iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments
TLDR
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
More filters
Proceedings ArticleDOI
K-Means Based on Resource Clustering for Smart Farming Problem in Fog Computing
TL;DR: This paper proposes a K-Means based clustering algorithm for a Smart Farming application, compared network performance with first come first serve (FCFS) algorithms, and demonstrates that K- means outperforms FCFS on energy consumption, network usage and end-to-end delay.
Journal ArticleDOI
A self-learning approach for proactive resource and service provisioning in fog environment
TL;DR: A framework for increasing resource management efficiency in the IoT ecosystem based on deep reinforcement learning (DRL) and the proposed deep neural network (DNN) method for estimating value functions improves adaptability to different oscillating conditions, learns past sensible strategies, and as a self-learning adaptive system by replicating interactions with the fog environment.
Proceedings ArticleDOI
Context Sensitive Health Monitoring Using Fog Computing
Pinjari Hameed,Anand Paul +1 more
TL;DR: This paper proposes to use fog computing to help monitor patients suffering from chronic diseases such that the data is collected and processed in an efficient manner and also analyses the security and deployment issues of this fog computing layer.
Proceedings ArticleDOI
Improving efficiency and availability in Smart Classroom environments
TL;DR: This work is to demonstrate methods of identification, prevention, and failure treatment in this of cloud computing infrastructure, to achieve greater efficiency of the ubiquitous and adaptive environment of Smart Classroom.
References
More filters
Journal ArticleDOI
Internet of Things (IoT): A vision, architectural elements, and future directions
TL;DR: In this article, the authors present a cloud centric vision for worldwide implementation of Internet of Things (IoT) and present a Cloud implementation using Aneka, which is based on interaction of private and public Clouds, and conclude their IoT vision by expanding on the need for convergence of WSN, the Internet and distributed computing directed at technological research community.
Journal ArticleDOI
CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms
TL;DR: The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
Book ChapterDOI
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.
Journal ArticleDOI
SmartSantander: IoT experimentation over a smart city testbed
Luis Sanchez,Luis Muñoz,Jose Antonio Galache,Pablo Sotres,Juan Ramón Santana,Verónica Gutiérrez,Rajiv Ramdhany,Alexander Gluhak,Srdjan Krco,Evangelos Theodoridis,Dennis Pfisterer +10 more
TL;DR: The IoT experimentation facility described in this paper is conceived to provide a suitable platform for large scale experimentation and evaluation of IoT concepts under real-life conditions to influence the definition and specification of Future Internet architecture design.
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.