A Meta-Heuristic Load Balancer for Cloud Computing Systems
This addresses load balancing for cloud computing systems, but it appears incremental as it builds on existing meta-heuristic methods.
The paper tackles the problem of allocating services in cloud computing systems to avoid node overload and maintain stability with minimal cost, and demonstrates a prototype meta-heuristic load balancer with experimental results.
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.