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An efficient grid-scheduling strategy based on a fuzzy matchmaking approach

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Abstract

Computational grids have become an appealing research area as they solve compute-intensive problems within the scientific community and in industry. A grid computational power is aggregated from a huge set of distributed heterogeneous workers; hence, it is becoming a mainstream technology for large-scale distributed resource sharing and system integration. Unfortunately, current grid schedulers suffer from the haste problem, which is the schedule inability to successfully allocate all input tasks. Accordingly, some tasks fail to complete execution as they are allocated to unsuitable workers. Others may not start execution as suitable workers are previously allocated to other peers. This paper is the first to introduce the scheduling haste problem. It also presents a reliable grid scheduler. The proposed scheduler selects the most suitable worker to execute an input grid task using a fuzzy inference system. Hence, it minimizes the turnaround time for a set of grid tasks. Moreover, our scheduler is a system-oriented one as it avoids the scheduling haste problem. Experimental results have shown that the proposed scheduler outperforms traditional grid schedulers as it introduces a better scheduling efficiency.

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References

  • Aggarwal M, Kent R (2005) An adaptive generalized scheduler for grid applications. In: The 19th annual international symposium on high performance computing systems and applications (HPCS’05), 2005, pp 15–18

  • Aggarwal M, Kent R, Ngom A (2005) Genetic algorithm based scheduler for computational grids. Int Symp High Per form Comput Syst Appl 15(18):209–215

    Google Scholar 

  • Berman F, Wolski R, Figueira S, Schopf J, Shao G (1996) Application-level scheduling on distributed heterogeneous networks. In: Proceedings of the 1996 ACM/IEEE conference on Supercomputing, 1996, p 39

  • Boutammine S, Millot D, Parrot C (2006) An adaptive Scheduling Method for Grid Computing. Euro-Par 2006:188–197

    Google Scholar 

  • Buyya R (1999) High performance cluster computing: systems and architectures. Prentice Hall, USA

    Google Scholar 

  • Buyya R, Vazhkudai S (2001) Compute power market: towards a market-oriented grid. In: The 1st international symposium on cluster computing and the grid, 2001, p 574

  • Casanova H, Kim M, Plank J, Dongarra J (1999) Adaptive scheduling for task farming with grid middleware. Int J Supercomput Appl High-Perform Comput 13(3):231–240

    Article  Google Scholar 

  • Chandak A, Sahoo B, Turuk A (2011) Heuristic task allocation srategies for computational grid. Int. J. Advanced Netw Appl 2(5):804–810

    Google Scholar 

  • Daoud M, Kharma N (2008) Research Note: a high performance algorithm for static task scheduling in heterogeneous distributed computing systems. Int J Parallel Distrib Comput 68(4):399–409

    Article  MATH  Google Scholar 

  • Foster I, Roy A, Sander V (2000) A quality of service architecture that combines resource reservation and application adaptation. In: The international workshop on quality of service, 2000, pp 181–188

  • Fujimoto N, Hagihara K (2004) A comparison among grid scheduling algorithms for independent coarse-grained tasks. In: Proceedings of the 2004 symposium on applications and the internet-workshops (SAINT 2004 Workshops), 2004, p 674

  • He L, Jarvis S, Spooner D, Bacigalupo D, Tan G, Nudd G (2005) Mapping DAG-based applications to multiclusters with background workload. In: The IEEE international symposium on cluster computing and the grid (CCGrid’05), 2005, pp 855–862

  • Heymann E, Senar M, Luque E, Livny M (2000) Adaptive scheduling for master-worker applications on the computational grid. In: Proceedings of the first international workshop on grid computing (GRID 2000)

  • Hsin C (2005) On the design of task scheduling in the heterogeneous computing environments. In: Computers and signal processing, (PACRIM. 2005), 2005, pp 396–399

  • Iavarasan E, Thambidurai P, Mahilmannan R (2005) Performance effective task scheduling algorithm for heterogeneous computing system. In: Proceedings of the 4th international symposium on parallel and distributed computing, 2005, pp 28–38

  • Jen M, Yuan F (2009) Service-oriented grid computing system for digital rights management (GC-DRM). Int J Expert Syst Appl 36(7):10708–10726

    Article  Google Scholar 

  • Jens V, Martin W, Roman B (2009) Services grids in industry—on-demand provisioning and allocation of grid-based business services. Int J Bus Inf Syst Eng 1(2):177–184

