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A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud Services

Authors
Sun, JoohyungCho, Hyeonjoong
Issue Date
2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Cloud computing; dynamic power management; energy-aware algorithm; flow network problem; optimal scheduling; real-time computing
Citation
IEEE ACCESS, v.10, pp.5697 - 5714
Indexed
SCIE
SCOPUS
Journal Title
IEEE ACCESS
Volume
10
Start Page
5697
End Page
5714
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/137603
DOI
10.1109/ACCESS.2022.3141086
ISSN
2169-3536
Abstract
To support ever-chainging user needs such as large storage volumes, web search, and highperformance computing, numerous companies have expanded their systems to cloud computing servers. Cloud environment systems generally consume large amounts of electrical power, leading to tremendously high operational costs. In addition, they require computing infrastructures to run various real-time applications such as financial analysis, cloud gaming, and web-based real-time services. To represent performance guarantees, the negotiated agreements in real-time computing, expressed as deadline (or latency), can be specified by service level agreements of cloud services between users and cloud server providers. Thus, a number of research works have started focusing on reducing the energy consumption and simultaneously satisfying the temporal constraint in a cloud environment. Although we previously proposed an optimal real-time scheduling algorithm for multiprocessors, it is difficult to use it for cloud environments handling a large number of cloud services because of the high computational complexity of Omega(N-3 logN), where N is the number of tasks. Thus, we introduce a real-time task scheduling algorithm for cloud computing servers, which alleviates the computational complexity of O(N-2) from the complexity of the previous algorithm using a novel flow network-based optimization method. To the best of our knowledge, our scheduling algorithm in a cloud environment, which ensures optimality for real-time tasks and achieves energy savings using dynamic power management simultaneously, is the first in the problem domain. We show that the proposed scheduling algorithm guarantees an optimal schedule for real-time tasks and achieves energy savings simultaneously. Our experimental results show that the proposed algorithm outperforms the latest existing algorithms in terms of both time complexity and energy efficiency.
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