Distributed Systems

Publications

2024


  1. Supporting business process variability through declarative process families ( and ), In Computers in Industry, volume 159-160, .

    Abstract

    Organizations use business process management systems to automate processes that they use to perform tasks or interact with customers. However, several variants of the same business process may exist due to, e.g., mergers, customer-tailored services, diverse market segments, or distinct legislation across borders. As a result, reliable support for process variability has been identified as a necessity. In this article, we introduce the concept of declarative process families to support process variability and present a procedure to formally verify whether a business process model is part of a specified process family. The procedure allows to identify potential parts in the process that violate the process family. By introducing the concept of process families, we allow organizations to deviate from their prescribed processes using normal process model notation and automatically verify if such a deviation is allowed. To demonstrate the applicability of the approach, a simple example process is used that describes several variants of a car rental process which is required to adhere to several process families. Moreover, to support the proposed procedure, we present a tool that allows business processes, specified as Petri nets, to be verified against their declarative process families using the NuSMV2 model checker.


    Keywords: Business processes, Variability, Declarative, Process families, Temporal logic, Verification


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  2. Poster: Towards Federated LLM-Powered CEP Rule Generation and Refinement (, , and ), In Proceedings of the 18th ACM International Conference on Distributed and Event-Based Systems (DEBS'24), ACM, .

    Abstract

    In traditional event processing systems, patterns representing situations of interest are typically defined by domain experts or learned from historical data. These approaches often make rule generation reactive, time-consuming, and susceptible to human error. In this paper, we propose and investigate the integration of large language models (LLMs) to automate and accelerate query translation and rule generation in event processing systems. Furthermore, we introduce a federated learning schema to refine the initially generated rules by examining them over distributed event streams, ensuring greater accuracy and adaptability. Preliminary results demonstrate the potential of LLMs as a key component in proactively expediting the autonomous rule-generation process. Moreover, our findings suggest that employing customized prompt engineering techniques can further enhance the quality of the generated rules.


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  3. Driving Towards Efficiency: Adaptive Resource-aware Clustered Federated Learning in Vehicular Networks (, , , , and ), In Proceedings of the 22nd Mediterranean Communication and Computer Networking Conference (MedComNet’24)., IEEE, .

    Abstract

    Guaranteeing precise perception for fully autonomous driving in diverse driving conditions requires continuous improvement and training. In vehicular networks, federated learning (FL) facilitates this by enabling model training without sharing raw sensory data. As an extension, clustered FL reduces communication overhead and aligns well with the dynamic nature of these networks. However, current literature on this topic does not consider critical dimensions of FL, including (1) the correlation between perception performance and the networking overhead, (2) the limited vehicle storage, (3) the need for training with freshly captured data, and (4) the impact of non-IID data and varying traffic densities. To fill these research gaps, we introduce AR-CFL, an Adaptive Resource-aware Clustered Federated Learning framework. AR-CFL utilizes clustered FL to collectively model the environment of connected vehicles, integrating models from all vehicles and ensuring universal accessibility to the refined model. AR-CFL dynamically enhances system efficiency by adaptively adjusting the number of clusters and specific in-cluster participant selection strategies. Using AR-CFL, we systematically study the scenario of online car detection model training on non-IID data across varied conditions. The evaluation results highlight the robust detection performance exhibited by the trained model employing the clustered FL approach, despite the constraints posed by limited vehicle storage capacity. Furthermore, our investigation unveils superior training performance with clustered FL in comparison to specific classical FL scenarios, increasing the training efficiency in terms of participating nodes by up to 25% and reducing cellular communication by 33%.


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  4. In-Network Management of Parallel Data Streams over Programmable Data Planes ( and ), In Proceedings of the 23rd International Federation for Information Processing Networking Conference (IFIP NETWORKING 2024), IEEE, .

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  5. Analog In-Network Computing through Memristor-based Match-Compute Processing (, , , and ), In Proceedings of the 43rd International Conference on Computer Communications (INFOCOM 2024), IEEE, .

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  6. dAQM: Derivative-based Active Queue Management (, and ), In Proceedings of the 23rd IFIP Networking Conference (NETWORKING 2024), IFIP, .

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  7. Adaptive In-Network Queue Management using Derivatives of Sojourn Time and Buffer Size (, and ), In Proceedings of the 37th Network Operations and Management Symposium (NOMS 2024), IEEE, .

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  8. APP-CEP: Adaptive Pattern-level Privacy Protection in Complex Event Processing Systems (, , and ), In The 10th International Conference on Information Systems Security and Privacy (ICISSP 2024)., SCITEPRESS, .

    Abstract

    Although privacy-preserving mechanisms endeavor to safeguard sensitive information at the attribute level, detected event patterns can still disclose privacy-sensitive knowledge in distributed complex event processing systems (DCEP). Events might not be inherently sensitive, but their aggregation into a pattern could still breach privacy. In this paper, we study in the context of APP-CEP the problem of integrating pattern-level privacy in event-based systems by selective assignment of obfuscation techniques to conceal private information. Compared to state-of-the-art techniques, we seek to enforce privacy independent of the actual events in streams. To support this, we acquire queries and privacy requirements using CEP-like patterns. The protection of privacy is accomplished through generating pattern dependency graphs, leading to dynamically appointing those techniques that have no consequences on detecting other sensitive patterns, as well as non-sensitive patterns required to provide acceptable Quality of Service. Besides, we model the knowledge that might be possessed by potential adversaries to violate privacy and its impacts on the obfuscation procedure. We assessed the performance of APP-CEP in a real-world scenario involving an online retailer’s transactions. Our evaluation results demonstrate that APP-CEP successfully provides a privacy-utility trade-off. Modeling the background knowledge also effectively prevents adversaries from realizing the modifications in the input streams.


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  9. Large-Scale Multipurpose Benchmark Datasets For Assessing Data-Driven Deep Learning Approaches For Water Distribution Networks (, , and ), In Proceedings of the 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI), MDPI, .

    Abstract

    Currently, the number of common benchmark datasets that researchers can use straight away for assessing data-driven deep learning approaches is very limited. Most studies provide data as configuration files. It is still up to each practitioner to follow a particular data generation method and run computationally intensive simulations to obtain usable data for model training and evaluation. In this work, we provide a collection of datasets that includes several small and medium size publicly available Water Distribution Networks (WDNs), including Anytown, Modena, Balerma, C-Town, D-Town, L-Town, Ky1, Ky6, Ky8, and Ky13. In total 1,394,400 hours of WDNs data operating under normal conditions is made available to the community.


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  10. Too Good To Be True: accuracy overestimation in (re)current practices for Human Activity Recognition (, and ), In 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), IEEE, .

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  11. Beyond Digital! Memristor-based Energy Efficient Analog Network Functions ( and ), In ICT.OPEN - CompSys Research for a Responsibly Digitalised Society, NWO ICT.OPEN, .

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  12. Towards Analog In-Network Computing for Supporting Cognitive and Energy-Efficient Network Functions ( and ), In 6th International Conference on Applications of Intelligent Systems (APPIS 2024), University of Las Palmas de Gran Canaria, Spain, .

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2023


  1. Cross-Instance Regulatory Compliance Checking of Business Process Event Logs (, , , , and ), In IEEE Transactions on Software Engineering, volume , .

