Enabling Personalized Composition and Adaptive Provisioning of Web Services

The proliferation of interconnected computing devices is fostering the emergence of environments where Web services made available to mobile users are a commodity. Unfortunately, inherent limitations of mobile devices still hinder the seamless access to Web services, and their use in supporting complex user activities. In this paper, we describe the design and implementation of a distributed, adaptive, and context-aware framework for personalized service composition and provisioning adapted to mobile users. Users specify their preferences by annotating existing process templates, leading to personalized service-based processes. To cater for the possibility of low bandwidth communication channels and frequent disconnections, an execution model is proposed whereby the responsibility of orchestrating personalized processes is spread across the participating services and user agents. In addition, the execution model is adaptive in the sense that the runtime environment is able to detect exceptions and react to them according to a set of rules

Search based software engineering: Trends, techniques and applications

© ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is available from the link below.In the past five years there has been a dramatic increase in work on Search-Based Software Engineering (SBSE), an approach to Software Engineering (SE) in which Search-Based Optimization (SBO) algorithms are used to address problems in SE. SBSE has been applied to problems throughout the SE lifecycle, from requirements and project planning to maintenance and reengineering. The approach is attractive because it offers a suite of adaptive automated and semiautomated solutions in situations typified by large complex problem spaces with multiple competing and conflicting objectives. This article provides a review and classification of literature on SBSE. The work identifies research trends and relationships between the techniques applied and the applications to which they have been applied and highlights gaps in the literature and avenues for further research.EPSRC and E

Systematic survey on evolution of cloud architectures

Cloud architectures are becoming an active area of research. The quality and durability of a software system are defined by its architecture. The architecture approaches that are used to build cloud-based systems are not available in a blended fashion to achieve an effective universal architecture solution. The paper aims to contribute to the systematic literature review (SLR) to assist researchers who are striving to contribute in this area. The main objective of this review is to systematically identify and analyse the recently published research topics related to software architecture for cloud with regard to research activity, used tools and techniques, proposed approaches, domains. The applied method is SLR based on four selected electronic databases proposed by (Kitchenham and Charters, 2007). Out of 400 classified publications, we regard 121 as relevant for our research domain. We outline taxonomy of their topics and domains, provide lists of used methods and proposed approaches. At present, there is little research coverage on software architectures for cloud, while other disciplines have become more active. The future work is to develop a secure architecture to achieve quality of service and service level agreements

Dynamic QoS/QoE-aware reliable service composition framework for edge intelligence

Edge intelligence has become popular recently since it brings smartness and copes with some shortcomings of conventional technologies such as cloud computing, Internet of Things (IoT), and centralized AI adoptions. However, although utilizing edge intelligence contributes to providing smart systems such as automated driving systems, smart cities, and connected healthcare systems, it is not free from limitations. There exist various challenges in integrating AI and edge computing, one of which is addressed in this paper. Our main focus is to handle the adoption of AI methods on resource-constrained edge devices. In this regard, we introduce the concept of Edge devices as a Service (EdaaS) and propose a quality of service (QoS) and quality of experience (QoE)-aware dynamic and reliable framework for AI subtasks composition. The proposed framework is evaluated utilizing three well-known meta-heuristics in terms of various metrics for a connected healthcare application scenario. The experimental results confirm the applicability of the proposed framework. Moreover, the results reveal that black widow optimization (BWO) can handle the issue more efficiently compared to particle swarm optimization (PSO) and simulated annealing (SA). The overall efficiency of BWO over PSO is 95%, and BWO outperforms SA with 100% efficiency. It means that BWO prevails SA and PSO in all and 95% of the experiments, respectively

An Analysis of Service Ontologies

Services are increasingly shaping the world’s economic activity. Service provision and consumption have been profiting from advances in ICT, but the decentralization and heterogeneity of the involved service entities still pose engineering challenges. One of these challenges is to achieve semantic interoperability among these autonomous entities. Semantic web technology aims at addressing this challenge on a large scale, and has matured over the last years. This is evident from the various efforts reported in the literature in which service knowledge is represented in terms of ontologies developed either in individual research projects or in standardization bodies. This paper aims at analyzing the most relevant service ontologies available today for their suitability to cope with the service semantic interoperability challenge. We take the vision of the Internet of Services (IoS) as our motivation to identify the requirements for service ontologies. We adopt a formal approach to ontology design and evaluation in our analysis. We start by defining informal competency questions derived from a motivating scenario, and we identify relevant concepts and properties in service ontologies that match the formal ontological representation of these questions. We analyze the service ontologies with our concepts and questions, so that each ontology is positioned and evaluated according to its utility. The gaps we identify as the result of our analysis provide an indication of open challenges and future work