    Article  Google Scholar 

  • Kadav A, Sanjeev K (2006) A workflow editor and scheduler for composing applications on computational grids. In: The 12th international conference on parallel and distributed systems, 2006, pp 127-132

  • Kiran M, Hassan A, Kuan L, Yee Y (2009) Execution time prediction of imperative paradigm tasks for grid scheduling optimization. Int J Comput Sci Netw Secur 9(2):155–163

    Google Scholar 

  • Kousalya K, Balasubramanie P (2008) An enhanced ant algorithm for grid scheduling problem. Int J Comput Sci Netw Secur 8(4):262–271

    Google Scholar 

  • Lee W, Squicciarini A, Bertino E (2009) The design and evaluation of accountable grid computing system. In: The 29th IEEE international conference on distributed computing systems (ICDCS ‘09), 2009, pp 145–154

  • Li M, Hadjinicolaou M (2008) Curriculum development on grid computing. Int J Educ Inf Technol 1(2):71–78

    Google Scholar 

  • Liu L, Yang Y, Lian L, Wanbin S (2006) Using ant optimization for super scheduling in computational grid. In: Proceedings of the IEEE Asia-Pacific conference on services computing, 2006

  • Malone W, Fikes R, Grant R, Howard M (1998) Enterprise: a market-like task scheduler for distributed computing environments. In: The ecology of computation, 1998, pp 177–205

  • Michael C, William L (2008) Multi-core CPUs, clusters, and grid computing: a tutorial. Int J Comput Econ 32(4):353–382

    Article  MATH  Google Scholar 

  • Min-Jen T, Yin-Kai H (2009) Distributed computing power service coordination based on peer-to-peer grids architecture. Int J Expert Syst Appl 36(2):3101–3118

    Article  Google Scholar 

  • Nithya LM, Shanmugam A (2011) Scheduling in computational grid with a new hybrid ant colony optimization algorithm. Eur J Sci Res 62(2):273–281

    Google Scholar 

  • Sacerdoti F, Katz M, Massie M, Culler D (2003) Wide area cluster monitoring with ganglia. In: The IEEE international conference on cluster computing, 2003, pp 289–298

  • Salehi M, Deldari H, Dorri B (2008) Balancing load in a computational grid applying adaptive, intelligent colonies of ants. Int J Comput Inform (Informatica) 32:327–335

    MATH  Google Scholar 

  • Saravanakumar E, Prathima G (2010) A novel load balancing algorithm for computational grid. Int J Comput Intell Tech 1(1):20–26

    Google Scholar 

  • Shah R, Veeravalli B, Misra M (2007) On the design of adaptive and decentralized load balancing algorithms with load estimation for computational grid environments. IEEE Trans Parallel Distrib Syst 18(12):1675–1686

    Article  Google Scholar 

  • Tchernykh A, Ramírez J, Avetisyan A, Kuzjurin N, Grushin D, Zhuk S (2005) Two level job-scheduling strategies for a computational grid. In: Proceedings of PPAM, 2005, pp 774–781

  • Tseng L, Chin Y, Wang S (2009) The anatomy study of high performance task scheduling algorithm for Grid computing system. Int J Comput Stand Interfaces 31(4):713–722

    Article  Google Scholar 

  • Waldspurger C, Hogg T, Huberman B, Kephart O, Stornetta S (1992) Spawn: a Distributed Computational Economy. IEEE Trans Softw Eng 18:103–177

    Article  Google Scholar 

  • Wolski R, Spring N, Hayes J (1999) The Network Weather Service: a distributed resource performance forecasting service for metacomputing. Int J Future Gener Comput Syst 15(5–6):757–768

    Article  Google Scholar 

  • Xiao L, Zhu Y, Lionel M, Xu Z (2005) GridIS: an Incentive-based Grid Scheduling. In: 19th IEEE international parallel and distributed processing symposium (IPDPS’05)

  • Yan H, Shen X, Li X, Wu M (2005) An improved ant algorithm for job scheduling in grid computing. In: The IEEE international conference on machine learning and cybernetics, 2005, pp 2957–2961

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Authors and Affiliations

  1. Department of Computer Engineering and Systems, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Ahmed I. Saleh

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  1. Ahmed I. Saleh
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Correspondence to Ahmed I. Saleh.

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Saleh, A.I. An efficient grid-scheduling strategy based on a fuzzy matchmaking approach. Soft Comput 17, 467–487 (2013). https://doi.org/10.1007/s00500-012-0920-7

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