    Abstract

    Event logs capture the execution of business processes, such that each task is represented by an event and each individual execution is a chronological sequence of events, called an event trace. Event logs allow after-the-act and runtime analysis of deployed business processes to verify whether their execution complies with regulations and business requirements. Checking the compliance of a single sequence of events in a trace is straightforward and a number of approaches have been proposed to address this. However, some regulations or business rules span multiple process instances and a cross-instance analysis is required. In order to check whether such requirements are maintained at all times, multiple traces need to be analysed together, which can result in a combinatorial computational complexity. In this paper, we present a novel approach that efficiently checks runtime regulatory compliance based on event logs, while supporting cross-instance rule evaluation and extensible function evaluation over sequences of attribute data values. The efficiency and applicability of the proposed method is tested in a two-pronged evaluation, showing a significant improvement over existing techniques with respect to capabilities as well as computational complexity. The approach presented in this paper is subject to a patent application, with patent number WO2021/248201.


    Keywords: Business process, Event log, Compliance, Regulations, Cross-instance, Instance-spanning, Runtime verification


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  2. Enough Hot Air: The Role of Immersion Cooling (, , and ), In Energy Informatics, .

    Abstract

    Air cooling is the traditional solution to chill servers in data centers. However, the continuous increase in global data center energy consumption combined with the increase of the racks’ power dissipation calls for the use of more efficient alternatives. Immersion cooling is one such alternative. In this paper, we quantitatively examine and compare air cooling and immersion cooling solutions. The examined characteristics include power usage efficiency (PUE), computing and power density, cost, and maintenance overheads. A direct comparison shows a reduction of about 50% in energy consumption and a reduction of about two-thirds of the occupied space, by using immersion cooling. In addition, the higher heat capacity of used liquids in immersion cooling compared to air allows for much higher rack power densities. Moreover, immersion cooling requires less capital and operational expenditures. However, challenging maintenance procedures together with the increased number of IT failures are the main downsides. By selecting immersion cooling, cloud providers must trade-off the decrease in energy and cost and the increase in power density with its higher maintenance and reliability concerns. Finally, we argue that retrofitting an air-cooled data center with immersion cooling will result in high costs and is generally not recommended.


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  3. Carbon Emission-Aware Job Scheduling for Kubernetes Deployments (, and ), In The Journal of Supercomputing, .

    Abstract

    Decreasing carbon emissions of data centers while guaranteeing Quality of Service (QoS) is one of the major challenges for efficient resource management of large-scale cloud infrastructures and societal sustainability. Previous works in the area of carbon reduction mostly focus on decreasing overall energy consumption, replacing energy sources with renewable ones, and migrating workloads to locations where lower emissions are expected. These measures do not consider the energy mix of the power used for the data center. In other words, all KWh of energy are considered the same from the point of view of emissions, which is rarely the case in practice. In this paper, we overcome this deficit by proposing a novel practical CO2-aware workload scheduling algorithm implemented in the Kubernetes orchestrator to shift non-critical jobs in time. The proposed algorithm predicts future CO2 emissions by using historical data of energy generation, selects time-shiftable jobs, and creates job schedules utilizing greedy sub-optimal CO2 decisions. The proposed algorithm is implemented using Kubernetes’ scheduler extender solution due to its ease of deployment with little overheads. The algorithm is evaluated with real-world workload traces and compared to the default Kubernetes scheduling implementation on several actual scenarios.


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  4. The Future is Analog: Energy-Efficient Cognitive Network Functions over Memristor-Based Analog Computations ( and ), In Proceedings of the 22nd ACM SIGCOMM Workshop on Hot Topics in Networks (HotNets 2023), ACM, .

    Abstract

    Current network functions build heavily on fixed programmed rules and lack capacity to support more expressive learning models, e.g. brain-inspired Cognitive computational models using neuromorphic computations. The major reason for this shortcoming is the huge energy consumption and limitation in expressiveness by the underlying TCAM-based digital packet processors. In this research, we show that recent emerging technologies from the analog domain have a high potential in supporting network functions with energy efficiency and more expressiveness, so called cognitive functions. We propose an analog packet processing architecture building on a novel technology named Memristors. We develop a novel analog match-action memory called Probabilistic Content-Addressable Memory (pCAM) for supporting deterministic and probabilistic match functions. We develop the programming abstractions and show the support of pCAM for an active queue management-based analog network function. The analysis over an experimental dataset of a memristor chip showed only 0.01 fJ/bit/cell of energy consumption for corresponding analog computations which is 50 times less than digital computations.


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  5. Memristor-based Network Switching Architecture for Energy Efficient Cognitive Computational Models ( and ), In Proceedings of the 18th International Symposium on Nanoscale Architectures (NanoArch 2023), ACM, .

    Abstract

    The Internet makes use of high performance network switches in order to route network traffic from end users to servers. Despite line-rate performance, the current switches consume huge energy and cannot support more expressive learning models, like cognitive functions using neuromorphic computations. The major reason is the use of transistors in the underlying Ternary Content-Addressable Memory (TCAM) which is volatile and supports digital computations only. These shortcomings can be bypassed by developing network memories building on novel components, like Memristors, due to their nonvolatile, nanoscale and analog storage/processing characteristics. In this paper, we propose the use of a novel memristor-based Probabilistic Associative Memory, PAmM, which provides both digital (deterministic) and analog (probabilistic) outputs for supporting cognitive computational models in network switches. The traditional digital operations can be supported by a memristor-based energy efficient TCAM, called TCAmMCogniGron. Building on PAmM and TCAmMCogniGron, we propose a novel network switching architecture and analyze its energy efficiency over the experimental dataset of a Nb-doped SrTiO3 memristive device. The results show that the proposed network switching architecture consumes only 0.01 fJ/bit/cell energy for analog compute operations which is at least 50 times less than the digital operations.


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  6. PAmM: Memristor-based Probabilistic Associative Memory for Neuromorphic Network Functions (, , and ), In Proceedings of the Non-Volatile Memory Technology Symposium (NVMTS 2023), IEEE, .

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  7. Towards Pattern-Level Privacy Protection in Distributed Complex Event Processing (, and ), In The 17th ACM International Conference on Distributed and Event-Based Systems (DEBS 2023), ACM press, .

    Abstract

    In event processing systems, detected event patterns can reveal privacy-sensitive information. In this paper, we propose and discuss how to integrate pattern-level privacy protection in event-based systems. Compared to state-of-the-art approaches, we aim to enforce privacy independent of the particularities of specific operators. We accomplish this by supporting the flexible integration of multiple obfuscation techniques and studying deployment strategies for privacy-enforcing mechanisms. Moreover, we share ideas on how to model the adversary’s knowledge to better select appropriate obfuscation techniques for the discussed deployment strategies. Initial results indicate that flexibly choosing obfuscation techniques and deployment strategies is essential to conceal privacy-sensitive event patterns accurately.


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  8. AQuA-CEP: Adaptive Quality-Aware Complex Event Processing in the Internet of Things (, and ), In Proceedings of the 17th ACM International Conference on Distributed and Event-Based Systems (DEBS 2023), ACM press, .

    Abstract

    Sensory data profoundly influences the quality of detected events in a distributed complex event processing system (DCEP). Since each sensor’s status is unstable at runtime, a single sensing assignment is often insufficient to fulfill the consumer’s quality requirements. In this paper, we study in the context of AQuA-CEP the problem of dynamic quality monitoring and adaptation of complex event processing by active integration of suitable data sources. To support this, in AQuA-CEP, queries to detect complex events are supplemented with consumer-definable quality policies that are evaluated and used to autonomously select (or even configure) suitable data sources of the sensing infrastructure. In addition, we studied different forms of expressing quality policies and analyzed how it affects the quality monitoring process. Various modes of evaluating and applying quality-related adaptations and their impacts on correlation efficiency are addressed, too. We assessed the performance of AQuA-CEP in IoT scenarios by utilizing the notion of the quality policy alongside the query processing adaptation using knowledge derived from quality monitoring. The results show that AQuA-CEP can improve the performance of DCEP systems in terms of the quality of results while fulfilling the consumer’s quality requirements. Quality-based adaptation can also increase the network’s lifetime by optimizing the sensor’s energy consumption due to efficient data source selection.


