Wenrui Li

WS-PSC monitor: a tool chain for monitoring temporal and timing properties in composite service based on property sequence chart

Web service composition is a new paradigm to develop distributed and reactive software-intensive systems. Due to the autonomous attribute of each basic service, validation of composite services must be extended from design time to run-time. In this paper, we describe a novel tool chain called WS-PSC Monitor to monitor temporal and timing properties in composite service based on graphical specification property sequence chart and timed property sequence chart. The tool chain provides a completely graphical front-end which can make software designers do not have to deal with any particular textual and logical formalism.

Web services property sequence chart monitor: a tool chain for monitoring BPEL-based web service composition with scenario-based specifications

Web service composition is a new paradigm to develop distributed and reactive software-intensive systems. Owing to the autonomous nature of basic services, the validation of composite service must be extended from design-time to run-time. Here, the authors describe a novel tool chain called web services property sequence chart monitor to monitor temporal, timing and probabilistic properties in composite service based on scenario-based property specifications called property sequence chart, timed property sequence chart and probabilistic timed property sequence chart, respectively. The tool chain provides a completely graphical front-end that eliminates the need to deal with any particular textual and logical formalism. Furthermore, the framework and implementation detail of the tool chain are also presented. Finally, the feasibility and usability of the tool have been validated by the case studies and performance measurement.

A Novel QoS Prediction Approach for Cloud Service Based on Bayesian Networks Model

Considered as the next generation computing model, cloud computing plays an important role in scientific and commercial computing and draws wide attention from both academia and industry. In the dynamic, complex and changeable cloud computing environment, Quality Of Service (QoS) is an important basis for the selection of different cloud services. Therefore, the prediction of cloud services QoS can help users to choose the most suitable service at hand. The software and hardware and resources of three-layer structure for cloud computing will impact on cloud services QoS, but existing QoS prediction approaches are not consider the three-layer structure on the influence of thecloud service QoS. The CPU usage, physical memory usage andthe number of processes of infrastructure layer have definitely influenced QoS. In order to address this limitation, in the paper, a Bayesian network model of QoS prediction for cloud servicesis proposed. Firstly, an initial and basic Bayesian network modelis established by collecting data from the infrastructure layer, the platform layer and the application layer. Then the Bayesian network is trained and updated to obtain the cloud service QoS prediction model. Finally, a set of experiments based on collected data from the real cloud service environment has been conducted to validate the proposed approach. Experimental results show that the prediction approach is effective and accurate.

Predicting Failures in Dynamic Composite Services with Proactive Monitoring Technique

Web service composition is a new paradigm to develop distributed and reactive software-intensive systems. Predicting and preventing failures of dynamic composite services is an important and challenge problem due to the dynamically evolving attribute. In previous work, we propose CASSANDRA, a novel proactive monitoring technique with the ability to predict and prevent the potential failures happening in dynamic evolvable system. In this paper, we concretize the approach into web service composition field. By combining runtime information and design-time specification of basic services, the approach can analyse future -step models ahead of the current service execution states. Then, this model can be used to check with a set of desired properties represented by property sequence chart. Initial experiments on an online medicine case study validates our approach and shows encouraging results.

A novel approach for QoS prediction based on Bayesian combinational model

As an important factor in evaluating service, QoS (Quality of Service) has drawn more and more concerns with the rapid increasing of Web services. However, due to the great volatility of services in Mobile Internet environments, such as internet of vehicles, Web services often do not work as announced and thus cause unacceptable problems. QoS prediction can avoid failure before it takes place, which is considered a more effective way to assure quality. However, Current QoS prediction approaches neither consider the highly dynamic of Web services, nor maintain good prediction performance all the time. Consequently we propose a novel Bayesian combinational model to predict QoS by continuously adjusting credit values of the basic models so as to keep good prediction accuracy. QoS attributes such as response time, throughput and reliability are used to validate the proposed model. Experimental results show that the model can provide stable prediction results in Mobile Internet environments.

