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Open AccessJournal ArticleDOI

Coding for Distributed Fog Computing

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TLDR
In this paper, the authors demonstrate the transformational role of coding in fog computing for leveraging such redundancy to substantially reduce the bandwidth consumption and latency of computing, and discuss two recently proposed coding concepts, minimum bandwidth codes and minimum latency codes.
Abstract: 
Redundancy is abundant in fog networks (i.e., many computing and storage points) and grows linearly with network size. We demonstrate the transformational role of coding in fog computing for leveraging such redundancy to substantially reduce the bandwidth consumption and latency of computing. In particular, we discuss two recently proposed coding concepts, minimum bandwidth codes and minimum latency codes, and illustrate their impacts on fog computing. We also review a unified coding framework that includes the above two coding techniques as special cases, and enables a trade-off between computation latency and communication load to optimize system performance. At the end, we will discuss several open problems and future research directions.

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Proceedings ArticleDOI

Wireless Distributed Computing: Processing Time Analysis and Optimization

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Leveraging User-Diversity in Energy-Efficient Edge-Facilitated Collaborative Fog Computing

TL;DR: In this paper, the authors investigated edge-facilitated collaborative fog computing to augment the computing capabilities of individual devices while optimizing for energy-efficiency, where computing load is optimally distributed among devices, taking into account their diversity in terms of computing and communication capabilities.
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Coded Computing via Binary Linear Codes: Designs and Performance Limits

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Communication-Efficient Computation Load Scheduling for Delay-Constrained Services

TL;DR: A dynamic communication-efficient computation load scheduling framework to complete the computation tasks with coded MapReduce considering arbitrary arrival and strict delay constraints over time-varying computing resource and proposes a dynamic online algorithm based on the predicted computing resource.
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Communication-Aware Computing for Edge Processing

TL;DR: In this article, the authors proposed a Universal Coded Edge Computing (UCEC) scheme for linear functions to simultaneously minimize the load of computation at the edge nodes, and maximize the physical-layer communication efficiency towards the mobile users.
References
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MapReduce: simplified data processing on large clusters

TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Journal ArticleDOI

MapReduce: simplified data processing on large clusters

TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
Proceedings Article

Spark: cluster computing with working sets

TL;DR: Spark can outperform Hadoop by 10x in iterative machine learning jobs, and can be used to interactively query a 39 GB dataset with sub-second response time.
Proceedings ArticleDOI

Fog computing and its role in the internet of things

TL;DR: This paper argues that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
Book ChapterDOI

Fog Computing and Its Role in the Internet of Things

TL;DR: This chapter argues that the above characteristics make the Fog the appropriate platform for a number of critical internet of things services and applications, namely connected vehicle, smart grid, smart cities, and in general, wireless sensors and actuators networks (WSANs).
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