- Title
- Multi-objective dynamic virtual machine consolidation algorithm for cloud data centers with highly energy proportional servers and heterogeneous workload
- Creator
- Khan, Md Anit; Paplinski, Andrew; Khan, Abdul; Murshed, Manzur; Buyya, Rajkumar
- Date
- 2022
- Type
- Text; Book chapter
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/189774
- Identifier
- vital:17492
- Identifier
-
https://doi.org/10.1007/978-3-031-05528-7_3
- Identifier
- ISBN:2199-1073 (ISSN)
- Abstract
- Present Dynamic VM Consolidation (DVMC) algorithms assume that optimal energy efficiency can be achieved via maximum load on Physical Machines (PMs). Such assumption has become invalid with the advent of the highly energy proportional PMs. Additionally, these algorithms consider only varying resource demand, ignoring dissimilarity of workload finishing time, aka the VM Release Time (VMRT), whereas both aspects are strongly associated with energy consumption. Consequently, traditional algorithms fail to proffer optimal performance under real Cloud scenarios. Although minimization of VM migration brings massive benefit for Cloud Data Center (CDC), it is complete opposite of what is needed to minimize energy consumption through DVMC. As such, our proposed multi-objective Stochastic Release Time aware DVMC (SRTDVMC) algorithm is unique in addressing concomitant minimization of energy consumption and VM migration in the presence of state-of-the-art PMs and heterogeneous workloads. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Relation
- New Frontiers in Cloud Computing and Internet of Things Chapter 3 p. 69-106
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2022, The Author(s)
- Subject
- Cloud data center energy efficiency; Dynamic VM consolidation; Highly energy proportional servers
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