AbstractsBusiness Management & Administration

Cloud computing is a latest market-oriented computing paradigm which can provide virtually unlimited scalable high performance computing resources. As a type of high-level middleware services for cloud computing, cloud workflow systems are a research frontier for both cloud computing and workflow technologies. Cloud workflows often underlie many large scale data/computation intensive e-science applications such as earthquake modelling, weather forecast and Astrophysics. At build-time modelling stage, these sophisticated processes are modelled or redesigned as cloud workflow specifications which normally contain the functional requirements for a large number of workflow activities and their non-functional requirements such as Quality of Service (QoS) constraints. At runtime execution stage, cloud workflow instances are executed by employing the supercomputing and data sharing ability of the underlying cloud computing infrastructures. In this thesis, we focus on scientific cloud workflow systems. In the real world, many scientific applications need to be time constrained, i.e. they are required to be completed by satisfying a set of temporal constraints such as local temporal constraints (milestones) and global temporal constraints (deadlines). Meanwhile, task execution time (or activity duration), as one of the basic measurements for system performance, often needs to be monitored and controlled by specific system management mechanisms. Therefore, how to ensure satisfactory temporal correctness (high temporal QoS), i.e. how to guarantee on-time completion of most, if not all, workflow applications, is a critical issue for enhancing the overall performance and usability of scientific cloud workflow systems. At present, workflow temporal verification is a key research area which focuses on time-constrained large-scale complex workflow applications in distributed high performance computing environments. However, existing studies mainly emphasise on monitoring and detection of temporal violations (i.e. violations of temporal constraints) at workflow runtime, there is still no comprehensive framework which can support the whole lifecycle of time-constrained workflow applications in order to achieve high temporal QoS. Meanwhile, cloud computing adopts a marketoriented resource model, i.e. cloud resources such as computing, storage and network are charged by their usage. Hence, the cost for supporting temporal QoS (including both time overheads and monetary cost) should be managed effectively in scientific cloud workflow systems. This thesis proposes a novel probabilistic temporal framework and its strategies for cost-effective delivery of high QoS in scientific cloud workflow systems (or temporal framework for short in this thesis). By investigating the limitations of conventional temporal QoS related research, our temporal framework can provide a systematic and cost-effective support for time-constrained scientific cloud workflow applications over their whole lifecycles. With a probability based temporal consistency model,…