Abstract
Large distributed platform for computationally exhaustive applications is provided by the Computational Grid (CG). Required jobs are allotted to the computational grid nodes in grid scheduling in order to optimize few characteristic qualities of service parameters. Availability is the most important parameter of the computational nodes which is the likelihood of computational nodes accessible for service in specified period of time. In this paper, emphasis has given on optimization of two quality of service (QoS) parameter makespan (MS) and availability grid system for the task execution. Since, the scheduling problem is NP-Hard, so a meta-heuristics-based evolutionary techniques are often applied to solve this. We have proposed NSGA II for this purpose. The performance estimation of the proposed Availability Aware NSGA II (AANSGA II) has been done by writing program in Java and integrated with gridsim. The simulation results evaluate the performance of the proposed algorithm.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Foster, I., Kesselman, C.: The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann Publishers is an Imprint of Elsevier (2004)
Berman, F.G., Anthony, F., Hey, J.G.: Grid Computing: Making the Global Infrastructure a Reality. John Wiley and Sons (2003)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, New York (1979)
Buyya, R., Murshed, M.: Gridsim a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concurr. Comput. Pract. Experience 14(13–15), 1175–1220 (2002)
Kumar, C., Prakash, S., Kumar, T., Sahu, D.P.: Variant of genetic algorithm and its applications. International Conference on Advances in Computer and Electronics Technology, Hong Kong, pp. 25–29 (2014)
Shi, Z., Dongarra, J.: Scheduling workflow applications on processors with different capabilities. Future Generation Comput. Syst. 2006(22), 665–675 (2006)
Prakash, S., Vidyarthi, D.P.: A novel scheduling model for computational grid using quantum genetic algorithm. J. Supercomput. Springer 65(2), 742–770 (2013)
Prakash, S., Vidyarthi, D.P.: Maximizing availability for task scheduling in computational grid using GA. Concurr. Comput. Practice Experience, Wiley 27(1), 193–210 (2015)
Braun, T.D., Sigel, H.J.N.: Beck A comparison of eleven static heuristic for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61, 810–837 (2001)
Abawajy, J.H.: Automatic job scheduling policy for grid computing. LNCS, Springer-Verlag, Berlin Heidelberg 3516, 101–104 (2005)
Xhafa, F., Abraham, A.: Meta-heuristics for grid scheduling problems. Stud. Comput. Intell. Series, Springer 146, 1–37 (2008)
Dora, D.P., Kaiwartya, O.P., Kumar, S., Prakash, S.: Secured time stable geocast (S-TSG) routing for VANETs. 3rd International Conference for Computer and Communication Technology, LNCS, Springer, pp. 1–6, (2015)
Koren, I., Krishna, C.M.: Fault tolerant systems. Morgan Kaufmann is an imprint of Elsevier (2007)
Xhafa, F., Abraham, A.: A genetic algorithm based schedulers for grid computing systems. Int. J. Innov. Computing, Inform. Control 3(6), 1–19 (2007)
Rajni, A., Chana, I.: Formal QoS policy based grid resource provisioning framework. J. Grid Comput. 10(2), 249–264 (2012)
Cooper, R.B.: Introduction to Queuing Theory, 2nd edn. Elsevier North Holland Publications (1981)
Kumar, C., Prakash, S., Kumar, T., Sahu, D.P.: Variant of genetic algorithm and its applications. Int. J. Artificial Intell. Neural Networks 4(4), 8–12 (2014)
Sahu, D.P., Singh, K., Prakash, S.: Deep auto-encoders for non-linear dimensionality reduction. J. Bioinform. Intell. Control 3(4), 23–27 (2014)
Kaiwartya, O.P., Sahu, D.P., Prakash, S., Vidyarthi, D.P: Energy aware scheduling for dependent task in computational grid using genetic algorithm. KSII Trans. Internet Inform. Syst. 9(5), 220–237 (2015)
Sahu, D.P., Singh, K., Prakash, S.: Review on resource scheduling models to optimize quality of service parameters in grid computing using meta-heuristics. Int. J. Comput. Appl. (2015)
Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Srinivas, N.: Deb, kalyanmoy.: multi-objective optimization using non-dominated sorting in genetic algorithms. Evol. Comput. 2(3), 221–248 (1994)
Prakash, S., Vidyarthi, D.P.: Observations on effect of IPC in GA based scheduling on computational grid. Int. J. Grid High Perform. Comput. 4(1), 67–80 (2012)
Prakash, S., Vidyarthi, D.P.: Immune genetic algorithm for scheduling in computational grid. J. Bio-Inspired Comput. 6(6), 397–408 (2014)
Kashyap, R., Vidyarthi, D.P.: Energy-aware scheduling model for computational grid. Concurr. Comput. Practice Exper. 24(12), 1377–1391 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer India
About this paper
Cite this paper
Sahu, D.P., Singh, K., Prakash, S. (2016). Maximizing Availability and Minimizing Markesan for Task Scheduling in Grid Computing Using NSGA II. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 381. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2526-3_24
Download citation
DOI: https://doi.org/10.1007/978-81-322-2526-3_24
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2525-6
Online ISBN: 978-81-322-2526-3
eBook Packages: EngineeringEngineering (R0)