A Prefetching Technique Using HMM Forward and Backward Chaining for the DFS in CloudAuthor : V.Thilaganga, M.Karthika and M.Maha Lakshmi
Volume 6 No.2 July-December 2017 pp 23-26
A general class of temporal probabilistic model have recently developed, which extends the Forward, Backward and Viterbi algorithm for hidden Markov models. The HMM (Midden Markov Model) is a probabilistic model of the joint probability of a collection of random variables with both observations and states. The algorithm is based on shrinking the state space of the HMM noticeably using such chains. The states through which the world passes are hidden, or unobserved. However, at each point in time also gets an observation that in some way reflects on the current state of the world. The Cloud Computing is a big deal for three reasons: It does not need any effort on Clients part to maintain or manage. It’s effectively infinite in size, so clients don’t need to worry about it running out of capacity. User can access cloud-based applications and services from anywhere all you need is a device with an Internet connection. In this Cloud Computing used the Distributed File Systems (DFS) for sharing and allocating the data during dynamic process .Those process are using some Prediction algorithms here using HMM Forward and Backward Chain. In this paper represents, Cloud Storage Server can Share the data among with the multiple users, using two prediction algorithms such as forward and backward chain in HMM.
Distributed FileSystem, Hidden Markov Model, Storage Server