Citation :

M. Rupasri, G.P.S.Varma, Indukuri Hemalatha, "Hybrid Harris Hawks and Reinforced Ant Colony Optimization for Energy-Aware Task Scheduling in Cloud Environments," International Journal of Electronics and Communication Engineering, vol. 13, no. 5, pp. 65-76, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I5P107

Abstract

Cloud computing task scheduling is a complex multi-objective optimization problem that involves balancing the makespan, energy consumption, cost, and Service-Level Agreement (SLA) compliance. Conventional heuristics may not adapt well to heterogeneous workloads, whereas single meta-heuristics may be prone to premature convergence. To address these issues, this study proposes a Hybrid Harris Hawks’ Optimization With Reinforced Ant Colony Optimization (HGO-RACO) model. The model enhances the global search ability of Harris Hawk’s Optimization (HHO) through adaptive escape energy dynamics and Levy flights, while leveraging the local search power of Ant Colony Optimization (ACO) via pheromone reinforcement. Simulation experiments using workload traces from NASA iPSC and HPC 2N in CloudSim demonstrated that HHO-RACO reduces makespan, power consumption, and SLA violations more effectively than PSO, GA, Firefly, and CEQACO. These results affirm that HHO-RACO provides scalable, energy-efficient, and robust scheduling suitable for dynamic cloud environments, supporting green- and performance-oriented cloud infrastructure.

Keywords

Cloud Task Scheduling, Harris Hawks Optimization (Hho), Reinforced Ant Colony Optimization (Raco), Energy-Aware Scheduling, Sla Violation Minimization.

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