Load balancing across parallel servers is an important class of congestion control problems that arises in service systems. An effective load balancer relies heavily on accurate, real-time congestion information to make routing decisions. However, obtaining such information can impose significant communication overheads, especially in demanding applications like those found in modern data centers. We introduce a framework for communication-aware load balancing and design new load balancing algorithms that perform exceptionally well even in scenarios with sparse communication patterns. Central to our approach is state approximation, where the load balancer first estimates server states through a communication protocol. Subsequently, it utilizes these approximate states within a load balancing algorithm to determine routing decisions. We demonstrate that by using a novel communication protocol, one can achieve accurate queue length approximation with sparse communication: for a maximal approximation error of x, the communication frequency only needs to be O(1/x^2). We further show, via a diffusion analysis, that a constant maximal approximation error is sufficient for achieving asymptotically optimal performance. Taken together, these results therefore demonstrate that highly performant load balancing is possible with very little communication. Through simulations, we observe that the proposed designs match or surpass the performance of state-of-the-art load balancing algorithms while drastically reducing communication rates by up to 90%.

翻译：并行服务器间的负载均衡是服务系统中一类重要的拥塞控制问题。有效的负载均衡器高度依赖于准确、实时的拥塞信息来制定路由决策。然而，获取此类信息可能带来显著的通信开销，特别是在现代数据中心等要求苛刻的应用场景中。本文提出了一个通信感知的负载均衡框架，并设计了即使在稀疏通信模式下也能表现优异的新型负载均衡算法。我们方法的核心在于状态近似：负载均衡器首先通过通信协议估计服务器状态，随后将这些近似状态应用于负载均衡算法中以确定路由决策。我们证明，通过采用一种新颖的通信协议，可以在稀疏通信条件下实现精确的队列长度近似：对于最大近似误差x，通信频率仅需O(1/x^2)。进一步通过扩散分析表明，恒定的最大近似误差足以实现渐近最优性能。综上所述，这些结果证明高性能负载均衡可以在极低通信开销下实现。仿真实验表明，所提出的设计方案在将通信速率大幅降低高达90%的同时，其性能达到或超越了最先进的负载均衡算法。