Priority Based Classical Data Encapsulated Scheduling For New Era in Computing Environment
P. Sunil Gavaskar1, D.Udaya Suriya Rajkumar2, P.Sudheer3
1Dr. P. Sunil Gavaskar, Sri Venkateswara University, Tirupathi, AndhraPradesh,India,
2D.Udaya Suriya Rajkumar, GGR College of Engineering, Department of Computer Science and Engineering, Vellore.
3P.Sudheer, GGR College of Engineering, Department of Computer Science and Engineering, Vellore.
Manuscript received on 20 August 2019. | Revised Manuscript received on 02 September 2019. | Manuscript published on 30 September 2019. | PP: 2562-2567 | Volume-8 Issue-11, September 2019. | Retrieval Number: K18590981119/2019©BEIESP | DOI: 10.35940/ijitee.K1859.0981119
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Abstract: In computational environment the data processing and data transactions are major issue, to overcome this problem of processing and reduce the band width in distributed models. The abstract mentioned here clearly state the novelty of the work regarding to data processing and data utilization. This paper shows that the data provided between nodes that are involved in clusters and that is useful to the data utilization schematic in all kinds of clusters. The proposed approach arranges data into data tables and distributes them transversely nodes in a cluster. It recommends jobs to be processed by the cluster then transfers packaged and encapsulated data into nodes to process the data in parallel. This technique employs an incremental data scheduled computation way that keep away from the costly enumeration of data pattern matching necessary by cluster methods. It improves the data locality by forwarding data to the job supporting cluster. This abstraction is enthused by the data processing and data reduce primitives presented in the various job processing environment and many other functional applications used in data grid, data intensive approaches. The Architectural interface is provided in between clusters and is used to achieve high performance on large clusters of commodity PCs. This approach reduces the data request and its processing when it is signed into data clusters. The proposed approach arranges data into data tables and distributes them across nodes in a cluster. It recommend jobs to be processed by the cluster then transfers packaged and encapsulated data into nodes to progression the data in parallel. It improves the data locality by forwarding data to the job supporting cluster. The proposed approach in this paper is helpful where number of nodes involve in cluster is always increasing due to the high end computations that are involved in distributed environment. I
Keywords: Data integration, Data processing, Scheduling, Data Priority, Data analysis .
Scope of the Article: Environmental Engineering