Grid workflow optimization regarding dynamically changing resources and conditions

Authors:
Erich Schikuta;Helmut Wanek;Irfan Ul Haq
Affiliations:
Department of Knowledge and Business Engineering, University of Vienna, Rathausstraße 19-9, A-1010 Vienna, Austria;Department of Knowledge and Business Engineering, University of Vienna, Rathausstraße 19-9, A-1010 Vienna, Austria;Department of Knowledge and Business Engineering, University of Vienna, Rathausstraße 19-9, A-1010 Vienna, Austria
Venue:
Concurrency and Computation: Practice & Experience - 2nd International Workshop on Workflow Management and Applications in Grid Environments (WaGe2007)
Year:
2008

Citing 0
Cited 4

Negotiating and Enforcing QoS and SLAs in Grid and Cloud Computing

GPC '09 Proceedings of the 4th International Conference on Advances in Grid and Pervasive Computing
User-centric, heuristic optimization of service composition in clouds

EuroPar'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part I
Dynamic service configurations for SLA Negotiation

Euro-Par 2010 Proceedings of the 2010 conference on Parallel processing
An Adaptive Heuristic Approach to Service Selection Problems in Dynamic Distributed Systems

GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automatic construction of workflows on the Grid is a challenging task. The problems that have to be solved are manifold: How can existing services be integrated into a workflow that is able to accomplish a specific task? How can an optimal workflow be constructed with respect to changing resource characteristics during the optimization process? How to cope with dynamically changing or incomplete knowledge of the goal function of the optimization process? and finally: How to react to service failures during workflow execution? In this paper, we propose a method to optimize a workflow based on a heuristic A* approach that allows to react to dynamics in the environment. Changes in the Grid infrastructure and in the users' requirements can be handled during the optimization process as well as during the execution of the workflow. Our algorithm also allows the workflow to recover from failing resources during the execution phase. Copyright © 2008 John Wiley & Sons, Ltd.