# Reinforced Workload Distribution Fairness

arXiv: https://arxiv.org/abs/2111.00008 · HTML: https://mlsanity.com/paper/2111.00008/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [Microservices: yesterday, today, and tomorrow](https://mlsanity.com/paper/1606.04036/) (2016)
- [MVFST-RL: An Asynchronous RL Framework for Congestion Control with Delayed Actions](https://mlsanity.com/paper/1910.04054/) (2019)
- [In-network Congestion-aware Load Balancing at Transport Layer](https://mlsanity.com/paper/1811.09731/) (2018)
- [Distributed Dispatching in the Parallel Server Model](https://mlsanity.com/paper/2008.00793/) (2020)

## Built on by (most cited first, 3 of 3)

- [Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center](https://mlsanity.com/paper/2201.11727/) (2022)
- [Multi-agent Attention Actor-Critic Algorithm for Load Balancing in Cellular Networks](https://mlsanity.com/paper/2303.08003/) (2023)
- [Group Fairness in Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2503.07817/) (2025)

## Abstract (arXiv)

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.