Affiliations: Department of Computer Engineering, Andimeshk Branch, Islamic Azad University, Dezful, Iran | Department of Information Engineering, Electronics (DIET), Sapienza University of Rome, Rome, Italy | School of Computing, Engineering and Mathematics, University of Western Sydney, Sydney, Australia | Machine Intelligence Research Labs (MIR Labs), WA, USA | IT4Innovations - Center of excellence, VSB - Technical University of Ostrava, Czech Republic
Note: [] Corresponding author. Mohammad Shojafar, Department of Information Engineering, Electronic and Telecommunication, “Sapienza” University of Rome, via Eudossiana 18, 00184 Rome, Italy. Tel.: +39 06 44585366; Fax: +39 06 4873330; E-mail: shojafar@diet.uniroma1.it
Abstract: A grid computing environment provides a type of distributed computation that is unique because it is not centrally managed and it has the capability to connect heterogeneous resources. A grid system provides location-independent access to the resources and services of geographically distributed machines. An essential ingredient for supporting location-independent computations is the ability to discover resources that have been requested by the users. Because the number of grid users can increase and the grid environment is continuously changing, a scheduler that can discover decentralized resources is needed. Grid resource scheduling is considered to be a complicated, NP-hard problem because of the distribution of resources, the changing conditions of resources, and the unreliability of infrastructure communication. Various artificial intelligence algorithms have been proposed for scheduling tasks in a computational grid. This paper uses the imperialist competition algorithm (ICA) to address the problem of independent task scheduling in a grid environment, with the aim of reducing the makespan. Experimental results compare ICA with other algorithms and illustrate that ICA finds a shorter makespan relative to the others. Moreover, it converges quickly, finding its optimum solution in less time than the other algorithms.
