PHWSOA: A Pareto-based Hybrid Whale-Seagull Scheduling for Multi-Objective Tasks in Cloud Computing

2025-12-10

AI Summary

To address the insufficient multi-objective co-optimization in cloud task scheduling, this paper proposes a Pareto-driven multi-objective scheduling algorithm that hybridizes Whale Optimization Algorithm (WOA) and Seagull Optimization Algorithm (SOA). Key innovations include Halton-sequence-based population initialization, a Pareto-guided mutation mechanism, and a dynamic virtual machine load redistribution strategy—collectively enhancing global exploration while mitigating premature convergence and improving local exploitation. Integrated into the CloudSim framework, the algorithm is evaluated on real-world workloads from NASA-iPSC and HPC2N. Experimental results demonstrate significant improvements over baseline methods (e.g., WOA, GA): 72.1% reduction in makespan, 36.8% enhancement in load balancing, and 23.5% reduction in economic cost. This work establishes an efficient and robust optimization framework for multi-objective resource scheduling in cloud environments.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Task scheduling is a critical research challenge in cloud computing, a transformative technology widely adopted across industries. Although numerous scheduling solutions exist, they predominantly optimize singular or limited metrics such as execution time or resource utilization often neglecting the need for comprehensive multi-objective optimization. To bridge this gap, this paper proposes the Pareto-based Hybrid Whale-Seagull Optimization Algorithm (PHWSOA). This algorithm synergistically combines the strengths of the Whale Optimization Algorithm (WOA) and the Seagull Optimization Algorithm (SOA), specifically mitigating WOA's limitations in local exploitation and SOA's constraints in global exploration. Leveraging Pareto dominance principles, PHWSOA simultaneously optimizes three key objectives: makespan, virtual machine (VM) load balancing, and economic cost. Key enhancements include: Halton sequence initialization for superior population diversity, a Pareto-guided mutation mechanism to avert premature convergence, and parallel processing for accelerated convergence. Furthermore, a dynamic VM load redistribution mechanism is integrated to improve load balancing during task execution. Extensive experiments conducted on the CloudSim simulator, utilizing real-world workload traces from NASA-iPSC and HPC2N, demonstrate that PHWSOA delivers substantial performance gains. Specifically, it achieves up to a 72.1% reduction in makespan, a 36.8% improvement in VM load balancing, and 23.5% cost savings. These results substantially outperform baseline methods including WOA, GA, PEWOA, and GCWOA underscoring PHWSOA's strong potential for enabling efficient resource management in practical cloud environments.

Problem

• Optimizes multi-objective task scheduling in cloud computing
• Improves makespan, load balancing, and cost simultaneously
• Overcomes limitations of existing single-objective optimization methods

Innovation

• Hybrid whale-seagull algorithm optimizes multi-objective cloud tasks
• Pareto dominance guides makespan, load balance, and cost optimization
• Halton initialization and parallel processing enhance convergence and diversity