A Fuzzy Logic Based Model for Representing and Evaluating Service Composition Properties

Abstract-Increase emphasis on Quality of Service and highly changing environments make management of composite services a time consuming and complicated task. Adaptation approaches aim to mitigate the management problem by adjusting composite services to the environment conditions, maintaining functional and quality levels, and reducing human intervention. This paper presents an adaptation approach that implements self-optimization based on fuzzy logic. The proposed optimization model performs service selection based on the analysis of historical and real QoS data, gathered at different stages during the execution of composite services. The use of fuzzy inference systems enables the evaluation of the measured QoS values, helps deciding whether adaptation is needed or not, and how to perform service selection. Experimental results show significant improvements in the global QoS of the use case scenario, providing reductions up to 17.1% in response time, 17.38% in cost and 40% in energy consumption.

show abstract

“…In the area of Web services, it has been used as a support tool for service selection, discovery and composition [35][36][37][38].…”

Section: Related Workmentioning

confidence: 99%

“…In [37], it is presented a fuzzy decision making model to locate and select services based on customer's preference or satisfaction degree. A generic model for representing and evaluating non-functional service properties is proposed in [38]. It aims to enable the selection of service compositions fitting the user's requirements.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Logic Based QoS Optimization Mechanism for Service Composition

Avila

Djemame

2013

2013 IEEE Seventh International Symposium on Service-Oriented System Engineering

View full text Add to dashboard Cite

show abstract

“…Here, sim is a realvalued variable between zero and one, where a value of one specifies the equivalence of the two workflows. This degree of similarity is derived within a fitness evaluation procedure that computes the similarity of services compositions with regard to their functional and non-functional properties by means of a fuzzylogic based model; details on this evaluation technique can be found in (Pfeffer, Krüssel, & Steglich, 2008).…”

Section: Realization and Examplementioning

confidence: 99%

Mixing Workflows and Components to Support Evolving Services

Pfeffer

Baude

Legrand

et al. 2010

International Journal of Adaptive, Resilient and Autonomic Systems

View full text Add to dashboard Cite

Composite distributed services involve local and remote services that get orchestrated according to specific business logic. This logic can be programmed by applying a traditional general-purpose programming language, but is generally described using a workflow language that coordinates a set of given services. The services involved in the composition, or the composition may need to evolve both at the business logic level (workflow level) and the global architecture level. This paper presents a solution to ease such evolution for compound distributed services and the authors’ proposal enables the evolution of both the business logic and the underlying architecture. This paper suggests relying on a distributed software component model to represent and easily manage the set of local or remote software entities (services) involved in the composition. Composite services are represented in a model that combines the use of a distributed and hierarchical software component model and new timed-automata based workflow language. This combination makes explicit the separation between functional and non-functional concerns, and as a consequence this approach helps in defining the required and various evolution procedures in context to compound services.

show abstract

“…This representation of user preferences enables the fast evaluation of the requested service composition by a fuzzy multiplication of the service composition properties. Thus service composition' properties are measured during or after execution [12].…”

Section: Related Workmentioning

confidence: 99%

“…Other works have been done in fuzzy logic based web service selection. In [12][13][14][15][16][17], various methods have been proposed for specifying fuzzy QoS constraints and for ranking Web services based on their fuzzy representation.…”

Section: Related Workmentioning

confidence: 99%

A Neuro-Fuzzy Model for QoS Based Selection of Web Service

Missaoui¹,

Barkaoui²

2010

JSEA

View full text Add to dashboard Cite

The automatic selection and composition of Web services rely strongly on the manner to deal with ambiguity inherent to the description of functionalities of these services and the client’s requests. Quality of Service (QoS) criteria become crucial in Web services selection and the problem of checking that a web service satisfies a given level of QOS is considered in recent research works. This paper presents a QoS based automatic classification method of web services. These services give generally similar functionalities and are offered by different providers. The main feature of our Web service selection model is to take advantage of the neuro-fuzzy logic for coping with the imprecision of QoS constraints values

show abstract

A Fuzzy Logic Based Model for Representing and Evaluating Service Composition Properties

Cited by 4 publications

References 17 publications

Fuzzy Logic Based QoS Optimization Mechanism for Service Composition

Fuzzy Logic Based QoS Optimization Mechanism for Service Composition

Mixing Workflows and Components to Support Evolving Services

A Neuro-Fuzzy Model for QoS Based Selection of Web Service

Contact Info

Product

Resources

About