@inproceedings{scholars8823,
       publisher = {EAI},
         journal = {COMPSE 2016 - 1st EAI International Conference on Computer Science and Engineering},
           title = {Experimental performance analysis of job scheduling algorithms on computational grid using real workload traces},
            note = {cited By 0; Conference of 1st EAI International Conference on Computer Science and Engineering, COMPSE 2016 ; Conference Date: 11 November 2016 Through 12 November 2016; Conference Code:130814},
            year = {2017},
            isbn = {9781631901362},
          author = {Shah, S. N. M. and Mahmood, A. K. and Rubab, S. and Hassan, M. F.},
        abstract = {Grid, an infrastructure for resource sharing, currently has shown its importance in many scientific applications requiring tremendously high computational power. Grid computing, whose resources are distributed, heterogeneous and dynamic in nature, introduces a number of fascinating issues in job scheduling. Grid scheduler is the core component of a grid and is responsible for efficient and effective utilization of heterogeneous and distributed resources. This paper presents comparative performance analysis of our proposed job scheduling algorithm with other well known job scheduling algorithms considering the quality of service parameters. The main thrust of this work was to conduct a quality of service based experimental performance evaluation of job scheduling algorithms on computational Grid in true dynamic environment. Experimental evaluation confirmed that proposed scheduling algorithms possess a high degree of optimality in performance, efficiency and scalability. This paper includes statistical analysis of real workload traces to present the nature and behavior of jobs.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032380630&partnerID=40&md5=99dd68cf9fce8f88f4a6c4ab1af52250},
        keywords = {Cluster computing; Distributed computer systems; Parallel processing systems; Quality control; Quality of service; Resource allocation; Scheduling; Scheduling algorithms, Cluster; Distributed systems; Grid scheduling; Parallel processing; Performance evaluation; Simulation; Task synchronization; Work-load models, Grid computing}
}