A Review on Resource Scheduling Models to Optimize Quality of Service Parameters in Grid Computing using Meta-heuristics

Abstract

Computational Grid (CG) is a wide network of computational resources that provides a distributed platform for high end compute intensive applications. The resources in the computational grid are usually heterogeneous and being a highly heterogeneous system, Computational Grid poses a number of constraints. It is difficult to allocate and schedule the applications properly to achieve the benefit of the grid resources from the applications point of view, as the resources are heterogeneous and dynamic in nature. There are no common scheduling strategies that fulfill all the needs with respect to both, user and the system. The available scheduling implementations consider specific characteristics of the available resources and the application. The complexity of application, user requirements and system heterogeneity prevents any scheduling procedure in achieving its best performance. The aim of a grid scheduling algorithm is to find an appropriate set of resources and maintain its user-demanded Quality of Service (QoS) requirements. Scheduling in CG is an NP-hard problem which requires an efficient solution. The problem, considered in this work, is task scheduling in Computational Grid (CG). Task scheduling in CG is a complex problem as many QoS parameters and system constraints are involved. This paper deliberates over the problem and various tools used in order to solve this problem.

References

• M. J. Quinn, Parallel Computing: Theory and Practices, Tata McGraw Hill, India, 2nd edition, 2002.
• A. S. Tanenbaum, Distributed Systems: Principles and Paradigms, Prentice Hall of India, 2nd edition, 2002.
• I. Foster and C. Kesselman, Grid 2: Blueprint for a New Grid Computing Infrastructure, Morgan Kaufmann Publishers Inc. San Francisco, CA, USA an Imprint of Elsevier, 2nd edition, 2003.
• Grid-Café, http://www. gridcafe. org, visited on February 2014.
• F. Berman, G. Fox and T. Hey, Grid Computing: Making the Global Infrastructure a Reality, John Wiley and Sons, New York, 2002.
• F. Xhafa and A. Abraham, Meta-heuristics for Scheduling in Distributed Computing Environments Studies in Computational Intelligence, Springer, 146:1–37, 2008.
• M. R. Garey and D. S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman and Co. , New York, 1979.
• F. Xhafa and A. Abraham, "Computational Models and Heuristic Methods for Grid Scheduling Problems", Future Generation Computer Systems, Elsevier, 26(4):608-621 2010.
• P. K. Tiwari and D. P. Vidyarthi, "Observing the Effect of Inter Process Communication in Auto Controlled Ant Colony Optimization based Scheduling on Computational Grid", Concurrency and Computation: Practice and Experience, Wiley, 26(1):241–270, 2014.
• L. Ferreira, N. Bieberstein, V. Berstis and J. Armstrong Introduction to Grid Computing with Globus, Redbook, IBM Corporation, 2003.
• B. Jacob, M. Brown, K. Fukul and J. Armstrong, Introduction to Grid Computing, Redbook, IBM Corporation, 2005.
• Z. Raza and D. P. Vidyarthi, "GA Based Scheduling Model for Computational Grid to Minimize Turnaround Time", International Journal of Grid and High Performance Computing, IGI Global, 1(4):70-90, 2009.
• K. Li, "Optimal Load Distribution in Non-Dedicated Heterogeneous Cluster and Grid Computing Environments", Systems Architecture, Elsevier, 54(1-2):111–123, 2008.
• Y. Li, Y. Yang, M. Ma and L. Zhou, "A Hybrid Load Balancing Strategy of Sequential Jobs for Grid Computing Environments", Future Generation Computer Systems, Elsevier, 25(8):819-828, 2009.
• I. Koren and C. M. Krishna, Fault Tolerant Systems, Morgan Kaufmann is an imprint of Elsevier, New York, 2007.
• S. Nesmachnow, B. Dorronsoro, J. Pecero and P. Bouvry, "Energy-aware Scheduling on Multicore Heterogeneous Grid Computing Systems", Journal of Grid Computing, Springer, 11(4):653-680, 2013.
• Q. Niu, F. Zhou and T. Zhou, "Quantum Genetic Algorithm for Hybrid Flow Shop Scheduling Problem to Minimize Total Completion Time", Lecture Notes in Computer Science, Springer, 6329(2):21-29, 2010.
• Y. Mingscheng, "Quantum Computation, Quantum Theory and AI", Artificial Intelligence, Elsevier, 174(2):162-176, 2010.
• J. Gu, X. Gu and M. Gu, "A Novel Parallel Quantum Genetic Algorithm for Stochastic Job Shop Scheduling", Journal of Mathematical Analysis and Applications, Elsevier, 355(1):63-81, 2009.
• K. Vivekanandan and D. Ramyachitra, "Bacteria Foraging Optimization for Protein Sequence Analysis on the Grid", Future Generation Computer Systems, Elsevier, 28(4):647-656, 2012.
• S. K. Nayak, S. K. Padhy and S. P. Panigrahi, "A Novel Algorithm for Dynamic Task Scheduling", Future Generation Computer System, Elsevier 28 (5):709-717, 2012.
• M. E. Moghaddam and R. Bonyadi, "An Immune-based Genetic Algorithm with Reduced Search Space Coding for Multiprocessor Task Scheduling Problem", International Journal of Parallel Programming, Springer, 40(2):225-257, 2012.
• R. Kashyap and D. P. Vidyarthi, "Security-aware Scheduling Model for Computational Grid", Concurrency and Computation: Practice and Experience, Wiley, 24(12):1377-1391, 2012.
• R. Kashyap and D. P. Vidyarthi, "Security Driven Scheduling model for Computational Grid using NSGA II", Journal of Grid Computing, Springer, 11(4):721-734, 2013.
• T. D. Braun, H. J. Sigel and N. Beck, "A Comparison of Eleven Static Heuristic for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems", Journal of Parallel and Distributed Computing, Elsevier, 61(6):810–837, 2001.
• S. Prakash and D. P. Vidyarthi, "Load Balancing in Computational Grid Using Genetic Algorithm", International Journal of Advances in Computing, Scientific and Academic Publishing, US, 1(1):8-17 2011.
• S. Prakash and D. P. Vidyarthi, "A Model for Load Balancing in Computational Grid", 18th IEEE Annual International Conference on High Performance Computing (HiPC'11) Bangalore, India, pp. 1-5, 2011.
• , S. Prakash and D. P. Vidyarthi, "Observations on Effect of IPC in GA Based Scheduling on Computational Grid", International Journal of Grid and High Performance Computing (IJGHPC), IGI Global, US, 4(1): 66-79, 2012.
• S. Prakash and D. P. Vidyarthi, "A Novel Scheduling Model for Computational Grid using Quantum Genetic Algorithm", Journal of Supercomputing, 65(2):742-770, Springer US, 2013.
• S. Prakash and D. P. Vidyarthi, "Maximizing Availability for Task Scheduling in Computational Grid using GA", Concurrency and Computation: Practice and Experience, 27(1),197-210, Wiley, UK, 2015.
• S. Prakash and D. P. Vidyarthi, "Immune Genetic Algorithm for Scheduling in Computational Grid", Journal of Bio-Inspired Computing, 6(6), 397-408, 2014.
• Prakash and D. P. Vidyarthi "A Hybrid GABFO Approach for Scheduling in Computational Grid", International Journal of Applied Evolutionary Computation (IJAEC) vol. 5(3), pp. 57-83, 2014.
• C. Kumar, S. Prakash, T. Kumar and D. P. Sahu, "Variant of genetic algorithm and its applications", International Journal of Artificial Intelligence and Neural Networks, vol. 4(4), pp. 8-12, 2014.

Index Terms

Keywords