TY - GEN
T1 - Latency Prediction for Delay-sensitive V2X Applications in Mobile Cloud/Edge Computing Systems
AU - Zhang, Wenhan
AU - Feng, Mingjie
AU - Krunz, Marwan
AU - Volos, Haris
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - Mobile edge computing (MEC) is a key enabler of delay-sensitive vehicle-to-everything (V2X) applications. Determining where to execute a task necessitates accurate estimation of the offloading latency. In this paper, we propose a latency prediction framework that integrates machine learning and statistical approaches. Aided by extensive latency measurements collected during driving, we first preprocess the data and divide it into two components: one that follows a trackable trend over time and the other that behaves like random noise. We then develop a Long Short-Term Memory (LSTM) network to predict the first component. This LSTM network captures the trend in latency over time. We further enhance the prediction accuracy of this technique by employing a k-medoids classification method. For the second component, we propose a statistical approach using a combination of Epanechnikov Kernel and moving average functions. Experimental results show that the proposed prediction approach reduces the prediction error to half of a standard deviation (STD) of the raw data.
AB - Mobile edge computing (MEC) is a key enabler of delay-sensitive vehicle-to-everything (V2X) applications. Determining where to execute a task necessitates accurate estimation of the offloading latency. In this paper, we propose a latency prediction framework that integrates machine learning and statistical approaches. Aided by extensive latency measurements collected during driving, we first preprocess the data and divide it into two components: one that follows a trackable trend over time and the other that behaves like random noise. We then develop a Long Short-Term Memory (LSTM) network to predict the first component. This LSTM network captures the trend in latency over time. We further enhance the prediction accuracy of this technique by employing a k-medoids classification method. For the second component, we propose a statistical approach using a combination of Epanechnikov Kernel and moving average functions. Experimental results show that the proposed prediction approach reduces the prediction error to half of a standard deviation (STD) of the raw data.
UR - http://www.scopus.com/inward/record.url?scp=85101248469&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85101248469&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM42002.2020.9348104
DO - 10.1109/GLOBECOM42002.2020.9348104
M3 - Conference contribution
AN - SCOPUS:85101248469
T3 - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
BT - 2020 IEEE Global Communications Conference, GLOBECOM 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Global Communications Conference, GLOBECOM 2020
Y2 - 7 December 2020 through 11 December 2020
ER -