Dynamically Partitioning Workflow over Federated Clouds For Optimising the Monetary Cost and Handling Run-Time Failures

Wen, Zhenyu, Qasha, Rawaa, Li, Zequn, Ranjan, Rajiv, Watson, Paul and Romanovsky, Alexander (2020) Dynamically Partitioning Workflow over Federated Clouds For Optimising the Monetary Cost and Handling Run-Time Failures. IEEE Transactions on Cloud Computing, 8 (4). pp. 1093-1107. ISSN 2168-7161

Preview

Text 07553525.pdf - Accepted Version Download (2MB) | Preview

Abstract

Several real-world problems in domain of healthcare, large scale scientific simulations, and manufacturing are organised as workflow applications. Efficiently managing workflow applications on the Cloud computing data-centres is challenging due to the following problems: (i) they need to perform computation over sensitive data (e.g. Healthcare workflows) hence leading to additional security and legal risks especially considering public cloud environments and (ii) the dynamism of the cloud environment can lead to several run-time problems such as data loss and abnormal termination of workflow task due to failures of computing, storage, and network services. To tackle above challenges, this paper proposes a novel workflow management framework call DoFCF (Deploy on Federated Cloud Framework) that can dynamically partition scientific workflows across federated cloud (public/private) data-centres for minimising the financial cost, adhering to security requirements, while gracefully handling run-time failures. The framework is validated in cloud simulation tool (CloudSim) as well as in a realistic workflow-based cloud platform (e-Science Central). The results showed that our approach is practical and is successful in meeting users security requirements and reduces overall cost, and dynamically adapts to the run-time failures.

Actions (login required)

View Item

Downloads