摘要
With quick development of grid techniques and growing complexity of grid applications, it is becoming critical for reasoning temporal properties of grid workflows to probe potential pitfalls and errors, in order to ensure reliability and trustworthiness at the initial design phase. A state Pi calculus is proposed and implemented in this work, which not only enables fexible abstraction and management of historical grid verification of grid workflows. Furthermore, a relaxed region system events, but also facilitates modeling and temporal analysis (RRA) approach is proposed to decompose large scale grid workflows into sequentially composed regions with relaxation of parallel workflow branches, and corresponding verification strategies are also decomposed following modular verification principles. Performance evaluation results show that the RRA approach can dramatically reduce CPU time and memory usage of formal verification.
With quick development of grid techniques and growing complexity of grid applications, it is becoming critical for reasoning temporal properties of grid workflows to probe potential pitfalls and errors, in order to ensure reliability and trustworthiness at the initial design phase. A state Pi calculus is proposed and implemented in this work, which not only enables fexible abstraction and management of historical grid verification of grid workflows. Furthermore, a relaxed region system events, but also facilitates modeling and temporal analysis (RRA) approach is proposed to decompose large scale grid workflows into sequentially composed regions with relaxation of parallel workflow branches, and corresponding verification strategies are also decomposed following modular verification principles. Performance evaluation results show that the RRA approach can dramatically reduce CPU time and memory usage of formal verification.
基金
supported by the National Basic Research 973 Program of China under Grant Nos.2011CB302805,2011CB302505
the National High Technology Research and Development 863 Program of China under Grant No.2011AA040501
the National Natural Science Foundation of China under Grant No.60803017
Fan Zhang is supported by IBM 2011-2012 Ph.D. Fellowship