Comparative Analysis of Dynamic Cost-Time Allocation Techniques in Cloud Environments

¹ Department of CSE Chandigarh University Mohali, India
² Department of CSE Chandigarh University Mohali, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

The cloud computing environment requires effective cloud resource optimization to manage the cost of execution and the time of completion and deal with workloads that may vary. Dynamic scheduling is necessary to maximize system performance due to the fact that traditional approaches tend to be static and are not dynamic enough to address dynamic situations. This paper introduces comparative research on dynamic cost-time allocation methods in the cloud setups with reference to three well known optimization strategies which include Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). The evaluation framework that has been proposed evaluates these algorithms under varying workloads based on the efficiency of task allocation, scalability and convergence behaviour. The outcomes of the experiments indicate the strong and weak aspects of each method and show that TLBO is more effective in converging much faster with less variance, PSO is effective in highly adaptable resources, and GA is effective in orderly exploration of complex allocation settings. The results will help to evolve the cost-time allocation strategies in cloud computing, providing useful information to the researchers and practitioners who can create intelligent, adaptive and resource-efficient scheduling solutions.