SELF-LEARNING OF PARAMETER WEIGHTS FOR TASK SCHEDULING IN GRID COMPUTING ENVIRONMENT - Donatas Sandonavičius; Aušra Gadeikytė; Giedrius Paulikas; Mindaugas Vaitkūnas; Gytis Vilutis; Gintaras Butkus (Mokslo Taikomieji Tyrimai Lietuvos Kolegijose)
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.