Open AccessReliability of Fault Tolerance in Cloud using Machine Learning Algorithm
Author(s) -
S. Harini Krishna*,
G. Niveditha,
K. Gnana Mayuri
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l3889.1081219
Subject(s) - cloud computing , fault tolerance , software fault tolerance , computer science , distributed computing , algorithm , reliability (semiconductor) , fault (geology) , consistency (knowledge bases) , fault coverage , real time computing , reliability engineering , engineering , artificial intelligence , operating system , power (physics) , physics , electrical engineering , quantum mechanics , seismology , electronic circuit , geology
The basic fault tolerance issues seen in cloud computing are identification and recovery. To fight with these issues, so many fault tolerance methods have been designed to decrease the faults. However, due to the reliability and web based service giving behavior, fault tolerance in cloud computing will be a huge challenge. The present model is not just on tolerating faults but also to decrease the possibility of future faults as well[4].The fault tolerance deals with the exact and constant operation of the fault segments. The processing on computing nodes can be done remotely in the real time cloud applications, so there could be more possibilities of errors. Hence there lies an immense necessity for fault tolerance to attain consistency to the real time computing on cloud infrastructure. The “fault tolerance” can be explained through fault processing that have two basic stages. The stages are (i) The effective error processing stage which is used to intended for carrying the “effective error” back to inactive state, i.e., before the error occurred (ii) The latent error processing stage intended for guaranteeing that the fault does not get effective once again.