Prediction of Web Services Reliability Based on Decision Tree Classification Method

With the development of the service-oriented computing(SOC),web service has an important and popular solution for the design of the application system to various enterprises.Nowadays,the numerous web services are provided by the service providers on the network,it becomes difficult for users to select the best reliable one from a large number of services with the same function.So it is necessary to design feasible selection strategies to provide users with the reliable services.Most existing methods attempt to select services according to accurate predictions for the quality of service(QoS)values.However,because the network and user needs are dynamic,it is almost impossible to accurately predict the QoS values.Furthermore,accurate prediction is generally time-consuming.This paper proposes a service decision tree based post-pruning prediction approach.This paper first defines the five reliability levels for measuring the reliability of services.By analyzing the quality data of service from the network,the proposed method can generate the training set and convert them into the service decision tree model.Using the generated model and the given predicted services,the proposed method classifies the service to the corresponding reliability level after discretizing the continuous attribute of service.Moreover,this paper applies the post-pruning strategy to optimize the generated model for avoiding the over-fitting.Experimental results show that the proposed method is effective in predicting the service reliability.

An Emergency Shelter Location Model Based on the Sense of Security and the Reliability Level

Based on the value function of the prospect theory,this paper constructs a security function,which is used to describe the victims’feelings about the distance in emergency evacuation.Since different paths between the demand points and the emergency shelters are generally of different importance degrees,they are divided into main paths and auxiliary paths.The security function values and the reliability levels of main paths and auxiliary paths are given different weights.The weighted sum of the security function values and the weighted sum of the reliability level function values of all demand points are maximized to determine the location and the number of the emergency shelters,the transfer paths,the reinforced edges and the incremental reliability level of the selected edge.In order to solve the model,a two-stage simulated annealing-particle swarm optimization algorithm is proposed.In this algorithm,the particle swarm optimization(PSO)algorithm is embedded into the simulated annealing(SA)algorithm.The cumulative probability operator and the cost probability operator are formed to determine the evolution of the particles.Considering the budget constraint,the algorithm eliminates the shelter combinations that do not meet the constraint,which greatly saves the calculation time and improves the efficiency.The proposed algorithm is applied to a case,which verifies its feasibility and stability.The model and the algorithm of this paper provide a basis for emergency management departments to make the earthquake emergency planning.