SELF-LEARNING OF PARAMETER WEIGHTS FOR TASK SCHEDULING IN GRID COMPUTING ENVIRONMENT

  • Donatas Sandonavičius Kaunas University of Technology
  • Aušra Gadeikytė Kaunas University of Technology
  • Giedrius Paulikas Kaunas University of Technology
  • Mindaugas Vaitkūnas Kaunas University of Technology
  • Gytis Vilutis Kaunas University of Technology
  • Gintaras Butkus Kaunas University of Applied Sciences
Keywords: Grid, Cloud, Quality of Service, Resource Broker, Self-learning of parameter weights

Abstract

The Grid computing environment is very important for solving scientific problems. To get the best performance from Grid, it is important to know where to send tasks. This paper is about one of the suggested methods for a Grid resource broker to find the best resources for the task. This method requires defining the parameters of the resources and knowing the importance of the weights of parameters. This paper also presents the self-learning method of parameter weights.

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Published
2021-12-09