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  9. Graph Neural Networks for Pressure Estimation in Water Distribution Systems (, , and ), In arXiv preprint arXiv:2311.10579, Arxiv, .

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  10. Memristor-based Probabilistic Content Addressable Memory for Cognitive Network Functions (, , and ), In Neuromorphic Computing Netherlands (NCN 2023) Workshop [Posters], .

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  11. Memristor-based Cognitive and Energy-Efficient Analog In-network Computing ( and ), In Neuromorphic Summer School [Posters], Kiel CRC Neurotronics and CogniGron, .

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2022


  1. On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions ( and ), In IEEE Access, IEEE, volume 10, .

    Abstract

    The high performance requirements of nowadays computer networks are limiting their ability to support important requirements of the future. Two important properties essential in assuring cost-efficient computer networks and supporting new challenging network scenarios are operating energy efficient and supporting cognitive computational models. These requirements are hard to fulfill without challenging the current architecture behind network packet processing elements such as routers and switches. Notably, these are currently dominated by the use of traditional transistor-based components. In this article, we contribute with an in-depth analysis of alternative architectural design decisions to improve the energy footprint and computational capabilities of future network packet processors by shifting from transistor-based components to a novel component named Memristor . A memristor is a computational component characterized by non-volatile operations on a physical state, mostly represented in form of (electrical) resistance. Its state can be read or altered by input signals, e.g. electrical pulses, where the future state always depends on the past state. Unlike in traditional von Neumann architectures, the principles behind memristors impose that memory operations and computations are inherently colocated. In combination with the non-volatility, this allows to build memristors at nanoscale size and significantly reduce the energy consumption. At the same time, memristors appear to be highly suitable to model cognitive functionality due to the state dependence transitions in the memristor. In cognitive architectures, our survey contributes to the study of memristor-based Ternary Content Addressable Memory (TCAM) used for storage of cognitive rules inside packet processors. Moreover, we analyze the memristor-based novel cognitive computational architectures built upon self-learning capabilities by harnessing from non-volatility and state-based response of memristors (including reconfigurable architectures, reservoir computation architectures, neural network architectures and neuromorphic computing architectures).


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  2. Correlating the Community Structure of Constraint Satisfaction Problems with Search Time ( and ), In International Journal on Artificial Intelligence Tools, .

    Abstract

    A constraint satisfaction problem (CSP) is, in its most general form, an NP-complete problem. One of the several classes of tractable problems that exist contains all the problems with a restricted structure of the constraint scopes. This paper studies community structure, a particular type of restricted structure underpinning a class of tractable SAT problems with potentially similar relevance to CSPs. Using the modularity, it explores the community structure of a wide variety of problems with both academic and industrial relevance. Its impact on the search times of several general solvers, as well as one that uses tree-decomposition, is also analysed to determine whether constraint solvers exploit this type of structure. Nearly all CSP instances have a strong community structure, and those belonging to the same class have comparable modularity values. For the general solvers, strong correlations between the community structure and the search times are not apparent. A more definite correlation exists between the modularity and the search times of the tree-decomposition, suggesting that it might, in part, be able to take advantage of the community structure. However, combined with the relatively strong correlation between the modularity and the tree-width, it could also indicate a similarity between these two measures.


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  3. Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand (, , and ), In Energies 2022, Vol. 15, Page 3425, Multidisciplinary Digital Publishing Institute, volume 15, .

    Abstract

    This article focuses on developing both statistical and machine learning approaches for forecasting hourly electricity demand in Ontario. The novelties of this study include (i) identifying essential factors that have a significant effect on electricity consumption, (ii) the execution of a Bayesian optimization algorithm (BOA) to optimize the model hyperparameters, (iii) hybridizing the BOA with the seasonal autoregressive integrated moving average with exogenous inputs (SARIMAX) and nonlinear autoregressive networks with exogenous input (NARX) for modeling separately short-term electricity demand for the first time, (iv) comparing the model’s performance using several performance indicators and computing efficiency, and (v) validation of the model performance using unseen data. Six features (viz., snow depth, cloud cover, precipitation, temperature, irradiance toa, and irradiance surface) were found to be significant. The Mean Absolute Percentage Error (MAPE) of five consecutive weekdays for all seasons in the hybrid BOA-NARX is obtained at about 3%, while a remarkable variation is observed in the hybrid BOA-SARIMAX. BOA-NARX provides an overall steady Relative Error (RE) in all seasons (1 6.56%), while BOA-SARIMAX provides unstable results (Fall: 0.73 2.98%; Summer: 8.41 14.44%). The coefficient of determination (R2) values for both models are >0.96. Overall results indicate that both models perform well; however, the hybrid BOA-NARX reveals a stable ability to handle the day-ahead electricity load forecasts.


    Keywords: Bayesian optimization algorithm, NARX, SARIMAX, electricity demand, short, term forecast


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  4. Optimal Joint Operation of Coupled Transportation and Power Distribution Urban Networks (, , and ), In Energy Informatics, .

    Abstract

    The number of Electric Vehicles (EVs) and consequently their penetration level into urban society is increasing which has imperatively reinforced the need for a joint stochastic operational planning of Transportation Network (TN) and Power Distribution Network (PDN). This paper solves a stochastic multi-agent simulation-based model with the objective of minimizing the total cost of interdependent TN and PDN systems. Capturing the temporally dynamic inter-dependencies between the coupled networks, an equilibrium solution results in optimized system cost. In addition, the impact of large-scale EV integration into the PDN is assessed through the mutual coupling of both networks by solving the optimization problems, i.e., optimal EV routing using traffic assignment problem and optimal power flow using branch flow model. Previous works in the area of joint operation of TN and PDN networks fall short in considering the time-varying and dynamic nature of all effective parameters in the coupled TN and PDN system. In this paper, a Dynamic User Equilibrium (DUE) network model is proposed to capture the optimal traffic distribution in TN as well as optimal power flow in PDN. A modified IEEE 30 bus system is adapted to a low voltage power network to examine the EV charging impact on the power grid. Our case study demonstrates the enhanced operation of the joint networks incorporating heterogeneous EV characteristics such as battery State of Charge (SoC), charging requests as well as PDN network’s marginal prices. The results of our simulations show how solving our defined coupled optimization problem reduces the total cost of the defined case study by 36% compared to the baseline scenario. The results also show a 45% improvement on the maximum EV penetration level with only minimal voltage deviation (less than 0.3%).


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  5. Network Testing Utilizing ProgrammableNetworking Hardware. (, , , and ), In IEEE Communications Magazine, IEEE, .

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  6. Towards adaptive quality-aware Complex Event Processing in the Internet of Things (, and ), In Proceedings of the 18th International Conference on Mobility, Sensing and Networking (MSN 2022), IEEE, .

    Abstract

    This paper investigates how to complement Complex Event Processing (CEP) with dynamic quality monitoring mechanisms and support the dynamic integration of suitable sensory data sources. In the proposed approach, queries to detect complex events are annotated with consumer-definable quality policies that are evaluated and used to autonomously assign (or even configure) suitable data sources of the sensing infrastructure. We present and study different forms of expressing quality policies and explore how they affect the process of quality monitoring including different modes of assessing and applying quality-related adaptations. A performance study in an IoT scenario shows that the proposed mechanisms in supporting quality policy monitoring and adaptively selecting suitable data sources succeed in enhancing the acquired quality of results while fulfilling consumers' quality requirements. We show that the quality-based selection of sensor sources also extends the network's lifetime by optimizing the data sources' energy consumption.


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  7. TCAmMCogniGron: Energy Efficient Memristor-Based TCAM for Match-Action Processing (, , and ), In Proceedings of the 7th International Conference on Rebooting Computing (ICRC 2022), IEEE, .

