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Coding and Information Theory for Distributed Storage Systems

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (15 August 2021) | Viewed by 1960

Special Issue Editors

Teletraffic Research Centre, The University of Adelaide, Adelaide, SA 5005, Australia
Interests: information theory; communication theory; network coding; causal inference; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Electrical Engineering and Computing, The University of Newcastle, Callaghan NSW 2308, Australia
Interests: information theory; communication theory; wireless communications; index coding
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, Shenzhen 518172, China
Interests: information theory; coding for distributed storage; distributed computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Data storage systems, housing massive amounts of information, have become an indispensable component in modern communication networks, as well as cloud computing and network applications. For information backup, data is split, duplicated, and stored in different locations, in a way that is robust to node failures; for distributed computations, data is partitioned into different sets and communicated to different computers for computations, the results of which are then communicated and combined to obtain the required computational result; for content distribution, information is duplicated at different local servers closer to the clients for easy and efficient access.

The trend towards ubiquitous data storage in current and future applications induces stringent requirements for data storage, especially in the aspects of reliability and security—not only for storing the data but also for disseminating the data to users and different nodes in the systems. The use of information theory and coding to study the fundamental limits of data storage systems and to innovate efficient coding schemes has gained significant attention from both academia and industry.

This Special Issue will collect original papers within the research area of coding for distributed storage, including the derivation of fundamental trade-offs in storage systems, the design of practical codes that enable efficient data access and update, and the construction of coding schemes that keep stored data confidential and protect the privacy of users. Papers on network coding, physical-layer network coding, secure network coding, and coded caching are also welcome.

Dr. Siu-Wai Ho
Prof. Lawrence Ong
Prof. Kenneth Shum
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Coded caching
  • Coding for distributed storage
  • Fundamental limits in data storage systems
  • Physical-layer network coding
  • Secure network coding
  • Secure storage systems

Published Papers (1 paper)

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Research

32 pages, 559 KiB  
Article
An Umbrella Converse for Data Exchange: Applied to Caching, Computing, and Shuffling
by Prasad Krishnan, Lakshmi Natarajan and V. Lalitha
Entropy 2021, 23(8), 985; https://doi.org/10.3390/e23080985 - 30 Jul 2021
Cited by 1 | Viewed by 1397
Abstract
The problem of data exchange between multiple nodes with storage and communication capabilities models several current multi-user communication problems like Coded Caching, Data Shuffling, Coded Computing, etc. The goal in such problems is to design communication schemes which accomplish the desired data exchange [...] Read more.
The problem of data exchange between multiple nodes with storage and communication capabilities models several current multi-user communication problems like Coded Caching, Data Shuffling, Coded Computing, etc. The goal in such problems is to design communication schemes which accomplish the desired data exchange between the nodes with the optimal (minimum) amount of communication load. In this work, we present a converse to such a general data exchange problem. The expression of the converse depends only on the number of bits to be moved between different subsets of nodes, and does not assume anything further specific about the parameters in the problem. Specific problem formulations, such as those in Coded Caching, Coded Data Shuffling, and Coded Distributed Computing, can be seen as instances of this generic data exchange problem. Applying our generic converse, we can efficiently recover known important converses in these formulations. Further, for a generic coded caching problem with heterogeneous cache sizes at the clients with or without a central server, we obtain a new general converse, which subsumes some existing results. Finally we relate a “centralized” version of our bound to the known generalized independence number bound in index coding and discuss our bound’s tightness in this context. Full article
(This article belongs to the Special Issue Coding and Information Theory for Distributed Storage Systems)
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