2018
- MAGNETIC: Multi-Agent Machine Learning-Based Approach for Energy Efficient Dynamic Consolidation in Data Centers (), In IEEE Transactions on Services Computing, volume 15, 2019.
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 These assumptions are no longer valid in modern servers executing heterogeneous workloads and lead to unrealistic or inefficient results.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.
BibTeX
urldoi - Variability in business processes: Automatically obtaining a generic specification (), In Information Systems, PERGAMON-ELSEVIER SCIENCE LTD, volume 80, 2019.
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, CORRECTNESSBibTeX
urlpdfdoi - Analytical tool for the modelling and simulation of curriculum: Towards automated design, assessment, and improvement (), In International Journal of Engineering Education, volume 35, 2019.
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 assuranceBibTeX
- Fostering higher cognitive skills through design thinking in digital hardware course: A case study (), In ICIC Express Letters, volume 13, 2019.
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 outcomesBibTeX
doi - The u-can-act Platform: A Tool to Study Intra-individual Processes of Early School Leaving and Its Prevention Using Multiple Informants (), In Frontiers in Psychology, volume 10, 2019.
- Energy management for user's thermal and power needs: A survey (), In Energy Reports, volume 5, 2019.
- IMOS: improved meta-aligner and Minimap2 on spark (), In BMC bioinformatics, Springer, volume 20, 2019.
BibTeX
- Development of a decision-aid for patients with depression considering treatment options: prediction of treatment response using a data-driven approach (), In ISPOR Europe 2019, Copenhagen, Denmark, 2019.
- Predictive CO2-Efficient Scheduling of Hybrid Electric and Thermal Loads (), In 2019 IEEE International Conference on Energy Internet (ICEI), 2019.
BibTeX
- ECiDA: Evolutionary Changes in Data Analysis (), In ICT.Open, Hilversum, The Netherlands, 2019.
- Office Multi-Occupancy Detection using BLE Beacons and Power Meters (), In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics, and Mobile Communication Conference, 2019.
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- Temporal Analysis of 911 Emergency Calls Through Time Series Modeling (), In The International Conference on Advances in Emerging Trends and Technologies, 2019.
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- Prediction of Imports of Household Appliances in Ecuador Using LSTM Networks (), In Conference on Information Technologies and Communication of Ecuador, 2019.
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- Time to get personal? The impact of researchers’ choices on the selection of treatment targets using the experience sampling methodology (), PsyArXiv, 2019.