# Reinforcement Learning for Optimal Load Distribution Sequencing in Resource-Sharing System

## TL;DR

This paper applies reinforcement learning, specifically Multi-armed bandit algorithms, to optimize load distribution sequencing in resource-sharing systems, reducing completion time through adaptive learning.

## Contribution

It introduces a novel application of MAB reinforcement learning to divisible load scheduling in virtualized resource-sharing environments.

## Key findings

- Performance improves with training iterations
- Global optimum achieved with sufficient sample size
- Reinforcement learning outperforms naive solutions

## Abstract

Divisible Load Theory (DLT) is a powerful tool for modeling divisible load problems in data-intensive systems. This paper studied an optimal divisible load distribution sequencing problem using a machine learning framework. The problem is to decide the optimal sequence to distribute divisible load to processors in order to achieve minimum finishing time. The scheduling is performed in a resource-sharing system where each physical processor is virtualized to multiple virtual processors. A reinforcement learning method called Multi-armed bandit (MAB) is used for our problem. We first provide a naive solution using the MAB algorithm and then several optimizations are performed. Various numerical tests are conducted. Our algorithm shows an increasing performance during the training progress and the global optimum will be acheived when the sample size is large enough.