Optimizing virtual machine placement for energy and SLA in clouds using utility functions
Abstract Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations to outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in cloud users’ demands. Consequently, these cloud data centers co...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2016-10-01
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| Series: | Journal of Cloud Computing: Advances, Systems and Applications |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13677-016-0067-7 |
| _version_ | 1817992223290032128 |
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| author | Abdelkhalik Mosa Norman W. Paton |
| author_facet | Abdelkhalik Mosa Norman W. Paton |
| author_sort | Abdelkhalik Mosa |
| collection | DOAJ |
| description | Abstract Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations to outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in cloud users’ demands. Consequently, these cloud data centers consume, and have the potential to waste, substantial amounts of energy. This energy consumption increases the operational cost and the CO2 emissions. The goal of this paper is to develop an optimized energy and SLA-aware virtual machine (VM) placement strategy that dynamically assigns VMs to Physical Machines (PMs) in cloud data centers. This placement strategy co-optimizes energy consumption and service level agreement (SLA) violations. The proposed solution adopts utility functions to formulate the VM placement problem. A genetic algorithm searches the possible VMs-to-PMs assignments with a view to finding an assignment that maximizes utility. Simulation results using CloudSim show that the proposed utility-based approach reduced the average energy consumption by approximately 6 % and the overall SLA violations by more than 38 %, using fewer VM migrations and PM shutdowns, compared to a well-known heuristics-based approach. |
| first_indexed | 2024-04-14T01:23:32Z |
| format | Article |
| id | doaj.art-71f90f8085f34d93ab3ac0971fcc7993 |
| institution | Directory Open Access Journal |
| issn | 2192-113X |
| language | English |
| last_indexed | 2024-04-14T01:23:32Z |
| publishDate | 2016-10-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Cloud Computing: Advances, Systems and Applications |
| spelling | doaj.art-71f90f8085f34d93ab3ac0971fcc79932022-12-22T02:20:32ZengSpringerOpenJournal of Cloud Computing: Advances, Systems and Applications2192-113X2016-10-015111710.1186/s13677-016-0067-7Optimizing virtual machine placement for energy and SLA in clouds using utility functionsAbdelkhalik Mosa0Norman W. Paton1School of Computer Science, University of ManchesterSchool of Computer Science, University of ManchesterAbstract Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations to outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in cloud users’ demands. Consequently, these cloud data centers consume, and have the potential to waste, substantial amounts of energy. This energy consumption increases the operational cost and the CO2 emissions. The goal of this paper is to develop an optimized energy and SLA-aware virtual machine (VM) placement strategy that dynamically assigns VMs to Physical Machines (PMs) in cloud data centers. This placement strategy co-optimizes energy consumption and service level agreement (SLA) violations. The proposed solution adopts utility functions to formulate the VM placement problem. A genetic algorithm searches the possible VMs-to-PMs assignments with a view to finding an assignment that maximizes utility. Simulation results using CloudSim show that the proposed utility-based approach reduced the average energy consumption by approximately 6 % and the overall SLA violations by more than 38 %, using fewer VM migrations and PM shutdowns, compared to a well-known heuristics-based approach.http://link.springer.com/article/10.1186/s13677-016-0067-7Cloud computingVirtual machine placementCloud resource managementUtility functionsEnergy-awareService level agreement (SLA) |
| spellingShingle | Abdelkhalik Mosa Norman W. Paton Optimizing virtual machine placement for energy and SLA in clouds using utility functions Journal of Cloud Computing: Advances, Systems and Applications Cloud computing Virtual machine placement Cloud resource management Utility functions Energy-aware Service level agreement (SLA) |
| title | Optimizing virtual machine placement for energy and SLA in clouds using utility functions |
| title_full | Optimizing virtual machine placement for energy and SLA in clouds using utility functions |
| title_fullStr | Optimizing virtual machine placement for energy and SLA in clouds using utility functions |
| title_full_unstemmed | Optimizing virtual machine placement for energy and SLA in clouds using utility functions |
| title_short | Optimizing virtual machine placement for energy and SLA in clouds using utility functions |
| title_sort | optimizing virtual machine placement for energy and sla in clouds using utility functions |
| topic | Cloud computing Virtual machine placement Cloud resource management Utility functions Energy-aware Service level agreement (SLA) |
| url | http://link.springer.com/article/10.1186/s13677-016-0067-7 |
| work_keys_str_mv | AT abdelkhalikmosa optimizingvirtualmachineplacementforenergyandslaincloudsusingutilityfunctions AT normanwpaton optimizingvirtualmachineplacementforenergyandslaincloudsusingutilityfunctions |