Performance Evaluation and Social Optimization of an Energy-Saving Virtual Machine Allocation Scheme Within a Cloud Envi
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Performance Evaluation and Social Optimization of an Energy-Saving Virtual Machine Allocation Scheme Within a Cloud Environment Xiu-Shuang Wang1 · Jing Zhu1 · Shun-Fu Jin1 Yutaka Takahashi3
· Wu-Yi Yue2 ·
Received: 27 November 2018 / Revised: 8 September 2019 / Accepted: 3 October 2019 © The Author(s) 2019
Abstract Achieving greener cloud computing is non-negligible for the open-source cloud platform. In this paper, we propose a novel virtual machine allocation scheme with a sleep-delay and establish a corresponding mathematical model. Taking into account the number of tasks and the state of the physical machine, we construct a two-dimensional Markov chain and derive the average latency of tasks and the energy-saving degree of the system in the steady state. Moreover, we provide numerical experiments to show the effectiveness of the proposed scheme. Furthermore, we study the Nash equilibrium behavior and the socially optimal behavior of tasks and carry out an improved adaptive genetic algorithm to obtain the socially optimal arrival rate of tasks. Finally,
This work was supported in part by the National Natural Science Foundation of China (Nos. 61872311, 61973261, 61472342) and Hebei Provincial Natural Science Foundation (No. F2017203141), China, and was supported in part by MEXT and JSPS KAKENHI (Nos. JP17H01825 and JP26280113), Japan.
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Shun-Fu Jin [email protected] Xiu-Shuang Wang [email protected] Jing Zhu [email protected] Wu-Yi Yue [email protected] Yutaka Takahashi [email protected]
1
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, Shandong, China
2
Department of Intelligence and Informatics, Konan University, Kobe 658-8501, Japan
3
Graduate School of Informatics, Kyoto University, Kyoto 606-8225, Japan
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X. Wang et al.
we present a pricing policy for tasks to maximize the social profit when managing the network resource within the cloud environment. Keywords Cloud computing · Resource allocation scheme · Mathematical analysis · Markov chain · Socially optimization · Genetic algorithm Mathematics Subject Classification 68M20 · 60K20 · 60K25 · 91A15
1 Introduction As a commercial infrastructure paradigm, cloud computing has revolutionized the IT industry [1,2]. However, the energy consumption of cloud computing shows a rising trend, while the resources themselves are highly underutilized [3,4]. This presents a bottleneck that restricts the improvement of cloud computing and reveals the great importance of greening the networks. Consolidation of virtual machines (VMs) is an effective technique to minimize the excess energy consumption resulting from the diversity of workload. Many scholars have targeted solving the consolidation problem to improve resource utilization and reduce energy consumption over the cloud environment. In [5], Fard et al. presented a dynamic VM consolidation technique, in which the detections of server overload and server underload were supported. By calculating the deviation between the utilization and the threshold of the overl
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