    Abstract

    The Internet relies heavily on programmable match-action processors for matching network packets against locally available network rules and taking actions, such as forwarding and modification of network packets. This match-action process must be performed at high speed, i.e., commonly within one clock cycle, using a specialized memory unit called Ternary Content Addressable Memory (TCAM). Building on transistor-based CMOS designs, state-of-the-art TCAM architectures have high energy consumption and lack resilient designs for incorporating novel technologies for performing appropriate actions. In this article, we motivate the use of a novel fundamental component, the ‘Memristor’, for the development of TCAM architecture for match-action processing. Memristors can provide energy efficiency, non-volatility and better resource density as compared to transistors. We have proposed a novel memristor-based TCAM architecture called TCAmMCogniGron, built upon the voltage divider principle and requiring only two memristors and five transistors for storage and search operations compared to sixteen transistors in the traditional TCAM architecture. We analyzed its performance over an experimental data set of Nb-doped SrTiO3-based memristor. The analysis of TCAmMCogniGron showed promising power consumption statistics of 16 uW and 1 uW for match and mismatch operations along with twice the improvement in resources density as compared to the traditional architectures.


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  8. Towards Energy Efficient Memristor-based TCAM for Match-Action Processing (, , and ), In Proceedings of the 13th International Green and Sustainable Computing Conference (IGSC 2022), IEEE, .

    Abstract

    Match-action processors play a crucial role of communicating end-users in the Internet by computing network paths and enforcing administrator policies. The computation process uses a specialized memory called Ternary Content Addressable Memory (TCAM) to store processing rules and use header information of network packets to perform a match within a single clock cycle. Currently, TCAM memories consume huge amounts of energy resources due to the use of traditional transistor-based CMOS technology. In this article, we motivate the use of a novel component, the memristor, for the development of a TCAM architecture. Memristors can provide energy efficiency, non-volatility, and better resource density as compared to transistors. We have proposed a novel memristor-based TCAM architecture built upon the voltage divider principle for energy efficient match-action processing. Moreover, we have tested the performance of the memristor-based TCAM architecture using the experimental data of a novel Nb-doped SrTiO3 memristor. Energy analysis of the proposed TCAM architecture for given memristor shows promising power consumption statistics of 16 μW for a match operation and 1 μW for a mismatch operation.


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  9. On the Use of the Conformance and Compliance Keywords During Verification of Business Processes (, and ), In Business Process Management Forum (C. Di Ciccio, R. Dijkman, A. del Río Ortega, S. Rinderle-Ma, eds.), Springer, .

    Abstract

    A wealth of techniques have been developed to help organizations understand their processes, verify correctness against requirements and diagnose potential problems. In general, these verification techniques allow us to check whether a business process conforms or complies with some specification, and each of them is specifically designed to solve a particular business problem at a stage of the BPM lifecycle. However, the terms conformance and compliance are often used as synonyms and their distinct differences in verification goals is blurring. As a result, the terminology used to describe the techniques or the corresponding verification activity does not always match with the precise meaning of the terms as they are defined in the area of verification. Consequently, confusion of these terms may hamper the application of the different techniques and the correct positioning of research. In this position paper, we aim to provide comprehensive definitions and a unified terminology throughout the BPM lifecycle. Moreover, we explore the consequences when these terms are used incorrectly. In doing so, we aim to improve adoption from research to practical applications by clarifying the relation between techniques and the intended verification goals.


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  10. Window-based Parallel Operator Execution with In-Network Computing (, and ), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems, ACM, .

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  11. Enhancing Flexibility for Dynamic Time-Sensitive Network Configurations (, , , , and ), In Proceedings of the 3rd KuVS Fachgespräch on Network Softwarization, Universität Tübingen, .

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  12. On the Incremental Reconfiguration of Time-sensitive Networks at Runtime (, , , , and ), In Proceedings of the IFIP Networking Conference., IFIP, .

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  13. Travel light: state shedding for efficient operator migration (, , and ), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems (DEBS'22), ACM press, .

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  14. CO2 Emission Aware Scheduling for Deep Neural Network Training Workloads (, and ), In 2022 IEEE International Conference on Big Data (Big Data), IEEE, .

    Abstract

    Machine Learning (ML) training is a growing workload in high-performance computing clusters and data centers; furthermore, it is computationally intensive and requires substantial amounts of energy with associated emissions. To the best of our knowledge, previous works in the area of load management have never focused on decreasing the carbon emission of ML training workloads. In this paper, we explore the potential emission reduction achievable by leveraging the iterative nature of the training process as well as the variability of CO 2 signal intensity as coming from the power grid. To this end, we introduce two emission-aware mechanisms to shift the training jobs in time and migrate them between geographical locations. We present experimental results on power and carbon emission of the training process together with delay overheads associated with emission reduction mechanisms, for various, representative, deep neural network models. The results show that following emission signals, one can effectively reduce emissions by an amount that varies from 13% to 57% of the baseline cases. Moreover, the experimental results show that the total delay overhead for applying emission-aware mechanisms multiple times is negligible compared to the jobs’ completion time.


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  15. FA2: Fast, Accurate Autoscaling for Serving Deep Learning Inference with SLA Guarantees (, , , and ), In Proceedings of the 28th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2022), IEEE, .

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  16. PANDA: performance prediction for parallel and dynamic stream processing (, , and ), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems, ACM press, .

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  17. Using Memristors for Energy Efficient Cognitive Network Functions (, , and ), In Symposium on Physics of Information in Matter [Poster Session], AMOLF, .

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  18. In-Network Computing Over Memristor-Based Cognitive Network Functions (, , and ), In Brain-Inspired Concepts and Materials for Information Processing (Brainspiration) Conference [Poster Session], University of Twente, .

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  19. Memristor-Based Cognitive Network Packet Processors ( and ), In Neuromorphic Computing Netherlands (NCN 2022) Workshop [Abstracts, Talks and Posters], Radboud University, .

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  20. Memristor-Based Cognitive and Energy Efficient In-Network Processing (, , and ), In Workshop on Bio-Inspired Information Pathways [Abstracts and Posters], CRC-1461 Neurotronics, University of Kiel, .

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2021


  1. Multi-Energy Management of Buildings in Smart Grids (), University of Groningen, .

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  2. Automated Service Composition Using AI Planning and Beyond (, and ), Chapter in (M. Aiello, A. Bouguettaya, D. A. Tamburri, W. J. van den Heuvel, eds.), Springer International Publishing, .

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  3. Energy Consumption Patterns and Load Forecasting with Profiled CNN-LSTM Networks (, and ), In Processes, volume 9, .

    Abstract

    By virtue of the steady societal shift to the use of smart technologies built on the increasingly popular smart grid framework, we have noticed an increase in the need to analyze household electricity consumption at the individual level. In order to work efficiently, these technologies rely on load forecasting to optimize operations that are related to energy consumption (such as household appliance scheduling). This paper proposes a novel load forecasting method that utilizes a clustering step prior to the forecasting step to group together days that exhibit similar energy consumption patterns. Following that, we attempt to classify new days into pre-generated clusters by making use of the available context information (day of the week, month, predicted weather). Finally, using available historical data (with regard to energy consumption) alongside meteorological and temporal variables, we train a CNN-LSTM model on a per-cluster basis that specializes in forecasting based on the energy profiles present within each cluster. This method leads to improvements in forecasting performance (upwards of a 10% increase in mean absolute percentage error scores) and provides us with the added benefit of being able to easily highlight and extract information that allows us to identify which external variables have an effect on the energy consumption of any individual household.


