A Meta-Heuristic Load Balancer for Cloud Computing Systems

TL;DR

This paper introduces a meta-heuristic load balancing strategy for cloud systems that optimizes resource utilization, minimizes costs, and maintains stability through a novel genetic algorithm-based approach.

Contribution

It proposes a new genetic algorithm seeded with other meta-heuristics for improved load balancing in cloud computing environments.

Findings

Prototype implementation demonstrates effective load distribution.

Experimental results show reduced migration costs.

The genetic algorithm outperforms traditional methods.

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.