Task Scheduling: A Reinforcement Learning Based Approach

Ciprian Paduraru, Catalina Patilea, Stefan Iordache

2023

Abstract

Nowadays, various types of digital systems such as distributed systems, cloud infrastructures, industrial devices and factories, and even public institutions need a scheduling engine capable of managing all kinds of tasks and jobs. As the global resource demand is unprecedented, we can classify task scheduling as a hot topic in today’s world. On a small scale, this process can be orchestrated by humans without the intervention of machines and algorithms. However, with large scale data streams, the scheduling process can easily exceed human capacity. An automated agent or robot capable of processing millions of requests per second is the ideal solution for efficient scheduling of flows. This work focuses on developing an agent that learns autonomously from experiences using reinforcement learning how to perform efficiently the scheduling process. Carefully designed environments are used to train the agent to have similar or better planning experiences than already existing methods such as heuristic algorithms, machine learning-based methods (supervised algorithms) and genetic algorithms. We also focused on designing a suitable dataset generator for the research community, a tool that generates random data starting from a user-supplied template in combination with different distribution strategies.

Paper Citation

in Harvard Style

Paduraru C., Patilea C. and Iordache S. (2023). Task Scheduling: A Reinforcement Learning Based Approach. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-623-1, pages 948-955. DOI: 10.5220/0011826100003393

in Bibtex Style

@conference{icaart23,
author={Ciprian Paduraru and Catalina Patilea and Stefan Iordache},
title={Task Scheduling: A Reinforcement Learning Based Approach},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2023},
pages={948-955},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011826100003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Task Scheduling: A Reinforcement Learning Based Approach
SN - 978-989-758-623-1
AU - Paduraru C.
AU - Patilea C.
AU - Iordache S.
PY - 2023
SP - 948
EP - 955
DO - 10.5220/0011826100003393