Job scheduling is state of the art problem in advanced computing system. To tackle the issue of larger Make span and Flow time, several techniques are being researched over. This paper works toward secretion of job scheduling policy where Burst time is considered for arranging the jobs in clusters. Proposed system is categorised into two phases: first phase arranges the jobs by following shortest job first scheduling. The queue thus formed is presented to round robin scheduler with time quantum that varies depending upon the burst time of job. Jobs arranged are arranged in batches of 10% of total jobs in queue. SJF scheduler considered is non primitive where RRS scheduler is primitive. Second phase executes the jobs by looking at the resource clusters. Multi-source shortest path dynamic algorithm is used for selection of job that can be assigned to the resource cluster. Once job execution is complete credits are assigned which will be from 0-10. Higher the credit more proficient is the result. Optimal result is obtained by the application of proposed system in terms of Make span and flow time. Simulation is conducted in MATLAB showing improvement of 6% in overall result.

Job Scheduling, SJF, RRS, PBS, Multi source shortest path

[1]. Armbrust, M. et al., 2010. A view of cloud computing. Communications of the ACM, 53(4), p.50. Available at: http://portal.acm.org/citation.cfm?doid=1721654.1721672.
[2]. Barbosa, J. & Monteiro, A.P., 2008. A list scheduling algorithm for scheduling multi-user jobs on clusters. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5336 LNCS, pp.123–136.
[3]. Depoorter, W., Vanmechelen, K. & Broeckhove, J., 2014. Advance reservation , co-allocation and pricing of network and computational resources in grids. Future Generation Computer Systems, 41, pp.1–15. Available at: http://dx.doi.org/10.1016/j.future.2014.07.004.
[4]. Elghirani, A. et al., 2008. Performance enhancement through hybrid replication and genetic algorithm co-scheduling in data grids. AICCSA 08 - 6th IEEE/ACS International Conference on Computer Systems and Applications, pp.436–443.
[5]. Horng, S.C. & Lin, S.S., 2015. Integrating Ant Colony System and Ordinal Optimization for Solving Stochastic Job Shop Scheduling Problem. Proceedings - International Conference on Intelligent Systems, Modelling and Simulation, ISMS, 2015-Octob, pp.70–75.
[6]. Kaur, M., Sharma, S. & Kaur, R., 2014. Optimization of Job Scheduling in Cloud Computing Environment. International Journal of Advanced Research in Computer Science and Software Engineering, 4(7), pp.2277–128.
[7]. Kliazovich, D., Bouvry, P. & Khan, S.U., 2013. DENS: Data center energy-efficient network-aware scheduling. Cluster Computing, 16(1), pp.65–75.
[8]. Li, B. et al., 2014. Resource availability-aware advance reservation for parallel jobs with deadlines. , pp.798–819.
[9]. Mirashe, S.P. & Kalyankar, N. V, 2010. Cloud Computing N. Antonopoulos & L. Gillam, eds. Communications of the ACM, 51(7), p.9. Available at: http://arxiv.org/abs/1003.4074.
[10]. Rajvir Kaur, S.K., 2014. Analysis of Job Scheduling Algorithms in Cloud Computing. International Journal of Computer Trends and Technology (IJCTT), 2(March), pp.12–22.
[11]. Ranganathan, K., Iamnitchi, A. & Foster, I., 2002. Improving data availability through dynamic model-driven replication in large peer-to-peer communities. 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGrid 2002, pp.0–5.
[12]. Rodger, J.A., 2016. Informatics in Medicine Unlocked Discovery of medical Big Data analytics : Improving the prediction of traumatic brain injury survival rates by data mining Patient Informatics Processing Software Hybrid Hadoop Hive. Informatics in Medicine Unlocked, 1(2015), pp.17–26. Available at: http://dx.doi.org/10.1016/j.imu.2016.01.002.
[13]. Singh, D., Singh, J. & Chhabra, A., 2012. High availability of clouds: Failover strategies for cloud computing using integrated checkpointing algorithms. Proceedings - International Conference on Communication Systems and Network Technologies, CSNT 2012, pp.698–703.
[14]. Suri, P.K. & Rani, S., 2017. Design of Task Scheduling Model for Cloud Applications in Multi Cloud Environment. , 2, pp.11–24.
[15]. Switalski, P. & Seredynski, F., 2014. Scheduling parallel batch jobs in grids with evolutionary metaheuristics. Journal of Scheduling, 18(4), pp.345–357. Available at: http://dx.doi.org/10.1007/s10951-014-0382-0.
[16]. Xhafa, F. et al., 2011. A GA+TS hybrid algorithm for independent batch scheduling in computational grids. Proceedings - 2011 International Conference on Network-Based Information Systems, NBiS 2011, pp.229–235.
[17]. Yousif, A., Abdullah, A.H. & Nor, S.M., 2011. SCHEDULING JOBS ON GRID COMPUTING USING. IEEE Access, 33(2), pp.155–164.