Non-Intrusive Continuous Monitoring of Smart City Platforms

Abstract

Smart city platforms provide several services to facilitate the development of applications. Such platforms typically manage several applications, deal with a large volume of data, and serve many devices and users that generate a high volume of requests. The large number of requests to handle and the complex operations to perform often cause overloads on the platform, degrading the quality of service provided to users and applications. In this context, monitoring the underlying computational infrastructure in which smart city platforms and applications are deployed and the platform operations is essential. The monitoring process can allow for examining fluctuations in the behavior of the platform's components to detect performance degradation and overloads (including unforeseen ones), contribute to avoiding interruptions in the platform's services, and increase its scalability to assimilate significant amounts of requests, devices, and users. This paper presents a strategy and architecture to enable the non-intrusive monitoring of operations on smart city platforms and their underlying infrastructure. The proposal covers monitoring at multiple levels and is based on the aspect-oriented programming (AOP) paradigm so that it is possible to monitor the platform's operations without intervening in the platform's implementation or generating coupling regarding monitoring. This paper presents the implementation of the monitoring architecture and its instantiation in the context of Smart Geo Layers (SGeoL), a platform that has been used in several real-world smart city applications. This paper also reports the results of computational experiments to evaluate the performance of the proposed monitoring architecture for response time to requests, CPU usage, and RAM utilization. The obtained results show an evident increase in response time with the number of simultaneous requests and a significant correlation between the response time and the CPU utilization in the deployment of the monitoring architecture.