The proliferation of cloud computing allows scientists to deploy computation and data intensive applications without infrastructure investment, where large generated datasets can be flexibly stored with multiple cloud service providers. Due to the pay-as-you-go model, the total application cost largely depends on the usage of computation, storage and bandwidth resources, and cutting the cost of cloud-based data storage becomes a big concern for deploying scientific applications in the cloud. In this paper, we propose a novel algorithm that can automatically decide whether a generated dataset should be 1) stored in the current cloud, 2) deleted and re-generated whenever reused or 3) transferred to cheaper cloud service for storage. The algorithm finds the trade-off among computation, storage and bandwidth costs in the cloud, which are three key factors for the cost of storing generated application datasets with multiple cloud service providers. Simulations conducted with popular cloud service providers' pricing models show that the proposed algorithm is highly cost-effective to be utilised in the cloud.

Nowadays, most business processes are running in a parallel, distributed and time-constrained manner. How to guarantee their on-time completion is a challenging issue. In the past few years, temporal checkpoint selection which selects a subset of workflow activities for verification of temporal consistency has been proved to be very successful in monitoring single, complex and large size scientific workflows. An intuitive approach is to apply those strategies to individual business processes. However, in such a case, the total number of checkpoints will be enormous, namely the cost for system monitoring and exception handling could be excessive. To address such an issue, we propose a brand new idea which selects time points along the workflow execution time line as checkpoints to monitor a batch of parallel business processes simultaneously instead of individually. Based on such an idea, a set of new definitions as well as a time-point based checkpoint selection strategy are presented in this paper. Our preliminary results demonstrate that it can achieve an order of magnitude reduction in the number of checkpoints while maintaining satisfactory on-time completion rates compared with the state-of-the-art activity-point based checkpoint selection strategy.

Due to the dynamic nature of cloud computing, how to achieve satisfactory QoS (Quality of Service) in cloud workflow systems becomes a challenge. Meanwhile, since QoS requirements have many dimensions, a unified system design for different QoS management components is required to reduce the system complexity and software development cost. Therefore, this paper proposes a generic QoS framework for cloud workflow systems. Covering the major stages of a workflow lifecycle, the framework consists of four components, viz. QoS requirement specification, QoS-aware service selection, QoS consistency monitoring and QoS violation handling. While there are many QoS dimensions, this paper illustrates a concrete performance framework as a case study and briefly touches others. We also demonstrate the system implementation and evaluate the effectiveness of the performance framework in our cloud workflow system.