Web Service Selection Based on Similarity Evaluation

2014 IEEE 7th International Conference on Service-Oriented Computing and Applications

2014

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In a highly competitive global service market, service consumers tend to select a provider from a large pool of functionally equivalent services based on Quality of Service (QoS). To gain competitive advantages, it is desirable for a service provider to adopt service customization, particularly QoS customization, as one of the key strategies to differentiate itself from the competitors. This, however, has raised new challenges in describing and publishing QoS information, identifying users, and adjusting QoS offerings. This research borrows the concept of attribute based access control and presents an attribute based service customization model. The attribute based access control policy language is extended to enable definition of QoS customization rules. An algorithm is proposed to support consumer identification and QoS customization. This paper also discusses a case study to illustrate the proposed approach.

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Section: Subject=attrset=andset|orsetmentioning

confidence: 99%

Section: Customized Service Selectionmentioning

confidence: 99%

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Attribute Based Service Customization and Selection

2014 IEEE 7th International Conference on Service-Oriented Computing and Applications

2014

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“…Suppose a user has m QoS constraints in a composite service request as

Q_{r} MathClass-rel= ([], {Q_{r}}^{1} MathClass-punc, {Q_{r}}^{2} MathClass-punc, MathClass-punc. MathClass-punc. MathClass-punc. MathClass-punc, {Q_{r}}^{m})

. For each dimension of QoS request,

{Q_{r}}^{m}

is presented as

{Q_{r}}^{m} MathClass-rel= ⟨ Name MathClass-punc, Type MathClass-punc, Willingness MathClass-punc, V alue ⟩

. In this study, the fitness function is define as follows:

{scriptF}_{itness} MathClass-rel= ({, \begin{matrix} falsenonefalsearray \\ arrayaxis λ \cdot T MathClass-open(g MathClass-close) \sum_{i = 1}^{m} w_{i} f_{i} ({Q_{r}}^{i}, {q_{g}}^{i}), if \prod_{i = 1}^{m} f_{i} ({Q_{r}}^{i}, {q_{g}}^{i}) = 0 \\ arrayaxis \\ arrayaxis T MathClass-open(g MathClass-close) \sum_{i = 1}^{m} w_{i} f_{i} ({Q_{r}}^{i}, {q_{g}}^{i}), otherwise \end{matrix})

…”

Section: Service Composition Architecturementioning

confidence: 99%

“…f i is the satisfaction function on the i th dimension which depends on the Willingness of QoS requirement. In our previous work , the Willingness of user's preference is classified into five categories: Top , Bot , Equivalent , Close , and Distant , they represent the tendency of the service consumer's preferences for the particular QoS requirement dimensions. The satisfaction function

f_{i} ((), {Q_{r}}^{i} MathClass-punc, {q_{g}}^{i})

is defined as follows:

f_{i} ((), {Q_{r}}^{i} MathClass-punc, {q_{g}}^{i}) MathClass-rel= ({, \begin{matrix} falsenonefalsearray \\ arrayaxis \frac{{Q_{r}}^{i} - {q_{g}}^{i}}{{Q_{r}}^{i}}, & arrayaxis if {Q_{r}}^{i} > {q_{g}}^{i} \\ arrayaxis 0, & arrayaxis otherwise \end{matrix}) MathClass-punc, with Willingness (i) MathClass-rel= Top

f_{i} ((), {Q_{r}}^{i} MathClass-punc, {q_{g}}^{i}) MathClass-rel= ({, \begin{matrix} falsenonefalsearray \\ arrayaxis \frac{{q_{g}}^{i} - Q_{r}^{i}}{{q_{g}}^{i}}, & arrayaxis if {Q_{r}}^{i} \end{matrix})

…”

Section: Service Composition Architecturementioning

confidence: 99%

Trust‐oriented QoS‐aware composite service selection based on genetic algorithms

Concurrency and Computation

2013

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SUMMARYService selection in service‐oriented computing has emerged to be an increasingly important research area. From the client's point of view, in addition to the QoS of a service or a service composition, the trust level becomes an important part. As the complexity of invocation in service composition has been greatly increased, a comprehensive mechanism, which could evaluate both the subjective aspect as trust expression and the objective aspect as QoS, is needed. In this paper, we provide a formal service composition architecture for service selection. In addition, we propose a trust evaluation method for the service composition plan based on the subjective probability theory, based on them, our trust‐oriented genetic algorithm (TOGA) is proposed to find a near‐optimal service composition plan with QoS constraints. Experimental results have illustrated that our proposed approach can discover the near‐optimal solution efficiently. Copyright © 2013 John Wiley & Sons, Ltd.

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Business Rule Driven Composite Service Optimisation and Selection

2015 IEEE International Conference on Services Computing

2015