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Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment

Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment

Priyanka H., Mary Cherian
Copyright: © 2021 |Volume: 11 |Issue: 3 |Pages: 20
ISSN: 2156-1834|EISSN: 2156-1826|EISBN13: 9781799862468|DOI: 10.4018/IJCAC.2021070105
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MLA

Priyanka H., and Mary Cherian. "Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment." IJCAC vol.11, no.3 2021: pp.72-91. http://doi.org/10.4018/IJCAC.2021070105

APA

Priyanka H. & Cherian, M. (2021). Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment. International Journal of Cloud Applications and Computing (IJCAC), 11(3), 72-91. http://doi.org/10.4018/IJCAC.2021070105

Chicago

Priyanka H., and Mary Cherian. "Effective Utilization of Resources Through Optimal Allocation and Opportunistic Migration of Virtual Machines in Cloud Environment," International Journal of Cloud Applications and Computing (IJCAC) 11, no.3: 72-91. http://doi.org/10.4018/IJCAC.2021070105

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Abstract

Cloud computing has become more prominent, and it is used in large data centers. Distribution of well-organized resources (bandwidth, CPU, and memory) is the major problem in the data centers. The genetically enhanced shuffling frog leaping algorithm (GESFLA) framework is proposed to select the optimal virtual machines to schedule the tasks and allocate them in physical machines (PMs). The proposed GESFLA-based resource allocation technique is useful in minimizing the wastage of resource usage and also minimizes the power consumption of the data center. The proposed GESFL algorithm is compared with task-based particle swarm optimization (TBPSO) for efficiency. The experimental results show the excellence of GESFLA over TBPSO in terms of resource usage ratio, migration time, and total execution time. The proposed GESFLA framework reduces the energy consumption of data center up to 79%, migration time by 67%, and CPU utilization is improved by 9% for Planet Lab workload traces. For the random workload, the execution time is minimized by 71%, transfer time is reduced up to 99%, and the CPU consumption is improved by 17% when compared to TBPSO.

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