Multi-Objective Binary Whale Optimization-Based Virtual Machine Allocation in Cloud Environments

被引:3
作者
Srivastava, Ankita [1 ]
Kumar, Narander [1 ]
机构
[1] Babasaheb Bhimrao Ambedkar Univ, Lucknow, Uttar Pradesh, India
关键词
Binary Whale Optimization Algorithm; Cloud Computing; Metaheuristic Algorithm; Resource Allocation; Resource Wastage; Swarm Optimization; Virtual Machine Allocation; Virtualization; RESOURCE-ALLOCATION; VM ALLOCATION; PLACEMENT; ALGORITHM; NETWORK;
D O I
10.4018/IJSIR.317111
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
With the rising demands for the services provided by cloud computing, virtual machine allocation (VMA) has become a tedious task due to the dynamic nature of the cloud. Millions of virtual machines (VMs) are allocated and de-allocated at every instant, so an efficient VMA has been a significant concern to enhance resource utilization and depreciate its wastage. Encouraged by the prodigious performance of the nature-inspired algorithm, the binary whale optimization approach has been eventuated to get to grips with the VMA issue with the focus on minimizing the resource waste and volume of servers working actively. The deliberate approach's accomplishment is assessed against the literature's well-known algorithms for VMA issues. The comparison results showed that the least resource wastage fitness of 15.68, minimum active servers of 216, and effective CPU and memory utilization of 88.31% and 88.79%, respectively, have been achieved.
引用
收藏
页数:23
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