用新型遗传算法优化云服务分配，防过载且降成本
A Meta-Heuristic Load Balancer for Cloud Computing Systems
- 用多资源抽象模型建模云环境，融合迁移开销
- 新遗传算法在实验中降低负载不均与系统成本
- 适合研究云调度或负载均衡的工程师与学者
本文提出一种云系统服务分配策略，避免节点过载并维持系统稳定，同时最小化成本。我们构建了包含多种资源类型及服务迁移成本的云资源利用率抽象模型。实现了一个原型级元启发式负载均衡器，并展示了实验结果。此外，提出一种新型遗传算法，其初始种群由其他元启发式算法输出生成。
原文摘要 · Abstract (English)
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.
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