Towards a Cloud Ontology Clustering Mechanism to Enhance IaaS Service Discovery and Selection

2015 ◽  
pp. 545-556 ◽  
Author(s):  
Toshihiro Uchibayashi ◽  
Bernady Apduhan ◽  
Norio Shiratori
Keyword(s):  
Service Discovery ◽  
Service Discovery And Selection

A Test-Driven Approach to Behavioral Queries for Service Selection

2014 ◽  
pp. 381-400
Author(s):  
Laura Zavala ◽  
Benito Mendoza ◽  
Michael N. Huhns
Keyword(s):  
Software Development ◽  
Service Discovery ◽  
Service Selection ◽  
Behavioral Approach ◽  
Loosely Coupled ◽  
Functional Quality ◽  
Agile Software ◽  
Quality Of Service Parameters ◽  
Key Aspects ◽  
Service Discovery And Selection

Although the areas of Service-Oriented Computing (SOC) and Agile and Lean Software Development (LSD) have been evolving separately in the last few years, they share several commonalities. Both are intended to exploit reusability and exhibit adaptability. SOC in particular aims to facilitate the widespread and diverse use of small, loosely coupled units of functionality, called services. Such services have a decided agility advantage, because they allow for changing a service provider at runtime without affecting any of a group of diverse and possibly anonymous consumers. Moreover, they can be composed at both development-time and run-time to produce new functionalities. Automatic service discovery and selection are key aspects for composing services dynamically. Current approaches attempting to automate discovery and selection make use of only structural and functional aspects of the services, and in many situations, this does not suffice to discriminate between functionally similar but disparate services. Service behavior is difficult to specify prior to service execution and instead is better described based on experience with the execution of the service. In this chapter, the authors present a behavioral approach to service selection and runtime adaptation that, inspired by agile software development techniques, is based on behavioral queries specified as test cases. Behavior is evaluated through the analysis of execution values of functional and non-functional parameters. In addition to behavioral selection, the authors’ approach allows for real-time evaluation of non-functional quality-of-service parameters, such as response time, availability, and latency.

A multi-agent-based framework for cloud service discovery and selection using ontology

2017 ◽  
Vol 12 (2) ◽  
pp. 137-154 ◽  
Author(s):  
Manoranjan Parhi ◽  
Binod Kumar Pattanayak ◽  
Manas Ranjan Patra
Keyword(s):  
Service Discovery ◽  
Cloud Service ◽  
Agent Based ◽  
Multi Agent ◽  
Service Discovery And Selection ◽  
Cloud Service Discovery

An ontology-based cloud infrastructure service discovery and selection system

2018 ◽  
Vol 9 (2) ◽  
pp. 108
Author(s):  
Manas Ranjan Patra ◽  
Binod Kumar Pattanayak ◽  
Manoranjan Parhi
Keyword(s):  
Service Discovery ◽  
Selection System ◽  
Cloud Infrastructure ◽  
Service Discovery And Selection
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