We consider the load balancing problem in large-scale heterogeneous systems with multiple dispatchers. We introduce a general framework called Local-Estimation-Driven (LED). Under this framework, each dispatcher keeps local (possibly outdated) estimates of queue lengths for all the servers, and the dispatching decision is made purely based on these local estimates. The local estimates are updated via infrequent communications between dispatchers and servers. We derive sufficient conditions for LED policies to achieve throughput optimality and delay optimality in heavy-traffic, respectively. These conditions directly imply delay optimality for many previous local-memory based policies in heavy traffic. Moreover, the results enable us to design new delay optimal policies for heterogeneous systems with multiple dispatchers. Finally, the heavy-traffic delay optimality of the LED framework directly resolves a recent open problem on how to design optimal load balancing schemes using delayed information.

中文翻译：

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具有多个调度器的大规模异构系统中的渐近最优负载平衡

我们考虑具有多个调度器的大规模异构系统中的负载平衡问题。我们介绍了一个称为局部估计驱动（LED）的通用框架。在这个框架下，每个调度器保持对所有服务器的队列长度的本地（可能是过时的）估计，并且调度决策完全基于这些本地估计做出。本地估计通过调度员和服务器之间不频繁的通信进行更新。我们推导出 LED 策略的充分条件，以分别在大流量中实现吞吐量最优和延迟最优。这些条件直接暗示了许多以前在大流量中基于本地内存的策略的延迟最优性。此外，结果使我们能够为具有多个调度程序的异构系统设计新的延迟优化策略。最后，