- Office Occupancy Detection based on Power Meters and BLE Beaconing ( ), University of Groningen, 2020.
- Optimization of energy distribution in smart grids ( ), University of Groningen, 2020.
- Unsupervised approach towards analysing the public transport bunching swings formation phenomenon ( ), In Public Transport, 2020.
- Prediction-Based Underutilized and Destination Host Selection Approaches for Energy-Efficient Dynamic VM Consolidation in Data Centers ( ), In The Journal of Supercomputing, 2020.
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.
- Efficient conditional compliance checking of business process models ( ), In Computers in Industry, ELSEVIER SCIENCE BV, volume 115, 2020.
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
- Infrastructure Aware Heterogeneous-Workloads Scheduling for Data Center Energy Cost Minimization ( ), In IEEE Transactions on Cloud Computing, volume 10, 2020.
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.
- Prediction of academic performance at undergraduate graduation: Course grades or grade point average? ( ), In Applied Sciences (Switzerland), volume 10, 2020.
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.
- The Internet of Everything: Smart things and their impact on business models ( ), In Journal of Business Research, 2020.
- Predicting academic success in higher education: literature review and best practices ( ), In International Journal of Educational Technology in Higher Education, volume 17, 2020.
© 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
- Workload Scheduling on heterogeneous Mobile Edge Cloud in 5G networks to Minimize SLA Violation ( ), In arXiv preprint arXiv:2003.02820, 2020.
- OpenBNG: Central office network functions on programmable data plane hardware ( ), In International Journal of Network Management, Wiley, 2020.
- Grußwort der Gastherausgeber zum Thema Fog Computing ( ), In Informatik Spektrum, Springer Science and Business Media LLC, volume 42, 2020.
- Theorie: processen van voortijdig schoolverlaten en begeleiding om dat te voorkomen ( ), 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, 2020.
- Procesonderzoek: processen van uitvallen, blijven en begeleiding ( ), 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, 2020.
- Towards Service-Oriented and Intelligent Microgrids ( ), In Proceedings of the 3rd International Conference on Applications of Intelligent Systems, Association for Computing Machinery, 2020.
- Predictive Multi-Objective Scheduling with Dynamic Prices and Marginal CO2-Emission Intensities ( ), In ACM e-Energy 2020, 2020.
- Sustainability Choices when Cooking Pasta ( ), In ACM e-Energy 2020, 2020.
- Operator as a Service: Stateful Serverless Complex Event Processing ( ), In Proceedings of the 2020 IEEE International Conference on Big Data, IEEE, 2020.
- The Community Structure of Constraint Satisfaction Problems and Its Correlation with Search Time ( ), In 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), 2020.
- Microbursts in Software and Hardware-based Traffic Load Generation ( ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2020.
- Flexible Content-based Publish/Subscribe over Programmable Data Planes ( ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2020.
- P4STA: High Performance Packet Timestamping with Programmable Packet Processors ( ), In Proceedings of the IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2020.