Green power aware approaches for scheduling independent tasks on a multi-core machine

Kassab, Ayham; Nicod, Jean-Marc; Philippe, Laurent; Rehn‐Sonigo, Veronika

doi:10.1016/j.suscom.2021.100590

Cited by 7 publications

(4 citation statements)

References 35 publications

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“…References [ [36] , [37] , [38] , [39] , [40] ] confirm the role of Particle Swarm Optimization algorithms in enhancing MEC by improving energy efficiency and reducing latency, thus improving service quality for IoT devices. Similarly, references [ [41] , [42] , [43] , [44] , [45] ] demonstrates that effective task scheduling can significantly reduce energy consumption. Together, these studies substantiate our study's objectives to optimize energy consumption and task scheduling efficiency in the MEC environment, showing that strategic task scheduling and the use of sophisticated algorithms like Particle Swarm Optimization are vital for enhancing energy efficiency and operational performance in such systems.…”

Section: Conclusion and Limitationmentioning

confidence: 94%

Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

Li,

2024

Heliyon

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Section: Conclusion and Limitationmentioning

confidence: 94%

Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

Li,

2024

Heliyon

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“…research and practical implementations (Hurta, Žilka, & Freiberg, 2022;Joy & Chowdhury, 2021;Kassab, Nicod, Philippe, & Rehn-Sonigo, 2021). Herein, we present a synthesis of our findings, followed by a critical discussion on the implications, challenges, and prospects.…”

mentioning

confidence: 90%

Untitled

2023

GMJ

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This systematic literature review delves into the integration of the Kalina Cycle with renewable energy sources, focusing on its efficiency, practicality, and future potential. The methodological framework is robust, involving a comprehensive search across electronic databases such as Web of Science, Google Scholar, ScienceDirect, IEEE Xplore, and Scopus, supplemented by grey literature and hand-searched references. The search, confined to the past decade, employs a strategic combination of keywords and Boolean operators. Articles are meticulously selected based on stringent inclusion and exclusion criteria, ensuring relevance and quality. Data extraction encompasses study context, findings, and methodologies, followed by a rigorous quality assessment using the PRISMA checklist. The synthesis of findings is twofold: quantitative results are analyzed through meta-analytical techniques, while qualitative data undergo thematic analysis. Two independent reviewers ensure the validity of the content, and the review commits to biennial updates for sustained relevance. This systematic review not only provides a comprehensive understanding of the Kalina Cycle's integration with renewable energies but also lays the groundwork for future research and practical applications in sustainable energy conversion.

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“…There is increasing interest in sustainable and energy efficient computing and recent work focuses on approaches for energy awareness for scheduling tasks on multi-core machines [19] and also identical parallel machines [20]. For larger computation tasks, approaches for energy-aware modelling and workload predication hope to optimise data centers [21].…”

Section: Evaluating Energy Usage In Computationmentioning

confidence: 99%

Measuring the Energy and Performance of Scientific Workflows on Low-Power Clusters

2022

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Scientific problems can be formulated as workflows to allow them to take advantage of cluster computing resources. Generally, the assumption is that the greater the resources dedicated to completing these tasks the better. This assumption does not take into account the energy cost of performing the computation and the specific characteristics of each workflow. In this paper, we present a unique approach to evaluating the energy consumption of scientific workflows on compute clusters. Two workflows from different domains, Astronomy and Bioinformatics, are presented and their execution is analyzed on a cluster of low powered small board computers. The paper presents a theoretical analysis of an energy-aware execution of workflows that can reduce the energy consumption of workflows by up to 68% compared to normal execution. We demonstrate that there are limitations to the benefits of increasing cluster sizes and there are trade-offs when considering energy vs. performance of the workflows and that the performance and energy consumption of any scientific workflow is heavily dependent on its underlying structure. The study concludes that the energy consumption of workflows can be optimized to improve both aspects of the workflow and motivates the development of an energy-aware scheduler.

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Green power aware approaches for scheduling independent tasks on a multi-core machine

Cited by 7 publications

References 35 publications

Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

Optimizing low-power task scheduling for multiple users and servers in mobile edge computing by the MUMS framework

Untitled

Measuring the Energy and Performance of Scientific Workflows on Low-Power Clusters

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