Bi-Level Selection Model for Web Services Search

Service registries and web service engines are the main approaches for discovering web services. Current service directories are mainly based on Universal Description, Discovery and Integration (UDDI), which is an industry standard for service registries, developed to solve the web service search problem. However, UDDI offers limited search functionalities which may return a huge number of irrelevant services. Another critical challenge in web service search and composition is the selection of web services, to be executed or to be composed, from the pool of matching services. Most of the current service selection proposals apply a weighted sum model (WSM) as an evaluation method for selection of services with the same functionality. In this paper, we propose a Bi-level service selection approach that selects the most appropriate web services from the pool of matching services that considers both the functional and non-functional requirements for service selection. The functional requirements are provided by the user as a set of input parameters provided for and output parameters desired from the web service. The user also provides a set of desired QoS values and the order of their preference for selection. The experimental results demonstrate the efficiency of service search in our bi-level model and the variety of user queries supported

Methods and algorithms for service selection and recommendation (preference and aggregation based)

In order for service users to get the best service that meets their requirements, they prefer to personalize their non-functional attributes, such as reliability and price. However, the personalization makes it challenging because service providers have to deal with conflicting non-functional attributes when selecting services for users. In addition, users may sometimes want to explicitly specify their trade-offs among non-functional attributes to make their preferences known to service providers. Typically, users\u27 service search requests with conflicting non-functional attributes may result in a ranked list of services that partially meet their needs. When this happens, it is natural for users to submit other similar requests, with varying preferences on non-functional attributes, in an attempt to find services that fully meet their needs. This situation produces a challenge for the users to choose an optimal service based on their preferences, from the multiple ranked lists that partially satisfy their request. Existing memory-based collaborative filtering (CF) service recommendation methods that employ this recommendation technique usually depend on non-functional attribute values obtained at service invocation to compute the similarity between users or items, and also to predict missing non-functional attributes. However, this approach is not sufficient because the non-functional attribute values of invoked services may not necessarily satisfy their personalized preferences. The main contributions of this work are threefold. First, a novel service selection method, which is based on fuzzy logic, that considers users\u27 personalized preferences and their trade-offs on non-functional attributes during service selection is presented. Second, a method that aggregates multiple ranked lists of services into a single aggregated ranked list, where top ranked services are selected for the user is also presented. Two algorithms were proposed: 1) Rank Aggregation for Complete Lists (RACoL), that aggregates complete ranked lists and 2) Rank Aggregation for Incomplete Lists (RAIL) to aggregate incomplete ranked lists. Finally, a CF-based service recommendation method that considers users\u27 personalized preference on non-functional attributes if proposed. Examples using real-world services are presented to evaluate the proposed methods and experiments are carried out to validate their performance --Abstract, page iii

Integrating Semantic Web Services Ranking Mechanisms Using a Common Preference Model

Service ranking has been long-acknowledged to play a fundamental role in helping users to select the best o erings among services retrieved from a search request. There exist many ranking mechanisms, each one providing ad hoc preference models that o er di erent levels of expressiveness. Consequently, applying a single mechanism to a particular scenario constrains the user to de ne preferences based on that mechanism's facilities. Furthermore, a more exible solution that uses several independent mechanisms will face interoperability issues because of the di erences between preference models provided by each ranking mechanism. In order to overcome these issues, we propose a Preference- based Universal Ranking Integration (PURI) framework that enables the combination of several ranking mechanisms using a common, holistic preference model. Using PURI, di erent ranking mechanisms are seamlessly and transparently integrated, o ering a single fa cade to de ne preferences using highly expressive facilities that are not only decoupled from the concrete mechanisms that perform the ranking process, but also allow to exploit synergies from the combination of integrated mechanisms. We also thoroughly present a particular application scenario in the SOA4All EU project and evaluate the bene ts and applicability of PURI in further domains

Qos-Aware Web Services Composition Using Grasp with Path Relinking

In service oriented scenarios, applications are created by composing atomic services and exposing the resulting added value logic as a service. When several alternative service providers are available for composition, quality of service (QoS) properties such as execution time, cost, or availability are taken into account to make the choice, leading to the creation of QoS-aware composite web services. Finding the set of service providers that result in the best QoS is a NPhard optimization problem. This paper presents QoS-Gasp, a metaheuristic algorithm for performing QoS-aware web service composition at runtime. QoS-Gasp is an hybrid approach that combines GRASP with Path Relinking. For the evaluation of our approach we compared it with related metaheuristic algorithms found in the literature. Experiments show that when results must be available in seconds, QoS-Gasp improves the results of previous proposals up to 40%. Beside this, QoS-Gasp found better solutions than any of the compared techniques in a 92% of the runs when results must be available in 100ms; i.e. it provides compositions with a better QoS, implying cost savings, increased availability and reduced execution times for the end-user.CICYT TIN2009-07366CICYT TIN2012-32273Junta de Andalucía P12-TIC-1867Junta de Andalucía TIC-590

