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

By Harshit Gupta, Amir Vahid Dastjerdi, Soumya Kanti Ghosh, Rajkumar Buyya. Software, Practice & Experience, 2016

1,559 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/0c06d0d41ed1a946a5927157312f5936349f62bf

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Citations in the corpus

Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study (2017, methodology): “Utilized simulation toolkit – For this purpose, we have adopted the (recently deployed) iFogSim toolkit [132].”
Resource Management in Fog/Edge Computing (2018, methodology): “The proposed model was evaluated on an extended iFogSim [60].”
Fog Computing: Survey of Trends, Architectures, Requirements, and Research Directions (2018, background): “[123] Modelled IoT and Fog environments and measure the impact of resource management techniques in terms of network congestion, latency, cost and energy consumption.”
Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “In [472], the authors developed a simulator to model IoT using fog environments and measured the impact of resource management techniques in latency, network congestion, energy consumption, and cost.”
Predictive Maintenance and Intelligent Sensors in Smart Factory: Review (2021, background): “In addition to the mentioned Internet of Things [75], there is a significant representation of publications focused on wearable sensor-based systems [76], deep learning [77], edge technology [78], graphene-based smart materials, blockchain, smart city, and grid.”
EdgeCloudSim: An environment for performance evaluation of Edge Computing systems (2017, methodology): “iFogSim is another simulator running on top of CloudSim [15].”
Survey on Multi-Access Edge Computing for Internet of Things Realization (2018, methodology): “[128] Develop a toolkit for modeling and simulation of resource management techniques in the IoT, edge and fog computing environments General IoT X”
Resource Scheduling in Edge Computing: A Survey (2021, methodology): “2) Test Environment: The performance evaluation of scheduling algorithms in current research is generally performed using simulation tools, including professional simulators for edge computing such as iFogSim [300], EdgeCloudSim [301], and MyiFogSim [302], and general simulation platforms like MATLA”
Computer Science Review (neutral): “For environments with a fog or edge hierarchy, IFogSim [113], also based on CloudSim, is frequently used.”
Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm (2017, methodology): “al [19] over the CloudSim [20] framework for modeling and simulating IoT, Edge and Fog Computing environments.”
HealthFog: An Ensemble Deep Learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in Integrated IoT and Fog Computing Environments (2019, methodology): “There is a need to solve the following challenges [24, 25, 11, 12, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 26, 39, 42, 43, 44] to recognize the full capability of IoT based fog-computing for healthcare systems: (a) An efficient IoT based Healthcare application is needed which can process a large amo”