A Soft Constraint-Based Approach to QoS-Aware Service Selection

Service-based systems should be able to dynamically seek replacements for faulty or underperforming services, thus performing self-healing. It may however be the case that available services do not match all requirements, leading the system to grind to a halt. In similar situations it would be better to choose alternative candidates which, while not fulfilling all the constraints, allow the system to proceed. Soft constraints, instead of the traditional crisp constraints, can help naturally model and solve replacement problems of this sort. In this work we apply soft constraints to model SLAs and to decide how to rebuild compositions which may not satisfy all the requirements, in order not to completely stop running systems

Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection

© 2008-2012 IEEE. Service selection is crucial to service composition in determining the composite Quality of Service (QoS). The proliferation of composable services on the Internet and the practical need for timely delivering optimized composite solutions motivate the adoption of population-based algorithms for QoS-aware service selection. However, existing population-based algorithms are generally complicated to use, and often used as a general approach to solving different optimization problems. We propose to develop specialized algorithms for QoS-aware service selection, based on the artificial bee colony algorithm (ABC). ABC is a new and simpler implementation of swarm intelligence, which has proven to be successful in solving many real-world problems, especially the numerical optimization problems. We develop an approximate approach for the neighborhood search of ABC, which enables effective local search in the discrete space of service selection in a way that is analogical to the search in a continuous space. We present three algorithms based on the approach. All the three algorithms are designed to improve the performance and meanwhile preserve the simplicity of ABC. Each algorithm applies a different technique to leverage the unique characteristics of the service selection problem. Experimental results show higher accuracy and convergence speed of the proposed algorithms over the state of the art algorithms

Non-Functional Properties in Service Selection

Service selection is an important step of the service composition process. Multiple services functionally equivalent might be offered by different providers but characterized by different non functional properties such as Quality of Service (QoS) values (e.g. execution price, success rate) and transactional properties (e.g. compensatable or not). Since the QoS of the selected services has an impact on the QoS of the produced composite service, the best set of services to be selected is the set that maximize the QoS of the composite service. In the literature, many approaches have been proposed for the QoS-aware service selection problem which has been formalized as an optimization problem. The talk will first overview some optimization techniques and their application to the service selection problem and next present a service selection approach based not only according to their functional requirements but also to their transactional properties.Universidad de Málaga. Campus de Excelencia Internacional Andalucí

Robust Multi-criteria Service Composition in Information Systems

Service compositions are used to implement business processes in a variety of application domains. A quality of service (QoS)-aware selection of the service to be composed involves multiple, usually conflicting and possibly uncertain QoS attributes. A multi-criteria solution approach is desired to generate a set of alternative service selections. In addition, the uncertainty of QoSattributes is neglected in existing solution approaches. Hence, the need for service reconfigurations is imposed to avoid the violation of QoS restrictions. The researched problem is NP-hard. This article presents a heuristic multicriteria service selection approach that is designed to determine a Pareto frontier of alternative service selections in a reasonable amount of time. Taking into account the uncertainty of response times, the obtained service selections are robust with respect to the constrained execution time. The proposed solution approach is based on the Nondominated Sorting Genetic Algorithm (NSGA)-II extended by heuristics that exploit problem specific characteristics of the QoS-aware service selection. The applicability of the solution approach is demonstrated by a simulation study

Q-CAD: QoS and Context Aware Discovery framework for adaptive mobile systems

This paper presents Q-CALl, a resource discovery framework that enables pervasive computing applications to discover and select the resource(s) best satisfying the user needs, taking the current execution context and quality-ofservice (QoS} requirements into account. The available resources are first screened, so that only those suirable to the current execution context of the application will be considered; the shortlisted resources are then evaluated against the QoS needs of the application, and a binding is established to the best available

Towards critical event monitoring, detection and prediction for self-adaptive future Internet applications

The Future Internet (FI) will be composed of a multitude of diverse types of services that offer flexible, remote access to software features, content, computing resources, and middleware solutions through different cloud delivery models, such as IaaS, PaaS and SaaS. Ultimately, this means that loosely coupled Internet services will form a comprehensive base for developing value added applications in an agile way. Unlike traditional application development, which uses computing resources and software components under local administrative control, FI applications will thus strongly depend on third-party services. To maintain their quality of service, those applications therefore need to dynamically and autonomously adapt to an unprecedented level of changes that may occur during runtime. In this paper, we present our recent experiences on monitoring, detection, and prediction of critical events for both software services and multimedia applications. Based on these findings we introduce potential directions for future research on self-adaptive FI applications, bringing together those research directions