HALO: Heterogeneity-Aware Load Balancing
HALO: Heterogeneity-Aware Load Balancing
复制标题
HALO：异构感知负载平衡
DOI：
--
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Naman Mittal
中科院分区：
文献类型：
--
作者：
Anshul Gandhi;Xi Zhang;Naman Mittal
负载均衡器 (LB) 在管理分布式系统的性能和资源利用率方面发挥着关键作用。然而，为大型分布式集群开发高效的 LB 具有挑战性，原因如下：(i) 大型集群每秒需要大量调度决策，(ii) 此类集群通常由计算能力差异很大的异构服务器组成，以及 (iii) 此类集群经常经历负载的显着变化。在本文中，我们提出了 HALO，一类用于集群系统的可扩展、异构感知的 LB。 HALO LB 基于简单的随机算法，该算法针对异质性进行了分析优化。我们为随机、循环和 Power-of-D LB 开发 HALO。我们使用分析、模拟和（基于 Apache）实施结果来说明 HALO 的优势，并证明其相对于其他同类 LB 的优越性。我们的结果表明，HALO LB 可显着缩短响应时间，并且不会在各种场景中产生额外的开销。
Load Balancers (LBs) play a critical role in managing the performance and resource utilization of distributed systems. However, developing efficient LBs for large, distributed clusters is challenging for several reasons: (i) large clusters require numerous scheduling decisions per second, (ii) such clusters typically consist of heterogeneous servers that widely differ in their computing power, and (iii) such clusters often experience significant changes in load. In this paper we propose HALO, a class of scalable, heterogeneity-aware LBs for cluster systems. HALO LBs are based on simple randomized algorithms that are analytically optimized for heterogeneity. We develop HALO for randomized, Round-Robin, and Power-of-D LBs. We illustrate the benefits of HALO and demonstrate its superiority over other comparable LBs using analytical, simulation, and (Apache-based) implementation results. Our results show that HALO LBs provide significantly lower response times without incurring additional overhead across a wide range of scenarios.