Date of Award


Publication Type

Master Thesis

Degree Name



Computer Science

First Advisor

Aggarwal, Akshaikumar (Computer Science)


Computer Science.



Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.


Scheduling is an active research area in the Computational Grid environment. The objective of grid scheduling is to deliver both the Quality of Service (QoS) requirement of the grid users, as well as high utilization of the resources. To obtain optimal scheduling in the generalized grid environment is an NP-complete problem. A large number of researchers have presented heuristic algorithms to find a near-global optimum for the static scheduling model of the grid. Relatively a smaller number of researchers have worked on the scheduling problem for the dynamic scheduling model. This thesis proposes a new resource characteristic based optimization method, which may be combined with Earlier Gap, Earliest Deadline First (EG-EDF) policy to schedule jobs in a dynamic environment. The proposed algorithm generates near-optimal solutions, which are better than those reported in the literature for a specific range of datasets. Extensive experimentation has proved the efficacy of our method.