Abstract
Gang scheduling combines time-sharing with space-sharing to ensure a short response time for interactive tasks and high overall system throughput. It has been widely studied in different areas including the Grid. Gang scheduling tries to assign the task belonging to one job to different Grid nodes. During the tasks assignment, there are three targets as follows: (1) to keep the Grid in higher resource utilization, (2) to keep the jobs in a low average waiting time and executing time, and, (3) to keep the system in fairness between jobs. In order to meet these targets, we propose a new model according to the waiting time of the jobs. Then we propose a new scheduling method ZERO–ONE scheduling with multiple targets (ZEROONEMT) to solve the Gang scheduling in the Grid. We have conducted extensive evaluations to compare our method with the existing methods based on a simulation environment and a real log from a Grid. In the experiments, in order to justify our method, different metrics, including adapted first come first served and largest job first served, are selected to test the performance of our methods. Experimental results illustrate that our proposed ZEROONEMT reduces the values in the average waiting time, the average response time, and the standard deviation of waiting time of all the jobs.
Similar content being viewed by others
References
Bote-Lorenzo, M., Dimitriadis, Y., G´omez-S´anchez, E.: Grid characteristics and uses: a grid definition grid computing. In: Across Grids Conference, Santiago de Compostela, Spain, vol. 2970, pp. 291–298. Springer-Verlag LNCS (2004)
Chunlin, L., Layuan, L.: The use of economic agents under price driven mechanism in grid resource management, J. Syst. Archit. 50(9), 521–535 (2004). ISSN 1383-7621, doi:10.1016/j.sysarc.2003.12.002
Sim, N., Konovalov, D., Coomans, D.: High-performance GRID computing in chemoinformatics. In: Brown, Steven, Tauler, Roma, Walczak, Beata, (eds.) Comprehensive Chemometrics: Chemical and Biochemical Data Analysis. pp. 507-539. Elsevier, Oxford (2009)
Garg, S., Vecchiola, C., et al.: Mandi: a market exchange for trading utility and cloud computing services. J. Supercomput. 64(3), 1153–1174 (2013)
Ergu, D., Kou, G., et al.: The analytic hierarchy process: task scheduling and resource allocation in cloud computing environment. J. Supercomput. 64(3), 835–848 (2013)
Qureshi, K., Rehman, A., Paul, M.: Enhanced GridSim architecture with load balancing. J. Supercomput. 57(3), 265–275 (2011)
Erdil, D., Lewis, M.: Dynamic grid load sharing with adaptive dissemination protocols. J. Supercomput. 59(3), 1139–1166 (2012)
Karaoglanoglou, K., Karatza, H.: Resource discovery in a grid system: directing requests to trustworthy virtual organizations based on global trust values. J. Syst. Softw. 84(3), 465–478 (2011). ISSN 0164-1212, doi:10.1016/j.jss.2010.10.043
Jingcheng, G., Yang, X., Jing, L., Wei, L., Philip C.C.L.: A survey of communication/networking in smart grids. Future Gener. Comput. Syst. 28(2), 391–404 (2012). ISSN 0167-739X, doi:10.1016/j.future.2011.04.014
Buyya, R., Murshed, M.: GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing. Concurr. Comput. Pract. Exp. 14(13–15), 1175–1220 (2002)
Khanli, L.M., Analoui, M.: An approach to grid resource selection and fault management based on ECA rules. Future Gener. Comput. Syst. 24(4) 296–316 (2008). ISSN 0167-739X, doi:10.1016/j.future.2007.05.002
Papazachos, Z., Karatza, H.: The impact of task service time variability on Gang scheduling performance in a two-cluster system. Simul. Model. Pract. Theory 17, 1276–1289 (2009). doi:10.1016/j.simpat.2009.05.002
Karatza, H.: Scheduling Gangs in a distributed system. Int. J. Simul. Syst. Sci. Technol. UK Simul. Soc. 7, 15–22 (2006)
Karatza, H.: Performance analysis of Gang scheduling in a partition able parallel system. In: Proceedings of the 20th Europe Conference in Model Simulation, Bonn (2006)
Karatza, H.: Performance of Gang scheduling policies in the presence of critical sporadic Gridlets in distributed systems. In: Proceedings of the symposium perform evaluation of computer telecommunication system, San Diego (2007), pp 547–554
Karatza, H.: Performance of gang scheduling strategies in a parallel system. Simul. model. Pract. Theory. 17(2), 430–441 (2009), ISSN 1569-190X, doi:10.1016/j.simpat.2008.10.001
Papazachos, Z., Karatza, H.: Gang scheduling in multi-core clusters implementing migrations. Fut. Gener. Comput. Syst. 27, 1153–1165 (2011)
Moschakis, I., Karatza, H.: Evaluation of gang scheduling performance and cost in a cloud computing system. J. Supercomput. 59(2), 975–992 (2012)
Papazachos, Z., Karatza, H.: Performance evaluation of bag of gangs scheduling in a heterogeneous distributed system. J. Syst. Softw. 83, 1346–1354 (2010)
Ro, C., Cao, Y.: Performance evaluation of gang scheduling policies with migration in a grid. Int. J. Contents. 6(4), 30–34 (2010)
Malhotra, M., Ciardo, G.: Dependability modeling using petri-net. IEEE Trans. Reliab. 44(3), 428–440 (1995)
http://aws.amazon.com/ec2/ [visited: 2012-2-14]
Morefield, C.: Application of 0-1 integer programming to multitarget tracking problems. IEEE Trans. Autom. Control 22(3), 302–312 (1977)
Fourer, R., Gay, D.M., Kernighan, B.W.: A modeling language mathematical programming. Manage. Sci. 36, 519–554 (1990)
IBM Corporation, 2010. ILOG. http://www.ilog.com/products/cplex/. (2011)
http://www.mathworks.cn/help/toolbox/optim/ug/bintprog.html [visited:2011-08-05]
Tsafrir, D., Feitelson, D.G.: Instability in parallel job scheduling simulation: the role of workload flurries. In 20th International Parallel and Distributed Processing Symposium (2006)
Krishnanand, K.N., Ghose, D., Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations. Robotics and Autonomous Systems, 56(7), 549–569 (2008), ISSN 0921-8890, doi:10.1016/j.robot.2007.11.003
Hao, Y., Liu, G., Wen N., An enhanced load balancing mechanism based on deadline control on GridSim. Fut. Gener. Comput. Syst. 28(4), 657–665 (2012), ISSN 0167-739X, doi:10.1016/j.future.2011.10.010
Acknowledgments
The work was partly supported by the National Natural Science Foundation of China (NSFC) under grant (No. 61303019), Specialized Research Fund for the Doctoral Program of Higher Education under Grant No. 20133201120012, Fujuan Educational Bureau B project (JB12189). We are grateful to the editors and the reviewers for their valuable comments.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hao, Y., Liu, G., Hou, R. et al. Performance Analysis of Gang Scheduling in a Grid. J Netw Syst Manage 23, 650–672 (2015). https://doi.org/10.1007/s10922-014-9312-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10922-014-9312-x