论文标题
云计算中资源分配的分层多代理优化
Hierarchical Multi-Agent Optimization for Resource Allocation in Cloud Computing
论文作者
论文摘要
在云计算中，一个重要的问题是，将服务节点的可用资源分配给请求的任务，并使目标函数最佳，即最大化资源利用率，收益和可用带宽。本文提出了一种层次多代理优化（HMAO）算法，以最大程度地利用资源利用，并使云计算的带宽成本最低。提出的HMAO算法是遗传算法（GA）和多代理优化（MAO）算法的组合。通过最大化资源利用率，可以实现改进的GA来找到一组用于部署请求任务的服务节点。提出了一种基于分散的MAO算法，以最大程度地降低带宽成本。我们通过Taguchi方法研究了HMAO算法的关键参数的影响，并评估了性能结果。与遗传算法（GA）和快速的非主导分类遗传（NSGA-II）算法相比，模拟结果表明，HMAO算法比现有解决方案更有效，可以解决资源分配问题，并具有大量要求的任务。此外，我们在在线资源分配中提供了HMAO算法的性能比较。
In cloud computing, an important concern is to allocate the available resources of service nodes to the requested tasks on demand and to make the objective function optimum, i.e., maximizing resource utilization, payoffs and available bandwidth. This paper proposes a hierarchical multi-agent optimization (HMAO) algorithm in order to maximize the resource utilization and make the bandwidth cost minimum for cloud computing. The proposed HMAO algorithm is a combination of the genetic algorithm (GA) and the multi-agent optimization (MAO) algorithm. With maximizing the resource utilization, an improved GA is implemented to find a set of service nodes that are used to deploy the requested tasks. A decentralized-based MAO algorithm is presented to minimize the bandwidth cost. We study the effect of key parameters of the HMAO algorithm by the Taguchi method and evaluate the performance results. When compared with genetic algorithm (GA) and fast elitist non-dominated sorting genetic (NSGA-II) algorithm, the simulation results demonstrate that the HMAO algorithm is more effective than the existing solutions to solve the problem of resource allocation with a large number of the requested tasks. Furthermore, we provide the performance comparison of the HMAO algorithm with the first-fit greedy approach in on-line resource allocation.
