Hybrid Genetic Algorithm and Water Wave Optimization Approach for QoS Aware Multi Objective Task Scheduling and Load Balancing in Cloud Environments
DOI:
https://doi.org/10.4108/eetiot.12172Keywords:
Cloud Computing, Task Scheduling, Load Balancing, Hybrid metaheuristic, Genetic Algorithm, Water Wave Optimization, Multi Objective Optimization, Virtual MachinesAbstract
Efficient task scheduling and load balancing in cloud computing continue to present significant challenges as a result of heterogeneous, dynamic workloads, particularly for AI/ML applications in edge-to-cloud and multi-cloud deployments. Classic and single-metaheuristic techniques sometimes converge prematurely, lack adaptability, and fail to manage multi-objective trade-offs. This paper introduces the Adaptive Evolutionary Wave Optimisation Scheduler (AEWOS), a novel hybrid metaheuristic that combines genetic algorithm evolutionary operators for global diversity generation with adaptive water wave optimisation for propagation, refraction towards elite solutions, and breaking-based localised refinement. A weighted composite fitness function with thirteen QoS metrics, diversity-variance-driven self-adaptive parameter optimisation, and constraint-aware repair techniques maintains virtual machine capacity and exclusive task allocation limits in AEWOS. CloudSim Plus's ability to conduct extensive simulations of Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Salp Swarm Algorithm (SSA), Gaussian Blackwinged Kite Algorithm (GBKA), Enhanced Osprey Optimisation Algorithm (EOOA), and Gazelle Coati Optimisation Algorithm (GCOA) represents the six advanced benchmarks that AEWOS consistently exceeds. Additionally, the use of actual portions of the ATLAS (Alibaba Trace for Learning-Augmented Scheduling) workload traces within machine computation across various scaling scenarios (500-2500 tasks on 20-100 heterogeneous virtual machines) demonstrates that AEWOS consistently outperforms these six advanced benchmarks. Significant improvements include 3-13 percentage point increases in resource utilisation, 8-22% reductions in makespan, 10-35% decreases in transmission and waiting durations, 40-70% relative decreases in imbalance degree, nearly constant throughput (0.272 tasks/s), 10-25 percentage point reductions in SLA violation rates, improved energy efficiency and scalability, and up to 65% reductions in task migration overhead. It has been demonstrated through an ablation study that the Adaptive Mechanism and Wave Propagation Model are the key factors that have an effect on system performance. AEWOS provides a self-adjusting, cohesive framework that improves the sustainability and resilience of multi-objective cloud resource management.
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