A modern organization's day to day operations rely heavily on the proper functioning of its IT systems. Increasingly, most of these systems are starting to be based on the Service Oriented Architectures (SOAs). SOAs enable the automatic creation of business applications from independently developed and deployable Web services. When a SOA component suffers failure it results in sub-par operational performance and ultimately hampers the revenue stream of the organization. There are two approaches to minimize the failure risk in SOA: (a) Fault Intolerance tries to eliminate the faults before it occurs in a system. (b) Fault-Tolerance techniques deal with faults that occur on run time and try averting that faults from turning into system failures. Most of the current fault management techniques focus on the latter. However, in our study we develop a hybrid model. The method proposed in this paper (FT-SOA) predicts the failure based on HSMM and clustering techniques for SOAs and Web services. We focus on failure prediction because it plays a major role in having a highly available system. The objective of the online fault prediction is to predict the occurrence of faults in the system, based on the current information such as: service weight, previous history and reputation. A new prediction method is created to assess the fault likelihood in the near future execution of a specific service in a composite solution. We compare our approach with similar techniques to show the applicability of our approach.
|Publication status||Published - 2014|