Optimized resource allocation in cloud computing for enhanced performance with modified particle swarm optimization

¹ Department of Computer Science and Engineering (Data Science), Vardhaman College of Engineering, Shamshabad, Hyderabad, India, 501218
² Department of EEE, Institute of Aeronautical Engineering, Hyderabad, India
³ Department of Computer Science Engineering, Hyderabad Institute of Technology and Management, Telangana, India
⁴ Department of Computer Science & Engineering, Silicon Institute of Technology, Bhubaneswar, Odisha, India, 751024
⁵ Department of Computer Science and Engineering, Builders Engineering College, Kangeyam Tirupur, Tamil Nadu, India, 638108
⁶ PS Consulting and Solutions
⁷ Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur District, Andhra Pradesh, India, 522302

^* Corresponding author: gsrinivasulu1678@vardhaman.org

Abstract

Cloud Computing (CC) offers abundant resources and diverse services for running a wide range of consumer applications, although it faces specific issues that need attention. Cloud customers aim to choose the most suitable resource that fulfills the requirements of consumers at a fair cost and within an acceptable timeframe; however, at times, they wind up paying more for a shorter duration. Many advanced algorithms focus on optimizing a single variable individually. Hence, an Optimized Resource Allocation in Cloud Computing (ORA-CC) Model is required to achieve equilibrium between opposing aims in Cloud Computing. The ORA-CC study aims to create a task processing structure with the decision-making ability to choose the best resource in real-time for handling diverse and complicated uses on Virtual Computers (VC). It will utilize a Modified Particle Swarm Optimization (MoPSO) method to meet a deadline set by the user. The fitness value is calculated by combining a base value with the enhanced estimation of resources based on the ORA-CC algorithm to create a robust arrangement. The ORA-CC technique's effectiveness is evaluated by contrasting it with a few current multi-objective restrictions applied to machine scheduling strategies utilizing the Cloudsim simulation. The comparison demonstrates that the suggested ORA-CC strategy offers more efficient resource allocation than other techniques.