Service Selection with Uncertain Context Information

The current evolution of Service-Oriented Computing in ubiquitous systems is leading to the development of context-aware services. Context-aware services are services of which the description is enriched with context information related to non-functional requirements, describing the service execution environment or its adaptation capabilities. This information is often used for discovery and adaptation purposes. However, in real-life systems, context information is naturally dynamic, uncertain, and incomplete, which represents an important issue when comparing the service description with user requirements. Uncertainty of context information may lead to an inexact match between provided and required service capabilities, and consequently to the non-selection of services. In this chapter, we focus on how to handle uncertain and incomplete context information for service selection. We consider this issue by presenting a service ranking and selection algorithm, inspired by graph-based matching algorithms. This graph-based service selection algorithm compares contextual service descriptions using similarity measures that allow inexact matching. The service description and non-functional requirements are compared using two kinds of similarity measures: local measures, which compare individually required and provided properties, and global measures, which take into account the context description as a whole.

Transactional-Aware Web Service Composition

Web Service (WS) composition consists in combining several WSs into a Composite WS (CWS), which becomes a value-added process. In order to provide reliable and fault-tolerant CWSs, several transactional-aware composition approaches have been proposed. However, as far as we know, no real classification survey of such approaches exists. This is the contribution of this chapter. Our classification distinguishes the more relevant and recent propositions in two groups: approaches based on WS transactional properties and the ones also integrating QoS criteria to the composition process. All these studied approaches are compared according to several criteria: the transactional model used or proposed, the control flow model used or automatically generated, the mechanism proposed to verify the transactional property of the composition, the step(s) of the composition process involved in, and the protocols or the standard languages used or extended. This classification allows underlining the lacks and the future directions which should be studied.

Verification of Non-Functional Requirements by Abstract Interpretation

This chapter investigates a formal approach to the verification of non-functional software requirements that are crucial in Service-oriented Systems, like portability, time and space efficiency, and dependability/robustness. The key-idea is the notion of observable, i.e., an abstraction of the concrete semantics when focusing on a behavioral property of interest. By applying an abstract interpretation-based static analysis of the source program, and by a suitable choice of abstract domains, it is possible to design formal and effective tools for non-functional requirements validation.

An Outlook on the Future of Services and Non-Functional Properties Management

Over the years a large number of technologies have been devised in order to describe service interfaces, e.g., WSDL (Booth & Liu, 2007), combine services in a process-oriented way, e.g., WS-BPEL (OASIS Web Services Business Process Execution Language (WSBPEL) TC, 2007), provide support for transactions, e.g., WS-Transaction, and cover non-functional properties (NFP) of services such as security aspects and the like, see for instance WS-Security and WS-Policy to name just a few (Erl, 2007). There is in an overwhelming stack of technologies and specifications dubbed WS-*, covering most aspects researchers have faced thus far. There remain nonetheless a number of outstanding issues (Papazoglou, Traverso, Dustdar, & Leymann, 2007) some of which are of a general technical nature, and some, indeed, are specifically related to NFPs. The latter will be dealt with in more detail in the next section.

The Gross Interest

Service popularity, e.g., how often a service is requested, can be an important non-functional property determining the life-cycle of a service. To capture it, the requesting behavior of clients needs to be modeled. In this work, we introduce and discuss: the importance of the service popularity, a generalized requesting model that can capture the requesting behavior of clients, a service popularity measure called “Gross Interest,” and a Gross Interest quantification method. Two extremely different sets of specifications for the proposed generalized requesting model which produce two different Gross Interest scenarios (rich and poor scenarios) are introduced and quantified. As an application example for the service popularity, we show a service replication protocol for Mobile Ad Hoc Networks (MANETs) which realizes and employs service popularity in its replication decisions.

