Robust and fault-tolerant scheduling for scientific workflows in cloud computing environments

CLOUD environments offer low-cost computing resources as a subscription-based service. These resources are elastically scalable and dynamically provisioned. Furthermore, new pricing models have been pioneered by cloud providers that allow users to provision resources and to use them in an efficient manner with significant cost reductions. As a result, scientific workflows are increasingly adopting cloud computing. Scientific workflows are used to model applications of high throughput computation and complex large scale data analysis. However, existing works on workflow scheduling in the context of clouds are either on deadline or cost optimization, ignoring the necessity for robustness. Cloud is not a utopian environment. Failures are inevitable in such large complex distributed systems. It is also well studied that cloud resources experience fluctuations in the delivered performance. Therefore, robust and fault-tolerant scheduling that handles performance variations of cloud resources and failures in the environment is essential in the context of clouds. This thesis presents novel workflow scheduling heuristics that are robust against performance variations and fault-tolerant towards failures. Here, we have presented and evaluated static and just-in-time heuristics using multiple fault-tolerant techniques. We have used different pricing models offered by the cloud providers and proposed schedules that are fault-tolerant and at the same time minimize time and cost. We have also proposed resource selection policies and bidding strategies for spot instances. The proposed heuristics are constrained by either deadline and budget or both. These heuristics are evaluated with the prominent state-of-the art workflows. Finally, we have also developed a multi-cloud framework for the Cloudbus workflow