Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enter
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Simultaneous application assignment and virtual machine placement via ant colony optimization for energy-efficient enterprise data centers Fares Alharbi1,2 • Yu-Chu Tian1 Zu-Guo Yu5
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Maolin Tang1 • Md Hasanul Ferdaus3 • Wei-Zhe Zhang4
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Received: 3 January 2020 / Revised: 12 September 2020 / Accepted: 14 September 2020 Ó Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Enterprise cloud data centers consume a tremendous amount of energy due to the large number of physical machines (PMs). These PMs host a huge number of virtual machines (VMs), on which a vast number of applications are deployed. Existing research uses two separate layers to manage data center resources: application assignment to VMs, and VM placement to PMs, each of which is a bin packing problem. While this consecutive two-layer bin packing (Consec2LBP) makes the problems easier to solve, it also limits further improvement in the quality of solution. To address this issue, an integrated any colony optimization approach is proposed in this paper to deal with both layers simultaneously. It formulates the two-layer resource management into an integrated two-layer bin packing (Int2LBP) optimization problem. Then, an integrated first fit-decreasing (FFD) algorithm Int2LBP_FFD is proposed to solve this optimization problem. Using the result of Int2LBP_FFD as an initial solution, an integrated ant colony system (ACS) algorithm Int2LBP_ACS is further developed to improve the quality of solution. Simulation experiments are conducted to demonstrate the effectiveness of our integrated approach. Keywords Data center Application assignment Virtual machine placement First-fit-decreasing Ant colony system energy
& Yu-Chu Tian [email protected] Fares Alharbi [email protected]
4
School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
5
The Key Laboratory of Intelligent Computing and Information Processing of the Ministry of Education of China, Xiangtan University, Xiangtan 411105, Hunan, China
Maolin Tang [email protected] Md Hasanul Ferdaus [email protected] Wei-Zhe Zhang [email protected] Zu-Guo Yu [email protected] 1
School of Computer Science, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia
2
Present Address: Shaqra University, Shaqra, Riyadh 11961, Saudi Arabia
3
Melbourne Institute of Technology, Melbourne, VIC 3000, Australia
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Cluster Computing
1 Introduction An enterprise data center consumes a tremendous amount of energy. Most of the energy consumption come from a large number of physical machines (PMs) in the data center. These PMs host a huge number of Virtual Machines (VMs) in which applications are deployed [19, 27]. The electricity cost of an enterprise data center with 50,000 PMs is approximately $52 Million per year [15]. A recent study [5] shows that about 60% of the total energy consumption of a data center result from the PMs. Reducing this tremendous amount of
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