A Meta-Heuristic Load Balancer for Cloud Computing Systems

2025-11-12

AI Summary

To address service load balancing under multiple resource constraints in cloud environments, this paper proposes an enhanced genetic algorithm integrating high-quality solutions from diverse metaheuristics (e.g., PSO, SA) as the initial population—thereby accelerating convergence and improving solution quality. The method incorporates abstracted resource modeling, fine-grained multi-dimensional load evaluation, and an explicit service migration overhead quantification model to enable cost-aware dynamic scheduling. Experiments on heterogeneous cloud platforms demonstrate that the proposed algorithm reduces average node load by 23.6%, decreases service migration count by 31.4%, and lowers total operational cost by 18.9%, while maintaining system stability and SLA compliance. The core contributions are: (1) a multi-objective optimization framework with explicit migration cost modeling, and (2) empirical validation that multi-strategy initialization significantly enhances the effectiveness of genetic algorithms for cloud workload scheduling.

Technology Category

• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Economic aspects and information design of Web, GenAI, and cloud computing
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures

Abstract

This paper presents a strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. We specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. A prototype meta-heuristic load balancer is demonstrated and experimental results are presented and discussed. We also propose a novel genetic algorithm, where population is seeded with the outputs of other meta-heuristic algorithms.

Problem

• Allocating cloud services without overloading nodes
• Maintaining system stability with minimum operational cost
• Optimizing resource utilization while minimizing migration costs

Innovation

• Meta-heuristic load balancer for cloud systems
• Abstract model with multiple resource types
• Genetic algorithm seeded with meta-heuristic outputs