A Cluster Based Energy efficient Q-Ant Colony Optimization(CEQACO) framework for cloud computing environment

Abstract

In cloud computing, resource allocation and cloudlet scheduling are fundamental issues when dealing with a medium to large number of tasks. To meet consumer expectations and achieve optimal performance, multiple cloudlets need to be executed simultaneously using available resources, while minimizing makespan and effectively balancing the load. Despite ongoing developments in cloud computing, this technology faces numerous challenges, one of which is task scheduling. Task scheduling involves allocating users' tasks to virtual machines (VMs) to minimize turnaround time and improve resource utilization. This is an NP-hard problem with a runtime complexity of O(mn), making it a challenging task to schedule n tasks on m resources. The process of task scheduling requires exploring a large solution space, and there is a lack of algorithms that can find the optimal solution in polynomial runtime.This paper proposes a Cluster-based Energy Efficient Q-Ant Colony Optimization (CEQACO) framework for cloud computing environments. The framework utilizes clustering techniques to group cloud virtual machines (VMs) based on their workload characteristics and applies a Q-Ant Colony Optimization algorithm to optimize the allocation of VMs to physical servers. The results show that the CEQACO framework can reduce time computation and energy by up to 6%, while still meeting the quality of service requirements of cloud users.