TIEO: Metaheuristic Driven Cost and Energy Aware Scheduling in Cloud Computing

Abstract

Task Scheduling (TS) in Cloud Computing (CC) plays a critical role in optimizing resource allocation, minimizing execution time, and reducing operational costs. This paper introduces a novel Tribal Intelligent Evolution Optimization (TIEO) algorithm for efficient task scheduling in cloud environments. TIEO mimics the collective intelligence and adaptive strategies of tribal societies, integrating evolutionary selection, cooperation, and adaptive learning to enhance scheduling efficiency. The proposed approach dynamically balances makespan, energy consumption, and cost by leveraging intelligent exploration-exploitation mechanisms. Comparative analysis with conventional algorithms such as PSO, ACO, and GA demonstrates that TIEO achieves a 17.8% reduction in makespan, 19.4% improvement in energy efficiency, and 22.1% enhancement in resource utilization. The results validate the effectiveness of TIEO in addressing dynamic workload variations, ensuring optimal task execution while maintaining a balanced trade-off between performance and cost in cloud computing environments.