Scheduling in volunteer computing networks, based on neural network prediction of the job execution time. Issue 4 (4th July 2019)

• Record Type: Journal Article
• Authors: Parkhomenko, S. S.; Ledeneva, T. M.
• Is Part Of: International journal of parallel, emergent and distributed systems. Volume 34:Issue 4(2019)
• Page Start: 430
• Page End: 447
• Publication Date: 2019-07-04
• Subjects: Volunteer computing network -- scheduling -- neural network -- genetic algorithm; Parallel computers -- Periodicals; Electronic data processing -- Distributed processing -- Periodicals; Computer algorithms -- Periodicals; 004.35
• DOI: 10.1080/17445760.2018.1496435
• Languages: English
• ISSNs: 1744-5760

Abstract

ABSTRACT: Improvement of scheduling which broadly speaking means the distribution of jobs to volunteers is very important for improving the effectiveness of volunteer computing networks operating on the basis of computing resources connected to the Internet. The scheduling strategy based on the prediction of the job execution time is chosen as the main strategy to solve this problem. Suggested approach includes a neural network mechanism for computing the predictive estimate of the job execution time and a genetic algorithm for distributing jobs to volunteers with adjustment of parameters that makes it possible to respond to changes in the computing environment. The features of the approach are illustrated by computing experiments. In addition, we consider an example of the distribution of jobs to two volunteers for a project consisting of three applications. Even approximate (interval) estimates of job execution time allowed reducing the total execution time of the project and thereby optimising the computing process.