A Prefetching Technique using HMM Forward Chaining for the DFS in Cloud

  V.Thilaganga,  Dr.M.Karthika,  Dr.X.Josphin Jasiline Anitha

In recent applications of Distributed File System for cloud have managed in providing high security. The Hidden Markov Model (HMM) has played a vital role in prediction analysis in various areas. Hidden Markov Model and Distributed File System(DFS) applications have been in a variety of problems in cloud. The HMM includes Evaluation, Decoding and Learning. The HMM problems can be used to solve various sequences analysis problems, such as forward, backward and Viterbi algorithms. Cloud computing synchronizes the operation of all systems, with techniques such as data center networking, the map-reduce framework with supports data intensive computing applications in parallel and distributed systems that provide lively resource allocation, permittingvarious operating system to exists on the same physical server. So Distributed File System provides more reliable services. The paper represents client and server process, which is a storage server, can directly pre-fetch the data from client machines. The prefetching techniques, uses Hidden Markov Model Forward chain and work Distributed File System for cloud. The prefetching techniques can be implemented to analyze the I/O from the client machine and then send the prefetching data to the relevant machine proactively. Finally, the storage server can forward the prefetching data, in prior to the request from the client machine.

Keywords- Distributed File System, Hidden Markov Model, Storage Server.

Unique Identification Number - IJEDR1701093
Page Number(s) - 604-609
Pubished in - Volume 5 | Issue 1 | March 2017
DOI (Digital Object Identifier) -   
Publisher - IJEDR (ISSN - 2321-9939)

  V.Thilaganga,  Dr.M.Karthika,  Dr.X.Josphin Jasiline Anitha,   "A Prefetching Technique using HMM Forward Chaining for the DFS in Cloud", International Journal of Engineering Development and Research (IJEDR), ISSN:2321-9939, Volume.5, Issue 1, pp.604-609, March 2017, Available at :http://www.ijedr.org/papers/IJEDR1701093.pdf