VMS-MCSA: virtual machine scheduling using modified clonal selection algorithm
| Autor: | Kashav Ajmera, Tribhuwan Kumar Tewari |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Schedule
Computer Networks and Communications Artificial immune system Computer science business.industry Distributed computing 020206 networking & telecommunications Cloud computing Symmetric multiprocessor system 02 engineering and technology computer.software_genre Scheduling (computing) Clonal selection algorithm Virtual machine Discrete optimization 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing business computer Software |
| Zdroj: | Cluster Computing. 24:3531-3549 |
| ISSN: | 1573-7543 1386-7857 |
| DOI: | 10.1007/s10586-021-03320-5 |
| Popis: | A huge cloud data center makes it possible to offer computing as a utility to customers. However, the main challenge is to fulfill the customer’s dynamic workload requirement seamlessly. Additionally, cloud data center consumes an enormous amount of power due to improper scheduling of virtual machines over the physical machines, which lead to inefficient usage of heterogeneous computing resources. So, to minimize energy consumption in the cloud data center, virtual machines should be scheduled in an energy-efficient way. In this paper, an Artificial Immune System based Virtual Machine Scheduling using Modified Clonal Selection Algorithm (VMS-MCSA) is proposed to schedule virtual machines energy efficiently. The classical Clonal Selection Algorithm(CSA) operators are modified such that they can be applied to the discrete optimization dynamic virtual machine scheduling problem. The randomized mutation operator is proposed, which reschedule VMs at each scheduling interval to handle the dynamicity of workload with minimum virtual machine migrations. Additionally, the VM-consolidation model was proposed for constraint-based virtual machine migration. The proposed VMS-MCSA algorithm is implemented on a cloudsim simulator, and the results show that the VM scheduling using VMS-MCSA is energy-efficient compared to other recent approaches. |
| Databáze: | OpenAIRE |
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