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

Zemni, Mohamed Anis; Carro, Manuel

doi:10.1007/978-3-642-17358-5_44

Cited by 19 publications

(10 citation statements)

References 6 publications

(8 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The problems have been modeled as a 0-1 knapsack problems [16], [17], constraint optimization problems [18], [19], multi-dimensional and/or multi-objective optimization problems [20], [21], weighted directed acyclic graph problems [22], [23], and mathematical programming problems [24], [25]. Accordingly, many algorithms have been proposed.…”

Section: Global Optimization Approachesmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review of Bio-Inspired Service Concretization

Wang

Shen

2017

IEEE Trans. Serv. Comput.

View full text Add to dashboard Cite

In service oriented computing, Web service selection is an important part of Web service composition. The Web service composition is achieved by solving the Web service concretization problem. The literature presents two types of Web service concretization approaches: local optimization approaches and global optimization approaches. There are three types of algorithmic methods in the global optimization approaches: optimal methods, sub-optimal methods, and soft constraints-based methods. The bio-inspired algorithms are sub-optimal methods. This paper will firstly present a hierarchical taxonomy of Web service concretization approaches. Then we conduct a systematic review on the current research of Web service concretization based on three bio-inspired algorithms, namely, ant colony optimization algorithms, genetic algorithms, and particle swarm optimization algorithms. Based on the findings from the systematic review, this paper also discusses the underlying applications of bio-inspired algorithms to the data-intensive service concretization problems. Abstract-In service oriented computing, Web service selection is an important part of Web service composition. The Web service composition is achieved by solving the Web service concretization problem. The literature presents two types of Web service concretization approaches: local optimization approaches and global optimization approaches. There are three types of algorithmic methods in the global optimization approaches: optimal methods, sub-optimal methods, and soft constraints-based methods. The bioinspired algorithms are sub-optimal methods. This paper will first present a hierarchical taxonomy of web service concretization approaches. Then we conduct a systematic review on the current research of Web service concretization based on three bio-inspired algorithms, namely, ant colony optimization algorithms, genetic algorithms, and particle swarm optimization algorithms. Based on the findings from the systematic review, this paper also discusses the underlying applications of bio-inspired algorithms to the dataintensive service concretization problems.

show abstract

Section: Global Optimization Approachesmentioning

confidence: 99%

“…Soft constraints-based algorithms are adopted in order to allow constraint violations in less likely feasible compositions when the requirements of the global QoS constraints are overly strict [19], [39], [40]. They are proposed to predict run-time service level agreement violations for composite services.…”

Section: Global Optimization Approachesmentioning

confidence: 99%

A Systematic Review of Bio-Inspired Service Concretization

Wang

Shen

2017

IEEE Trans. Serv. Comput.

View full text Add to dashboard Cite

show abstract

“…An overconstrained SL means that its encompassing SLOs contain very restrictive constraints that cannot be satisfied. As the root of this problem is that SLOs are expressed as hard constraints that must be satisfied at all costs to infer the matchmaking, its solution may come through the use of soft constraints [Zemni et al 2010]. In particular, if SLOs are expressed as soft constraints, where the user expresses their significance through using weights or levels, then not all of them have to be satisfied when matching.…”

Section: Soft Constraintsmentioning

confidence: 99%

A survey on service quality description

et al. 2013

Self Cite

View full text Add to dashboard Cite

Quality of service (QoS) can be a critical element for achieving the business goals of a service provider, for the acceptance of a service by the user, or for guaranteeing service characteristics in a composition of services, where a service is defined as either a software or a software-support (i.e., infrastructural) service which is available on any type of network or electronic channel. The goal of this article is to compare the approaches to QoS description in the literature, where several models and metamodels are included. consider a large spectrum of models and metamodels to describe service quality, ranging from ontological approaches to define quality measures, metrics, and dimensions, to metamodels enabling the specification of quality-based service requirements and capabilities as well as of SLAs (Service-Level Agreements) and SLA templates for service provisioning. Our survey is performed by inspecting the characteristics of the available approaches to reveal which are the consolidated ones and which are the ones specific to given aspects and to analyze where the need for further research and investigation lies. The approaches here illustrated have been selected based on a systematic review of conference proceedings and journals spanning various research areas in computer science and engineering, including: distributed, information, and telecommunication systems, networks and security, and service-oriented and grid computing.

show abstract

“…Therefore, no service behavior is considered and it is not possible to quantitatively reason on similarity/preference involving different services: it is not possible to widen the results of a query by obtaining similar services. In [19], a semiring-based framework is used to model and compose QoS features of WSs. However, no notion of similarity relationship is given in in [19].…”

Section: Related Workmentioning

confidence: 99%

“…In [19], a semiring-based framework is used to model and compose QoS features of WSs. However, no notion of similarity relationship is given in in [19].…”

Section: Related Workmentioning

confidence: 99%

Preference and Similarity-Based Behavioral Discovery of Services

Arbab

Santini

2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. We extend Constraint Automata by replacing boolean constraints with semiring-based soft constraints. The obtained general formal tool can be used to represent preference-based and similarity-based queries, which allow a user more freedom in choosing the behavior of the service to finally use, among all possible choices. A user states his preferences through a "soft" query, and obtains results that satisfy this query with different levels of preference. The soft requirements may involve a parameter data of the service operations, or the (names of the) operations themselves. Moreover, we introduce a first implementation of the search procedure by using declarative (Soft) Constraint Programming.

show abstract

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

Cited by 19 publications

References 6 publications

A Systematic Review of Bio-Inspired Service Concretization

A Systematic Review of Bio-Inspired Service Concretization

A survey on service quality description

Preference and Similarity-Based Behavioral Discovery of Services

Contact Info

Product

Resources

About