Hybrid Intellectual Scheme for Scheduling of Heterogeneous Workflows based on Evolutionary Approach and Reinforcement Learning

Mikhail Melnik, Ivan Dolgov, Denis Nasonov

2020

Abstract

Scheduling of workload in distributed computing systems is a well-known optimization proble. A workload may include single independent software packages. However, the computational process in scientific and industrial fields is often organized as composite applications or workflows which are represented by collection of interconnected computing packages that solve a common problem. We identified three common computing modes: batch, stream and iterative. The batch mode is a classic way for one-time execution of software packages with an initially specified set of input data. Stream mode corresponds to launch of a software package for further continuous processing of active data in real time. Iterative mode is a launching of a distributed application with global synchronization at each iteration. Each computing mode has its own specifics for organization of computation process. But at the moment, there are new problems that require organization of interaction between computing modes (batch, stream, iterative) and to develop optimization algorithms for this complex computations that leads to formation of heterogeneous workflows. In this work, we present a novel developed hybrid intellectual scheme for organizing and scheduling of heterogeneous workflows based on evolutionary computing and reinforcement learning methods.

Paper Citation

in Harvard Style

Melnik M., Dolgov I. and Nasonov D. (2020). Hybrid Intellectual Scheme for Scheduling of Heterogeneous Workflows based on Evolutionary Approach and Reinforcement Learning. In Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA; ISBN 978-989-758-475-6, SciTePress, pages 200-211. DOI: 10.5220/0010112802000211

in Bibtex Style

@conference{ecta20,
author={Mikhail Melnik and Ivan Dolgov and Denis Nasonov},
title={Hybrid Intellectual Scheme for Scheduling of Heterogeneous Workflows based on Evolutionary Approach and Reinforcement Learning},
booktitle={Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA},
year={2020},
pages={200-211},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010112802000211},
isbn={978-989-758-475-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computational Intelligence (IJCCI 2020) - Volume 1: ECTA
TI - Hybrid Intellectual Scheme for Scheduling of Heterogeneous Workflows based on Evolutionary Approach and Reinforcement Learning
SN - 978-989-758-475-6
AU - Melnik M.
AU - Dolgov I.
AU - Nasonov D.
PY - 2020
SP - 200
EP - 211
DO - 10.5220/0010112802000211
PB - SciTePress