English

Abstract

Cloud computing is upcoming a mainstream feature of information technology. More progressively enterprises deploy their software systems in the cloud environment. The applications in cloud are usually large scale and containing a lot of distributed cloud components. Building cloud applications is highly reliable for challenging and critical research issues. Information processing systems has increased the significance of its correct and continuous operation even in the presence of faulty components. To address this issue, proposes a cloud framework to build fault-tolerant cloud applications. We first propose fault detection algorithms to identify significant components from the huge amount of cloud components. Then, we present an efficient fault-tolerance strategy selection algorithm to determine the most suitable fault-tolerance strategy for each significant component. Software fault tolerance is widely adopted to increase the overall system reliability in critical applications. System reliability can be enhanced by employing functionally equivalent components to tolerate component failures. Fault-tolerance strategies introduced a three well- known techniques are in the following with formulas for calculating the failure probabilities of the fault-tolerant modules. Our work will mainly be driven toward the implementation of the framework to measure the strength of fault tolerance service and to make an in-depth analysis of the cost benefits among all the stakeholders. An algorithm is proposed to automatically determine an efficient fault-tolerance strategy for the significant cloud components. Using real failure traces and model, we evaluate the proposed resource provisioning policies to determine their performance, cost as well as cost efficiency. The experimental results show that by tolerating faults of a small part of the most important components, the reliability of cloud applications can be highly improved.