评估云Web服务的加权轮询负载均衡

Weikun Wang, G. Casale
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引用次数: 48

摘要

加权轮循负载平衡是云负载平衡器中提供的一种常见路由策略。然而,缺乏有效的机制来决定分配给每个服务器的权重,以实现系统的整体最优收益。本文首先通过实验探讨了概率路由与加权轮询负载均衡策略之间的关系。从实验中发现,这两种策略之间具有相似的行为,这使得根据文献中研究的排队理论启发式算法和优化算法估计的路由概率来分配权重成为可能。我们特别关注基于多类工作负载的封闭队列网络的算法,该算法可用于描述具有不同用户的服务水平协议的应用程序。我们还比较了排队理论方法与不需要指定应用程序随机模型的简单启发式方法的效率。结果表明,在吞吐量最大化方面,排队理论算法比与VM容量成比例的路由产生明显更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating Weighted Round Robin Load Balancing for Cloud Web Services
Weighted round robin load balancing is a common routing policy offered in cloud load balancers. However, there is a lack of effective mechanisms to decide the weights assigned to each server to achieve an overall optimal revenue of the system. In this paper, we first experimentally explore the relation between probabilistic routing and weighted round robin load balancing policies. From the experiment a similar behavior is found between these two policies, which makes it possible to assign the weights according to the routing probability estimated from queueing theoretic heuristic and optimization algorithms studied in the literature. We focus in particular on algorithms based on closed queueing networks for multi-class workloads, which can be used to describe application with service level agreements differentiated across users. We also compare the efficiency of queueing theoretic methods with simple heuristics that do not require to specify a stochastic model of the application. Results indicate that queueing theoretical algorithms yield significantly better results than routings proportional to the VM capacity with respect to throughput maximization.
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