Performance Modeling of IoT-Cloud Gateway Under Diverse Traffic Characteristics

Abstract

The presence of diverse traffic types is well-established in IoT networks. Various probability distributions have been found to describe packets inter-arrival time contrasting with the familiar exponential distribution in traditional networks. These findings suggest the need to develop appropriate traffic models for performance analysis of IoT network systems. An essential component in IoT network is gateway as it provides connectivity to the core Internet. The IoT gateway also performs functions such as protocol translation and traffic aggregation. Therefore, efficient design of the IoT gateway is necessary for better network management. The paper presents a new analytical model, N-Gamma/M/1, for analyzing the performance of IoT gateway. The equivalence of N-Gamma/M/1 and Gamma/M/1 models is proved mathematically. Additionally, an in-depth performance evaluation of the IoT gateway under various arrival patterns is conducted through simulation. The numerical analysis of the proposed N-Gamma/M/1 model emphasizes the need for more buffers at the gateway when traffic from input devices has varying values of gamma distribution parameters. It is also noted that the IoT gateway experiences a longer mean queue length resulting in higher mean waiting time and packet loss when inter-arrival time distribution of packets follows generalized Pareto, Weibull and lognormal distributions with different parameter values. This makes the task of IoT network management challenging. Adaptive and intelligent resource allocation policies along with dynamic congestion control algorithms may provide a solution to minimize packet loss and ensure quality of service.