Optimization techniques for task scheduling criteria in IaaS cloud computing atmosphere using nature inspired hybrid spotted hyena optimization algorithm

Summary Cloud computing has garnered unprecedented growth in recent years in the field of Information Technology. It has emerged as a high‐performance computing option owing to its infrastructure that comprises of heterogeneous collection of autonomous computers and adaptable network architecture. T...

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Vydáno v:Concurrency and computation Ročník 34; číslo 24
Hlavní autoři: Natesan, Gobalakrishnan, Ali, Javid, Krishnadoss, Pradeep, Chidambaram, Raman, Nanjappan, Manikandan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken, USA John Wiley & Sons, Inc 01.11.2022
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Témata:
Algorithms
Cloud computing
Computer architecture
Genetic algorithms
Heuristic
Heuristic methods
Heuristic scheduling
Machine learning
meta‐heuristic
Optimization
optimization and scheduling
Optimization techniques
Parameters
QoS parameters
Resource utilization
Scheduling
Task scheduling
ISSN:1532-0626, 1532-0634
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Shrnutí:Summary Cloud computing has garnered unprecedented growth in recent years in the field of Information Technology. It has emerged as a high‐performance computing option owing to its infrastructure that comprises of heterogeneous collection of autonomous computers and adaptable network architecture. The tasks that are scheduled in an optimized manner for their execution could be classified under NP‐hard problems. Though meta‐heuristic scheduling algorithms emerge as scheduling options, they need to be much more consistent while dealing with the dynamic set up of the cloud environment. In this paper, we had proposed a multi‐objective meta‐heuristic scheduling algorithm namely Quasi Oppositional Genetic Spotted Hyena Optimization (QOGSHO) algorithm that globally optimizes the makespan, resource consumption and SLA violation QoS parameters, thereby improving the performance. The algorithm proposed is an amalgamated product of meta‐heuristic algorithms like Quasi Oppositional Based Learning (QOBL), Spotted Hyena Optimization (SHO), and Genetic Algorithm (GA). The performance efficiency of the proposed QOGSHO algorithm had been compared with various scheduling algorithms using uniform datasets by varying the data instance sizes in a simulated cloud environment. The obtained results clearly justify the task scheduling efficiency of the proposed algorithm with respect to the QoS parameters namely makespan, resource utilization and SLA violation.
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ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.7228