The Effects of Relative Importance of User Constraints in Cloud of Things Resource Discovery: A Case Study

TL;DR

This paper investigates how user constraints influence resource discovery algorithms in the Cloud of Things, analyzing the behavior of SAW, TOPSIS, and VIKOR methods and their solution quality using Pareto-optimality.

Contribution

It provides a comparative analysis of three decision analysis algorithms and evaluates the impact of user constraints on their effectiveness in Cloud of Things resource discovery.

Findings

User constraints significantly affect algorithm outcomes

SAW, TOPSIS, and VIKOR show different sensitivities to constraints

Solutions are evaluated for Pareto-optimality to assess quality

Abstract

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 Internet of Things, named as Cloud of Things, plays a key role in managing the connected things, their data and services. One of the main challenges in Cloud of Things is the resource discovery of the smart objects and their reuse in different contexts. Most of the existent work uses some kind of multi-criteria decision analysis algorithm to perform the resource discovery, but do not evaluate the impact that the user constraints has in the final solution. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi-objective decision analyses algorithms and the impact of user constraints on them. We evaluated the quality of the proposed solutions using the Pareto-optimality concept.