Abstract
Decisions are made in grid scheduling based on complexity of the requirement of the job, either as data or computation intensive. However the major challenges are the data locality, transfer and execution of data in suitable locations. The existing solutions had solved the issues considering only the individual constraints but solutions considering all the challenges are not available, thus demanding the need for a unique solution. A novel solution of introducing cognitive science into grid workflow environment is proposed to reduce the make span by considering all the challenges together. Cognitive Artificial Intelligence is used to develop a machine with intelligence which receives the request for data sets and reduce the size of data sets by partitioning and a unique algorithm, namely Cognitive Mode Algorithm (CMA) is also proposed for effective allocation of data sets based on the request from the user. Here the data sets are partitioned by considering the size and available network bandwidth. The replication of partitioned data sets across different sites is done and stored in partitioned metadata repository. This will minimize the recurrence of partition for the same data request in future. A Data Location Matrix (DLM) is also constructed by having the distance of the site and the details of the data sets stored in that site. Prediction of the next request from the same site is also made to reduce the data availability time. Cognitive mode algorithm focuses on learning, thinking, and perception to show the intelligence. The results shows that it reduces the data transfer time, execution time and data availability time which in turn will reduce the overall make span.
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References
Manish parashar,”Grid computing introduction and overview”
Kavitha Ranganathan” Decoupling Computation and Data Scheduling in Distributed Data-Intensive Applications”
David G. Cameron, Rub´en Carvajal-Schiaffino David G. Cameron, Rub´en Carvajal-Schiaffino”Evaluating Scheduling and Replica optimization strategies in optor sim” CERN, European Organization for Nuclear Research, 1211 Geneva, Switzerland.
Mohamed HH, Epema DHJ. An evaluation of the close-to-files processor and data Co-allocation policy in multiclusters. In: International conference on cluster computing. USA: IEEE Society Press; 2004. p. 287–98.
Mansouri N, Dastghaibyfard GH. A dynamic replica management strategy in Data Grid. Journal of Network and Computer Applications 2012;35(4):1297–303.
Najme Mansouri, Gholam Hosein Dastghaibyfard, Ehsan Mansouri,” Combination ofdata replication and scheduling algorithm for improving data availability in Data Grids”
Park S-M, Kim J-H, Go Y-B, Yoon W-S. Dynamic Grid replication strategy based on internet hierarchy, in international workshop on grid and cooperative com- puting. Lecture Note in Computer Science 2003;1001:1324–31
Sashi K, Thanamani A. Dynamic replication in a Data Grid using a modified BHR region based algorithm. Future Generation Computer Systems 2011;27(2): 202–10.
The Data Grid project. /http://eu-datagrid.web.cern.ch/eu-datagrid/S.
Carsten Ernemann, Volker Hamscher, Uwe Schwiegelshohn, Ramin Yahyapour” On Advantages of Grid Computing for Parallel Job Scheduling”
Stuart J. Russell and Peter Norvig” Artificial Intelligence A Modern Approach”.12 Stefka Fidanova“Simulated Annealing for Grid Scheduling Problem”
McClatchey R, Anjum A, Stockinger H, Ali A, Willers I, Thomas M, “scheduling in Data intensive and network aware (DIANA) grid scheduling”, Journal of Grid Computing,2012
Solomonoff”Some recent work in artificial intelligence”
Jim Davies” The Role of Artificial Intelligence Research Methods in Cognitive Science”.
Jiehai Cheng, Wei Li,”Research of the application of Grid computing on geographical information system”
Parvin Asadzadeh, Rajkumar Buyya
Chun Ling Kei, Deepa Nayar, and Srikumar Venugopal” Global Grids and Software Toolkits: A Study of Four Grid Middleware Technologies”.
McClatchey R, Anjum A, Stockinger H, Ali A, Willers I, Thomas M. Data intensive and network aware (DIANA) Grid scheduling. Journal of Grid Computing 2007;5:43–64.
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Iswarya, N.D., Mohamed, M.A.M., Vijaya, N. (2015). Cognitive Science Based Scheduling In Grid Environment. In: Selvaraj, H., Zydek, D., Chmaj, G. (eds) Progress in Systems Engineering. Advances in Intelligent Systems and Computing, vol 366. Springer, Cham. https://doi.org/10.1007/978-3-319-08422-0_30
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DOI: https://doi.org/10.1007/978-3-319-08422-0_30
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