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A Tensor-Based Framework for Software-Defined Cloud Data Center

Published: 12 December 2016 Publication History

Abstract

Multimedia has been exponentially increasing as the biggest big data, which consist of video clips, images, and audio files. Processing and analyzing them on a cloud data center have become a preferred solution that can utilize the large pool of cloud resources to address the problems caused by the tremendous amount of unstructured multimedia data. However, there exist many challenges in processing multimedia big data on a cloud data center, such as multimedia data representation approach, an efficient networking model, and an estimation method for traffic patterns. The primary purpose of this article is to develop a novel tensor-based software-defined networking model on a cloud data center for multimedia big-data computation and communication. First, an overview of the proposed framework is provided, in which the functions of the representative modules are briefly illustrated. Then, three models,—forwarding tensor, control tensor, and transition tensor—are proposed for management of networking devices and prediction of network traffic patterns. Finally, two algorithms about single-mode and multimode tensor eigen-decomposition are developed, and the incremental method is employed for efficiently updating the generated eigen-vector and eigen-tensor. Experimental results reveal that the proposed framework is feasible and efficient to handle multimedia big data on a cloud data center.

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 12, Issue 5s
Special Section on Multimedia Big Data: Networking and Special Section on Best Papers From ACM MMSYS/NOSSDAV 2015
December 2016
288 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/3001754
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 12 December 2016
Accepted: 01 April 2016
Revised: 01 April 2016
Received: 01 December 2015
Published in TOMM Volume 12, Issue 5s

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Author Tags

  1. Big data
  2. data center
  3. software defined networks
  4. tensor

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  • (2021)Tensor-Train-Based Higher Order Dominant Z-Eigen Decomposition for Multi-Modal Prediction and Its Cloud/Edge ImplementationIEEE Transactions on Network Science and Engineering10.1109/TNSE.2020.30275108:2(1353-1366)Online publication date: 1-Apr-2021
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