A Cache Partition Policy of CCN based on Content Popularity

2016 ◽  
Vol 92 ◽  
pp. 9-16
Author(s):  
Pu Gong ◽  
HuanYu Wu
Keyword(s):  
Content Popularity ◽  
Cache Partition

Machine-learning-based cache partition method in cloud environment

2021 ◽  
Author(s):  
Jiefan Qiu ◽  
Zonghan Hua ◽  
Lei Liu ◽  
Mingsheng Cao ◽  
Dajiang Chen
Keyword(s):  
Machine Learning ◽  
Cloud Environment ◽  
Partition Method ◽  
Cache Partition

scholarly journals Content Popularity Prediction Towards Location-Aware Mobile Edge Caching

2019 ◽  
Vol 21 (4) ◽  
pp. 915-929 ◽  
Author(s):  
Peng Yang ◽  
Ning Zhang ◽  
Shan Zhang ◽  
Li Yu ◽  
Junshan Zhang ◽  
...  
Keyword(s):  
Location Aware ◽  
Content Popularity ◽  
Popularity Prediction ◽  
Edge Caching

On the Interplay between Content Popularity and Performance in P2P Systems

2013 ◽  
pp. 3-21 ◽  
Author(s):  
Edmundo de Souza e Silva ◽  
Rosa M. M. Leão ◽  
Daniel Sadoc Menasché ◽  
Antonio A. de A. Rocha
Keyword(s):  
P2p Systems ◽  
Content Popularity ◽  
And Performance

Analysis of Content Popularity in Social Bookmarking Systems

2010 ◽  
pp. 233-257 ◽  
Author(s):  
Symeon Papadopoulos ◽  
Fotis Menemenis ◽  
Athena Vakali ◽  
Ioannis Kompatsiaris
Keyword(s):  
Social Relations ◽  
Online Social Networks ◽  
Large Scale ◽  
Selection Process ◽  
Online Content ◽  
Social Bookmarking ◽  
Content Selection ◽  
Content Consumption ◽  
Content Popularity ◽  
The Masses

The recent advent and wide adoption of Social Bookmarking Systems (SBS) has disrupted the traditional model of online content publishing and consumption. Until recently, the majority of content consumed by people was published as a result of a centralized selection process. Nowadays, the large-scale adoption of the Web 2.0 paradigm has diffused the content selection process to the masses. Modern SBS-based applications permit their users to submit their preferred content, comment on and rate the content of other users and establish social relations with each other. As a result, the evolution of popularity of socially bookmarked content constitutes nowadays an overly complex phenomenon calling for a multiaspect analysis approach. This chapter attempts to provide a unified treatment of the phenomenon by studying four aspects of popularity of socially bookmarked content: (a) the distributional properties of content consumption, (b) its evolution in time, (c) the correlation between the semantics of online content and its popularity, and (d) the impact of online social networks on the content consumption behavior of individuals. To this end, a case study is presented where the proposed analysis framework is applied to a large dataset collected from Digg, a popular social bookmarking and rating application.

Joint User Access Mode Selection and Content Popularity Prediction in Non-Orthogonal Multiple Access-Based F-RANs

2020 ◽  
Vol 68 (1) ◽  
pp. 654-666
Author(s):  
Shi Yan ◽  
Lin Qi ◽  
Yangcheng Zhou ◽  
Mugen Peng ◽  
G. M. Shafiqur Rahman
Keyword(s):  
Multiple Access ◽  
Mode Selection ◽  
Access Mode ◽  
Content Popularity ◽  
User Access ◽  
Popularity Prediction

scholarly journals Active Content Popularity Learning via Query-by-Committee for Edge Caching

2019 ◽  
Author(s):  
Srikanth Bommaraveni ◽  
Thang X. Vu ◽  
Satyanarayana Vuppala ◽  
Symeon Chatzinotas ◽  
Bjorn Ottersten
Keyword(s):  
Content Popularity ◽  
Active Content ◽  
Edge Caching

scholarly journals Intelligent data cache based on content popularity and user location for Content Centric Networks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hsin-Te Wu ◽  
Hsin-Hung Cho ◽  
Sheng-Jie Wang ◽  
Fan-Hsun Tseng
Keyword(s):  
Data Cache ◽  
Total Size ◽  
Hit Rate ◽  
Cache Size ◽  
Content Popularity ◽  
Content Centric Network ◽  
Cache Scheme ◽  
And Storage ◽  
User Location ◽  
Content Cache

AbstractContent cache as well as data cache is vital to Content Centric Network (CCN). A sophisticated cache scheme is necessary but unsatisfied currently. Existing content cache scheme wastes router’s cache capacity due to redundant replica data in CCN routers. The paper presents an intelligent data cache scheme, viz content popularity and user location (CPUL) scheme. It tackles the cache problem of CCN routers for pursuing better hit rate and storage utilization. The proposed CPUL scheme not only considers the location where user sends request but also classifies data into popular and normal content with correspond to different cache policies. Simulation results showed that the CPUL scheme yields the highest cache hit rate and the lowest total size of cache data with compared to the original cache scheme in CCN and the Most Popular Content (MPC) scheme. The CPUL scheme is superior to both compared schemes in terms of around 8% to 13% higher hit rate and around 4% to 16% lower cache size. In addition, the CPUL scheme achieves more than 20% and 10% higher cache utilization when the released cache size increases and the categories of requested data increases, respectively.

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