Please use this identifier to cite or link to this item:
|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|
|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|
|Appears in Collections:||Статьи, проиндексированные в SCOPUS, WOS|
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.