Di Hou

A novel heuristic algorithm for QoS-aware end-to-end service composition

Many works have been carried out to find the efficient algorithms for QoS-aware service composition in recent years. Nevertheless, on one hand, some of these works only consider the local QoS attributes in Web services composition; on the other hand, some ideas derived from QoS selection algorithms for network routing are directly applied in service composition without any adaption. A service composition model with end-to-end QoS constraints has been presented in this paper. An improved heuristics HCE based on the observation of characteristic of end-to-end service composition is proposed as a novel solution. Simulation results reveal the better performance of proposed heuristic compared to the other two heuristics, HMCOP and generic CE algorithm.

An Improved Heuristic for QoS-Aware Service Composition Framework

Service Oriented Architecture (SOA) and Service Oriented Computing (SOC) are prevailing paradigms for sharing and reusing resources. Service composition is a methodology widely used in SOA and SOC to build new value-added services on primitive services on-the-fly to support online Business-to-Business collaborations. Requirements of customers to these composite services include functionality and non-functionality. Since many services can have the similar functionality, the non-functionality of composite services, such as Quality of Services (QoS), is the important metrics to distinguish a service from each other and find an optimal program to meet the requirements of customers. This paper firstly proposes a system model from the view of resource and value, and then we introduce an improved heuristic algorithm for the selection of composite services with multiple constraints. The simulation experiments show an outperforming result of proposal algorithm in both utility performance and time cost comparing with the other heuristic algorithms.

A Study on Context-aware Privacy Protection for Personal Information

By using personal information in a pervasive computing environment, context-aware applications can provide appropriate services for people. This personal information is often involved in personal privacy. In order to protect personal privacy concerns about personal information, privacy role is proposed to control access personal information. We also construct an information system about the privacy decision of personal information disclosure based on peoples interaction history. In the initial period of personal information disclosure, the privacy decision is made by people and the information system is constructed based on the decision data. Then privacy disclosure policies are extracted from this information system using rough set theory. According to deducing from the privacy disclosure policies and peoples context information, the context-aware application is assigned to an adequate privacy role. It reduces the distraction of privacy decision for people. A case study further shows the proposed method is effective. Finally, it provides about the overload performance of privacy role analysis engine.

Software aging estimation and prediction of a real VOD system based on PCA and neural networks

The phenomenon of software aging refers to the exhaustion of operating system resource, fragmentation and accumulation of errors, which results in progressive performance degradation or transient failures or even crashes of applications. In this paper, we investigate the software aging patterns of a real VOD system. First, we collect data on several system resource usage and application server. Then, non-parametric statistical methods and linear regression models are adopted to detect aging and estimate trends in the data sets. Finally, artificial neural network (ANN) models are constructed to model the extracted data series of systematic parameters and to predict software aging of the VOD system. In order to reduce the complexity of ANN and to improve its efficiency, principal component analysis (PCA) is used to reduce the dimensionality of input variables of ANN. The experimental results show that the software aging prediction model based on ANN is superior to the time series models in the aspects of prediction precision. Based on the models employed here, software rejuvenation policies can be triggered by actual measurements.

An Improved Calculus for Secure Dynamic Services Composition

With the increased interest in the Web services composition, more and more enterprises and businesses depend on this paradigm. Open, distributed and dynamic properties of the schema, there is a pressing need for secure services in daily transactions. Orchestration and choreography language provide basic services standards and interaction, collaboration, and negotiation standards among services, but they are not give any secure manners or secure operation styles and specifications. Despite the interest of such security mechanisms, a formal module of them is still lacking. For giving general guide to implement secure orchestration and choreography language, we give a formal approach to carry out those goals. To this target, we emphasize on those by designing an extension of the Spi calculus with Secure Global Calculus. The Spi calculus precisely identifies orchestration secure properties of each principal from a local viewpoint. The secure global calculus describes an interaction secure choreography scenario from a vantage point of view. We called our method SpiG4WSC calculus. We believe that the combination of strong practical needs for dynamic secure Web services composition and the theoretical foundations will lead to a bridge between practice and theories. The contribution of this paper are (1) giving the syntax and semantic of SpiG4WSC calculus; (2)applying the calculus to give a model to presenting the secure orchestration, emphasizing on the formal basis for secure services; (3)describing the secure choreography, giving the formal frame for interaction processes.