Quality of Service Monitoring Strategies in Service Oriented Architecture Environments using Processor Hardware Performance Metrics

The sharp growth in data-intensive applications such as social, professional networking and online commerce services, multimedia applications, as well as the convergence of mobile, wireless, and internet technologies, is greatly influencing the shape and makeup of on-demand enterprise computing environments. In response to the global needs for on-demand computing services, a number of trends have emerged, one of which is the growth of computing infrastructures in terms of the number of computing node entities and the widening in geophysical distributions of deployed node elements. Another development has been the increased complexity in the technical composition of the business computing space due to the diversity of technologies that are employed in IT implementations. Given the huge scales in infrastructure sizes and data handling requirements, as well as the dispersion of compute nodes and technology disparities that are associated with emerging computing infrastructures, the task of quantifying performance for capacity planning, Service Level Agreement (SLA) enforcements, and Quality of Service (QoS) guarantees becomes very challenging to fulfil. In order to come up with a viable strategy for evaluating operational performance on computing nodes, we propose the use of on-chip registers called Performance Monitoring Counters (PMCs), which form part of the processor hardware. The use of PMC measurements is largely non-intrusive and highlights performance issues associated with runtime execution on the CPU hardware architecture. Our proposed strategy of employing PMC data thus overcomes major shortcomings of existing benchmarking approaches such as overheads in the software functionality and the inability to offer detailed insight into the various stages of CPU and memory hardware operation.

Negotiation of Service Level Agreements

Non-functional properties are an essential constituent of service level agreements as they describe those quality-of-service parameters that are not related to the actual function of a service. Thus, non-functional properties let providers create distinguishing service offers and let consumers discriminate between various offers that provide the same function. The negotiation of non-functional properties is how service level agreements are commonly established. This chapter introduces various forms, models, specifications, and realizations of service level agreement negotiation to provide a broad background of the current state-of-the-art. Although different in various details, the described systems share a number of common features. Based on them, a holistic architecture is defined combining previous work into one coherent framework. The architecture is applicable to different negotiation models and protocols, and covers all functions of the negotiation phase. Based on this architecture, particular challenges and areas of future work are motivated. These mostly revolve around increasing the acceptance of service level agreement negotiation and enhancing interoperability.

Performance Modeling for Quality of Service Prediction in Service-Oriented Systems

With the introduction of services, systems become more flexible as new services can easily be composed out of existing services. Services are increasingly used in mission-critical systems and applications, and therefore, considering Quality of Service (QoS) properties is an essential part of the service selection. Quality prediction techniques support the service provider in determining possible QoS levels that can be guaranteed to a customer or in deriving the operation costs induced by a certain QoS level. In this chapter, we present an overview on our work on modeling service-oriented systems for performance prediction using the Palladio Component Model. The prediction builds upon a model of a service-based system, and evaluates this model in order to determine the expected service quality. The presented techniques allow for early quality prediction, without the need for the system being already deployed and operating. We present the integration of our prediction approach into an SLA management framework. The emerging trend to combine event-based communication and Service-Oriented Architecture (SOA) into Event-based SOA (ESOA) induces new challenges to our approach, which are topic of a special subsection.

Towards Aligning and Matchmaking QoS-Based Web Service Specifications

QoS plays an important role in all service life-cycle activities, and consequently, has grabbed the researchers’ attention. Concerning QoS-based service description, the various approaches proposed adopt different meta-models and propose different QoS models mostly covering domain-independent NFPs and metrics. This lack of a common QoS meta-model and model causes serious accuracy problems in QoS-based service matchmaking. While mapping between QSDs is not difficult as they rely on similar meta-models, mapping between equivalent metrics specified even with the same meta-model is challenging. For this reason, a novel QoS metric matching algorithm has been proposed for metrics specified in the OWL-Q language. In this chapter, this algorithm is exploited for aligning OWL-Q specifications. Moreover, two novel QSM algorithms are proposed that advance the state-of-the-art by solving the problems of non-coverage of QoS demand metrics by QoS offers, erroneous matchmaking metrics, limited service categorization, and non-useful result production for over-constrained QoS demands.

Considering Quality of a Service in an Intentional Approach

The success of service-based applications is based on service technologies such as Web services. Nevertheless, the benefits of the Service-Oriented Architecture (SOA) remain mainly at the software level, since business people are often unable to fully exploit its benefits due to their unfamiliarity with such software level technology. The intentional Service-Oriented Architecture (iSOA) suggests a move from the function-driven SOA to intention-driven SOA in order to provide service description understandable by business practitioners. However, such transposition from business to implementation level should also consider Quality of Service (QoS) aspects. In this paper, we propose modeling the Quality of intentional Service (QoiS) by introducing the quality goals and their qualitative and quantitative evaluation. We also propose populating the intentional service registry of the iSOA architecture with the QoiS description.