EFO: Resource Aware Scheduling Model for Cloud Computing Using Meta-Heuristic Algorithm

Efficient resource management is critical for ensuring optimal task scheduling in cloud environments, where diverse and fluctuating workloads demand dynamic solutions. This paper presents a novel Resource-Aware Scheduling approach leveraging the Electric Fish Optimization Algorithm (EFOA) to enhance scheduling performance in cloud computing. The proposed algorithm simulates the electric field navigation mechanism of electric fish, facilitating effective exploration and exploitation in large search spaces. EFOA optimizes task allocation by considering key parameters such as resource utilization, makespan, energy consumption, and throughput. Comparative analysis demonstrates that EFOA outperforms traditional algorithms, improving resource utilization by 21.2%, reducing energy consumption by 16%, and minimizing makespan by 19.6%. The proposed algorithm is especially effective in handling heterogeneous workloads and resource-intensive cloud applications, ensuring both cost efficiency and system reliability.