Research Area: Metaheuristic Computing
The term grid computing is used to describe an infrastructure that connects geographically distributed computers and heterogeneous platforms owned by multiple organizations allowing their computational power, storage capabilities and other resources to be selected and shared. Allocating jobs to computational grid resources in an efficient manner is one of the main challenges facing any grid computing system; this allocation is called job scheduling in grid computing.
This thesis studies the application of hybrid meta-heuristics to the job scheduling problem in grid computing, which is recognized as being one of the most important and challenging issues in grid computing environments. Similar to job scheduling in traditional computing systems, this allocation is known to be an NP hard problem. Meta-heuristic approaches such as the Genetic Algorithm (GA), Variable Neighborhood Search (VNS) and Ant Colony Optimization (ACO) have all proven their effectiveness in solving different scheduling problems. However, hybridizing two or more meta-heuristics shows better performance than applying a stand-alone approach.
The experiments show that the proposed schedulers achieved impressive results compared to other traditional, heuristic and meta-heuristic approaches selected from the bibliography. To model the dynamic version of the problem, a simple simulator, which uses the rescheduling technique, is designed and new problem instances are generated, by using a well-known methodology, to evaluate the performance of the proposed hybrid schedulers. The experimental results show that the use of rescheduling provides significant improvements in terms of the make-span compared to other non-rescheduling approaches.
Name of the Researcher: Muhanad Tahrir Younis
Name of the Supervisor(s): Shengxiang Yang
Year of Completion: 2018
University: De Montfort University
Thesis Link: Home Page Url
