2022
- On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions (), In IEEE Access, IEEE, volume 10, 2022.
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).
BibTeX
urldoi - Correlating the Community Structure of Constraint Satisfaction Problems with Search Time (), In International Journal on Artificial Intelligence Tools, 2022.
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.
BibTeX
urldoi - Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand (), In Energies 2022, Vol. 15, Page 3425, Multidisciplinary Digital Publishing Institute, volume 15, 2022.
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 forecastBibTeX
urldoi - Optimal Joint Operation of Coupled Transportation and Power Distribution Urban Networks (), In Energy Informatics, 2022.
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%).
BibTeX
url - Network Testing Utilizing ProgrammableNetworking Hardware. (), In IEEE Communications Magazine, IEEE, 2022.
- Towards adaptive quality-aware Complex Event Processing in the Internet of Things (), In Proceedings of the 18th International Conference on Mobility, Sensing and Networking (MSN 2022), IEEE, 2022.
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.
BibTeX
urldoi - TCAmMCogniGron: Energy Efficient Memristor-Based TCAM for Match-Action Processing (), In Proceedings of the 7th International Conference on Rebooting Computing (ICRC 2022), IEEE, 2022.
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.
BibTeX
urldoi - Towards Energy Efficient Memristor-based TCAM for Match-Action Processing (), In Proceedings of the 13th International Green and Sustainable Computing Conference (IGSC 2022), IEEE, 2022.
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.
BibTeX
urldoi - On the Use of the Conformance and Compliance Keywords During Verification of Business Processes (), In Business Process Management Forum (C. Di Ciccio, R. Dijkman, A. del Río Ortega, S. Rinderle-Ma, eds.), Springer, 2022.
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.
BibTeX
urldoi - Window-based Parallel Operator Execution with In-Network Computing (), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems, ACM, 2022.
- Enhancing Flexibility for Dynamic Time-Sensitive Network Configurations (), In Proceedings of the 3rd KuVS Fachgespräch on Network Softwarization, Universität Tübingen, 2022.
- On the Incremental Reconfiguration of Time-sensitive Networks at Runtime (), In Proceedings of the IFIP Networking Conference., IFIP, 2022.
BibTeX
- Travel light: state shedding for efficient operator migration (), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems (DEBS'22), ACM press, 2022.
- CO2 Emission Aware Scheduling for Deep Neural Network Training Workloads (), In 2022 IEEE International Conference on Big Data (Big Data), IEEE, 2022.
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.
BibTeX
urldoi - FA2: Fast, Accurate Autoscaling for Serving Deep Learning Inference with SLA Guarantees (), In Proceedings of the 28th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2022), IEEE, 2022.
BibTeX
- PANDA: performance prediction for parallel and dynamic stream processing (), In Proceedings of the 16th ACM International Conference on Distributed and Event-Based Systems, ACM press, 2022.
- Using Memristors for Energy Efficient Cognitive Network Functions (), In Symposium on Physics of Information in Matter [Poster Session], AMOLF, 2022.
- In-Network Computing Over Memristor-Based Cognitive Network Functions (), In Brain-Inspired Concepts and Materials for Information Processing (Brainspiration) Conference [Poster Session], University of Twente, 2022.
- Memristor-Based Cognitive Network Packet Processors (), In Neuromorphic Computing Netherlands (NCN 2022) Workshop [Abstracts, Talks and Posters], Radboud University, 2022.
- Memristor-Based Cognitive and Energy Efficient In-Network Processing (), In Workshop on Bio-Inspired Information Pathways [Abstracts and Posters], CRC-1461 Neurotronics, University of Kiel, 2022.