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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE access Ročník 10; s. 5697 - 5714
Hlavní autoři: Sun, Joohyung, Cho, Hyeonjoong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Cloud computing
Complexity
dynamic power management
Dynamic scheduling
Energy consumption
Energy efficiency
energy-aware algorithm
flow network problem
Heuristic algorithms
Multiprocessing
optimal scheduling
Optimization
Power consumption
Power management
Real time
real-time computing
Real-time systems
Scheduling
Servers
Task analysis
Task scheduling
Virtual machine monitors
ISSN:2169-3536, 2169-3536
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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 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 <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.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3141086