Large scale computations from various scientific endeavors are composed as workflows that access shared data and high performance systems. Similarly, business applications in cloud computing systems use distributed infrastructure as part of mainstream business models. Recent advances in grid and cloud computing provide tools to monitor and manage execution. However they do not not provide predictable bounds on the Quality of Service (QoS) that can be expected in such variable multi-user distributed environments. Understanding the dynamic properties of resources and coordinated control of resources and workflows is critical especially for deadline-sensitive workflows such as weather prediction.

In this dissertation we revisit the software stack that supports the multi-tier services and propose and evaluate the WORDS (Workflow ORchestrator for Distributed Systems) architecture that abstracts the differences between specific resource models and provides a clear separation of concerns between the resource-level and application-level tools. In the context of the WORDS architecture we explore interfaces and mechanisms necessary for providing predictable quality of service to web service workflows with time and accuracy constraints.

We make the following four primary contributions. First, we propose a resource abstraction across grid and cloud resource control mechanisms that enables higher-levels tools to abstract the differences between systems. Second, we propose a probabilistic Quality of Service (QoS) model that enables providers to quantify the variation in resource availability; both for resource procurement due to competition and for the duration of the resource request from failures at various levels. Third, we use performability analysis through a Markov Reward Model to quantify the loss in performance and study the impact on cost due to availability variations. Finally, we propose a multi-phase orchestration approach that balances performance, reliability and cost considerations for a set of workflows.