Abstract
Identifying critical components are of great significance to the overall reliability of service-oriented systems (SOSs). As the size of the SOS increases, identifying critical components in the process of predicting the SOS reliability can reduce the number of components that need to be predicted and shorten the prediction time. Moreover, predicting the reliability of critical components can also ensure the stability of the SOS. Therefore, we transform the relationships among service components of the SOS into a service dependency graph. Then, an improved weighted LeaderRank algorithm (IW-LeaderRank) is proposed to measure the importance of components and obtain the sequence of critical components. Through experimental analysis, the method can accurately and efficiently identify critical components in SOSs.
Highlights
Service-oriented systems (SOSs) are defined as loosely coupled software applications based on service-oriented architecture (SOA) [1]
Based on the Service Dependency Graph of SOS (SDGS), this paper uses service components represents the reliability of the service-oriented systems (SOSs), and reducing the reliability of critical service the IW-LeaderRank to identify the critical components of the SOS
The results of the LeaderRank algorithm can be converged to a stable value, especially for large complex directional networks.After the LeaderRank algorithm sorts all the nodes in the network, the results show that the LeaderRank algorithm rank is better than the PageRank algorithm
Summary
Service-oriented systems (SOSs) are defined as loosely coupled software applications based on service-oriented architecture (SOA) [1]. A lot of resources need to be spent in the process of evaluating SOSs. the reliability prediction of the SOS is a major challenge [2]. The idea of the LeaderRank algorithm [18] is to add a common node (ground node) to the known a directional with N + 1 nodes: Network G ( N + 1, M + 2N ). Since the public node has a two-way directed network G( N , M ) , and add a reverse connection to the unidirectional connection to obtain connection with other nodes in the network, G constitutes a strong connected graph, which can avoid Ncomplex. Public nodeThe hasLeaderRank a two-way aisolated directional with nodes in the network and ensure of the connection with are other in the network, G constitutes a strong connected graph, which can avoid algorithm steps as nodes follows: isolated nodes in the complex and ensure the0,convergence ofvalues the algorithm.
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