scholarly journals EmuFog: Extensible and scalable emulation of large-scale fog computing infrastructures

2017 ◽  
Author(s):  
Ruben Mayer ◽  
Leon Graser ◽  
Harshit Gupta ◽  
Enrique Saurez ◽  
Umakishore Ramachandran
Keyword(s):  
Large Scale ◽  
Fog Computing

Characterizing the Capability of Vehicular Fog Computing in Large-scale Urban Environment

2018 ◽  
Vol 23 (4) ◽  
pp. 1050-1067 ◽  
Author(s):  
Xiaoyan Kui ◽  
Yue Sun ◽  
Shigeng Zhang ◽  
Yong Li
Keyword(s):  
Urban Environment ◽  
Large Scale ◽  
Fog Computing

scholarly journals Towards Distributed Data Management in Fog Computing

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Vasileios Moysiadis ◽  
Panagiotis Sarigiannidis ◽  
Ioannis Moscholios
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Large Scale ◽  
Fog Computing ◽  
Cloud Services ◽  
Smart Devices ◽  
Distributed Data ◽  
Storage Allocation ◽  
Huge Amount ◽  
Distributed Data Storage

In the emerging area of the Internet of Things (IoT), the exponential growth of the number of smart devices leads to a growing need for efficient data storage mechanisms. Cloud Computing was an efficient solution so far to store and manipulate such huge amount of data. However, in the next years it is expected that Cloud Computing will be unable to handle the huge amount of the IoT devices efficiently due to bandwidth limitations. An arising technology which promises to overwhelm many drawbacks in large-scale networks in IoT is Fog Computing. Fog Computing provides high-quality Cloud services in the physical proximity of mobile users. Computational power and storage capacity could be offered from the Fog, with low latency and high bandwidth. This survey discusses the main features of Fog Computing, introduces representative simulators and tools, highlights the benefits of Fog Computing in line with the applications of large-scale IoT networks, and identifies various aspects of issues we may encounter when designing and implementing social IoT systems in the context of the Fog Computing paradigm. The rationale behind this work lies in the data storage discussion which is performed by taking into account the importance of storage capabilities in modern Fog Computing systems. In addition, we provide a comprehensive comparison among previously developed distributed data storage systems which consist of a promising solution for data storage allocation in Fog Computing.

scholarly journals Multiactivation Pooling Method in Convolutional Neural Networks for Image Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Qi Zhao ◽  
Shuchang Lyu ◽  
Boxue Zhang ◽  
Wenquan Feng
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Big Data ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Large Scale ◽  
Fog Computing ◽  
Feature Extractor ◽  
Benchmark Datasets ◽  
Classification Tasks

Convolutional neural networks (CNNs) are becoming more and more popular today. CNNs now have become a popular feature extractor applying to image processing, big data processing, fog computing, etc. CNNs usually consist of several basic units like convolutional unit, pooling unit, activation unit, and so on. In CNNs, conventional pooling methods refer to 2×2 max-pooling and average-pooling, which are applied after the convolutional or ReLU layers. In this paper, we propose a Multiactivation Pooling (MAP) Method to make the CNNs more accurate on classification tasks without increasing depth and trainable parameters. We add more convolutional layers before one pooling layer and expand the pooling region to 4×4, 8×8, 16×16, and even larger. When doing large-scale subsampling, we pick top-k activation, sum up them, and constrain them by a hyperparameter σ. We pick VGG, ALL-CNN, and DenseNets as our baseline models and evaluate our proposed MAP method on benchmark datasets: CIFAR-10, CIFAR-100, SVHN, and ImageNet. The classification results are competitive.

Distributed Optimization of Collaborative Regions in Large-Scale Inhomogeneous Fog Computing

2018 ◽  
Vol 36 (3) ◽  
pp. 574-586 ◽  
Author(s):  
Xinchen Lyu ◽  
Chenshan Ren ◽  
Wei Ni ◽  
Hui Tian ◽  
Ren Ping Liu
Keyword(s):  
Large Scale ◽  
Distributed Optimization ◽  
Fog Computing

scholarly journals Advanced Technique for Efficiently and Securely Accessing data in the Fog and Cloud Network