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  4. TCEP: Transitions in Operator Placement to Adapt to Dynamic Network Environments. (, , , , and ), In In Journal of Computer and Systems Sciences (JCSS), Special Issue on Algorithmic Theory of Dynamic Networks and its Applications., Elsevier, volume 122, .

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  5. Employability prediction: a survey of current approaches, research challenges and applications (, , , and ), In Journal of Ambient Intelligence and Humanized Computing, .

    Abstract

    Student employability is crucial for educational institutions as it is often used as a metric for their success. The job market landscape, however, more than ever dynamic, is evolving due to the globalization, automation, and recent advances in Artificial Intelligence. Identifying the significant factors affecting employability, as well as the requirements of the new job market can tremendously help all stakeholders. Knowing their weaknesses and strengths, students might better plan their career. Instructors can focus on more appropriate skill sets to meet the requirements of rapidly evolving labor markets. Program managers can anticipate and improve their curriculum to build new competencies, both for educating, training and reskilling current and future workers. All these combined efforts certainly can contribute to increasing employability. Data driven and machine learning techniques have been extensively used in various fields of educational data mining. More and more studies are investigating data mining techniques for the prediction of employability. Yet, these studies show a lot of variation, for instance, with respect to the data used, the methods adopted, or even the research questions posed. In this paper, we aim to depict a clear picture of the art, clarifying for each standard step of data mining process, the differences, and similarities of these studies, along with further suggestions. Thus, this survey provides a comprehensive roadmap, enabling the application of data mining for employability.


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  6. OpenBNG: Central office network functions on programmable data plane hardware (, , , , , , , , , and ), In International Journal of Network Management, Wiley, volume 31, .

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  7. SVNN: an efficient PacBio-specific pipeline for structural variations calling using neural networks (, and ), In BMC bioinformatics, BioMed Central, volume 22, .

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  8. Adaptive On-the-fly Changes in Distributed Processing Pipelines (, , and ), In Frontiers in Big Data, Frontiers, .

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  9. A multi-robot allocation model for multi-object based on Global Optimal Evaluation of Revenue (, , , and ), In International Journal of Advanced Robotic Systems, volume 18, .

    Abstract

    The problem of global optimal evaluation for multi-robot allocation has gained attention constantly, especially in a multi-objective environment, but most algorithms based on swarm intelligence are difficult to give a convergent result. For solving the problem, we established a Global Optimal Evaluation of Revenue method of multi-robot for multi-tasks based on the real textile combing production workshop, consumption, and different task characteristics of mobile robots. The Global Optimal Evaluation of Revenue method could traversal calculates the profit of each robot corresponding to different tasks with global traversal over a finite set, then an optimization result can be converged to the global optimal value avoiding the problem that individual optimization easy to fall into local optimal results. In the numerical simulation, for fixed set of multi-object and multi-task, we used different numbers of robots allocation operation. We then compared with other methods: Hungarian, the auction method, and the method based on game theory. The results showed that Global Optimal Evaluation of Revenue reduced the number of robots used by at least 17%, and the delay time could be reduced by at least 16.23%.


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  10. Digital Twins: an enabler for digital transformation ( and ), Chapter in The Digital Transformation handbook, .

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  11. Accelerating the Performance of Data Analytics using Network-centric Processing (), In Proceedings of the 15th ACM International Conference on Distributed and Event-based Systems, ACM, .

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  12. Leveraging Flexibility of Time-Sensitive Networks for dynamic Reconfigurability (, , , , and ), In Proceedings of IFIP Networking 2021, IFIP, .

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  13. Leveraging PIFO Queues for Scheduling in Time-Sensitive Networks (, , , , , and ), In In the Proceedings of the IEEE International Symposium on Local and Metropolitan Area Networks (LANMAN 2021)., IEEE, .

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  14. Workload Distribution on Heterogeneous Platforms ( and ), In 2021 International Conference on Computer, Information and Telecommunication Systems (CITS), volume , .

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  15. P4-CoDel: Experiences on Programmable Data Plane Hardware (, , , , and ), In Proceedings of the IEEE International Conference on Communications (ICC 2021): Next-Generation Networking and Internet Symposium, IEEE, .

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  16. Towards QoE-Driven Optimization of Multi-Dimensional Content Streaming (, , , , , , and ), In Proceedings of the Conference on Networked Systems 2021 (NetSys 2021), European Association of Software Science and Technology, .

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  17. Iot based smart water quality monitoring: Recent techniques, trends and challenges for domestic applications (, and ), volume 13, .

    Abstract

    Safe water is becoming a scarce resource, due to the combined effects of increased population, pollution, and climate changes. Water quality monitoring is thus paramount, especially for domestic water. Traditionally used laboratory-based testing approaches are manual, costly, time consuming, and lack real-time feedback. Recently developed systems utilizing wireless sensor network (WSN) technology have reported weaknesses in energy management, data security, and communication coverage. Due to the recent advances in Internet-of-Things (IoT) that can be applied in the development of more efficient, secure, and cheaper systems with real-time capabilities, we present here a survey aimed at summarizing the current state of the art regarding IoT based smart water quality monitoring systems (IoT-WQMS) especially dedicated for domestic applications. In brief, this study probes into common water-quality monitoring (WQM) parameters, their safe-limits for drinking water, related smart sensors, critical review, and ratification of contemporary IoT-WQMS via a proposed empirical metric, analysis, and discussion and, finally, design recommendations for an efficient system. No doubt, this study will benefit the developing field of smart homes, offices, and cities.


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2020


  1. Office Occupancy Detection based on Power Meters and BLE Beaconing (), University of Groningen, .

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  2. Optimization of energy distribution in smart grids (), University of Groningen, .

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  3. Unsupervised approach towards analysing the public transport bunching swings formation phenomenon (, , , and ), In Public Transport, .

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  4. Prediction-Based Underutilized and Destination Host Selection Approaches for Energy-Efficient Dynamic VM Consolidation in Data Centers ( and ), In The Journal of Supercomputing, .

    Abstract

    Improving the energy efficiency while guaranteeing quality of services (QoS) is one of the main challenges of efficient resource management of large-scale data centers. Dynamic virtual machine (VM) consolidation is a promising approach that aims to reduce the energy consumption by reallocating VMs to hosts dynamically. Previous works mostly have considered only the current utilization of resources in the dynamic VM consolidation procedure, which imposes unnecessary migrations and host power mode transitions. Moreover, they select the destinations of VM migrations with conservative approaches to keep the service-level agreements , which is not in line with packing VMs on fewer physical hosts. In this paper, we propose a regression-based approach that predicts the resource utilization of the VMs and hosts based on their historical data and uses the predictions in different problems of the whole process. Predicting future utilization provides the opportunity of selecting the host with higher utilization for the destination of a VM migration, which leads to a better VMs placement from the viewpoint of VM consolidation. Results show that our proposed approach reduces the energy consumption of the modeled data center by up to 38% compared to other works in the area, guaranteeing the same QoS. Moreover, the results show a better scalability than all other approaches. Our proposed approach improves the energy efficiency even for the largest simulated benchmarks and takes less than 5% time overhead to execute for a data center with 7600 physical hosts.


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  5. Efficient conditional compliance checking of business process models (, and ), In Computers in Industry, ELSEVIER SCIENCE BV, volume 115, .

