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A scalable framework for wireless distributed computing

Li, S ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/TNET.2017.2702605
  3. Abstract:
  4. We consider a wireless distributed computing system, in which multiple mobile users, connected wirelessly through an access point, collaborate to perform a computation task. In particular, users communicate with each other via the access point to exchange their locally computed intermediate computation results, which is known as data shuffling. We propose a scalable framework for this system, in which the required communication bandwidth for data shuffling does not increase with the number of users in the network. The key idea is to utilize a particular repetitive pattern of placing the data set (thus a particular repetitive pattern of intermediate computations), in order to provide the coding opportunities at both the users and the access point, which reduce the required uplink communication bandwidth from users to the access point and the downlink communication bandwidth from access point to users by factors that grow linearly with the number of users. We also demonstrate that the proposed data set placement and coded shuffling schemes are optimal (i.e., achieve the minimum required shuffling load) for both a centralized setting and a decentralized setting, by developing tight information-theoretic lower bounds. © 1993-2012 IEEE
  5. Keywords:
  6. Coding ; Edge computing ; Scalability ; Wireless distributed computing ; Bandwidth ; Information theory ; Communication bandwidth ; Computation tasks ; Data shuffling ; Distributed computing systems ; Downlink communications ; Information-theoretic lower bounds ; Repetitive pattern ; Uplink communication ; Distributed computer systems
  7. Source: IEEE/ACM Transactions on Networking ; Volume 25, Issue 5 , 2017 , Pages 2643-2654 ; 10636692 (ISSN)
  8. URL: https://ieeexplore.ieee.org/document/7935426