Reinforced Workload Distribution Fairness

Zhiyuan Yao (1, 2) , Zihan Ding (3) , Thomas Heide Clausen (2)

1 Cisco Systems France
2 X - École polytechnique
3 Princeton University

Network load balancers are central components in data centers, that distributes workloads across multiple servers and thereby contribute to offering scalable services. However, when load balancers operate in dynamic environments with limited monitoring of application server loads, they rely on heuristic algorithms that require manual configurations for fairness and performance. To alleviate that, this paper proposes a distributed asynchronous reinforcement learning mechanism to-with no active load balancer state monitoring and limited network observations-improve the fairness of the workload distribution achieved by a load balancer. The performance of proposed mechanism is evaluated and compared with stateof-the-art load balancing algorithms in a simulator, under configurations with progressively increasing complexities. Preliminary results show promise in RLbased load balancing algorithms, and identify additional challenges and future research directions, including reward function design and model scalability.

Domaines: Intelligence artificielle [cs.AI]; Réseaux et télécommunications [cs.NI]

Zhiyuan Yao, Zihan Ding, Thomas Heide Clausen. Reinforced Workload Distribution Fairness. 5th Workshop on Machine Learning for Systems at 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Dec 2021, Sydney, Australia. ⟨hal-03405824⟩