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A probabilistic strategy for temporal constraint management in scientific workflow systems
Author(s) -
Liu Xiao,
Ni Zhiwei,
Chen Jinjun,
Yang Yun
Publication year - 2011
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.1739
Subject(s) - workflow , computer science , workflow technology , workflow management system , workflow engine , constraint (computer aided design) , probabilistic logic , distributed computing , temporal database , redundancy (engineering) , scheduling (production processes) , data mining , database , artificial intelligence , mathematical optimization , engineering , mechanical engineering , mathematics , operating system
In scientific workflow systems, it is critical to ensure the timely completion of scientific workflows. Therefore, temporal constraints as a type of QoS (Quality of Service) specification are usually required to be managed in scientific workflow systems. Specifically, temporal constraint management includes two basic tasks: setting temporal constraints at workflow build‐time and updating temporal constraints at workflow run‐time. For constraint setting, the current work mainly adopts user‐specified temporal constraints without considering the system performance. Hence, it may result in frequent temporal violations which deteriorate the overall workflow execution effectiveness. As regards constraint updating, although not well investigated, so far is in fact of great importance to workflow management tasks such as workflow scheduling and exception handling. In this paper, with a systematic analysis of the above issues, we propose a probabilistic strategy for temporal constraint management which utilizes a novel probability‐based temporal consistency model. Specifically for constraint setting, a negotiation process between the client and the service provider is designed to support the setting of coarse‐grained temporal constraints and then automatically derive the fine‐grained temporal constraints; for constraint updating, the probability time deficit/redundancy propagation process is proposed to update run‐time fine‐grained temporal constraints when workflow execution is either ahead of or behind the schedule. The effectiveness of our strategy is demonstrated through a case study on an example scientific workflow process in our scientific workflow system. Copyright © 2011 John Wiley & Sons, Ltd.