Fault Tolerance Approach To Improve Performance Computation Of Biological Jobs Using Cloud Computing

Various biological jobs are executed in cloud environment to utilize the resources and improve the performance of the computation. Free from unnecessary yoke related to supervising those biological computing resources and get rid of the expenditure of maintaining the resources. Reliability is a complex issue in a cloud environment, a key to improve it by using Fault Tolerance Approach (FTM). Proactive and Reactive Fault Tolerance strategies are there to provide services continuously in spite of failures or faults occur to offer highly reliable cloud service. The paper proposes a framework which integrates Fault tolerance and Anti-fragility to survey on both fault prediction and recovery method. This framework has three stages (i) Fault Detection Watcher (FDW) (ii) Fault Overseer (FO) (iii) Fault Resilience. FDW is used to identify faults either known or unknown. Fault overseer is used as a monitoring mechanism for proactive policy predict and recover known faults and Fault Resilience is for fault tolerance as reactive policy detect and recover unknown faults. Network and application faults are concerned with fault overseer; Fault induction and event log are the phases. Database and VM failures look after by Fault resilience stage. Fragments and replication are core objective of this phase. Proposed approach validates using reliability metrics in cloudsim simulator. The experiment result revealed the probability of proposed approach under the conditions of availability, reliability and performance. This paper shows a novel framework in an integrated manner, of proactive and reactive policies in terms of fault overseer and anti-fragility mechanism to execute biological job.