Bayesian Probabilistic Monitor: A New and Efficient Probabilistic Monitoring Approach Based on Bayesian Statistics

Modern software systems deal with increasing dependability requirements which specify non-functional aspect of a system correct operation. Usually, probabilistic properties are used to formulate dependability requirements like performance, reliability, safety, and availability. Probabilistic monitoring techniques, as an important assurance measure, has drawn more and more interest. Despite currently several approaches has been proposed to monitor probabilistic properties, it still lacks of a general and efficient monitoring approach for monitoring probabilistic properties. This paper puts forward a novel probabilistic monitoring approach based on Bayesian statistics, called Bayesian Probabilistic Monitor (BaProMon). By calculating Bayesian Factor, the approach can check whether the runtime information can provide sufficient evidences to support the null or alternative hypothesis. We give the corresponding algorithms and validate them via simulated-based experiments. The experimental results show that BaProMon can effectively monitor QoS properties. The results also indicate that our approach is superior to other approaches.

A Combinational QoS-Prediction Approach Based on RBF Neural Network

Quality of Service (QoS) is considered as an important factor to determine the success of a Web Service. Currently, many QoS prediction approaches focus on time series models. However, these approaches only consider linear and nonlinear time series. Analysis of real QoS datasets shows that they are characterized by other behaviors. Incomplete characteristics analysis of existing prediction approaches will result in wrong prediction results. Furthermore, the collected QoS values may miss some data, which will also impact the prediction accuracy. RBF (Radial Basis Function) neural network model can manage the complex linear and nonlinear relationship, with great flexibility and adaptability. Therefore, we propose a novel combinational prediction approach for QoS based on RBF, which chooses the optimal model from the established linear or nonlinear prediction model, and dynamic gray prediction model according to the data characteristics. Next, the predicted results of these models are passed into the RBF training model as the input, and then used for prediction. Using a public QoS dataset and four real-world QoS datasets, we evaluate the proposed approach by comparing it with previous approach. The experimental results show that our approach is better and improves the accuracy and validity.

Sky-MCSP-R: An Efficient Graph-Based Web Service Composition Approach

Aiming at optimizing Web service composition which satisfies users multiple QoS constraints, an efficient graph-based Web service composition approach, named Skyline improved Multi Constraint Shortest Path-Relax (Sky-MCSP-R), is proposed. Firstly, the approach selects Skyline services from candidate service spaces, thus it can construct the model of Web service composition directly on these high quality candidate services, reducing the whole number of nodes of the model. Secondly, the approach uses MCSP-K algorithm which uses over constraint mechanism to compose basic services, and reduces the constraint intensity so to make algorithm MCSP-K produce as many feasible solutions as possible. Thirdly, the approach uses Relax algorithm to optimize the solutions. Experimental results show that the approach improves the efficiency of Web service composition and keeps a high optimization rate, reducing no solution phenomenons. The results also indicate that our approach is superior to previous approaches.

A Web Service QoS Forecasting Approach Based on Multivariate Time Series

In order to accurately forecast Quality of Service (QoS) of different Web Services, this paper proposes a novel QoS forecasting approach called MulA-LMRBF (Multi-step fore-casting with Advertisement and Levenberg-Marquardt improved Radial Basis Function) based on multivariate time series. Considering the correlation among different QoS attributes, we use phase-space reconstruction to map historical multivariate QoS data into a dynamic system, use Average Dimension (AD) to estimate the embedding dimension and delay time of reconstructed phase space. We also add the short-term QoS advertisement data of service provider to form a more comprehensive data set. Then, RBF (Radial Basis Function) neural network improved by the Levenberg-Marquardt (LM) algorithm is used to update the weight of the neural network dynamically, which improves the forecasting accuracy and realizes the dynamic multiple-step forecasting. The experimental results demonstrate that MulA-LMRBF is better than previous approaches in term of precision and is more suitable for multi-step forecasting.

Game-Based Monitors for Scenario-Based Specification

Run-time verification techniques based on monitors have become the basic means of detecting software failures in dynamic and open environments. One challenging problem is how the monitor can provide sufficient indications before the real failures, so that the system has enough time to act before the failures cause serious harm. To this end, this paper proposes the main idea on how to generate monitors from a scenario-based specification called property sequence chart based on game theory. The monitors are interpreted in multivalued semantics: satisfied, infinitely controllable, system finitely controllable, system urgently controllable, environment finitely controllable, environment urgently controllable, violated. Through the multi-valued semantics definition, the monitors can provide enough information to help the system to take measures for failure prevention or recovery.