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Machine learning for dynamic resource allocation at network edge
Author(s): Bong Jun Ko; Kin K. Leung; Theodoros Salonidis
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Paper Abstract

With the proliferation of smart devices, it is increasingly important to exploit their computing, networking, and storage resources for executing various computing tasks at scale at mobile network edges, bringing many benefits such as better response time, network bandwidth savings, and improved data privacy and security. A key component in enabling such distributed edge computing is a mechanism that can flexibly and dynamically manage edge resources for running various military and commercial applications in a manner adaptive to the fluctuating demands and resource availability. We present methods and an architecture for the edge resource management based on machine learning techniques. A collaborative filtering approach combined with deep learning is proposed as a means to build the predictive model for applications’ performance on resources from previous observations, and an online resource allocation architecture utilizing the predictive model is presented. We also identify relevant research topics for further investigation.

Paper Details

Date Published: 4 May 2018
PDF: 10 pages
Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 106350J (4 May 2018); doi: 10.1117/12.2306095
Show Author Affiliations
Bong Jun Ko, IBM Thomas J. Watson Research Ctr. (United States)
Kin K. Leung, Imperial College London (United Kingdom)
Theodoros Salonidis, IBM Thomas J. Watson Research Ctr. (United States)


Published in SPIE Proceedings Vol. 10635:
Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX
Michael A. Kolodny; Dietrich M. Wiegmann; Tien Pham, Editor(s)

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