iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments

摘要

Internet of Things (IoT) aims to bring every object (e.g. smart cameras,wearable, environmental sensors, home appliances, and vehicles) online, hencegenerating massive amounts of data that can overwhelm storage systems and dataanalytics applications. Cloud computing offers services at the infrastructurelevel that can scale to IoT storage and processing requirements. However, thereare applications such as health monitoring and emergency response that requirelow latency, and delay caused by transferring data to the cloud and then backto the application can seriously impact their performances. To overcome thislimitation, Fog computing paradigm has been proposed, where cloud services areextended to the edge of the network to decrease the latency and networkcongestion. To realize the full potential of Fog and IoT paradigms forreal-time analytics, several challenges need to be addressed. The first andmost critical problem is designing resource management techniques thatdetermine which modules of analytics applications are pushed to each edgedevice to minimize the latency and maximize the throughput. To this end, weneed a evaluation platform that enables the quantification of performance ofresource management policies on an IoT or Fog computing infrastructure in arepeatable manner. In this paper we propose a simulator, called iFogSim, tomodel IoT and Fog environments and measure the impact of resource managementtechniques in terms of latency, network congestion, energy consumption, andcost. We describe two case studies to demonstrate modeling of an IoTenvironment and comparison of resource management policies. Moreover,scalability of the simulation toolkit in terms of RAM consumption and executiontime is verified under different circumstances.