Service-Oriented Middleware for the Future Internet: State of the Art and Research Directions

International audienceService-oriented computing is now acknowledged as a central paradigm for Internet computing, supported by tremendous research and technology development over the last ten years. However, the evolution of the Internet, and in particular, the latest Future Internet vision, challenges the paradigm. Indeed, service-oriented computing has to face the ultra large scale and heterogeneity of the Future Internet, which are orders of magnitude higher than those of today's service-oriented systems. This article aims at contributing to this objective by identifying the key research directions to be followed in light of the latest state of the art. This article more specifically focuses on research challenges for service-oriented middleware design, therefore investigating service description, discovery, access and composition in the Future Internet of services

Service Re-Selection for Disruptive Events in Mobile Environments: A Heuristic Technique for Decision Support at Runtime

Modern service-based processes in mobile environments are highly complex due to the necessary spatial–temporal coordination between multiple participating users and the consideration of context information. Due to the dynamic nature of mobile environments, disruptive events occur at runtime, which require a re-selection of the planned service compositions respecting multiple users and context-awareness. Thereby, when re-selecting services the features performance, solution quality, solution robustness and alternative solutions are essential and contribute to the efficacy of service systems. This paper presents an optimization-based heuristic technique based on a stateful representation that uses a region-based approach to re-select services considering multiple users, context information and in particular disruptive events at runtime. The evaluation results, which are based on a real-world scenario from the tourism domain, show that the proposed heuristic is superior compared to competing artifacts

Improving Cloud System Reliability Using Autonomous Agent Technology

Cloud computing platforms provide efficient and flexible ways to offer services and computation facilities to users. Service providers acquire resources according to their requirements and deploy their services in cloud. Service consumers can access services over networks. In cloud computing, virtualization techniques allow cloud providers provide computation and storage resources according to users’ requirement. However, reliability in the cloud is an important factor to measure the performance of a virtualized cloud computing platform. Reliability in cloud computing includes the usability and availability. Usability is defined as cloud computing platform provides functional and easy-to-use computation resources to users. In order to ensure usability, configurations and management policies have to be maintained and deployed by cloud computing providers. Availability of cloud is defined as cloud computing platform provides stable and reliable computation resources to users. My research concentrates on improving usability and availability of cloud computing platforms. I proposed a customized agent-based reliability monitoring framework to increase reliability of cloud computing

Context Aware Web-Service Monitoring

Monitoring the correct behaviour of a service-based system is a necessity and a key challenge in Service Oriented Computing. Several efforts have been directed towards the development of approaches dealing with the monitoring activity of service-based systems. However, these approaches are in general not suitable when dealing with modifications in service-based systems. Furthermore, existing monitoring approaches do not take into consideration the context of the users and how this context may affect the monitor activity. Consequently, a holistic monitor approach, capable of dealing with the dynamic nature of service-based systems and of taking into consideration the user context, would be highly desirable. In this thesis we present a monitor adaptation framework capable of dealing with changes in a service-based system and different types of users interacting with it. More specifically, the framework obtains a set of monitor rules, necessary to verify the correct behaviour of a service-based system, for a particular user. Moreover, the monitor rules verifying the behaviour of a service-based system relate to properties of the context types defined for a user. The main contributions of our work include the general characterisation of a user interacting with a service-based system and the generation of suitable monitor rules.The proposed framework can be applied to any service composition without the need of further modifications. Our work complements previous research carried on in the area of web service monitoring. More specifically, our work generates a set of suitable monitor rules - related to the user context - which are deployed in a run-time monitor component. Our framework has been tested and validated in several cases considering different scenarios

Sentiment analysis as a service

This research focuses on the design and development of a service composition based framework that enables the execution of services for social media based sentiment analysis. Our research develops novel analytical models, composition techniques and algorithms which use services as a mean for sentiment abstraction, processing and analysis from large scale social media data. Current sentiment analysis techniques require specialized skill of data science and machine learning. Moreover, traditional approaches rely on laborious and time-consuming activities such as manual dataset labelling, data model training and validation. This makes overall sentiment analysis process a challenging task. In comparison, services are `ready-made' software solutions that can be composed on-demand for developing complex applications without indulging in the domain specific details. This thesis investigates a novel approach that transforms traditional social media based sentiment analysis process into a service composition driven solution. In this thesis, we begin by developing a novel service framework that replaces the traditional sentiment analysis tasks with online services. Our framework includes a new service model to present services required for sentiment analysis. We develop a semantic service composition model and algorithm that dynamically composes various services for data collection, noise filtering and sentiment extraction. In particular, we focus on abstracting sentiment based on location and time. We then focus on enhancing the flexibility of our proposed service framework to compose appropriate sentiment analysis services for highly dynamic and changing features of social media platforms. In addition, we aim to efficiently process and analyze large scale social media data. In order to enhance our service composition framework, we propose a novel approach to formalize social media platforms as cloud enabled services. We develop a functional and quality of service (QoS) model that captures various dynamic features of social media platforms. In addition, we devise a cloud based service model to access social media platforms as services by using the Ontology Web Language for Service (OWL-S). Secondly, we integrate the QoS model into our existing composition framework. It enables our framework to dynamically assess the QoS of multiple social media platforms, and simultaneously compose appropriate services to extract, process, analyze and integrate the sentiment results from large scale data. Finally, we concentrate on efficient utilization of the sentiment analysis extracted from large scale data. We formulate a meta-information composition model that transforms and stores sentiment obtained from large streams of data into re-usable information. Later, the re-usable information is on-demand integrated and delivered to end users. To demonstrate the performance and test the effectiveness of our proposed models, we develop prototypes to evaluate our composition framework. We design several scenarios and conduct a series of experiments using real-world social media datasets. We present the results and discuss the outcomes which validate the performance of our research