Machine learning for dynamic resource allocation at network edge

Bong Jun Ko; Kin K. Leung; Theodoros Salonidis

doi:10.1117/12.2306095

4 May 2018 Machine learning for dynamic resource allocation at network edge

Bong Jun Ko, Kin K. Leung, Theodoros Salonidis

Author Affiliations +

Proceedings Volume 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX; 106350J (2018) https://doi.org/10.1117/12.2306095
Event: SPIE Defense + Security, 2018, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Bong Jun Ko, Bong Jun Ko, Kin K. Leung, Kin K. Leung, Theodoros Salonidis, Theodoros Salonidis, "Machine learning for dynamic resource allocation at network edge", Proc. SPIE 10635, Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR IX, 106350J (4 May 2018); doi: 10.1117/12.2306095; https://doi.org/10.1117/12.2306095

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