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

Zhi Zhao, Hang Xiao, Wei Rang

TL;DR

This paper tackles multi-objective cloud task scheduling by introducing PHWSOA, a Pareto-based hybrid of the Whale and Seagull Optimization Algorithms. It combines Halton-sequence initialization, Pareto-guided mutation, dynamic VM load redistribution, and parallel evaluation to optimize makespan, VM load balancing, and cost. Across NASA-iPSC and HPC2N traces, PHWSOA demonstrates substantial improvements over baselines, highlighting its potential for efficient, scalable cloud resource management. The approach offers a practical framework for achieving balanced trade-offs in real-world cloud environments.

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.

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

TL;DR

This paper tackles multi-objective cloud task scheduling by introducing PHWSOA, a Pareto-based hybrid of the Whale and Seagull Optimization Algorithms. It combines Halton-sequence initialization, Pareto-guided mutation, dynamic VM load redistribution, and parallel evaluation to optimize makespan, VM load balancing, and cost. Across NASA-iPSC and HPC2N traces, PHWSOA demonstrates substantial improvements over baselines, highlighting its potential for efficient, scalable cloud resource management. The approach offers a practical framework for achieving balanced trade-offs in real-world cloud environments.

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.

Paper Structure

This paper contains 20 sections, 18 equations, 5 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related work
  3. System Architecture and Scheduling Issues
  4. System design
  5. Problem Statement
  6. Algorithm and Modeling
  7. Whale Optimization Algorithm
  8. Seagull Optimization Algorithm
  9. Mathematical modeling
  10. A Hybrid pareto-based whale-seagull optimization algorithm
  11. Halton Sequence Population Initialization
  12. VM Load-Aware Redistribution Policy
  13. Parallel Processing Acceleration
  14. Introducing a variation operator
  15. Maintaining the Pareto Solution Set
  16. ...and 5 more sections

Figures (5)

  • Figure 1: System workflow.
  • Figure 2: Example of dominated, non-dominated and pareto front solution set for a bi-criteria optimization problem.
  • Figure 3: Flowchart of PHWSOA algorithm.
  • Figure 4: Tasks on NASA-iPSC instances. (a) makespan, (b) throughpu, (c) virtual machine load, (d) cost.
  • Figure 5: Tasks on HPC2N instances. (a) makespan, (b) throughpu, (c) virtual machine load, (d) cost.