.png){kind=icon}
.png){kind=icon}
Return
A Scalable Framework for Wireless Distributed Computing
.png){kind=icon}
OA
AI
DOI：10.1109/TNET.2017.2702605.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Wireless distributed computing
edge computing
coding
information theory
scalability
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
3.6
Papers:
4.4K
Citations:
9.5K