# Distributed Dispatching in the Parallel Server Model

arXiv: https://arxiv.org/abs/2008.00793 · HTML: https://mlsanity.com/paper/2008.00793/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Reinforced Workload Distribution Fairness](https://mlsanity.com/paper/2111.00008/) (2021)
- [Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center](https://mlsanity.com/paper/2201.11727/) (2022)
- [Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game](https://mlsanity.com/paper/2206.01451/) (2022)
- [Reinforcement Learning: From Algorithms To Foundation Models](https://mlsanity.com/paper/2607.17560/) (2026)

## Abstract (arXiv)

With the rapid increase in the size and volume of cloud services and data centers, architectures with multiple job dispatchers are quickly becoming the norm. Load balancing is a key element of such systems. Nevertheless, current solutions to load balancing in such systems admit a paradoxical behavior in which more accurate information regarding server queue lengths degrades performance due to herding and detrimental incast effects. Indeed, both in theory and in practice, there is a common doubt regarding the value of information in the context of multi-dispatcher load balancing. As a result, both researchers and system designers resort to more straightforward solutions, such as the power-of-two-choices to avoid worst-case scenarios, potentially sacrificing overall resource utilization and system performance. A principal focus of our investigation concerns the value of information about queue lengths in the multi-dispatcher setting. We argue that, at its core, load balancing with multiple dispatchers is a distributed computing task. In that light, we propose a new job dispatching approach, called Tidal Water Filling, which addresses the distributed nature of the system. Specifically, by incorporating the existence of other dispatchers into the decision-making process, our protocols outperform previous solutions in many scenarios. In particular, when the dispatchers have complete and accurate information regarding the server queues, our policies significantly outperform all existing solutions.