The International Arab Journal of Information Technology (IAJIT)

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A Network Performance Aware QoS Based Workflow Scheduling for Grid Services

Grids enable sharing, selection and aggregation of geographically distributed resources among various organizations. They are now emerging as promising computing paradigms for resource and compute intensive scientific workflow applications modeled as a Directed Acyclic Graph (DAG) with intricate inter-task dependencies. Job scheduling is an important and challenging issue in a grid environment. There are various scheduling algorithm proposed for grid environments to distribute the load among processors and maximize resource utilization while reducing task execution time. Task execution time is not the only parameter to be improved; various Quality of Service (QoS) parameters are also to be considered in job scheduling in grid computing. In this Research we have studied the existing QoS based Task scheduling, work flow scheduling and formulated the problem. The possible solutions are developed for the problems identified in existing algorithms. The scheduling of dependent task (work flow) is more challenging than independent task scheduling. The scheduling of both dependent and independent tasks with satisfying QOS requirements of users is a very challenging issue in grid computing. This paper proposes a Novel Network aware QoS workflow scheduling method for Grid Services. The proposed scheduling algorithm considers network and QoS constraints. The goal of the proposed scheduling algorithm is to implement the workflow schedule so that it reduces execution time and resource cost and yet meets the deadline imposed by the user. The experimental result shows that the proposed algorithm improves the success ratio of tasks and throughput of resources while reducing makespan and workflow execution cost.


[1] Abrishami S., Naghibzadeh M., and Epema D., Cost-Driven Scheduling of Grid Workflows Using Partial Critical Paths, IEEE Transactions 902 The International Arab Journal of Information Technology, Vol. 15, No. 5, September 2018 on Parallel and Distributed Systems, vol. 23, no. 8, pp. 1400-1414, 2012.

[2] Abrishami S. and Naghibzadeh M., Deadline- Constrained Workflow Scheduling in Software as A Service Cloud, Scientia Iranica, vol. 19, no. 3, pp. 680-689, 2012.

[3] Amalarethinam G. and Selvi K., An Efficient Dual Objective Grid Workflow Scheduling Algorithm, International Journal of Computer Applications, vol. 33, no. 1, pp. 7-12, 2011.

[4] Gharooni-Fard G., Moein-Darbari F., Deldari H., and Morvaridi A., Scheduling of Scientific Workflows using AChaos- Genetic Algorithm, in Proceedings of International Conference on Computational Science, Netherlands, pp. 1439- 1448, 2010.

[5] Hasham K., Peris A., Anjum A., Evans D., Hufnagel D., Huedo E., Hern ndez J., McClatchey R., Gowdy S., and Metson S., CMS Workflow Execution using Intelligent Job Scheduling and Data Access Strategies, IEEE Transactions on Nuclear Science, vol. 58, no. 3, pp. 1221-1232, 2011.

[6] Hassan M. and Abdullah A., A New Grid Resource Discovery Framework, The International Arab Journal of Information Technology, vol. 8, no. 1, pp. 99-107, 2011.

[7] Hsu C., Huang K., and Wang F., Online Scheduling of Workflow Applications in Grid Environments, Future Generation Computer Systems, vol. 27, no. 6, pp. 860-870, 2011.

[8] Ijaz S., Munir E., Anwar W., and Nasir W., Efficient Scheduling Strategy for Task Graphs in Heterogeneous Computing Environment, The International Arab Journal of Information Technology, vol. 10, no. 5, pp. 486-492, 2013.

[9] Nadia R. and Zimeo E., Time and Cost-Driven Scheduling of Data Parallel Tasks in Grid Workflows, IEEE Systems Journal, vol. 3, no. 1, pp. 104-120, 2009.

[10] Rahman M., Hassan R., Ranjan R., and Buyya R., Adaptive Workflow Scheduling for Dynamic Grid and Cloud Computing Environment, Concurrency Computation Practice Experience, vol. 25, no. 13, pp. 1816- 1842, 2013.

