Load Balancing Using Sparse Communication
提出一种基于状态近似的框架，通过让服务器仅在必要时通信，将负载均衡的通信量减少90%以上，同时保持近优性能，适用于带宽有限的大型服务系统。
Smarter Load Balancing with Fewer Messages Modern data centers rely on real-time information to route jobs efficiently—but constant communication between servers and load balancers can overwhelm the network. In their paper, “Load Balancing Using Sparse Communication,” Gal Mendelson and Kuang Xu present a breakthrough: high-performance load balancing using drastically fewer messages. They introduce a flexible framework based on state approximation and develop new algorithms and communication protocols that maintain near-optimal performance even when communication is sparse. The key insight is that servers can monitor how wrong the load balancer’s estimate is and communicate only when necessary. Their approach reduces communication by over 90% while sacrificing little quality, as proven through both theory and simulation. These results offer a scalable and practical solution for large service systems where bandwidth is precious.