# A Multi-Agent System Approach to Load-Balancing and Resource Allocation for Distributed Computing

arXiv: https://arxiv.org/abs/1509.06420 · HTML: https://mlsanity.com/paper/1509.06420/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2015.

## Built on by (most cited first, 1 of 1)

- [Task Allocation with Load Management in Multi-Agent Teams](https://mlsanity.com/paper/2207.08279/) (2022)

## Abstract (arXiv)

In this research we use a decentralized computing approach to allocate and schedule tasks on a massively distributed grid. Using emergent properties of multi-agent systems, the algorithm dynamically creates and dissociates clusters to serve the changing resource demands of a global task queue. The algorithm is compared to a standard First-in First-out (FIFO) scheduling algorithm. Experiments done on a simulator show that the distributed resource allocation protocol (dRAP) algorithm outperforms the FIFO scheduling algorithm on time to empty queue, average waiting time and CPU utilization. Such a decentralized computing approach holds promise for massively distributed processing scenarios like SETI@home and Google MapReduce.