A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud Services
To support ever-chainging user needs such as large storage volumes, web search, and high-performance 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 opera...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9673774/ |
| _version_ | 1798015660841762816 |
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| author | Joohyung Sun Hyeonjoong Cho |
| author_facet | Joohyung Sun Hyeonjoong Cho |
| author_sort | Joohyung Sun |
| collection | DOAJ |
| description | To support ever-chainging user needs such as large storage volumes, web search, and high-performance 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 <italic>service level agreements</italic> 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 <inline-formula> <tex-math notation="LaTeX">$\Omega (N^{3}logN)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is the number of tasks. Thus, we introduce a real-time task scheduling algorithm for cloud computing servers, which alleviates the computational complexity of <inline-formula> <tex-math notation="LaTeX">$O(N^{2})$ </tex-math></inline-formula> 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. |
| first_indexed | 2024-04-11T15:37:23Z |
| format | Article |
| id | doaj.art-cceb294912e849e79a4baf6552488a94 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T15:37:23Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cceb294912e849e79a4baf6552488a942022-12-22T04:15:55ZengIEEEIEEE Access2169-35362022-01-01105697571410.1109/ACCESS.2022.31410869673774A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud ServicesJoohyung Sun0https://orcid.org/0000-0002-3177-6595Hyeonjoong Cho1https://orcid.org/0000-0003-1487-895XElectronics and Telecommunications Research Institute, Daejeon, South KoreaDepartment of Computer Convergence Software, Korea University, Sejong City, South KoreaTo support ever-chainging user needs such as large storage volumes, web search, and high-performance 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 <italic>service level agreements</italic> 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 <inline-formula> <tex-math notation="LaTeX">$\Omega (N^{3}logN)$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is the number of tasks. Thus, we introduce a real-time task scheduling algorithm for cloud computing servers, which alleviates the computational complexity of <inline-formula> <tex-math notation="LaTeX">$O(N^{2})$ </tex-math></inline-formula> 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.https://ieeexplore.ieee.org/document/9673774/Cloud computingdynamic power managementenergy-aware algorithmflow network problemoptimal schedulingreal-time computing |
| spellingShingle | Joohyung Sun Hyeonjoong Cho A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud Services IEEE Access Cloud computing dynamic power management energy-aware algorithm flow network problem optimal scheduling real-time computing |
| title | A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud Services |
| title_full | A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud Services |
| title_fullStr | A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud Services |
| title_full_unstemmed | A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud Services |
| title_short | A Lightweight Optimal Scheduling Algorithm for Energy-Efficient and Real-Time Cloud Services |
| title_sort | lightweight optimal scheduling algorithm for energy efficient and real time cloud services |
| topic | Cloud computing dynamic power management energy-aware algorithm flow network problem optimal scheduling real-time computing |
| url | https://ieeexplore.ieee.org/document/9673774/ |
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