An Effective Framework of Light-Weight Handling for Three-Level Fine-Grained Recoverable Temporal Violations in Scientific Workflows

Temporal violations may often take place and deteriorate the overall QoS of scientific workflows. To handle temporal violations in an automatic and cost-effective fashion, we need to resolve the following issues: 1) how to define fine-grained recoverable temporal violations, 2) which light-weight effective exception handling strategies to be facilitated. This paper proposes an effective exception handling framework. Based on a probability based temporal consistency model, the probability range for recoverable temporal violations is divided into three levels of fine-grained temporal violations. Afterwards, three corresponding light-weight exception handling strategies including TDA (Time Deficit Allocation), ACOWR (Ant Colony Optimisation based two-stage Workflow local Rescheduling) and TDA+ACOWR (the combined strategy of TDA and ACOWR) are presented. The experimental results demonstrate the excellent performance of our framework in reducing both local and global temporal violations.

[1]  Xiao Liu,et al.  Handling Recoverable Temporal Violations in Scientific Workflow Systems: A Workflow Rescheduling Based Strategy , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[2]  Xiao Liu,et al.  A probabilistic strategy for temporal constraint management in scientific workflow systems , 2011, Concurr. Comput. Pract. Exp..

[3]  Wil M. P. van der Aalst,et al.  Workflow Exception Patterns , 2006, CAiSE.

[4]  Rajkumar Buyya,et al.  Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms , 2006, Sci. Program..

[5]  Jinjun Chen,et al.  Multiple states based temporal consistency for dynamic verification of fixed‐time constraints in Grid workflow systems , 2007, Concurr. Comput. Pract. Exp..

[6]  Hai Zhuge,et al.  A timed workflow process model , 2001, J. Syst. Softw..

[7]  Sanjeev Baskiyar,et al.  Scheduling Mixed Tasks with Deadlines in Grids Using Bin Packing , 2008, 2008 14th IEEE International Conference on Parallel and Distributed Systems.

[8]  Jinjun Chen,et al.  Temporal dependency-based checkpoint selection for dynamic verification of temporal constraints in scientific workflow systems , 2011, TSEM.

[9]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[10]  Hai Jin,et al.  Peer-to-Peer Based Grid Workflow Runtime Environment of SwinDeW-G , 2007, Third IEEE International Conference on e-Science and Grid Computing (e-Science 2007).

[11]  Yan Ma,et al.  Double-layer Scheduling Strategy of Load Balancing in Scientific Workflow , 2009, 2009 15th International Conference on Parallel and Distributed Systems.

[12]  Jun Zhang,et al.  An Ant Colony Optimization Approach to a Grid Workflow Scheduling Problem With Various QoS Requirements , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[13]  Radu Prodan,et al.  Overhead Analysis of Scientific Workflows in Grid Environments , 2008, IEEE Transactions on Parallel and Distributed Systems.

[14]  Gustavo Alonso,et al.  Exception Handling in Workflow Management Systems , 2000, IEEE Trans. Software Eng..

[15]  Rajkumar Buyya,et al.  Workflow scheduling algorithms for grid computing , 2008 .

[16]  Ian J. Taylor,et al.  Workflows and e-Science: An overview of workflow system features and capabilities , 2009, Future Gener. Comput. Syst..