Learning a Hybrid Proactive and Reactive Caching Policy in Wireless Edge Under Dynamic Popularity

Caching at wireless edge is a promising way to satisfy the explosively increasing mobile data demands, if future content popularity is known in advance. However, the time-varying nature of content popularity makes the popularity prediction far from perfect, which inevitably degrades the gain from ca...

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Main Authors: Kaiqiang Qi, Shengqian Han, Chenyang Yang
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8809698/
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author Kaiqiang Qi
Shengqian Han
Chenyang Yang
author_facet Kaiqiang Qi
Shengqian Han
Chenyang Yang
author_sort Kaiqiang Qi
collection DOAJ
description Caching at wireless edge is a promising way to satisfy the explosively increasing mobile data demands, if future content popularity is known in advance. However, the time-varying nature of content popularity makes the popularity prediction far from perfect, which inevitably degrades the gain from caching. In this paper, we resort to a hybrid proactive and reactive policy to deal with the dynamics of popularity, in particular the hybrid of proactive probabilistic caching policy and least recently used policy, which are appropriate respectively for the contents with low and high dynamic popularity. We divide the contents requested in a region into two classes, where one can be modeled by independent reference model (IRM) and the other can be modeled by shot noise model (SNM). To maximize the total successful offloading ratio achieved by caching the two classes of contents, we optimize the hybrid caching policy including both the cache resource allocation to each class of contents and the probability of caching each IRM content. We find the optimal solution to the problem for general case, and provide a closed-form solution in a special case to gain insights. To provide a viable solution for practical use, we propose a heuristic method to obtain the optimal allocation fraction, and predict the popularity distribution and the allocation fraction using neural networks with historical data. We validate our analytical results by simulation results via synthetic datasets. We evaluate the performance of the proposed hybrid caching policy via synthetic data generated by SNM and two real datasets, and compare it with the proactive policy, the reactive policy and the existing hybrid proactive and reactive policy.
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spelling doaj.art-9c2a52c45fc14538a38723f2f007d7b12022-12-21T22:00:57ZengIEEEIEEE Access2169-35362019-01-01712078812080110.1109/ACCESS.2019.29368668809698Learning a Hybrid Proactive and Reactive Caching Policy in Wireless Edge Under Dynamic PopularityKaiqiang Qi0Shengqian Han1https://orcid.org/0000-0002-2085-3292Chenyang Yang2https://orcid.org/0000-0003-0058-0765School of Electronics and Information Engineering, Beihang University (BUAA), Beijing, ChinaSchool of Electronics and Information Engineering, Beihang University (BUAA), Beijing, ChinaSchool of Electronics and Information Engineering, Beihang University (BUAA), Beijing, ChinaCaching at wireless edge is a promising way to satisfy the explosively increasing mobile data demands, if future content popularity is known in advance. However, the time-varying nature of content popularity makes the popularity prediction far from perfect, which inevitably degrades the gain from caching. In this paper, we resort to a hybrid proactive and reactive policy to deal with the dynamics of popularity, in particular the hybrid of proactive probabilistic caching policy and least recently used policy, which are appropriate respectively for the contents with low and high dynamic popularity. We divide the contents requested in a region into two classes, where one can be modeled by independent reference model (IRM) and the other can be modeled by shot noise model (SNM). To maximize the total successful offloading ratio achieved by caching the two classes of contents, we optimize the hybrid caching policy including both the cache resource allocation to each class of contents and the probability of caching each IRM content. We find the optimal solution to the problem for general case, and provide a closed-form solution in a special case to gain insights. To provide a viable solution for practical use, we propose a heuristic method to obtain the optimal allocation fraction, and predict the popularity distribution and the allocation fraction using neural networks with historical data. We validate our analytical results by simulation results via synthetic datasets. We evaluate the performance of the proposed hybrid caching policy via synthetic data generated by SNM and two real datasets, and compare it with the proactive policy, the reactive policy and the existing hybrid proactive and reactive policy.https://ieeexplore.ieee.org/document/8809698/Dynamic popularityproactive and reactive cachingcache resource allocationshot noise modelindependent reference modelreal datasets
spellingShingle Kaiqiang Qi
Shengqian Han
Chenyang Yang
Learning a Hybrid Proactive and Reactive Caching Policy in Wireless Edge Under Dynamic Popularity
IEEE Access
Dynamic popularity
proactive and reactive caching
cache resource allocation
shot noise model
independent reference model
real datasets
title Learning a Hybrid Proactive and Reactive Caching Policy in Wireless Edge Under Dynamic Popularity
title_full Learning a Hybrid Proactive and Reactive Caching Policy in Wireless Edge Under Dynamic Popularity
title_fullStr Learning a Hybrid Proactive and Reactive Caching Policy in Wireless Edge Under Dynamic Popularity
title_full_unstemmed Learning a Hybrid Proactive and Reactive Caching Policy in Wireless Edge Under Dynamic Popularity
title_short Learning a Hybrid Proactive and Reactive Caching Policy in Wireless Edge Under Dynamic Popularity
title_sort learning a hybrid proactive and reactive caching policy in wireless edge under dynamic popularity
topic Dynamic popularity
proactive and reactive caching
cache resource allocation
shot noise model
independent reference model
real datasets
url https://ieeexplore.ieee.org/document/8809698/
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AT shengqianhan learningahybridproactiveandreactivecachingpolicyinwirelessedgeunderdynamicpopularity
AT chenyangyang learningahybridproactiveandreactivecachingpolicyinwirelessedgeunderdynamicpopularity