Hybrid Optimization Algorithm with the Combination of PSO and Genetic Algorithm for Task Scheduling in Cloud Computing
Year: 2024
Type: article
Abstract: A type of parallel and distributed architecture known as cloud computing is made up of a variety of virtualized, interconnected, and powerfully provisioned computer systems. These systems are introduced as unified computing assets, depending on the SLAs that are formed by communication between clients and service providers. For modern organizations, where goals frequently include reducing energy and resource usage, lower response time, load balancing, etc., efficiency in cloud environments is crucial. In cloud computing, task scheduling has proven to be crucial for efficient development that strikes a compromise between improved load distribution and shorter completion times. Within a server pool, load balancing distribution is utilized to effectively distribute incoming server requests. The goal of efficient balancing is to minimize energy use while maximizing system output. Numerous methods were presented to balance load in last year, however they were unable to produce the expected outcomes. This work presents a hybrid optimization approach for cloud computing load balancing, combining PSO and genetic algorithms. The suggested approach is put into practice in MATLAB, and the energy consumption, response time, and overall migrations are compared with the outcomes of the PSO algorithm and ant colony optimization. It is analyzed that proposed algorithm performs well in terms of all parameters as compared to existing optimization algorithm. © 2001 Elsevier Science. All rights reserved
Authors: Kavita Rani, Om Prakash Sangwan, Ritu Garg
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G4010078214
OpenAlex ID: W6893152933
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