Hybrid Artificial Bee Colony and Bat Algorithm for Efficient Resource Allocation in Edge-Cloud Systems

Abstract

Integrating edge and cloud computing systems builds up a powerhouse, a framework for realizing real-time data processing and conducting large-scale computation tasks. However, efficient resource allocation and task scheduling are outstanding challenges in these dynamic, heterogeneous environments. This paper proposes an innovative hybrid algorithm that amalgamates the features of the Bat Algorithm (BA) and Artificial Bee Colony (ABC) to meet such challenges. The ABC algorithm's solid global search capabilities and the BA's efficient local exploitation are merged for efficient task scheduling and resource allocation. Dynamic adaptation of the proposed hybrid algorithm accommodates such conditions by balancing exploration and exploitation through periodic solution exchanges. Experimental evaluations highlight that the proposed algorithm can minimize execution time and costs involving resource utilization by guaranteeing proper management of task dependencies using a Directed Acyclic Graph (DAG) model. Compared to the available methods, the proposed hybrid technique generates better performance metrics concerning reduced makespan, improved resource utilization, and lower computational delays concerning resource optimization in an edge-cloud context.