2021 ◽  
pp. 573-578
Author(s):  
Dhanashri M. Kale ◽  
Dr. Vilas M. Thakare
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Fog Computing ◽  
Future Research ◽  
Advanced Technique ◽  
Computing Architecture ◽  
Use Of Technology ◽  
Radio Access ◽  
Architecture Evaluation ◽  
Cloud Network

With the increased use of technology , the fog computing network is being used on a large scale The integration of fog computing into cloud computing network is full of advantages and increases features. The network currently is secured but also subjected to various challenges. In this paper, we have reviewed five different schemes which are : Architecture Harmonization Between Cloud Radio Access Networks and Fog Networks, Fog Computing Architecture, Evaluation, and Future Research Directions, Indie Fog: An Efficient Fog-Computing Infrastructure for the Internet of Things, A Framework of Fog Computing: Architecture, Challenges, and Optimization, A Critical Analysis on Integration of Fog Computing and Cloud Computing. These schemes discussed here provide certain features but there are some limitations in it. So we propose a new scheme that helps to overcome the challenges of these previous schemes.

scholarly journals Developing applications in large scale, dynamic fog computing: A case study

2020 ◽  
Vol 50 (5) ◽  
pp. 519-532 ◽  
Author(s):  
Nam Ky Giang ◽  
Rodger Lea ◽  
Victor C.M. Leung
Keyword(s):  
Large Scale ◽  
Fog Computing

scholarly journals Multitier Fog Computing With Large-Scale IoT Data Analytics for Smart Cities

2018 ◽  
Vol 5 (2) ◽  
pp. 677-686 ◽  
Author(s):  
Jianhua He ◽  
Jian Wei ◽  
Kai Chen ◽  
Zuoyin Tang ◽  
Yi Zhou ◽  
...  
Keyword(s):  
Data Analytics ◽  
Large Scale ◽  
Smart Cities ◽  
Fog Computing

Large-Scale Data Streaming in Fog Computing and Its Applications

2021 ◽  
pp. 50-65
Author(s):  
Oshin Sharma ◽  
Anusha S.
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Fog Computing ◽  
Data Streaming ◽  
Cloud Data ◽  
Large Scale Data ◽  
Emerging Trends ◽  
Iot Devices ◽  
Data Centres ◽  
Scale Data

The emerging trends in fog computing have increased the interests and focus in both industry and academia. Fog computing extends cloud computing facilities like the storage, networking, and computation towards the edge of networks wherein it offloads the cloud data centres and reduces the latency of providing services to the users. This paradigm is like cloud in terms of data, storage, application, and computation services, except with a fundamental difference: it is decentralized. Furthermore, these fog systems can process huge amounts of data locally and can be installed on hardware of different types. These characteristics make fog suitable for time- and location-based applications like internet of things (IoT) devices which can process large amounts of data. In this chapter, the authors present fog data streaming, its architecture, and various applications.

scholarly journals An Engagement Model Based on User Interest and QoS in Video Streaming Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaoying Tan ◽  
Yuchun Guo ◽  
Mehmet A. Orgun ◽  
Liyin Xue ◽  
Yishuai Chen
Keyword(s):  
Large Scale ◽  
Fog Computing ◽  
User Engagement ◽  
Mobile Video ◽  
User Interest ◽  
User Behaviors ◽  
Proposed Model ◽  
Large Scale Dataset ◽  
Qoe Model

With the surging demand on high-quality mobile video services and the unabated development of new network technology, including fog computing, there is a need for a generalized quality of user experience (QoE) model that could provide insight for various network optimization designs. A good QoE, especially when measured as engagement, is an important optimization goal for investors and advertisers. Therefore, many works have focused on understanding how the factors, especially quality of service (QoS) factors, impact user engagement. However, the divergence of user interest is usually ignored or deliberatively decoupled from QoS and/or other objective factors. With an increasing trend towards personalization applications, it is necessary as well as feasible to consider user interest to satisfy aesthetic and personal needs of users when optimizing user engagement. We first propose an Extraction-Inference (E-I) algorithm to estimate the user interest from easily obtained user behaviors. Based on our empirical analysis on a large-scale dataset, we then build a QoS and user Interest based Engagement (QI-E) regression model. Through experiments on our dataset, we demonstrate that the proposed model reaches an improvement in accuracy by 9.99% over the baseline model which only considers QoS factors. The proposed model has potential for designing QoE-oriented scheduling strategies in various network scenarios, especially in the fog computing context.

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