MARL-LDB: Multi-Agent Reinforcement Learning Load Balancing Algorithm in a Distributed Computing System

Abstract

The load balancing problem in a heterogeneous distributed computing system is considered. The problem is mathematically formulated as a stochastic game. As a multi-agent system, the heterogeneous distributed computing system can be viewed as a system with trainable agents at each computational node. There are some computational nodes that receive tasks according to a pre-established distribution (Uniform, Poisson). Tasks must be distributed among computational nodes in such a way that the average load on each computational node's resources (CPU, RAM, Storage) is the same. Based on the Proximal Policy Optimization (PPO) algorithm, a multi-agent reinforcement learning algorithm has been developed to solve the stochastic game. As a result of the experimental results, the proposed algorithm is capable of solving the formulated problem and is stable under various scenarios of computing system changes.