  This paper investigates the network load balancing problem in data centers
(DCs) where multiple load balancers (LBs) are deployed, using the multi-agent
reinforcement learning (MARL) framework. The challenges of this problem consist
of the heterogeneous processing architecture and dynamic environments, as well
as limited and partial observability of each LB agent in distributed networking
systems, which can largely degrade the performance of in-production load
balancing algorithms in real-world setups.
Centralised-training-decentralised-execution (CTDE) RL scheme has been proposed
to improve MARL performance, yet it incurs -- especially in distributed
networking systems, which prefer distributed and plug-and-play design scheme --
additional communication and management overhead among agents. We formulate the
multi-agent load balancing problem as a Markov potential game, with a carefully
and properly designed workload distribution fairness as the potential function.
A fully distributed MARL algorithm is proposed to approximate the Nash
equilibrium of the game. Experimental evaluations involve both an event-driven
simulator and real-world system, where the proposed MARL load balancing
algorithm shows close-to-optimal performance in simulations, and superior
results over in-production LBs in the real-world system.

Artificial Intelligence;Distributed, Parallel, and Cluster Computing;Systems and Control;Systems and Control