iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments

TLDR

IoT generates massive data that overwhelms storage and analytics, and while cloud services can scale, they introduce latency that hampers time‑critical applications; fog computing extends cloud to the network edge to reduce latency and congestion. This work proposes iFogSim, an evaluation platform to quantify the performance of resource‑management policies in IoT and fog environments for real‑time analytics. iFogSim models IoT and fog infrastructures, assigns analytics modules to edge devices to minimize latency and maximize throughput, and includes case studies that compare resource‑management policies while assessing RAM usage and execution time. The toolkit’s scalability was verified, showing that RAM consumption and execution time remain manageable across varying simulation scenarios.

Abstract

Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.