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Energy aware resource allocation via MS-SLnO in cloud data center

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A Correction to this article was published on 01 August 2023

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Abstract

Cloud computing has evolved as a cutting-edge platform of consumer and business opportunities. It also allows the users to view apps and data from any place. Companies will borrow services from the cloud for storage and other operational tasks, which dramatically reduces the technology costs. Also, it gives the benefits of company-wide platform accessibility built on the pay-as-you-go model. There is no need of purchasing specific product licenses. However, resource allocation becomes the most important pitfalls in cloud computing. This paper aims to implement a new optimization-assisted resource allocation approach that maximizes sales with minimized costs. Prior to this allocation, the workload (allocated tasks) clustering is performed using an advanced k-means clustering with the Lion with Advanced Mating Process (LAAM) that fine-tunes the centroid. The clustering of tasks is undergone depending on the QoS (trust) and task time. Finally, a hybrid algorithm termed as Moth Search Adapted Sealion optimization (MS-SLnO) for optimal resource allocation under the consideration of Power Usage Effectiveness (PUE), CPU usage, and execution time. Furthermore, the suggested work’s performance was compared to that of the latest systems in regard to energy use, execution time, and resource usage.

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No new data were generated or analysed in support of this research.

Change history

Abbreviations

SLnO:

Sea Lion Optimization Algorithm

PUE:

Power Usage Effectiveness

ETC:

Expected Time To Complete

PM:

Physical Machines

MSA:

Moth Search Algorithm

LAAM:

Lion With Advanced Mating Process

VM:

Virtual Machines

CCSA:

Chaotic Squirrel Search Algorithm

ADA:

Adaptive Dragonfly Algorithm

FF:

Firefly

MS:

Make Span

IaaS:

Infrastructure As A Service

MGWO:

Modified Mean Grey Wolf Optimization Algorithm

DA:

Dragonfly Algorithm

ANN:

Artificial Neural Network

TS:

Task Scheduling

SCAS:

Security And Cost Aware Scheduling

PSO:

Particle Swarm Optimization

PE:

Processing Elements

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amity University Uttar Pradesh, Lucknow Campus, Lucknow, India

    Shubham Singh & Pawan Singh

  2. Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad, India

    Sudeep Tanwar

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  1. Shubham Singh
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  2. Pawan Singh
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  3. Sudeep Tanwar
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Correspondence to Shubham Singh.

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The original online version of this article was revised: The first author's given name was incorrectly spelled as "Shubhuam" in the original publication of this article.

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Singh, S., Singh, P. & Tanwar, S. Energy aware resource allocation via MS-SLnO in cloud data center. Multimed Tools Appl 82, 45541–45563 (2023). https://doi.org/10.1007/s11042-023-15521-8

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