Autonomic management of virtualized resources in cloud computing

The last five years have witnessed a rapid growth of cloud computing in business, governmental and educational IT deployment. The success of cloud services depends critically on the effective management of virtualized resources. A key requirement of cloud management is the ability to dynamically match resource allocations to actual demands, To this end, we aim to design and implement a cloud resource management mechanism that manages underlying complexity, automates resource provisioning and controls client-perceived quality of service (QoS) while still achieving resource efficiency. The design of an automatic resource management centers on two questions: when to adjust resource allocations and how much to adjust. In a cloud, applications have different definitions on capacity and cloud dynamics makes it difficult to determine a static resource to performance relationship. In this dissertation, we have proposed a generic metric that measures application capacity, designed model-independent and adaptive approaches to manage resources and built a cloud management system scalable to a cluster of machines. To understand web system capacity, we propose to use a metric of productivity index (PI), which is defined as the ratio of yield to cost, to measure the system processing capability online. PI is a generic concept that can be applied to different levels to monitor system progress in order to identify if more capacity is needed. We applied the concept of PI to the problem of overload prevention in multi-tier websites. The overload predictor built on the PI metric shows more accurate and responsive overload prevention compared to conventional approaches. To address the issue of the lack of accurate server model, we propose a model-independent fuzzy control based approach for CPU allocation. For adaptive and stable control performance, we embed the controller with self-tuning output amplification and flexible rule selection. Finally, we build a QoS provisioning framework that supports multi-objective QoS control and service differentiation. Experiments on a virtual cluster with two service classes show the effectiveness of our approach in both performance and power control. To address the problems of complex interplay between resources and process delays in fine-grained multi-resource allocation, we consider capacity management as a decision-making problem and employ reinforcement learning (RL) to optimize the process. The optimization depends on the trial-and-error interactions with the cloud system. In order to improve the initial management performance, we propose a model-based RL algorithm. The neural network based environment model, which is learned from previous management history, generates simulated resource allocations for the RL agent. Experiment results on heterogeneous applications show that our approach makes efficient use of limited interactions and find near optimal resource configurations within 7 steps. Finally, we present a distributed reinforcement learning approach to the cluster-wide cloud resource management. We decompose the cluster-wide resource allocation problem into sub-problems concerning individual VM resource configurations. The cluster-wide allocation is optimized if individual VMs meet their SLA with a high resource utilization. For scalability, we develop an efficient reinforcement learning approach with continuous state space. For adaptability, we use VM low-level runtime statistics to accommodate workload dynamics. Prototyped in a iBalloon system, the distributed learning approach successfully manages 128 VMs on a 16-node close correlated cluster

A Calculus for Orchestration of Web Services

Service-oriented computing, an emerging paradigm for distributed computing based on the use of services, is calling for the development of tools and techniques to build safe and trustworthy systems, and to analyse their behaviour. Therefore, many researchers have proposed to use process calculi, a cornerstone of current foundational research on specification and analysis of concurrent, reactive, and distributed systems. In this paper, we follow this approach and introduce CWS, a process calculus expressly designed for specifying and combining service-oriented applications, while modelling their dynamic behaviour. We show that CWS can model all the phases of the life cycle of service-oriented applications, such as publication, discovery, negotiation, orchestration, deployment, reconfiguration and execution. We illustrate the specification style that CWS supports by means of a large case study from the automotive domain and a number of more specific examples drawn from it

A KPN based Model for Describing and Verifying the Interaction of Web Services

Correct interaction between Web services is essential for successful Web service composition. This paper proposes a Web Service Interaction Model (IWSN) that aims to ensure correct interaction between Web services, improve the scalability of Web service composition, solve behavioral compatibility issues in the process of Web service interaction, and promote the application of service composition technology in related fields. The Kahn Process Network (KPN) supports parallel computing based on data streams and channels, and the proposed Web Service Interaction Model in this article is based on the KPN. The formal semantics of the IWSN model are based on process algebra Pi calculus, and the model's properties are discussed. Finally, an application case is used to demonstrate how the IWSN model can be applied to Web service composition and interaction