Infrastructure Aware Scientific Workflows and Infrastructure Aware Workflow Managers in Science Gateways

The workflow interoperability problem was successfully solved by the SHIWA project if the workflows to be integrated were running in the same grid infrastructure. However, in the more generic case when the workflows were running in different infrastructures the problem has not been solved yet. In the current paper we show a solution for this problem by introducing a new type of workflow called infrastructure-aware workflow. These are scientific workflows extended with new node types that enable the on-the-fly creation and destruction of the required infrastructures in the clouds. The paper shows the semantics of these new types of nodes and workflows and also how they can solve the workflow interoperability problem. The paper also describes how these new type of workflows can be implemented by a new service called Occopus, and how this service can be integrated with the existing SHIWA Simulation Platform services like the WS-PGRADE/gUSE portal to provide the required functionalities of solving the workflow interoperability problem.

[1]  Youngki Lee,et al.  Orchestrator: An active resource orchestration framework for mobile context monitoring in sensor-rich mobile environments , 2010, 2010 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[2]  Miklós Kozlovszky,et al.  DCI Bridge: Executing WS-PGRADE Workflows in Distributed Computing Infrastructures , 2014, Science Gateways for Distributed Computing Infrastructures.

[3]  Gabor Kecskemeti,et al.  Infrastructure Aware Scientific Workflows and Their Support by a Science Gateway , 2015, 2015 7th International Workshop on Science Gateways.

[4]  Chenn-Jung Huang,et al.  An adaptive resource management scheme in cloud computing , 2013, Eng. Appl. Artif. Intell..

[5]  Gábor Terstyánszky,et al.  Enabling scientific workflow sharing through coarse-grained interoperability , 2014, Future Gener. Comput. Syst..

[6]  Gábor Terstyánszky,et al.  Exploring Workflow Interoperability for Neuroimage Analysis on the SHIWA Platform , 2013, Journal of Grid Computing.

[7]  Thomas Steinke,et al.  The MoSGrid Science Gateway - A Complete Solution for Molecular Simulations. , 2014, Journal of chemical theory and computation.

[8]  Péter Kacsuk,et al.  One Click Cloud Orchestrator: Bringing Complex Applications Effortlessly to the Clouds , 2014, Euro-Par Workshops.

[9]  Miklós Kozlovszky,et al.  WS-PGRADE/gUSE Generic DCI Gateway Framework for a Large Variety of User Communities , 2012, Journal of Grid Computing.

[10]  Jianxin Li,et al.  An Efficient Resource Management System for On-Line Virtual Cluster Provision , 2009, 2009 IEEE International Conference on Cloud Computing.

[11]  Pter Kacsuk,et al.  Science Gateways for Distributed Computing Infrastructures , 2014, Springer International Publishing.

[12]  Richard Grunzke,et al.  Metadata Management in the MoSGrid Science Gateway - Evaluation and the Expansion of Quantum Chemistry Support , 2017, Journal of Grid Computing.

[13]  Bernd Freisleben,et al.  Data Flow Driven Scheduling of BPEL Workflows Using Cloud Resources , 2010, 2010 IEEE 3rd International Conference on Cloud Computing.

[14]  Johan Montagnat,et al.  Fine-Grain Interoperability of Scientific Workflows in Distributed Computing Infrastructures , 2013, Journal of Grid Computing.

[15]  K. K. Ramakrishnan,et al.  Towards a ubiquitous cloud computing infrastructure , 2010, 2010 17th IEEE Workshop on Local & Metropolitan Area Networks (LANMAN).

[16]  Péter Kacsuk,et al.  WS-PGRADE/gUSE and Clouds , 2014, Science Gateways for Distributed Computing Infrastructures.

[17]  Frank Leymann,et al.  On-demand Provisioning of Infrastructure, Middleware and Services for Simulation Workflows , 2013, 2013 IEEE 6th International Conference on Service-Oriented Computing and Applications.

[18]  Matt Welsh,et al.  Resource aware programming in the Pixie OS , 2008, SenSys '08.

[19]  Yuan Luo,et al.  Effectiveness of Hybrid Workflow Systems for Computational Science , 2012, ICCS.

[20]  Rizos Sakellariou,et al.  Adaptive resource configuration for Cloud infrastructure management , 2013, Future Gener. Comput. Syst..

[21]  Tamás Kiss.  Science Gateways for the Broader Take-up of Distributed Computing Infrastructures , 2012, Journal of Grid Computing.

[22]  Tamas Kiss,et al.  Extending Scientific Workflow Systems to Support MapReduce Based Applications in the Cloud , 2015, 2015 7th International Workshop on Science Gateways.

[23]  Johan Tordsson,et al.  Three fundamental dimensions of scientific workflow interoperability: Model of computation, language, and execution environment , 2010, Future Gener. Comput. Syst..

[24]  Bernd Freisleben,et al.  On-Demand Resource Provisioning for BPEL Workflows Using Amazon's Elastic Compute Cloud , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.