Decomposed multi-objective bin-packing for virtual machine consolidation
In this paper, we describe a novel solution to the problem of virtual machine (VM) consolidation, otherwise known as VM-Packing, as applicable to Infrastructure-as-a-Service cloud data centers. Our solution relies on the observation that virtual machines are not infinitely variable in resource consu...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2016-02-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-47.pdf |
| _version_ | 1818946486935748608 |
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| author | Eli M. Dow |
| author_facet | Eli M. Dow |
| author_sort | Eli M. Dow |
| collection | DOAJ |
| description | In this paper, we describe a novel solution to the problem of virtual machine (VM) consolidation, otherwise known as VM-Packing, as applicable to Infrastructure-as-a-Service cloud data centers. Our solution relies on the observation that virtual machines are not infinitely variable in resource consumption. Generally, cloud compute providers offer them in fixed resource allocations. Effectively this makes all VMs of that allocation type (or instance type) generally interchangeable for the purposes of consolidation from a cloud compute provider viewpoint. The main contribution of this work is to demonstrate the advantages to our approach of deconstructing the VM consolidation problem into a two-step process of multidimensional bin packing. The first step is to determine the optimal, but abstract, solution composed of finite groups of equivalent VMs that should reside on each host. The second step selects concrete VMs from the managed compute pool to satisfy the optimal abstract solution while enforcing anti-colocation and preferential colocation of the virtual machines through VM contracts. We demonstrate our high-performance, deterministic packing solution generation, with over 7,500 VMs packed in under 2 min. We demonstrating comparable runtimes to other VM management solutions published in the literature allowing for favorable extrapolations of the prior work in the field in order to deal with larger VM management problem sizes our solution scales to. |
| first_indexed | 2024-12-20T08:15:47Z |
| format | Article |
| id | doaj.art-885ad7dc22614eab9d93d3fda891cde3 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-20T08:15:47Z |
| publishDate | 2016-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-885ad7dc22614eab9d93d3fda891cde32022-12-21T19:47:07ZengPeerJ Inc.PeerJ Computer Science2376-59922016-02-012e4710.7717/peerj-cs.47Decomposed multi-objective bin-packing for virtual machine consolidationEli M. Dow0Industries and Solutions, IBM Research, Yorktown Heights, NY, United StatesIn this paper, we describe a novel solution to the problem of virtual machine (VM) consolidation, otherwise known as VM-Packing, as applicable to Infrastructure-as-a-Service cloud data centers. Our solution relies on the observation that virtual machines are not infinitely variable in resource consumption. Generally, cloud compute providers offer them in fixed resource allocations. Effectively this makes all VMs of that allocation type (or instance type) generally interchangeable for the purposes of consolidation from a cloud compute provider viewpoint. The main contribution of this work is to demonstrate the advantages to our approach of deconstructing the VM consolidation problem into a two-step process of multidimensional bin packing. The first step is to determine the optimal, but abstract, solution composed of finite groups of equivalent VMs that should reside on each host. The second step selects concrete VMs from the managed compute pool to satisfy the optimal abstract solution while enforcing anti-colocation and preferential colocation of the virtual machines through VM contracts. We demonstrate our high-performance, deterministic packing solution generation, with over 7,500 VMs packed in under 2 min. We demonstrating comparable runtimes to other VM management solutions published in the literature allowing for favorable extrapolations of the prior work in the field in order to deal with larger VM management problem sizes our solution scales to.https://peerj.com/articles/cs-47.pdfVirtual machine managementConsolidationIaaS |
| spellingShingle | Eli M. Dow Decomposed multi-objective bin-packing for virtual machine consolidation PeerJ Computer Science Virtual machine management Consolidation IaaS |
| title | Decomposed multi-objective bin-packing for virtual machine consolidation |
| title_full | Decomposed multi-objective bin-packing for virtual machine consolidation |
| title_fullStr | Decomposed multi-objective bin-packing for virtual machine consolidation |
| title_full_unstemmed | Decomposed multi-objective bin-packing for virtual machine consolidation |
| title_short | Decomposed multi-objective bin-packing for virtual machine consolidation |
| title_sort | decomposed multi objective bin packing for virtual machine consolidation |
| topic | Virtual machine management Consolidation IaaS |
| url | https://peerj.com/articles/cs-47.pdf |
| work_keys_str_mv | AT elimdow decomposedmultiobjectivebinpackingforvirtualmachineconsolidation |