Modeling and Performance Analysis of Software Rejuvenation Policies for Multiple Degradation Systems

Software rejuvenation is a preventive and proactive technology to counteract the phenomenon of software aging and system failures and to improve the system reliability. In this paper we present and analyze three software rejuvenation policies for an operational software system with multiple degradations, called preemptive rejuvenation, delayed rejuvenation and mixed rejuvenation. These policies consider both history data and current running state, and the rejuvenation action is triggered on the basis of predetermined performance threshold and rejuvenation interval respectively. Continuous-time Markov chains are used to describe the analytic models. To evaluate these polices expediently, we utilize deterministic and stochastic Petri nets to solve the models. Numerical results show that the deployment of software rejuvenation in the system leads to significant improvement in availability and throughput. These three rejuvenation policies are better than the standard rejuvenation policy, and the mixed policy is the best one.

A Rough Wavelet Network Model with Genetic Algorithm and its Application to Aging Forecasting of Application Server

According to the characteristics of the operational behavior and runtime state of application sever, the resource consumption time series are observed and modeled by rough neural network (RWN). The dimensionality of input variables of RWN is reduced by information entropy reduction method, and the structure and parameters of RWN are optimized with adaptive genetic algorithm (GA). Judging by the model, we can get the aging threshold before application server failed and preventively maintenance the application server before systematic parameter value reaches the threshold. The experiments are carried out to validate the efficiency of the proposed forecasting model and show that the aging forecasting model based on RWN with adaptive genetic algorithm is superior to the neural network (NN) model and wavelet network (WN) model in the aspects of convergence rate and forecasting precision.

A Mixed Software Rejuvenation Policy for Multiple Degradations Software System

Software rejuvenation is a preventive and proactive technology to counteract the phenomenon of software aging and system failures, and to improve the system reliability. In this paper we present a mixed software rejuvenation policy for an operational software system with multiple degradation states, which considers both the history information and the current running state. By this policy, the system is rejuvenated when it achieves to a degradation threshold or it comes to the pre-determined rejuvenation interval. For comparison, standard rejuvenation policy is also discussed. Continuous-time Markov chains are used to describe the multiple degradation states model. To evaluate these polices expediently, we utilize deterministic and stochastic Petri nets (DSPN) to solve the models. Numerical results show that the deployment of software rejuvenation in the system leads to significant improvement in availability and throughput. And the mixed rejuvenation policy is better than the standard rejuvenation policy.

Uncertain Privacy Decision about Access Personal Information in Pervasive Computing Environments

In pervasive computing environments, context- aware applications provide services for people by using personal information. People also make privacy decision about personal information disclosure as privacy concerns. Privacy decision depends on peoples interaction situation. The trust of information collector and personal information sensitivity often enable people to make uncertain privacy decision. Therefore, we construct a fuzzy objective information system that consists of personal interaction situation history. The privacy disclosure policies are extracted from this information system under using rough set theory. The context-aware applications are assigned to an adequate privacy role with the privacy disclosure policies and situation of peoples interaction. Finally, we provide a working example and the initial performance evaluation.

Context-Aware Learning Privacy Disclosure Policy from Interaction History

Context-aware applications use personal information to provide appropriate services for people. This personal information is often involved in personal privacy. In order to balance the personal privacy concerns against enjoying context-aware services, we propose a conceptual framework and privacy role to control access personal information. Privacy disclosure policy can be learned from peoples interaction history about personal information disclosure using rough set theory. According to deducing from the privacy disclosure policy and context information, a context-aware application is assigned to an adequate privacy role. A case analysis and the initial performance evaluation show that the proposed method is effective.