A monitoring approach for runtime service discovery

Effective runtime service discovery requires identification of services based on different service characteristics such as structural, behavioural, quality, and contextual characteristics. However, current service registries guarantee services described in terms of structural and sometimes quality characteristics and, therefore, it is not always possible to assume that services in them will have all the characteristics required for effective service discovery. In this paper, we describe a monitor-based runtime service discovery framework called MoRSeD. The framework supports service discovery in both push and pull modes of query execution. The push mode of query execution is performed in parallel to the execution of a service-based system, in a proactive way. Both types of queries are specified in a query language called SerDiQueL that allows the representation of structural, behavioral, quality, and contextual conditions of services to be identified. The framework uses a monitor component to verify if behavioral and contextual conditions in the queries can be satisfied by services, based on translations of these conditions into properties represented in event calculus, and verification of the satisfiability of these properties against services. The monitor is also used to support identification that services participating in a service-based system are unavailable, and identification of changes in the behavioral and contextual characteristics of the services. A prototype implementation of the framework has been developed. The framework has been evaluated in terms of comparison of its performance when using and when not using the monitor component

A comparative study of process mediator components that support behavioral incompatibility

Most businesses these days use the web services technology as a medium to allow interaction between a service provider and a service requestor. However, both the service provider and the requestor would be unable to achieve their business goals when there are miscommunications between their processes. This research focuses on the process incompatibility between the web services and the way to automatically resolve them by using a process mediator. This paper presents an overview of the behavioral incompatibility between web services and the overview of process mediation in order to resolve the complications faced due to the incompatibility. Several state-of the-art approaches have been selected and analyzed to understand the existing process mediation components. This paper aims to provide a valuable gap analysis that identifies the important research areas in process mediation that have yet to be fully explored.Comment: 20 Pages, 9 figures and 8 Tables; International Journal on Web Service Computing (IJWSC), September 2011, Volume 2, Number

Two-fold Semantic Web service matchmaking – applying ontology mapping for service discovery

Semantic Web Services (SWS) aim at the automated discovery and orchestration of Web services on the basis of comprehensive, machine-interpretable semantic descriptions. Since SWS annotations usually are created by distinct SWS providers, semantic-level mediation, i.e. mediation between concurrent semantic representations, is a key requirement for SWS discovery. Since semantic-level mediation aims at enabling interoperability across heterogeneous semantic representations, it can be perceived as a particular instantiation of the ontology mapping problem. While recent SWS matchmakers usually rely on manual alignments or subscription to a common ontology, we propose a two-fold SWS matchmaking approach, consisting of (a) a general-purpose semantic-level mediator and (b) comparison and matchmaking of SWS capabilities. Our semantic-level mediation approach enables the implicit representation of similarities across distinct SWS by grounding service descriptions in so-called Mediation Spaces (MS). Given a set of SWS and their respective grounding, a SWS matchmaker automatically computes instance similarities across distinct SWS ontologies and matches the request to the most suitable SWS. A prototypical application illustrates our approach

Describing and Verifying Monitoring Capabilities for Service Based Systems

Monitoring the operation of Service Based Systems (SBS) to ensure compliance with a set of service level agreements (SLAs) for example cannot always rely on a pre-specified monitoring infrastructure, where all the information and components required for monitoring are a priori known and available. This because new services with unknown monitoring infrastructures and capabilities may be dynamically assembled to an SBS. To address the need for dynamic configuration of SBS monitoring infrastructures, this paper proposes a model for describing the monitoring capabilities of different services of an SBS and discusses the process for verifying the monitorability of required properties based on these capabilities

Application of ontologies for the integration of network monitoring platforms

This is an electronic version of the paper presented at the European Workshop on Mechanisms for Mastering Future Internet, held in Salzburg on 2008This paper presents an ontology-based approach to integrate the measurements provided by different network monitoring tools and platforms. The combination of such measurements is valuable to network operators, enabling the development of new management applications. The use of ontologies provides some advantages over current syntactic solutions: classification, inference and querying capabilities are some of them. Moreover, they can reduce the complexity of information integration, providing solutions that can be applied to existing network monitoring infrastructures.This work has been partially funded by the European Union under the project FP7-MOMENT (INFSO-ICT-215225)

ServeNet: A Deep Neural Network for Web Services Classification

Automated service classification plays a crucial role in service discovery, selection, and composition. Machine learning has been widely used for service classification in recent years. However, the performance of conventional machine learning methods highly depends on the quality of manual feature engineering. In this paper, we present a novel deep neural network to automatically abstract low-level representation of both service name and service description to high-level merged features without feature engineering and the length limitation, and then predict service classification on 50 service categories. To demonstrate the effectiveness of our approach, we conduct a comprehensive experimental study by comparing 10 machine learning methods on 10,000 real-world web services. The result shows that the proposed deep neural network can achieve higher accuracy in classification and more robust than other machine learning methods.Comment: Accepted by ICWS'2