CARE: Resource Allocation Using Sparse Communication

We propose a new framework for studying effective resource allocation in a load balancing system under sparse communication, a problem that arises, for instance, in data centers. At the core of our approach is state approximation, where the load balancer first estimates the servers' states via a carefully designed communication protocol, and subsequently feeds the said approximated state into a load balancing algorithm to generate a routing decision. Specifically, we show that by using a novel approximation algorithm and server-side-adaptive communication protocol, the load balancer can obtain good queue-length approximations using a communication frequency that decays quadratically in the maximum approximation error. Furthermore, using a diffusion-scaled analysis, we prove that the load balancer achieves asymptotically optimal performance whenever the approximation error scales at a lower rate than the square-root of the total processing capacity, which includes as a special case constant-error approximations. Using simulations, we find that the proposed policies achieve performance that matches or outperforms the state-of-the-art load balancing algorithms while reducing communication rates by as much as 90%. Taken as a whole, our results demonstrate that it is possible to achieve good performance even under very sparse communication, and provide strong evidence that approximate states serve as a robust and powerful information intermediary for designing communication-efficient load balancing systems.