Machine learning for dynamic resource allocation at network edge

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Title: Machine learning for dynamic resource allocation at network edge
Authors: Ko, BJ
Leung, KK
Salonidis, T
Item Type: Conference 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.
Editors: Kolodny, MA
Wiegmann, DM
Pham, T
Issue Date: 4-May-2018
Date of Acceptance: 15-Apr-2018
ISSN: 0277-786X
Publisher: Proceedings of SPIE
Journal / Book Title: Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX
Volume: 10635
Copyright Statement: © 2018 SPIE.
Sponsor/Funder: IBM United Kingdom Ltd
Funder's Grant Number: 4603317662
Conference Name: 9th Conference on Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR part of the SPIE Defense + Commercial Sensing Conference
Keywords: Science & Technology
Physical Sciences
Edge computing
resource allocation
machine learning
collaborative filtering
Publication Status: Published
Start Date: 2018-04-15
Finish Date: 2018-04-19
Conference Place: Orlando, FL
Online Publication Date: 2018-05-04
Appears in Collections:Faculty of Engineering
Electrical and Electronic Engineering

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