Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

Zhang, Qiqi; Geng, Shaojin; Cai, Xingjuan

doi:10.32604/cmes.2023.022287

Cited by 4 publications

(1 citation statement)

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“…By harnessing the diverse array of computing resources available within distributed heterogeneous systems, it becomes feasible to adeptly meet diverse application requirements [3]. Furthermore, leveraging these resources allows for enhanced system performance, improved energy utilization efficiency, and the facilitation of innovation and development across diverse fields [4]. In the orchestration of these resources, scheduling algorithms assume a pivotal role, ensuring their efficient allocation and utilization.…”

Section: Introductionmentioning

confidence: 99%

Online Energy-Aware Scheduling for Deadline-Constrained Applications in Distributed Heterogeneous Systems

Liu,

Du,

Chen

et al. 2024

International Journal of Aerospace Engineering

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In the current computing environment, the significance of distributed heterogeneous systems has gained prominence. The research on scheduling problems in distributed systems that consider energy consumption has garnered substantial attention due to its potential to enhance system stability, achieve energy savings, and contribute to environmental preservation. However, efficient scheduling in such systems necessitates not only the consideration of energy consumption but also the ability to adapt to the dynamic nature of the system. To tackle these challenges, we propose an online energy-aware scheduling algorithm for deadline-constrained applications in distributed heterogeneous systems, leveraging dynamic voltage and frequency scaling (DVFS) techniques. First, the algorithm models the continuously arriving applications and heterogeneous processors and proposes a novel task-sorting method to prioritize tasks, ensuring that more applications are completed within their respective deadlines. Second, the algorithm controls the selection range of processors based on the task’s subdeadline and assigns the task to the processor with the minimum energy consumption. Through experiments conducted with randomly generated applications, our approach consistently exhibits superior performance when compared to similar scheduling algorithms.

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Section: Introductionmentioning

confidence: 99%