An Automated End-to-End Multi-Agent Qos Based Architecture for Selection of Geospatial Web Services

Abstract

ABSTRACT:Over the past decade, Service-Oriented Architecture (SOA) and Web services have gained wide popularity and acceptance from researchers and industries all over the world. SOA makes it easy to build business applications with common services, and it provides like: reduced integration expense, better asset reuse, higher business agility, and reduction of business risk. Building of framework for acquiring useful geospatial information for potential users is a crucial problem faced by the GIS domain. Geospatial Web services solve this problem. With the help of web service technology, geospatial web services can provide useful geospatial information to potential users in a better way than traditional geographic information system (GIS). A geospatial Web service is a modular application designed to enable the discovery, access, and chaining of geospatial information and service s across the web that are often both computation and data-intensive that involve diverse sources of data and complex processing functions. With the proliferation of web services published over the internet, multiple web services may provide similar functionality, but with different non-functional properties. Thus, Quality of Service (QoS) offers a metric to differentiate the services and their service providers. In a quality-driven selection of web services, it is important to consider non-functional properties of the web service so as to satisfy the constraints or requirements of the end users. The main intent of this paper is to build an automated endto-end multi-agent based solution to provide the best-fit web service to service requester based on QoS.

An Augmented Geospatial Service Web Based on QoS Constraints and Geospatial Service Semantic Relationships

“…In terms of the optimization of geospatial service composition, Gui et al [23] proposed a preliminary QoS evaluation model that introduces output quality factors, combines process quality factors, and designs a services instances' optimization selection algorithm based on linear programming. Shah et al [24] constructed a QoS-based automation solution using end-to-end multi-agents to provide service requesters with the most suitable web services. Sun [25] proposed a method of decomposing the quality of QoS indicators in the Internet of Things environment, which can improve the QoS calculation speed of the service composition.…”