Semantic Service Substitution in Pervasive Environments

A computing infrastructure where everything is a service offers many new system and application possibilities. Among the main challenges, however, is the issue of service substitution for the application execution in such heterogeneous environments. An application would like to continue to execute even when a service disappears, or it would like to benefit from the environment by using better services with better QoS when possible. In this article, we define a generic service model and describe the equivalence relations between services considering the functionalities they propose and their non functional QoS properties. We define semantic equivalence relations between services and equivalence degree between non functional QoS properties. Using these relations we propose semantic substitution mechanisms upon the appearance and disappearance of services that fits the application needs. We developed a prototype as a proof of concept and evaluated its efficiency over a real use case

Representing and reasoning with qualitative preferences for compositional systems

Many applications call for techniques for representing and reasoning about preferences, i.e., relative desirability over a set of alternatives. Preferences over the alternatives are typically derived from preferences with respect to the various attributes of the alternatives (e.g., a student\u27s preference for one course over another may be influenced by his preference for the topic, the time of the day when the course is offered, etc.). Such preferences are often qualitative and conditional. When the alternatives are expressed as tuples of valuations of the relevant attributes, preferences between alternatives can often be expressed in the form of (a) preferences over the values of each attribute, and (b) relative importance of certain attributes over others. An important problem in reasoning with multi-attribute qualitative preferences is dominance testing, i.e., to find if one alternative (assignment to all attributes) is preferred over another. This problem is hard (PSPACE-complete) in general for well known qualitative conditional preference languages such as TCP-nets. We provide two practical approaches to dominance testing. First, we study a restricted unconditional preference language, and provide a dominance relation that can be computed in polynomial time by evaluating the satisfiability of an appropriately constructed logic formula. Second, we show how to reduce dominance testing for TCP-nets to reachability analysis in an induced preference graph. We provide an encoding of TCP-nets in the form of a Kripke structure for CTL. We show how to compute dominance using NuSMV, a model checker for CTL. We address the problem of identifying a preferred outcome in a setting where the outcomes or alternatives to be compared are composite in nature (i.e., collections of components that satisfy certain functional requirements). We define a dominance relation that allows us to compare collections of objects in terms of preferences over attributes of the objects that make up the collection, and show that the dominance relation is a strict partial order under certain conditions. We provide algorithms that use this dominance relation to identify only (sound), all (complete), or at least one (weakly complete) of the most preferred collections. We establish some key properties of the dominance relation and analyze the quality of solutions produced by the algorithms. We present results of simulation experiments aimed at comparing the algorithms, and report interesting conjectures and results that were derived from our analysis. Finally, we show how the above formalism and algorithms can be used in preference-based service composition, substitution, and adaptation

A user perspective of quality of service in m-commerce

This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2004 Springer VerlagIn an m-commerce setting, the underlying communication system will have to provide a Quality of Service (QoS) in the presence of two competing factors—network bandwidth and, as the pressure to add value to the business-to-consumer (B2C) shopping experience by integrating multimedia applications grows, increasing data sizes. In this paper, developments in the area of QoS-dependent multimedia perceptual quality are reviewed and are integrated with recent work focusing on QoS for e-commerce. Based on previously identified user perceptual tolerance to varying multimedia QoS, we show that enhancing the m-commerce B2C user experience with multimedia, far from being an idealised scenario, is in fact feasible if perceptual considerations are employed

The Role of the Mangement Sciences in Research on Personalization

We present a review of research studies that deal with personalization. We synthesize current knowledge about these areas, and identify issues that we envision will be of interest to researchers working in the management sciences. We take an interdisciplinary approach that spans the areas of economics, marketing, information technology, and operations. We present an overarching framework for personalization that allows us to identify key players in the personalization process, as well as, the key stages of personalization. The framework enables us to examine the strategic role of personalization in the interactions between a firm and other key players in the firm's value system. We review extant literature in the strategic behavior of firms, and discuss opportunities for analytical and empirical research in this regard. Next, we examine how a firm can learn a customer's preferences, which is one of the key components of the personalization process. We use a utility-based approach to formalize such preference functions, and to understand how these preference functions could be learnt based on a customer's interactions with a firm. We identify well-established techniques in management sciences that can be gainfully employed in future research on personalization.CRM, Persoanlization, Marketing, e-commerce,

Location-aware deep learning-based framework for optimizing cloud consumer quality of service-based service composition

The expanding propensity of organization users to utilize cloud services urges to deliver services in a service pool with a variety of functional and non-functional attributes from online service providers. brokers of cloud services must intense rivalry competing with one another to provide quality of service (QoS) enhancements. Such rivalry prompts a troublesome and muddled providing composite services on the cloud using a simple service selection and composition approach. Therefore, cloud composition is considered a non-deterministic polynomial (NP-hard) and economically motivated problem. Hence, developing a reliable economic model for composition is of tremendous interest and to have importance for the cloud consumer. This paper provides “A location-aware deep learning framework for improving the QoS-based service composition for cloud consumers”. The proposed framework is firstly reducing the dimensions of data. Secondly, it applies a combination of the deep learning long short-term memory network and particle swarm optimization algorithm additionally to considering the location parameter to correctly forecast the QoS provisioned values. Finally, it composes the ideal services need to reduce the customer cost function. The suggested framework's performance has been demonstrated using a real dataset, proving that it superior the current models in terms of prediction and composition accuracy

Meter Scoping Study

This report presents a summary of metering technology and cost information from past studies in an attempt to identify key barriers to more widespread implementation

Survey of Service Description Languages and Their Issues in Cloud Computing

Along with the growing popularity of cloud computing technology, the amount of available cloud services and their usage frequency are increasing. In order to provide a mechanism for the efficient enforcement of service-relevant operations in cloud environment, such as service discovery, service provision, and service management, a completed and precise service specification model is highly required. In this paper, we conducted a survey on existing service description languages applied in three different domains - general services, Web/SOA services, and cloud services. We discussed and compared the past literature from seven major aspects, which are: (1) domain, (2) coverage, (3) purpose, (4) representation, (5) semantic expressivity, (6) intended users, and (7) features. Additionally, two core dimensions semantic expressivity and coverage are employed to categorize and analyse the key service description languages by using Magic Quadrant methodology. These two dimensions are regarded as the most essential factors for the evaluation of a service description model. Based on this analysis, we concluded that Unified Service Description Language (USDL) is the language with the widest coverage from business, technical and operational aspects, while OWL-S is the one that has the highest semantic expressivity. At last, critical research issues on cloud service description languages are identified and analysed. The solution of these issues requires more research efforts on the standardization of cloud service specification, which will eventually enhance the development of cloud industry