Research Of Situation Awareness Technology Oriented Service Quality Guarantee

Distributed, heterogeneous, autonomous and dynamic change characteristics of Web services cause many uncertain factors of service computing environment such as loose, independence, and heterogeneous, which make stricter requirements of non functional properties of the service providers, namely, the service quality of service(Qo S). With the continuous improvement of complex degree and non functional properties requires of system software, guarantying the Qo S of Web services has become an important issue facing platform designers and application developers.For the service quality assurance of Web service, many researchers take the establishment of Qo S model to manage the Web service. The existing Qo S management model can manage the Qo S data, but the dynamic Qo S cannot be updated dynamically during the service operation. That will cause the Qo S data distortion, and can’t update the Qo S safeguard strategy in the service operation process. In order to solve the dynamic Qo S in the process of Qo S service operation, many researchers propose Web service dynamic assessment method to real-time evaluate the Web service running process Qo S. In order to solve the dynamic Qo S of Web service runtime, many researchers propose Web service dynamic assessment methods to evaluate the Web service runtime Qo S in real-time. Through real-time collection and computing various attribute data of Web service running process, users can get timely and accurate Qo S data information. And then it can take real-time service quality assurance measures to protect the service quality of Web service. However, this dynamic Qo S assessment method needs to collect the server side, the user data, which not only consumes time, but also greatly increases the overhead of the system.In order to overcome the disadvantage of Qo S guarantee method in Web service, situation awareness technology is introduced to Qo S security. Through the perception of the current status and future trends of Web services, replacement service and other measures can be taken before the decline or failure of Web services, so as to ensure the quality of the service. Since it was proposed in 1985, situation awareness has been widely used in data fusion field, especially in the field of network security awareness. The most commonly used model of situation awareness can be summarized into four steps :(1) Collect original data of perception field;(2) analysis the original data with the help of situation awareness algorithm, perceiving situation map;(3) Combining with the situation map generated by the previous step to extract a deeper trend;(4) to predict the future development trend perception. The focus of this paper is to introduce awareness technology to Qo S security research, completing Qo S situation awareness and forecast. Through the construction of situation awareness model, the extraction and recognition of current situation of service component is completed, and service quality assurance strategy are given according to the current situation.The main work of this paper:In this paper, the core technology of Web service and service quality assurance strategy are studied deeply. Aiming at the existing problems of the field of service quality guarantee, the situation awareness technology was introduces into the research field to establish situation awareness model of Web service. Situation awareness model is divided into three parts, respectively trend extraction recognition and forecast. At the same time, by constructing situation awareness index system which contain implication, equivalence, plug-in satisfaction and not meet the four conditions and four kinds of variable points, current status and future development trend of Web service were measured, and active measures were taken to guarantee quality of service.In the construction of situation awareness model, Qo S trend extraction is divided into state and trends. The extraction of state can be obtained by comparing with the Qo S basic meet interval designated by the system. While the trend extraction need to be solved by least squares method and decision tree algorithm. Finally, Qo S situation map containing three kinds of element is established to obtain the dynamic Qo S of component service. In the process of recognition by the least squares trend, when the slope of P is large, the accumulation increases quickly relatively, quickly reaches the threshold, decreased recognition effect. Therefore, a new trend identification solution was put forward according to the grade of slope P. At the same time, this paper presents a hierarchical genetic algorithm of adaptive threshold setting method to further enhance the accuracy of trend recognition. Set three threshold value range of trend recognition through experience, and then use the optimization characteristics of genetic algorithm to find three thresholds with the minimum fitting error.Qo S situation map can be obtained with the help of trend recognition. The traditional Web service Qo S prediction methods require a large amount of historical data, only for conventional Qo S forecast. The grey forecasting requires less data, intuitive principle, fit computational complexity and good prediction. Based on those traits of grey forecast, definition of four kinds of variable point was given on the basis of Qo S status and trend of component services. Through the forecast of the change point in the next period time and comprehensive analysis, the decisive measure of replacing the corresponding component services was taken in order to guarantee the quality of service components and achieve the purpose of service composition security.