BACOA: Meta Heuristic Driven Hybrid Scheduling Algorithm for Improved Resource Allocation in Cloud Environment
Yerukala Naveen, Srinivasa Babu Kasturi, C P Ramya, B. Gayathri, Santhosh Kumar Medishetti
Abstract
Energy-aware scheduling is a critical aspect of optimizing task execution in cloud computing environments, where efficient resource management can significantly reduce operational costs. This paper introduces the Butterfly Ant Colony Optimization Algorithm (BACOA), a novel hybrid approach designed to enhance local and global search capabilities. BACOA combines the exploratory strength of the Butterfly Optimization Algorithm (BOA) and the exploitation capabilities of Ant Colony Optimization (ACO) to minimize energy consumption, communication cost, and computation cost in Task Scheduling (TS). By balancing these parameters, BACOA achieves a more energy-efficient task scheduling process while reducing the overhead costs associated with communication and computation in cloud systems. Simulation results demonstrate that BACOA outperforms existing algorithms, achieving superior energy savings by 16%, and reduced communication cost and computation cost by 21.06% and 18% respectively, the proposed algorithm making it ideal for scalable cloud infrastructures.