    Abstract

    When checking compliance of business processes against a set of business rules or regulations, the ability to handle and verify conditions in both the model and the rules is essential. Existing design-time verification approaches, however, either completely lack support for the verification of conditions or propose costly verification methods that also consider the full data perspective. This paper proposes a novel light-weight verification method, which is preferable over expensive approaches that include the data perspective when considering structural properties of a business process model. This novel approach generates partial models that capture only relevant execution states to the conditions under investigation. The resulting model can be verified using existing model checking techniques. The computation of such partial models fully abstracts conditions from the full models and specifications, thus avoiding the analysis of the full data perspective. The proposed method is complete with respect to the analyzed execution paths, while significantly reducing the state space complexity by pruning unreachable states given the conditions under investigation. This approach offers the ability to check if a process is compliant with rules and regulations on a much more fine-grained level, and it enables a more precise formulation of the conditions that should and should not hold in the processes. The approach is particularly useful in dynamic environments where processes are constantly changing and efficient conditional compliance checking is a necessity. The approach – implemented in Java and publicly available – is evaluated in terms of performance and practicability, and tested over both synthetic datasets and a real-life case from the Australian telecommunications sector.


    Keywords: Business process models, Formal verification, Conditional compliance, Data perspective, Temporal logic


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  6. Infrastructure Aware Heterogeneous-Workloads Scheduling for Data Center Energy Cost Minimization (, , and ), In IEEE Transactions on Cloud Computing, volume 10, .

    Abstract

    A huge amount of energy consumption, the cost of this usage and environmental effects have become serious issues for commercial cloud providers. Solar energy is a promising clean energy source, to provide some portion of the Internet data center's (IDC's) energy usage which can reduce environmental effects and total energy costs. Moreover, due to the high energy consumption of the cooling system, considering cooling power in job scheduling can provide efficient solutions to reduce total energy consumption. In this article, we investigate the problem of minimizing the energy cost of an IDC and propose an algorithm which schedules heterogeneous IDC workloads, by considering available renewable energy, cooling subsystem, and electricity rate structure. We evaluate the effectiveness and feasibility of our algorithm using real and synthetic workload traces. The simulation results illustrate how our proposed solution reduces the data center's energy cost by up to 46 percent compared to previous solutions. Moreover, results show that our solution is capable of reducing energy cost of data centers under different weather conditions, and rate structures.


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  7. Prediction of academic performance at undergraduate graduation: Course grades or grade point average? ( and ), In Applied Sciences (Switzerland), volume 10, .

    Abstract

    Predicting the academic standing of a student at the graduation time can be very useful, for example, in helping institutions select among candidates, or in helping potentially weak students in overcoming educational challenges. Most studies use individual course grades to represent college performance, with a recent trend towards using grade point average (GPA) per semester. It is unknown however which of these representations can yield the best predictive power, due to the lack of a comparative study. To answer this question, a case study is conducted that generates two sets of classification models, using respectively individual course grades and GPAs. Comprehensive sets of experiments are conducted, spanning different student data, using several well-known machine learning algorithms, and trying various prediction window sizes. Results show that using course grades yields better accuracy if the prediction is done before the third term, whereas using GPAs achieves better accuracy otherwise. Most importantly, variance analysis on the experiment results reveals interesting insights easily generalizable: individual course grades with short prediction window induces noise, and using GPAs with long prediction window causes over-simplification. The demonstrated analytical approach can be applied to any dataset to determine when to use which college performance representation for enhanced prediction.


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  8. The Internet of Everything: Smart things and their impact on business models (, , , , and ), In Journal of Business Research, .

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  9. Predicting academic success in higher education: literature review and best practices ( and ), In International Journal of Educational Technology in Higher Education, volume 17, .

    Abstract

    © 2020, The Author(s). Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success, through which student attributes to focus on, up to which machine learning method is more appropriate to the given problem. This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.


    Keywords: Data mining, Guidelines, Higher education, Prediction, Review, Student success


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  10. Workload Scheduling on heterogeneous Mobile Edge Cloud in 5G networks to Minimize SLA Violation (, , and ), In arXiv preprint arXiv:2003.02820, .

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  11. OpenBNG: Central office network functions on programmable data plane hardware (, , , , , , , , , and ), In International Journal of Network Management, Wiley, .

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  12. Grußwort der Gastherausgeber zum Thema Fog Computing (, , and ), In Informatik Spektrum, Springer Science and Business Media LLC, volume 42, .

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  13. Theorie: processen van voortijdig schoolverlaten en begeleiding om dat te voorkomen (, , , and ), Chapter in Voortijdig schoolverlaten voorkomen Perspectieven van wetenschap, praktijk en beleid (M. A. E. van der Gaag, N. R. Snell, G. G. Bron, E. S. Kunnen, eds.), Uitgeverij Acco, .

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  14. Procesonderzoek: processen van uitvallen, blijven en begeleiding (, , , , , and ), Chapter in Voortijdig schoolverlaten voorkomen Perspectieven van wetenschap, praktijk en beleid (M. A. E. van der Gaag, N. R. Snell, G. G. Bron, E. S. Kunnen, eds.), Uitgeverij Acco, .

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  15. Towards Service-Oriented and Intelligent Microgrids (, and ), In Proceedings of the 3rd International Conference on Applications of Intelligent Systems, Association for Computing Machinery, .

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  16. Predictive Multi-Objective Scheduling with Dynamic Prices and Marginal CO2-Emission Intensities ( and ), In ACM e-Energy 2020, .

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  17. Sustainability Choices when Cooking Pasta (, and ), In ACM e-Energy 2020, .

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  18. Operator as a Service: Stateful Serverless Complex Event Processing (, , , and ), In Proceedings of the 2020 IEEE International Conference on Big Data, IEEE, .

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  19. The Community Structure of Constraint Satisfaction Problems and Its Correlation with Search Time ( and ), In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), .

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  20. Microbursts in Software and Hardware-based Traffic Load Generation (, and ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, .

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  21. Flexible Content-based Publish/Subscribe over Programmable Data Planes (, , , and ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, .

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  22. P4STA: High Performance Packet Timestamping with Programmable Packet Processors (, , , and ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, .

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2019


  1. MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers (, , and ), In IEEE Transactions on Services Computing, volume 15, .

    Abstract

    Improving the energy efficiency of data centers while guaranteeing Quality of Service (QoS), together with detecting performance variability of servers caused by either hardware or software failures, are two of the major challenges for efficient resource management of large-scale cloud infrastructures. Previous works in the area of dynamic Virtual Machine (VM) consolidation are mostly focused on addressing the energy challenge, but fall short in proposing comprehensive, scalable, and low-overhead approaches that jointly tackle energy efficiency and performance variability. Moreover, they usually assume over-simplistic power models, and fail to accurately consider all the delay and power costs associated with VM migration and host power mode transition. These assumptions are no longer valid in modern servers executing heterogeneous workloads and lead to unrealistic or inefficient results. In this paper, we propose a centralized-distributed low-overhead failure-aware dynamic VM consolidation strategy to minimize energy consumption in large-scale data centers. Our approach selects the most adequate power mode and frequency of each host during runtime using a distributed multi-agent Machine Learning (ML) based strategy, and migrates the VMs accordingly using a centralized heuristic. Our Multi-AGent machine learNing-based approach for Energy efficienT dynamIc Consolidation (MAGNETIC) is implemented in a modified version of the CloudSim simulator, and considers the energy and delay overheads associated with host power mode transition and VM migration, and is evaluated using power traces collected from various workloads running in real servers and resource utilization logs from cloud data center infrastructures. Results show how our strategy reduces data center energy consumption by up to 15 percent compared to other works in the state-of-the-art (SoA), guaranteeing the same QoS and reducing the number of VM migrations and host power mode transitions by up to 86 and 90 percent, respectively. Moreover, it shows better scalability than all other approaches, taking less than 0.7 percent time overhead to execute for a data center with 1,500 VMs. Finally, our solution is capable of detecting host performance variability due to failures, automatically migrating VMs from failing hosts and draining them from workload.


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  2. Variability in business processes: Automatically obtaining a generic specification (, , and ), In Information Systems, PERGAMON-ELSEVIER SCIENCE LTD, volume 80, .

