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

Abstract

Summary 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.

Optimized IoT service placement in the fog

“…To apply the different approaches in a fog environment, we use the fog simulation toolkit iFogSim [17] to simulate the fog landscape and cloud resources.…”

“…In this example, we assume that the application had no previous deployment time, so that w t A 1 = 0, and in the closest neighbor colony with the control node N the average deployment time is T w N (t). The response time r A 1 is calculated according to (6)-(11) as shown in (17).…”

Leveraging Fog Computing for Scalable IoT Datacenter Using Spine-Leaf Network Topology

“…Rather, the Fog layer is optimized to support Internet of things (IoT) and Internet of Everything (IoE) smarter mobility, seamless resource and interface heterogeneity, handshake with the cloud, and distributed data analytics. This is to address requirements of IoT/IoE applications that require low latency with a wide and dense geographical distribution [1]. In context, this computing concept leverages both edge and cloud computing.…”

“…With today's IoT, it is possible to bring consumer electronic devices including home appliances such as medical devices, fridges, cameras, and sensors into the Internet environment [1]. Machine-to-machine communication which enables "everything" connectivity to the Internet network is not only a reality but also an integral part of day-to-day living and interactions.…”

Software Defined Network-based Scalable Resource Discovery for Internet of Things

“…The system model has been sim ula ted using iFogSim [16 ] sim ula tion toolkit. iFogSim sim ula tion toolkit has been dev el oped upon the CloudSim framew ork which has been used extensiv el y to sim ula te Cloud, Mobile Cloud, Vehicular Cloud environmen t.…”