Efficient Resource Discovery
Project Overview
Over the last few years, the number of smart objects connected to the Internet has grown exponentially in comparison to the number of services and applications. The integration between Cloud Computing and the Internet of Things, termed the Cloud of Things, plays a key role in managing connected things, their data, and services. One of the main challenges in the Cloud of Things is the resource discovery process that enables applications to locate and use specific resources. Most existing approaches employ multi-criteria decision analysis techniques that act as a black box, taking user constraints as input parameters to select a set of resources as output. However, these approaches do not evaluate the quality and characteristics of the selected resources with respect to varying criteria and their possible weights reflecting user needs.
This project analyses the quality and characteristics of three multi-criteria decision analysis techniques. The techniques examined are SAW, TOPSIS, and VIKOR, with a focus on understanding the impact of user constraints on their behaviour. By systematically varying input parameters and criteria weights, the research reveals how each algorithm responds to different user requirements in the resource discovery process.
Results show important differences in how these algorithms perform across capacity and diversity metrics. The three algorithms exhibit statistical equivalency regarding capacity, meaning they select resources of comparable quality. However, the VIKOR algorithm achieves the highest diverseness on average, indicating that its proposed set of solutions possesses different characteristics compared to those selected by SAW and TOPSIS, making it particularly suitable for scenarios where resource variety is valued.
Team