    Abstract

    The existence of different process variants is inevitable in many modern organizations. However, variability in business process support has proven to be a challenge as it requires a flexible business process specification that supports the required process variants, while at the same time being compliant with policies and regulations. Declarative approaches could support variability, by providing rules constraining process behavior and thereby allowing different variants. However, manual specification of these rules is complicated and error-prone. As such, tools are required to ensure that duplication and overlap of rules is avoided as much as possible, while retaining maintainability. In this paper, we present an approach to represent different process variants in a single compound prime event structure, and provide a method to subsequently derive variability rules from this compound prime event structure. The approach is evaluated by conducting an exploratory evaluation on different sets of real-life business process variants, including a real-life case from the Dutch eGovernment, to demonstrate the effectiveness and applicability of the approach.


    Keywords: Business Process Model, Declarative Variability Modeling, Event Structure, Temporal Logic, PROCESS MODELS, CORRECTNESS


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  3. Analytical tool for the modelling and simulation of curriculum: Towards automated design, assessment, and improvement (), In International Journal of Engineering Education, volume 35, .

    Abstract

    © 2019 TEMPUS Publications. Continuous quality improvement cycle is essential in educational systems allowing institutions to meet the evolving needs of the market. As such, it is required by all accreditation agencies. Curriculum revision is a critical step of this cycle. This study proposes a modelling paradigm to automate the design, analysis and improvement of curriculum. Based on proven theoretical principles, this novel graph-based approach captures both pre-requisite and cognitive dependencies among courses, enabling an optimal learning environment for students. The presented tool allows an easy and fast analysis of the impact of potential course revisions on all other courses, hence enabling a better continuous quality improvement process, thus providing benefits to many stakeholders in the education system, namely managers, instructors, students and employers. The proposed modelling paradigm is explained and illustrated on a capstone project course offered in the College of Computer Science and IT.


    Keywords: Accreditation, Automated tool, Curriculum design, Curriculum development, Engineering education, Quality assurance


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  4. Fostering higher cognitive skills through design thinking in digital hardware course: A case study (, and ), In ICIC Express Letters, volume 13, .

    Abstract

    © 2019 ICIC International. All rights reserved. Computer Science students are reportedly facing many issues in acquiring higher cognitive skills (e.g., analysis, and design). Digital hardware is one of the first courses in a typical Computer Science curriculum where students need to master these skills while analyzing and designing sequential circuits. This study investigates the pedagogical effectiveness of the Design Thinking methodology in improving students' higherorder cognitive skills in the digital hardware course. Design Thinking was embedded in the digital hardware course through a real-world design challenge where teams of students iteratively collaborated. The design problem was purposely set to necessitate knowledge and skills yet to be covered hence fostering in students' curiosity and eagerness to learn new topics, thus engaging students as active learners and meaning creators. The study demonstrates a significant gain in test scores. It also describes how to easily embed the Design Thinking process in the digital hardware curriculum.


    Keywords: Analysis, Continuous quality improvement, Design, Design Thinking, Digital Logic, Higherorder cognitive skills, Student learning outcomes


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  5. The u-can-act Platform: A Tool to Study Intra-individual Processes of Early School Leaving and Its Prevention Using Multiple Informants (, , , , and ), In Frontiers in Psychology, volume 10, .

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  6. Energy management for user's thermal and power needs: A survey ( and ), In Energy Reports, volume 5, .

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  7. IMOS: improved meta-aligner and Minimap2 on spark (, and ), In BMC bioinformatics, Springer, volume 20, .

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  8. Development of a decision-aid for patients with depression considering treatment options: prediction of treatment response using a data-driven approach (, , , , , , , , , and ), In ISPOR Europe 2019, Copenhagen, Denmark, .

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  9. Predictive CO2-Efficient Scheduling of Hybrid Electric and Thermal Loads ( and ), In 2019 IEEE International Conference on Energy Internet (ICEI), .

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  10. ECiDA: Evolutionary Changes in Data Analysis (, , , , , , , and ), In ICT.Open, Hilversum, The Netherlands, .

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  11. Office Multi-Occupancy Detection using BLE Beacons and Power Meters (, and ), In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics, and Mobile Communication Conference, .

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  12. Temporal Analysis of 911 Emergency Calls Through Time Series Modeling (, , and ), In The International Conference on Advances in Emerging Trends and Technologies, .

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  13. Prediction of Imports of Household Appliances in Ecuador Using LSTM Networks (, , and ), In Conference on Information Technologies and Communication of Ecuador, .

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  14. Time to get personal? The impact of researchers’ choices on the selection of treatment targets using the experience sampling methodology (, , , , , , , , , and ), PsyArXiv, .

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2018


  1. The Web Was Done by Amateurs: A Reflection on One of the Largest Collective Systems Ever Engineered (), Springer, .

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  2. The non-existent average individual: Automated personalization in psychopathology research by leveraging the capabilities of data science (), University of Groningen, .

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  3. Shedding Light on the Dark Corners of the Internet: A Survey of Tor Research (, and ), In Journal of Network and Computer Applications, Elsevier, volume 114, .

    Abstract

    Anonymity services have seen high growth rates with increased usage in the past few years. Among various services, Tor is one of the most popular peer-to-peer anonymizing service. In this survey paper, we summarize, analyze, classify and quantify 26 years of research on the Tor network. Our research shows that ‘security’ and ‘anonymity’ are the most frequent keywords associated with Tor research studies. Quantitative analysis shows that the majority of research studies on Tor focus on ‘deanonymization’ the design of a breaching strategy. The second most frequent topic is analysis of path selection algorithms to select more resilient paths. Analysis shows that the majority of experimental studies derived their results by deploying private testbeds while others performed simulations by developing custom simulators. No consistent parameters have been used for Tor performance analysis. The majority of authors performed throughput and latency analysis.


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  4. A Formal Model for Compliance Verification of Service Compositions (, and ), In Ieee transactions on services computing, volume 11, .

    Abstract

    Business processes design and execution environments increasingly need support from modular services in service compositions to offer the flexibility required by rapidly changing requirements. With each evolution, however, the service composition must continue to adhere to laws and regulations, resulting in a demand for automated compliance checking. Existing approaches, if at all, either offer only verification after the fact or linearize models to such an extent that parallel information is lost. We propose a mapping of service compositions to Kripke structures by using colored Petri nets. The resulting model allows preventative compliance verification using well-known temporal logics and model checking techniques while providing full insight into parallel executing branches and the local next invocation. Furthermore, the mapping causes limited state explosion, and allows for significant further model reduction. The approach is validated on a case study from a telecom company in Australia and evaluated with respect to performance and expressiveness. We demonstrate that the proposed mapping has increased expressiveness while being less vulnerable to state explosion than existing approaches, and show that even large service compositions can be verified preventatively with existing model checking techniques.


    Keywords: Service Composition, Business process, Compliance, Verification, Temporal Logic, Colored Petri net, Kripke structure, COMPLIANCE-CHECKING, BUSINESS, SPECIFICATION, SUPPORT


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  5. Fast and Energy-Efficient CNFET Adders with CDM and Sensitivity-Based Device-Circuit Co-Optimization (, and ), In IEEE Transactions on Nanotechnology, volume 17, .

