A proactive mobile edge cache policy based on the prediction by partial matching

The proactive caching has been an emerging approach to cost-effectively boost the network capacity and reduce access latency. While the performance of which extremely relies on the content prediction. Therefore, in this paper, a proactive cache policy is proposed in a distributed manner considering...

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Main Authors: Li, Lincan, Kwong, Chiew Foong, Liu, Qianyu
Format: Article
Language:English
Published: ASTES Publishers 2020
Subjects:
Online Access:https://eprints.nottingham.ac.uk/64085/
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author Li, Lincan
Kwong, Chiew Foong
Liu, Qianyu
author_facet Li, Lincan
Kwong, Chiew Foong
Liu, Qianyu
author_sort Li, Lincan
building Nottingham Research Data Repository
collection Online Access
description The proactive caching has been an emerging approach to cost-effectively boost the network capacity and reduce access latency. While the performance of which extremely relies on the content prediction. Therefore, in this paper, a proactive cache policy is proposed in a distributed manner considering the prediction of the content popularity and user location to minimise the latency and maximise the cache hit rate. Here, a backpropagation neural network is applied to predict the content popularity, and prediction by partial matching is chosen to predict the user location. The simulation results reveal our proposed cache policy is around 27%-60% improved in the cache hit ratio and 14%-60% reduced in the average latency, compared with the two conventional reactive policies, i.e., LFU and LRU policies.
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spelling nottingham-640852020-12-18T08:56:06Z https://eprints.nottingham.ac.uk/64085/ A proactive mobile edge cache policy based on the prediction by partial matching Li, Lincan Kwong, Chiew Foong Liu, Qianyu The proactive caching has been an emerging approach to cost-effectively boost the network capacity and reduce access latency. While the performance of which extremely relies on the content prediction. Therefore, in this paper, a proactive cache policy is proposed in a distributed manner considering the prediction of the content popularity and user location to minimise the latency and maximise the cache hit rate. Here, a backpropagation neural network is applied to predict the content popularity, and prediction by partial matching is chosen to predict the user location. The simulation results reveal our proposed cache policy is around 27%-60% improved in the cache hit ratio and 14%-60% reduced in the average latency, compared with the two conventional reactive policies, i.e., LFU and LRU policies. ASTES Publishers 2020-10-20 Article PeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/64085/1/A%20proactive%20mobile%20edge%20cache%20policy%20based%20on%20the%20prediction%20by%20partial%20matching.pdf Li, Lincan, Kwong, Chiew Foong and Liu, Qianyu (2020) A proactive mobile edge cache policy based on the prediction by partial matching. Advances in Science, Technology and Engineering Systems Journal, 5 (5). pp. 1154-1161. ISSN 24156698 Cache Latency; Cache Hit Rate; Content Prediction; Location Prediction http://dx.doi.org/10.25046/aj0505140 doi:10.25046/aj0505140 doi:10.25046/aj0505140
spellingShingle Cache
Latency; Cache Hit Rate; Content Prediction; Location Prediction
Li, Lincan
Kwong, Chiew Foong
Liu, Qianyu
A proactive mobile edge cache policy based on the prediction by partial matching
title A proactive mobile edge cache policy based on the prediction by partial matching
title_full A proactive mobile edge cache policy based on the prediction by partial matching
title_fullStr A proactive mobile edge cache policy based on the prediction by partial matching
title_full_unstemmed A proactive mobile edge cache policy based on the prediction by partial matching
title_short A proactive mobile edge cache policy based on the prediction by partial matching
title_sort proactive mobile edge cache policy based on the prediction by partial matching
topic Cache
Latency; Cache Hit Rate; Content Prediction; Location Prediction
url https://eprints.nottingham.ac.uk/64085/
https://eprints.nottingham.ac.uk/64085/
https://eprints.nottingham.ac.uk/64085/