Semantics-aware planning methodology for automatic web service composition

Service-Oriented Computing (SOC) has been a major research topic in the past years. It is based on the idea of composing distributed applications even in heterogeneous environments by discovering and invoking network-available Web Services to accomplish some complex tasks when no existing service can satisfy the user request. Service-Oriented Architecture (SOA) is a key design principle to facilitate building of these autonomous, platform-independent Web Services. However, in distributed environments, the use of services without considering their underlying semantics, either functional semantics or quality guarantees can negatively affect a composition process by raising intermittent failures or leading to slow performance. More recently, Artificial Intelligence (AI) Planning technologies have been exploited to facilitate the automated composition. But most of the AI planning based algorithms do not scale well when the number of Web Services increases, and there is no guarantee that a solution for a composition problem will be found even if it exists. AI Planning Graph tries to address various limitations in traditional AI planning by providing a unique search space in a directed layered graph. However, the existing AI Planning Graph algorithm only focuses on finding complete solutions without taking account of other services which are not achieving the goals. It will result in the failure of creating such a graph in the case that many services are available, despite most of them being irrelevant to the goals. This dissertation puts forward a concept of building a more intelligent planning mechanism which should be a combination of semantics-aware service selection and a goal-directed planning algorithm. Based on this concept, a new planning system so-called Semantics Enhanced web service Mining (SEwsMining) has been developed. Semantic-aware service selection is achieved by calculating on-demand multi-attributes semantics similarity based on semantic annotations (QWSMO-Lite). The planning algorithm is a substantial revision of the AI GraphPlan algorithm. To reduce the size of planning graph, a bi-directional planning strategy has been developed

Novel optimization schemes for service composition in the cloud using learning automata-based matrix factorization

A thesis submitted to the University of Bedfordshire, in partial fulfilment of the requirements for the degree of Doctor of PhilosophyService Oriented Computing (SOC) provides a framework for the realization of loosely couple service oriented applications (SOA). Web services are central to the concept of SOC. They possess several benefits which are useful to SOA e.g. encapsulation, loose coupling and reusability. Using web services, an application can embed its functionalities within the business process of other applications. This is made possible through web service composition. Web services are composed to provide more complex functions for a service consumer in the form of a value added composite service. Currently, research into how web services can be composed to yield QoS (Quality of Service) optimal composite service has gathered significant attention. However, the number and services has risen thereby increasing the number of possible service combinations and also amplifying the impact of network on composite service performance. QoS-based service composition in the cloud addresses two important sub-problems; Prediction of network performance between web service nodes in the cloud, and QoS-based web service composition. We model the former problem as a prediction problem while the later problem is modelled as an NP-Hard optimization problem due to its complex, constrained and multi-objective nature. This thesis contributed to the prediction problem by presenting a novel learning automata-based non-negative matrix factorization algorithm (LANMF) for estimating end-to-end network latency of a composition in the cloud. LANMF encodes each web service node as an automaton which allows v it to estimate its network coordinate in such a way that prediction error is minimized. Experiments indicate that LANMF is more accurate than current approaches. The thesis also contributed to the QoS-based service composition problem by proposing four evolutionary algorithms; a network-aware genetic algorithm (INSGA), a K-mean based genetic algorithm (KNSGA), a multi-population particle swarm optimization algorithm (NMPSO), and a non-dominated sort fruit fly algorithm (NFOA). The algorithms adopt different evolutionary strategies coupled with LANMF method to search for low latency and QoSoptimal solutions. They also employ a unique constraint handling method used to penalize solutions that violate user specified QoS constraints. Experiments demonstrate the efficiency and scalability of the algorithms in a large scale environment. Also the algorithms outperform other evolutionary algorithms in terms of optimality and calability. In addition, the thesis contributed to QoS-based web service composition in a dynamic environment. This is motivated by the ineffectiveness of the four proposed algorithms in a dynamically hanging QoS environment such as a real world scenario. Hence, we propose a new cellular automata-based genetic algorithm (CellGA) to address the issue. Experimental results show the effectiveness of CellGA in solving QoS-based service composition in dynamic QoS environment

Microservice Transition and its Granularity Problem: A Systematic Mapping Study

Microservices have gained wide recognition and acceptance in software industries as an emerging architectural style for autonomic, scalable, and more reliable computing. The transition to microservices has been highly motivated by the need for better alignment of technical design decisions with improving value potentials of architectures. Despite microservices' popularity, research still lacks disciplined understanding of transition and consensus on the principles and activities underlying "micro-ing" architectures. In this paper, we report on a systematic mapping study that consolidates various views, approaches and activities that commonly assist in the transition to microservices. The study aims to provide a better understanding of the transition; it also contributes a working definition of the transition and technical activities underlying it. We term the transition and technical activities leading to microservice architectures as microservitization. We then shed light on a fundamental problem of microservitization: microservice granularity and reasoning about its adaptation as first-class entities. This study reviews state-of-the-art and -practice related to reasoning about microservice granularity; it reviews modelling approaches, aspects considered, guidelines and processes used to reason about microservice granularity. This study identifies opportunities for future research and development related to reasoning about microservice granularity.Comment: 36 pages including references, 6 figures, and 3 table