Abstract
Classical scheduling mechanisms don’t satisfy the requirements for the end user, especially if the number of the jobs has increased massively in grid computing environment. To meet the expectations for non-trivial applications, the efficiency of the system has to be improved and the resources have to be ultimately utilized. Thus, backfilling technique becomes highly required due to its efficiency in exploiting the resources by filling the gaps that was created in the scheduler by short jobs. There are two well-known mechanisms, which are Extensible Argonne Scheduling System (EASY) and Conservative Backfilling (CONS). EASY is very aggressive and well uses the resources, however it causes a delay for the jobs ahead in the queue, while CONS solve this issue at the expense of system efficiency. In addition, and to further improve the scheduling quality, schedule-based approach has to be implemented. This approach provides information for the incoming job parameters and the resources capabilities; thus, the mechanism schedules the jobs in advance. This approach has shown a significant improvement compared with queue-based approach. In this paper, a new mechanism is proposed, namely Swift Gap. This mechanism implements schedule-based approach and applies multi-level scheduling method. In the first stage, the mechanism finds the right place for the newly arrival job, while in the second stage it manipulates the jobs’ positions for further optimization. Moreover, this paper introduces the completion time scheme. This scheme minimizes both start time and processing time. The evaluation has shown the significant impact of Swift Gap alongside the completion time rule compared to CONS and EASY.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Klusacek, D., Rudová, H.: Improving QoS in computational Grids through schedule-based approach. In: Scheduling and Planning Applications Workshop at the Eighteenth International Conference on Automated Planning and Scheduling (ICAPS 2008), Sydney, Australia (2008)
Azmi, Z.R.M., et al.: Scheduling grid jobs using priority rule algorithms and gap filling techniques. Int. J. Adv. Sci. Technol. 37, 61–76 (2011)
Sajat, M.S., et al.: A critical review on energy-efficient medium access control for wireless and mobile sensor networks. J. Telecommun. Electron. Comput. Eng. (JTEC) 8(10), 89–94 (2016)
Fareed, A., et al.: Channel impulse response equalization based on genetic algorithm in mode division multiplexing. J. Telecommun. Electron. Comput. Eng. (JTEC) 10(2–4), 149–154 (2018)
Fazea, Y.: Numerical simulation of helical structure mode-division multiplexing with nonconcentric ring vortices. Opt. Commun. 437, 303–311 (2019)
Fazea, Y.: Mode division multiplexing and dense WDM-PON for Fiber-to-the-Home. Optik 183, 994–998 (2019)
Fazea, Y., Alobaedy, M.M., Ibraheem, Z.T.: Performance of a direct-detection spot mode division multiplexing in multimode fiber. J. Opt. Commun. 40, 161–166 (2019)
Fazea, Y., Amphawan, A.: 5 × 5 25 Gbit/s WDM-MDM. J. Opt. Commun. 36(4), 327–333 (2015)
Lee, C.B.: On the user-scheduler relationship in high-performance computing (2009)
Dakkak, O., Nor, S.A., Arif, S.: Analyzing the QoS criteria from end user’s perspective in computational grid environment. In: TENCON 2017–2017 IEEE Region 10 Conference. IEEE (2017)
Klusacek, D.: Dealing with uncertainties in grids through the event-based scheduling approach. In: Fourth Doctoral Workshop on Mathematical and Engineering Methods in Computer Science (MEMICS 2008) (2008)
Zotkin, D., Keleher, P.J.: Job-length estimation and performance in backfilling schedulers. In: 1999 Proceedings of the Eighth International Symposium High Performance Distributed Computing. IEEE (1999)
Mu’alem, A.W., Feitelson, D.G.: Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with backfilling. IEEE Trans. Parallel Distrib. Syst. 12(6), 529–543 (2001)
Lifka, D.A.: The ANL/IBM SP scheduling system. In: Workshop on Job Scheduling Strategies for Parallel Processing. Springer (1995)
Jackson, D., Snell, Q., Clement, M.: Core algorithms of the Maui scheduler. In: Workshop on Job Scheduling Strategies for Parallel Processing. Springer (2001)
Chlumsky, V., Klusácek, D., Ruda, M.: The extension of torque scheduler allowing the use of planning and optimization in grids. Comput. Sci. 13(2), 5–19 (2012)
Glover, F., Laguna, M.: Tabu Search*. Springer (2013)
Dakkak, O., Nor, S.A., Arif, S.: Proposed algorithm for scheduling in computational grid using backfilling and optimization techniques. J. Telecommun. Electron. Comput. Eng. (JTEC) 8(10), 133–138 (2016)
Rizal, Z., et al.: Combinatorial Rules Approach to Improve Priority Rules Scheduler in Grid Computing Environment (2012)
Fibich, P., Matyska, L., Rudová, H.: Model of grid scheduling problem. In: Exploring Planning and Scheduling for Web Services, Grid and Autonomic Computing, pp. 17–24 (2005)
Xhafa, F., Abraham, A.: Computational models and heuristic methods for Grid scheduling problems. Future Gener. Comput. Syst. 26(4), 608–621 (2010)
Gritsenko, A.V., et al.: Decomposition analysis and machine learning in a workflow-forecast approach to the task scheduling problem for high-loaded distributed systems. Mod. Appl. Sci. 9(5), 38 (2015)
Feitelson, D.G.: Metrics for parallel job scheduling and their convergence. In: Workshop on Job Scheduling Strategies for Parallel Processing. Springer (2001)
Dakkak, O., et al.: From grids to clouds: recap on challenges and solutions. In: AIP Conference Proceedings. AIP Publishing (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Dakkak, O., Nor, S.A., Arif, S., Fazea, Y. (2020). Improving QoS for Non-trivial Applications in Grid Computing. In: Saeed, F., Mohammed, F., Gazem, N. (eds) Emerging Trends in Intelligent Computing and Informatics. IRICT 2019. Advances in Intelligent Systems and Computing, vol 1073. Springer, Cham. https://doi.org/10.1007/978-3-030-33582-3_52
Download citation
DOI: https://doi.org/10.1007/978-3-030-33582-3_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33581-6
Online ISBN: 978-3-030-33582-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)