An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks

QoS identification for untrustworthy Web services is critical in QoS management in the service computing since the performance of untrustworthy Web services may result in QoS downgrade. The key issue is to intelligently learn the characteristics of trustworthy Web services from different QoS levels, then to identify the untrustworthy ones according to the characteristics of QoS metrics. As one of the intelligent identification approaches, deep neural network has emerged as a powerful technique in recent years. In this paper, we propose a novel two-phase neural network model to identify the untrustworthy Web services. In the first phase, Web services are collected from the published QoS dataset. Then, we design a feedforward neural network model to build the classifier for Web services with different QoS levels. In the second phase, we employ a probabilistic neural network (PNN) model to identify the untrustworthy Web services from each classification. The experimental results show the proposed approach has 90.5% identification ratio far higher than other competing approaches.Comment: 8 pages, 5 figure

A Trustworthy Approach to the Adaptive Composition of GeoServices

AbstractFor the automatic generation of geographical information service chain, this article defines the quality of service (QoS) metrics based on service response time, reliability, and matching degree, among others, and the error propagation model. Based on the semantic matching and trustworthiness assessment of the geographical information services, this article proposes a trustworthy adaptive composition framework and implementation algorithm for geographical information services, ensuring the composition of service chains to better meet various QoS constraints. The effectiveness of this approach is proven in the simulation experiments

Academic Panel: Can Self-Managed Systems be trusted?

Trust can be defined as to have confidence or faith in; a form of reliance or certainty based on past experience; to allow without fear; believe; hope: expect and wish; and extend credit to. The issue of trust in computing has always been a hot topic, especially notable with the proliferation of services over the Internet, which has brought the issue of trust and security right into the ordinary home. Autonomic computing brings its own complexity to this. With systems that self-manage, the internal decision making process is less transparent and the ‘intelligence’ possibly evolving and becoming less tractable. Such systems may be used from anything from environment monitoring to looking after Granny in the home and thus the issue of trust is imperative. To this end, we have organised this panel to examine some of the key aspects of trust. The first section discusses the issues of self-management when applied across organizational boundaries. The second section explores predictability in self-managed systems. The third part examines how trust is manifest in electronic service communities. The final discussion demonstrates how trust can be integrated into an autonomic system as the core intelligence with which to base adaptivity choices upon

QoS based Effective and Efficient Selection of Web Service and Retrieval of Search Information

Web services are integrated software components for the support of interoperable machine to machine interaction over a network. Web services have been widely employed for building service-oriented applications in both industry and academia in recent years. The number of publicly available Web services is steadily increasing on the Internet. However, this proliferation makes it hard for a user to select a proper Web service among a large amount of service candidates. An inappropriate service selection may cause many problems to the resulting applications. In this paper, a novel collaborative filtering-based Web service recommender system is proposed to help the users and select services with optimal QoS performance. Our recommender system employ an effective and efficient selection of web services and relevant retrieval of information and makes personalized service recommendation to users based on the clustering results. Compared with existing service recommendation methods, the proposed approach achieves considerable improvement on the recommendation accuracy and the QoS performance metrics adopted in this paper shows the better accuracy and relevant web services

Context-Aware Trustworthy IoT Energy Services Provisioning

We propose an IoT energy service provisioning framework to ensure consumers' Quality of Experience (QoE). A novel context-aware trust assessment model is proposed to evaluate the trustworthiness of providers. Our model adapts to the dynamic nature of energy service providers to maintain QoE by selecting trustworthy providers. The proposed model evaluates providers' trustworthiness in various contexts, considering their behavior and energy provisioning history. Additionally, a trust-adaptive composition technique is presented for optimal energy allocation. Experimental results demonstrate the effectiveness and efficiency of the proposed approaches.Comment: 15 pages, 12 figures, This paper is accepted in the 21th International Conference on Service Oriented Computing (ICSOC 2023

Trust-Based Service Selection

Service Oriented Architecture (SOA) is an architectural style that builds enterprise solutions based on services. In SOA, the lack of trust between different parties affects the adoption of such architecture. Trust is as significant a factor for successful online interactions as it is in real life communities, and consequently, it is an important factor that is used as a criterion for service selection. In the context of online services and SOA, the literature shows that the field of trust is not mature. Trust definition and the consideration of the essentials of trust aspects do not reflect the true nature of trust online. This thesis proposes a trust-based service selection solution, which requires establishing trust for services and supporting service selection based on trust. This work considers building trust for service providers besides rating services, an area that is neglected in the literature. This work follows progressive steps to arrive at a solution. First, this work develops a trust definition and identifies trust principles, which cover different aspects of trust. Next, SOA is extended to build a trust-based SOA that supports trust-based service selection. In particular, a new component, the trust mediator, which is responsible for trust establishment is added to the architecture. Accordingly, a trust mediator framework is built according to the trust definition and principles to identify its main components. Subsequently, this work identifies the trust information, or metrics, for services and service providers. Accordingly, trust models are built to evaluate trust rates for the applicable metrics, services, and service providers. Moreover, this work addresses the trust bootstrapping challenge. The proposed trust bootstrapping approach addresses different challenges in the literature such as whitewashing and cold start. This approach is implemented through experiments, evaluations, and scenarios

QoS and trust prediction framework for composed distributed systems

The objective of this dissertation is to propose a comprehensive framework to predict the QoS and trust (i.e, the degree of compliance of a service to its specification) values of composed distributed systems created out of existing quality-aware services. We improve the accuracy of the predictions by building context-aware models and validating them with real-life case studies. The context is the set of environmental factors that affect QoS attributes (such as response time and availability), and trust of a service or a composed system. The proposed framework uses available context-QoS dependency information of individual services and information about the interaction patterns among the services to make predictions for the QoS and trust values of the composed system at the design phase of the development lifecycle. Such predictions made in the early phases of the system development lifecycle will reduce cost, time, and effort. We demonstrate the use of these predictions in selecting the optimum set of services to create composed systems using heuristic optimization algorithms. Additionally, the prediction model is used at runtime with fast heuristic techniques to build adaptable composed systems. The empirical results show the proposed context-dependent framework performs well in providing more accurate predictions than the prevalent approaches