    Abstract

    Since integrated circuit technology entered into the nanoscale regime, energy efficiency has become one of the most significant challenges. The carbon nanotube field effect transistor (CNFET) is one of the highly appreciated nanoscale devices for replacement due to its similar process to the current CMOS technology. The big question in this paper is what are the other specific controllable parameters in CNFET technology for designers to design high-performance and energy-efficient circuits and how much these parameters impact the circuit characteristics? In this regard, two energy-efficient full adders, as the crucial building blocks of digital systems, in 32 nm CNFET technology are designed. Cell design methodology as an efficient logic style is used for the new designs, and CNFET-SEA is used for the optimization. The CNFET-SEA, which is a modification of simple exact algorithm (SEA), is proposed as an appropriate sizing algorithm for circuits in CNFET technology. The sensitivity analysis, as a new approach, is used in the CNFET-SEA algorithm to obtain better sizing results in shorter runtime. The number of tubes, the diameter of tubes, and pitch are considered as the three specific device parameters in the CNFET technology for device-circuit co-optimization, and their effect on the circuit characteristics is investigated. The simulation results show a 15-97% delay, 8-87% power-delay product (PDP), and 22-99% energy-delay product improvement for the proposed full adders compared with the referenced ones. The PDP optimization with CNFET-SEA in comparison with SEA shows 11-20% improvement with a significant runtime reduction for selected adders.


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  6. Zero-queue ethernet congestion control protocol based on available bandwidth estimation (, and ), In Journal of Network and Computer Applications, Elsevier, volume 105, .

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  7. Multi-User Low Intrusive Occupancy Detection (, , and ), In Sensors, MDPI, volume 18, .

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  8. Topological Considerations on Decentralised Energy Exchange in the Smart Grid ( and ), In Procedia Computer Science, volume 130, .

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  9. Learning behind glass walls: learning style and partition-room, is there a correlation? (, , and ), In International Journal of Innovation Science, .

    Abstract

    © 2018, Emerald Publishing Limited. Purpose: This study aims to investigate how a very particular learning environment, namely, partition rooms, affect students’ teaching experience and further explore if students’ learning styles is a pertinent determinant. Partition rooms are very common in Saudi Arabia when lectures are held by male instructors for female students. The male instructor delivers his lesson behind a glass wall, creating an environment of limited visual and auditory interaction. Various digital tools are present, meant to overcome the gap caused by the lack of direct student–teacher contact. Design/methodology/approach: The researchers collected data from a sample of 109 female students who are studying at Level 4 Computer Science Department, College of Computer Sciences and Information Technology, at a public university in Saudi Arabia. All of them experienced a minimum of two courses undertaken in a partition room. The survey consists of two parts with a total of 53 questions. The first 20 questions were adopted from the perceptual learning style preference questionnaire (PLSP). Findings: Research findings reveal that students are affected differently by the various dimensions of the partition room depending on their learning style. Originality/value: There are fewer results in the literature that study learners of our particular group, namely, Saudi females. The study focuses on students studying IT and related fields. This study is almost unique, as most studies of the kind are related to the experience of females learning English as a foreign language. Therefore, the authors’ research gives much-needed insight into the conditions and perceptions of female students studying toward their degree in a technical field.


    Keywords: Cultural specific education, Educational technology, Female education, Learning environment, Partition room, Saudi education, Technology efficacy


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  10. A smarter electricity grid for the Eastern Province of Saudi Arabia: Perceptions and policy implications (, , and ), In Utilities Policy, volume 50, .

    Abstract

    © 2017 Elsevier Ltd Saudi Arabia aspires to transition toward a smarter electricity grid with increased reliance on renewable energy, where customers will use or produce green energy and where smart meters will enable customers to tailor their behavior and decrease their carbon footprint. The success of the transition is dependent on householder acceptance. This research studies the public's disposition toward a smarter grid. The Eastern Province of Saudi Arabia is taken as a case study through a field questionnaire to assess public knowledge about energy sources and environmental impacts on the environments, people's disposition toward a smarter electric grid, and the main motivations for undergoing this transition. A logit model is used to investigate determinants. Stated willingness is taken as a variable representing an individual's disposition. We found that the public is willing to use green energy, accept smart meters, or become co-producers. However, their fear of unknown technologies and perceptions about their high cost are major obstacles to their adoption. Enhancive knowledge, especially about ecological sensitivity, and governmental incentives will help to win public acceptance. Also, government subsidies that lower prices should be cut and dynamic pricing should be implemented to motivate electricity saving behavior.


    Keywords: Kingdom of Saudi Arabia, Renewable energy, Residential area, Smart grid, Smart metering, Social acceptance, Solar energy


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  11. Exploring the emotional dynamics of subclinically depressed individuals with and without anhedonia: An experience sampling study (, , , , and ), In Journal of Affective Disorders, volume 228, .

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  12. Topological Considerations on the Use of Batteries to Enhance the Reliability of HV-Grids (, , and ), In Journal of Energy Storage, volume 18, .

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  13. A task-based greedy scheduling algorithm for minimizing energy of mapreduce jobs ( and ), In Journal of grid computing, Springer, volume 16, .

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  14. Adaptive Provisioning of Heterogeneous Cloud Resources for Big Data Processing (, , , and ), In Big Data and Cognitive Computing, volume 2, .

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  15. The impact of digital technology on female students' learning experience in partition-rooms: Conditioned by social context (, , and ), In IEEE Transactions on Education, volume 61, .

    Abstract

    Contribution: As expected, a partition-room environment negatively affects students' learning. An unexpected result of this study is that female students occasionally choose not to use the technology available in partition-rooms, to avoid undesirable facial exposure. Background: The main purpose of partition-rooms is to prevent male instructors from seeing female students' faces. In learning environments where instructors and students are physically separated, technology is expected to play an integral role in bridging the gap. In one side of partition-rooms, female students use their own mobile devices, such as laptops, tablets and mobile phones, for course activities and communication; in the other side, the instructor has various digital teaching equipment provided by the institution. Research Question: What effect does a partition-room's physical environment have on female students' academic performance, satisfaction, technology efficacy, and perceived learning? What effect does a partition-room's social environment have on female students' academic performance, satisfaction, technology efficacy, and perceived learning? Methodology: Both quantitative and qualitative approaches were followed. Quantitative results were obtained from a student questionnaire. Qualitative data was gathered in a focus group session. Findings: The communication benefits offered by technology are impaired by both the physical context and the cultural-social context. The latter emerged during focus group discussions where students said that their faces might by revealed in the light emitted by their devices. Thus, local culture and social context limit the benefits of using digital technology in the classroom.


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  16. Personalized Physical Activity Coaching: A Machine Learning Approach (, , , and ), In Sensors, volume 18, .

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  17. Robustness of reconfigurable complex systems by a multi-agent simulation: Application on power distribution systems (, , , and ), In 2018 Annual IEEE International Systems Conference (SysCon), .

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  18. Low-power Appliance Recognition using Recurrent Neural Networks (, , and ), In Applications of Intelligent Systems, .

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  19. A remotely piloted aerial system for a faster processing of traffic collisions towards reducing the resulting road congestion (, and ), In , .

    Abstract

    This paper presents the motivation, design, implementation, and testing of a remotely piloted aerial system, designed to facilitate police officers processing traffic collisions. A drone remotely controlled by the police officer can reach faster the accident scene and act as the police officer's eye, ear, and voice in the sky. A complete system prototype has been constructed and tested to validate the proposed system. The results show that the system performance is promising in terms of system functionality, safety, and cost.


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  20. Household CO2-efficient energy management ( and ), In Energy Informatics, Springer, .

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  21. Mining Sequential Patterns for Appliance Usage Prediction (, , , and ), In International Conference on Smart Cities and Green ICT Systems, .

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  22. Modeling 911 emergency events in Cuenca-Ecuador using geo-spatial data (, , and ), In International Conference on Technology Trends, .

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  23. LOD-GF: an integral linked open data generation framework (, , , , , and ), In Conference on Information Technologies and Communication of Ecuador, .

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  24. One for All, All for One: A Heterogeneous Data Plane for Flexible P4 Processing ( and ), In IEEE 26th International Conference on Network Protocols (ICNP), IEEE, .

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