Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12258/4255
Title: Decomposition analysis and machine learning in a workflow-forecast approach to the task scheduling problem for high-loaded distributed systems
Authors: Gritsenko, A. V.
Гриценко, А. В.
Keywords: Hierarchical clustering;Machine learning;Resource management systems;Supervised learning;Task scheduling;Workflow prediction
Issue Date: 2015
Publisher: Canadian Center of Science and Education
Citation: Gritsenko, A.V., Demurchev, N.G., Kopytov, V.V., Shulgin, A.O. Decomposition analysis and machine learning in a workflow-forecast approach to the task scheduling problem for high-loaded distributed systems // Modern Applied Science. - 2015. -Volume 9. - Issue 5. - Pages 38-49
Series/Report no.: Modern Applied Science
Abstract: The aim of this paper is to provide a description of machine learning based scheduling approach for high-loaded distributed systems that have patterns of tasks/queries that occur recurrently in workflow. The core of this approach is to predict the future workflow of the system depending on previous tasks/queries using supervised learning. First of all, the workflow is analyzed using hierarchical clustering to reveal sets of tasks/queries. Revealed sets of tasks/queries then undergo restructuring to represent patterns of recurrent tasks/queries. Later these patterns become the object of the forecasting process performed using neural network. Information on predicted tasks/queries is used by the resource management system (RMS) to perform efficient schedule. To estimate the performance of the described method it was at first realized as a module of the simulation tool Alea that models the work of high-performance distributed systems and then compared with other state-of-the-art scheduling algorithms. The simulation was produced for two datasets: in one of the experiments the proposed method showed best results, and in the other it was inferior to just a single method, though it was much better than commonly used standard scheduling algorithms
URI: https://www.scopus.com/record/display.uri?eid=2-s2.0-84928547510&origin=resultslist&sort=plf-f&src=s&nlo=1&nlr=20&nls=afprfnm-t&affilName=north+caucasus+federal+university&sid=c2b5101ac6f3f5921983751f044515f2&sot=afnl&sdt=sisr&cluster=scopubyr%2c%222015%22%2ct&sl=53&s=%28AF-ID%28%22North+Caucasus+Federal+University%22+60070541%29%29&ref=%28Decomposition+analysis+and+machine+learning+in+a+workflow-forecast+approach+to+the+task+scheduling+problem+for+high-loaded+distributed+systems%29&relpos=0&citeCnt=5&searchTerm=
http://hdl.handle.net/20.500.12258/4255
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