iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments
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In this paper, the authors 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.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.read more
Citations
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Journal ArticleDOI
Fog of Everything: Energy-Efficient Networked Computing Architectures, Research Challenges, and a Case Study
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Proceedings ArticleDOI
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FogBus: A Blockchain-based Lightweight Framework for Edge and Fog Computing
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Resource Management Approaches in Fog Computing: a Comprehensive Review
TL;DR: This paper provides a systematic literature review (SLR) on the resource management approaches in fog environment in the form of a classical taxonomy to recognize the state-of-the-art mechanisms on this important topic and providing open issues as well.
References
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Fog Computing: A Platform for Internet of Things and Analytics
TL;DR: This chapter proposes a hierarchical distributed architecture that extends from the edge of the network to the core nicknamed Fog Computing, and pays attention to a new dimension that IoT adds to Big Data and Analytics: a massively distributed number of sources at the edge.
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Journal ArticleDOI
Assessment of the Suitability of Fog Computing in the Context of Internet of Things
TL;DR: Results show that as the number of applications demanding real-time service increases, the fog computing paradigm outperforms traditional cloud computing.