Cost Effective Hybrid Fault Tolerant Scheduling Model for Cloud Computing Environment

Abstract

Cloud computing provides flexible and cost effective way for end users to access data from multiplatform environment. Despite of support by the features of cloud computing, there are also chances of resource failure. Hence there is a need of a fault tolerant mechanism to achieve undisrupted performance of cloud services. The task reallocation and duplication are the two commonly used fault tolerant mechanisms. But task replication method results in huge storage and computational overhead, when the number of tasks is increasing gradually. If the number of faults is high, it incurs more storage overhead and time complexity based on task criticality. In order to solve these issues, we propose to develop a Cost Effective Hybrid Fault Tolerant Scheduling (CEHFTS) Model for cloud computing. In this model, the Failure Occurrence Probability (FoP) of each VM is estimated by finding the previous failures and successful executions. Then an adaptive fault recovery timer is maintained during a fault, which is adjusted based on the type of faults. Experimental results have shown that CEHFTS model achieves 43% reduced storage cost and 13% reduced response delay for critical tasks, when compared to existing technique.

High Efficient Virtual Machine Migration Using Glow Worm Swarm Optimization Method for Cloud Computing

“…Cloud computing environment may suffer from internal and external faults [13]. The fault probability (FP) of task ti is modelled by an exponential distribution given as follows.…”