Application Of CSP Based Probe Selection Algorithm In Network Fault Diagnosis

The core of network management is fault management,and fault management focuses on fault diagnosis.The traditional fault diagnosis technology is a passive technology based on alarm correlation analysis.With the increase of network size and complexity,the number of alarms increases rapidly,making the traditional passive fault diagnosis technology based on alarm correlation analysis unsuitable.On the other hand,active detection technology actively sends probe information to the network to collect network status information for fault diagnosis,and has the features of active adaptability,which overcomes the shortcomings of traditional technologies to some extent.However,probe information will introduce additional management traffic and storage space.In order to reduce the extra cost of introducing probe information,it is necessary to select an optimized detection method to perform.Aiming at the problem of detection selection in fault detection stage,this paper proposes an improved greedy increase algorithm and a probe selection algorithm based on CSP.Through the analysis of the traditional greedy increase algorithm,it is found that the traditional greedy increase algorithm does not consider the different meanings of each network node for the probe selection problem,when performing greedy selection.By default,all network nodes have the same importance.This is contrary to the facts.Therefore,this paper designs a method for assigning weights to different network nodes to represent their importance,and determines the weights of nodes by the number of probes passing through each node.The weights of the probes are calculated based on the weights of the nodes.When the greedy selection is made,the probe with the largest weight is preferentially selected,thereby ensuring that the probes passing through the most important nodes are selected first.On the basis,a probe selection algorithm based on CSP is proposed.The algorithm use the characteristics of the optimal probe set to construct a constraint set and to select instantiations compatible with the constraint set,and then removes redundancy of the probe set and optimize it.Aiming at the problem of detection selection in the fault diagnosis stage,this paper proposes an improved heuristic search algorithm and a pre-selection algorithm based on CSP.Through the analysis of the traditional heuristic search algorithm,it is found that the traditional heuristic search algorithm only selects one probe at a time,resulting in too many selections and complex calculations;This paper designs a method for selecting a group of probes at a time instead of selecting one probe at a time,and adjusts the heuristic selection strategy,and further preferentially selects the most probably successful detection.As the number of selections is reduced,the calculation amount is reduced.At the same time,as more probes for each selection result in more heuristic information,the accuracy of selection increases.A pre-selection algorithm based on CSP is further proposed.In this algorithm,the characteristics of the optimal probe set are analyzed to construct a constraint set.Using a backtracking search algorithm to find instantiations compatible with the constraint set,a detection set that satisfies the optimal set feature is obtained.The simulation experiment of the proposed algorithm is carried out in this paper.The experimental results show that the proposed algorithm is superior to the traditional algorithm and shows that the improvement of the algorithm is effective.At the same time,it proves that the work of this paper has positive significance to solving the problem of probe selection in the active detection process.