Towards self-optimization of publish/subscribe IoT systems using continuous performance monitoring

TL;DR

This paper presents a methodology for continuous performance monitoring and self-optimization of publish/subscribe IoT systems using Stochastic Petri nets to improve response times and system efficiency.

Contribution

It introduces a novel approach combining performance monitoring with self-optimization in IoT publish/subscribe systems through stochastic modeling.

Findings

Effective performance assessment using Stochastic Petri nets

System self-optimizes response times in real-time

Improved IoT system responsiveness and reliability

Abstract

Today, more and more embedded devices are being connected through a network, generally Internet, offering users different services. This concept refers to Internet of Things (IoT), bringing information and control capabilities in many fields like medicine, smart homes, home security, etc. Main drawbacks of IoT environment are its dependency on Internet connectivity and need continuous devices power. These dependencies may affect system performances, namely request processing response times. In this context, we propose in this paper a continuous performance monitoring methodology, applied on IoT systems based on Publish/subscribe communication model. Our approach assesses performances using Stochastic Petri net modeling, and self-optimizes whenever poor performances are detected. Our approach relies on a Stochastic Petri nets modelling and analysis to assess performances. We target improving performances, in particular response times, by online modification of influencing factors.