[11] Rahman M., Ranjan R., and Buyya R., Cooperative and Decentralized Workflow Scheduling in Global Grids, Future Generation Computer Systems, vol. 26, no. 5, pp. 753-768, 2010.

[12] Smith W., Foster I., and Taylor V., Scheduling with Advanced Reservations, in Proceedings of International Parallel and Distributed Processing Symposium, Cancun, pp. 127-132, 2000.

[13] Su S., Li J., Huang Q., Huang X., Shuang K., and Wang J., Cost-Efficient Task Scheduling for Executing Large Programs in the Cloud, Parallel Computing, vol. 39, no. 4-5, pp. 177- 188, 2013.

[14] Tao Y., Jin H., Wu S., Shi X., and Shi L., Dependable Grid Workflow Scheduling Based on Resource Availability, Journal of Grid Computing, vol. 11, no. 1, pp. 47-61, 2013.

[15] Vasques J. and Veiga L., A Decentralized Utility-Based Grid Scheduling Algorithm, in Proceedings of the 28th Annual ACM Symposium on Applied Computing, Coimbra, pp. 619-624, 2013.

[16] Wu Q., Zhu M., Gu Y., Brown P., Lu X., Lin W., and Liu Y., A Distributed Workflow Management System with Case Study of Real- Life Scientific Applications on Grids, Journal of Grid Computing, vol. 10, no. 3, pp. 367-393, 2012.

[17] Wu X., Deng M., Zhang R., Zeng B., and Zhou S., A Task Scheduling Algorithm Based on QoS-Driven in Cloud Computing, in Proceedings of ITQM in Elsevier, China, pp. 1162-1169, 2013.

[18] Yousaf M. and Welzl M., Network-Aware HEFT Scheduling for Grid, The Scientific World Journal, vol. 2014, pp. 1-13, 2014.

[19] Yu J., Buyya R., and Ramamohanarao K., Meta- Heuristics for Scheduling in Distributed Computing Environments, Springer, 2008.

[20] Yu J., Buyya R., and Tham C., QoS-based Scheduling of Workflow Applications on Service Grids, in Proceedings of the 1st IEEE International Conference on E-Science and Grid Computing, Australia, pp. 1-9, 2005.

[21] Zhao H. and Sakellariou R., Advance Reservation Policies for Workflows, in Proceedings of Job Scheduling Strategies for Parallel Processing, Saint-Malo, pp. 47-67, 2007. A Network Performance Aware QoS Based Workflow Scheduling for Grid Services 903 Shinu John is a Professor in the department of Computer Science and Engineering at the St. Thomas College of Engineering and Technology, Kannur, India. He obtained his Ph. D. from Anna University, Chennai. He received his M.E. and B.E. degrees in Computer Science and Engineering from Anna University, India and the M.S. University, Tirunelveli, India respectively. He is a member of the System Software Group at MAM College of Engineering, India and has published many papers in various national, international journals and conferences. His research interests include Grid Computing, Mobile Computing and Computer Networks. He is a life member of Computer Society of India, the Indian Society for Technical Education (ISTE), Institution of Engineers and a member of IEEE since 2006. Maluk Mohamed obtained his Ph.D. from the Indian Institute of Technology (IIT) Madras in 2006, Masters in Engineering from the National Institute of Technology, Tiruchirappalli in 1995 and Bachelors from the Bharathidasan University in 1993. He is currently a professor in the Department of Computer Science and Engineering, M.A.M. College of Engineering, India. He coordinates research activities for the System Software Group at MAMCE. His research interests include distributed computing and its family ie; grid computing, mobile computing, cloud computing and wireless sensor networks, software engineering and distributed databases. He has guided 5 Ph. D, 1 M.S. (By research) and 32 M. Tech., scholars and is currently guiding 5 Ph.D., and 3 M. Tech., research scholars. He is a member of the Board of Studies, in Anna University and JNTU Anantapur. He is the principal investigator for a number of funded projects like Cyberspace Security and Cloud API.