LSQ：多调度器的大规模异构系统中的负载均衡
LSQ: Load Balancing in Large-Scale Heterogeneous Systems with Multiple Dispatchers
摘要：
如今，云基础设施的快速增长和服务器异构程度的增加对传统负载均衡策略的效率甚至可行性提出了挑战。在这样的异构系统中，许多负载均衡器，传统的解决方案，如JSQ，招致一个巨大的通信开销和有害的incast效应，由于羊群行为。替代的低通信策略，如JSQ（d）和最近提出的JIQ，要么不稳定，要么性能较差。我们介绍了本地最短队列（LSQ）系列的负载均衡算法。在这些算法中，每个调度器维护自己的、本地的、可能过时的服务器队列长度视图，并在其本地视图上继续使用JSQ。很少使用较小的通信开销来更新此本地视图。我们正式证明，只要这些本地估计的服务器队列长度的误差是有界的期望，整个系统是强稳定的。最后，在模拟中，我们展示了简单而稳定的LSQ策略如何表现出吸引人的性能，并显着优于现有的低通信策略，同时使用等效的通信预算。特别是，我们的简单政策往往优于JSQ，因为它们减少了羊群行为。我们进一步展示了如何依靠智能服务器（即，高级拉式通信），我们可以进一步提高性能并降低通信开销。
英文摘要：
Nowadays, the efficiency and even the feasibility of traditional load-balancing policies are challenged by the rapid growth of cloud infrastructure and the increasing levels of server heterogeneity. In such heterogeneous systems with many load-balancers, traditional solutions, such as JSQ, incur a prohibitively large communication overhead and detrimental incast effects due to herd behavior. Alternative low-communication policies, such as JSQ(d) and the recently proposed JIQ, are either unstable or provide poor performance. We introduce the Local Shortest Queue (LSQ) family of load balancing algorithms. In these algorithms, each dispatcher maintains its own, local, and possibly outdated view of the server queue lengths, and keeps using JSQ on its local view. A small communication overhead is used infrequently to update this local view. We formally prove that as long as the error in these local estimates of the server queue lengths is bounded in expectation, the entire system is strongly stable. Finally, in simulations, we show how simple and stable LSQ policies exhibit appealing performance and significantly outperform existing low-communication policies, while using an equivalent communication budget. In particular, our simple policies often outperform even JSQ due to their reduction of herd behavior. We further show how, by relying on smart servers (i.e., advanced pull-based communication), we can further improve performance and lower communication overhead.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
