Malcolm: Multi-agent Learning for Cooperative Load Management at Rack Scale

Abstract

We consider the problem of balancing the load among servers in dense racks for microsecond-scale workloads. To balance the load in such settings tens of millions of scheduling decisions have to be made per second. Achieving this throughput while providing microsecond-scale latency and high availability is extremely challenging. To address this challenge, we design a fully decentralized load-balancing framework. In this framework, servers collectively balance the load in the system. We model the interactions among servers as a cooperative stochastic game. To find the game's parametric Nash equilibrium, we design and implement a decentralized algorithm based on multi-agent-learning theory. We empirically show that our proposed algorithm is adaptive and scalable while outperforming state-of-the art alternatives. In homogeneous settings, Malcolm performs as well as the best alternative among other baselines. In heterogeneous settings, compared to other baselines, for lower loads, Malcolm improves tail latency by up to a factor of four. And for the same tail latency, Malcolm achieves up to 60% more throughput compared to the best alternative among other baselines.