RecCac: Recommendation-Empowered Cooperative Edge Caching for Internet of Things

doi:10.12142/ZTECOM.202102002

ZTE Communications ›› 2021, Vol. 19 ›› Issue (2): 2-10.DOI: 10.12142/ZTECOM.202102002

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RecCac: Recommendation-Empowered Cooperative Edge Caching for Internet of Things

HAN Suning¹, LI Xiuhua¹(), SUN Chuan¹, WANG Xiaofei², LEUNG Victor C. M.^3,⁴

^1.Chongqing University, Chongqing 400000, China
^2.Tianjin University, Tianjin 300072, China
^3.Shenzhen University, Shenzhen 518000, China
^4.The University of British Columbia, Vancouver V6T 1Z4, Canada

Received:2021-03-01 Online:2021-06-25 Published:2021-07-27
About author:HAN Suning received the bachelor’s degree in software engineering from Tiangong University, China in 2020. He is currently pursuing the master’s degree with the School of Big Data and Software Engineering, Chongqing University, China. His current research interests include mobile edge computing, big data and recommender system.|LI Xiuhua (lixiuhua1988@gmail.com) received the B.S. degree from the Honors School, Harbin Institute of Technology, China in 2011, the M.S. degree from the School of Electronics and Information Engineering, Harbin Institute of Technology, in 2013, and the Ph.D. degree from the Department of Electrical and Computer Engineering, The University of British Columbia, Canada in 2018. He joined Chongqing University through One-Hundred Talents Plan of Chongqing University, China in 2019. He is currently a tenure-track Assistant Professor with the School of Big Data & Software Engineering, and the Dean of the Institute of Intelligent Network and Edge Computing at Key Laboratory of Dependable Service Computing in Cyber Physical Society, Chongqing University. His current research interests are 5G/B5G mobile Internet, mobile edge computing and caching, big data analytics, and machine learning.|SUN Chuan is a Ph.D. student with the School of Big Data & Software Engineering, Chongqing University, China. He received his B.S. degree from Wuhan University of Science and Technology, China in 2017. His current research interests include multi-access edge computing, recommender systems, and machine learning.|WANG Xiaofei is currently a professor with the Tianjin Key Laboratory of Advanced Networking, School of Computer Science and Technology, Tianjin University, China. He got his master’s and doctoral degrees from Seoul National University, South Korea in 2006 and 2013, and was a postdoctoral fellow at The University of British Columbia, Canada from 2014 to 2016. Focusing on research of social-aware cloud computing, cooperative cell caching, and mobile traffic offloading, he has authored over 100 technical papers in IEEE JSAC, IEEE TWC, IEEE Wireless Communications, IEEE Communications Magazine, IEEE TMM, IEEE INFOCOM, and IEEE SECON. He was a recipient of the National Thousand Talents Plan (Youth) of China. He received the Scholarship for Excellent Foreign Students in the IT Field from NIPA of South Korea from 2008 to 2011, the Global Outstanding Chinese Ph.D. Student Award of the Ministry of Education of China in 2012, and the Peiyang Scholar of Tianjin University. In 2017, he received the Fred W. Ellersick Prize from the IEEE Communication Society.|Victor C. M. LEUNG is currently a Distinguished Professor of computer science and software engineering with Shenzhen University, China. He is also an Emeritus Professor of electrical and computer engineering and the Director of the Laboratory for Wireless Networks and Mobile Systems, The University of British Columbia, Canada. His research is in the broad areas of wireless networks and mobile systems. He has published widely in archival journals and refereed conference proceedings in these areas; several of his papers have won Best Paper Awards. He is a fellow of the Royal Society of Canada, Canadian Academy of Engineering, and Engineering Institute of Canada.
Supported by:
National Key R&D Program of China(2018YFB2100100);National NSFC(61902044);Chongqing Research Program of Basic Research and Frontier Technology(CSTC2019-jcyjmsxmX0589);Key Research Program of Chongqing Science and Technology Commission(CSTC2017jcyjBX0025);Fundamental Research Funds for the Central Universities(2020CDJQY-A022);Chinese National Engineering Laboratory for Big Data System Computing Technology, and Canadian NSERC

Abstract

Abstract:

Edge caching is an emerging technology for supporting massive content access in mobile edge networks to address rapidly growing Internet of Things (IoT) services and content applications. However, the edge server is limited with the computation/storage capacity, which causes a low cache hit. Cooperative edge caching jointing neighbor edge servers is regarded as a promising technique to improve cache hit and reduce congestion of the networks. Further, recommender systems can provide personalized content services to meet user’s requirements in the entertainment-oriented mobile networks. Therefore, we investigate the issue of joint cooperative edge caching and recommender systems to achieve additional cache gains by the soft caching framework. To measure the cache profits, the optimization problem is formulated as a 0–1 Integer Linear Programming (ILP), which is NP-hard. Specifically, the method of processing content requests is defined as server actions, we determine the server actions to maximize the quality of experience (QoE). We propose a cache-friendly heuristic algorithm to solve it. Simulation results demonstrate that the proposed framework has superior performance in improving the QoE.

Key words: IoT, recommender systems, cooperative edge caching, soft caching

HAN Suning, LI Xiuhua, SUN Chuan, WANG Xiaofei, LEUNG Victor C. M.. RecCac: Recommendation-Empowered Cooperative Edge Caching for Internet of Things[J]. ZTE Communications, 2021, 19(2): 2-10.

Figures/Tables 5

Table 1 Key Parameters

Notation	Definition
N	Number of BSs
M	Number of IDs
F	Total number of contents
C	Cache size of a BS
$D f$	Size of the content f
$p f$	The content f requested probability
$a m, n$	The association of the ID and the BS
$s n, f$	The cache state of the content f in the BS n
$w m, f$	Rating (i.e., preference) for the content f of the ID m
$v m, n$	The wireless transmission rate between the ID and the BS
$π m, n, f$	System action
$d m, n, f L$ , $d f E$ , $d f C$	Transmission delay of the content f between the BS n and the ID m, BS and BS, BS and CS, respectively
$r m, n, f L$ , $r m, n, f E$ , $r m, f C$	Content satisfaction in different server actions

Table 1 Key Parameters

Notation	Definition
N	Number of BSs
M	Number of IDs
F	Total number of contents
C	Cache size of a BS
$D f$	Size of the content f
$p f$	The content f requested probability
$a m, n$	The association of the ID and the BS
$s n, f$	The cache state of the content f in the BS n
$w m, f$	Rating (i.e., preference) for the content f of the ID m
$v m, n$	The wireless transmission rate between the ID and the BS
$π m, n, f$	System action
$d m, n, f L$ , $d f E$ , $d f C$	Transmission delay of the content f between the BS n and the ID m, BS and BS, BS and CS, respectively
$r m, n, f L$ , $r m, n, f E$ , $r m, f C$	Content satisfaction in different server actions

Figure 1 Cooperative edge caching supporting IoT architecture

Figure 2 QoE versus different numbers of contents

Figure 3 QoE versus different balance parameters

Figure 4 Hit Ratio versus different numbers of BSs

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RecCac: Recommendation-Empowered Cooperative Edge Caching for Internet of Things

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