Specification and monitoring of data-centric temporal properties for service-based systems

  • Authors:

  • Guoquan Wu;Jun Wei;Chunyang Ye;Hua Zhong;Tao Huang;Hong He

  • Affiliations:

  • Institute of Software, Chinese Academy of Sciences, China;Institute of Software, Chinese Academy of Sciences, China and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China;Institute of Software, Chinese Academy of Sciences, China and Middleware Systems Research Group, University of Toronto, Canada;Institute of Software, Chinese Academy of Sciences, China;Institute of Software, Chinese Academy of Sciences, China and State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, China;Shandong University at Weihai, China

  • Venue:
  • Journal of Systems and Software
  • Year:
  • 2012

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Abstract

Service-based systems operate in a very dynamic environment. To guarantee functional and non-functional objective at runtime, an adaptation mechanism is usually expected to monitor software changes, make appropriate decisions, and act accordingly. However, existing runtime monitoring solutions consider only the constraints on the sequence of messages exchanged between partner services and ignore the actual data contents inside the messages. As a result, it is difficult to monitor some dynamic properties such as how message data of interest is processed between different participants. To address this issue, we propose an efficient, non-intrusive online monitoring approach to dynamically analyze data-centric properties for service-oriented applications involving multiple participants. By introducing Par-BCL - a Parametric Behavior Constraint Language for Web services - to define monitoring parameters, various data-centric temporal behavior properties for Web services can be specified and monitored. This approach broadens the monitored patterns to include not only message exchange orders, but also data contents bound to the parameters. To reduce runtime overhead, we statically analyze the monitored properties and combine two different indexing mechanisms to optimize monitoring. The experiments show that our solution is efficient and promising.