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边缘支持的移动P2P社交网络中为提高流量卸载服务可靠性的锚点选择

  

  • 修回日期:2020-06-01 出版日期:2020-06-25 发布日期:2020-07-14

Anchor Vertex Selection for Enhanced Reliability of Traffic Offloading Service in Edge-Enabled Mobile P2P Social Networks

Hengda Zhang(),Xiaofei Wang(),Hao Fan(),Taotao Cai(),Jianxin Li(),Xiuhua Li(),VictorC.M. Leung()   

  1. College of Intelligence and Computing, Tianjin University,Tianjin 300350,China
    School of Information Technology, Deakin University, Melbourne 3217, Australia
    School of Big Data and Software Engineering,Chongqing University,Chongqing 401331,China
    Leung. Department of Electrical and Computer Engineering,the University of British Columbia, Vancouver, BC V6T 1Z4, Canada
  • Revised:2020-06-01 Online:2020-06-25 Published:2020-07-14
  • About author:Hengda Zhang received his B.S. degree in software engineering from China University of Petroleum, Qingdao,China,in 2018. He is currently working towards his M.S. degree in computer science with the College of Intelligence and Computing at Tianjin University.His research interests include network big data and social graph.|Xiaofei Wang[corresponding author]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 and doctor degrees in Seoul National University from 2006 to 2013,and was a post-doctoral fellow with the University of British Columbia from 2014 to 2016. Focusing on the 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,IEEE TMM,IEEE INFOCOM,and IEEE SECON.He was a recipient of National Thousand Talents Plan(Youth)of China.He received the“Scholarship for Excellent Foreign Students in IT Field”by NIPA of South Korea from 2008 to 2011, the“Global Outstanding Chinese Ph.D. Student Award”by the Ministry of Education of China in 2012, and the Peiyang Scholar from Tianjin University. In 2017,he received the“Fred W.Ellersick Prize”from IEEE Communications Society.|Hao Fan received his B.S.degree in information and computing science from Hebei University of Technology,Tianjin,China,in 2018. He is currently pursuing his graduate degree at the College of Intelligence and Computing, Tianjin University, Tianjin, China. His current research interests include network big data and edge artificial intelligence.|Taotao Cai received his M.E degree in computer science from Shenzhen University, China, in 2016. He is currently a Ph.D. student at Deakin University, Australia. His research interests include algorithmic aspects of geo-social network analysis and database query processing＆optimization.|Jianxin Li received his Ph.D. degree in computer science from Swinburne University of Technology, Australia, in 2009. He is an associate professor in data science at the School of Information Technology,Deakin University. His research interests include graph database query processing and optimization,social network computing, complex network representation learning,and personalized online learning analytics. Jianxin is also a granted assessor in Australia Research Council Discovery Programs and Linkage Programs,and serves as invited reviewers for many top journals and program committee members in many top conferences.|Xiuhua Li (S’12-M’19) received his B.S. degree from the Honors School,Harbin Institute of Technology, Harbin, China, in 2011, his M.S. degree from the School of Electronics and Information Engineering, Harbin Institute of Technology, in 2013, and his Ph.D. degree from the Department of Electrical and Computer Engineering, the University of British Columbia,Vancouver,BC,Canada,in 2018.He joined Chongqing University through One-Hundred Talents Plan of Chongqing University 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 associated with Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University),Education Ministry,China. His current research interests are 5G/B5G mobile Internet,mobile edge computing and caching,big data analytics and machine learning.|VICTOR C.M.LEUNG is a Distinguished Professor in computer science and software engineering at Shenzhen University, China. He is also an Emeritus Professor of Electrical and Computer Engineering and director of the Laboratory for Wireless Networks and Mobile Systems at the University of British Columbia (UBC),Canada. His research is in the broad areas of wireless networks and mobile systems,and he has published widely in these areas. Dr. Leung is serving on the editorial boards of IEEE Transactions on Green Communications and Networking, IEEE Transactions on Cloud Computing, IEEE Access, IEEE Network, and several other journals. He received the 1977 APEBC Gold Medal,1977-1981 NSERC Postgraduate Scholarships,IEEE Vancouver Section Centennial Award, 2011 UBC Killam Research Prize, 2017 Canadian Award for Telecommunications Research,2018 IEEE TCGCC Distinguished Technical Achievement Recognition Award,and 2018 ACM MSWiM Reginald Fessenden Award.He co-authored papers that won the 2017 IEEE ComSoc Fred W.Ellersick Prize,2017 IEEE Systems Journal Best Paper Award, 2018 IEEE CSIM Best Journal Paper Award,and 2019 IEEE TCGCC Best Journal Paper Award. He is a fellow of IEEE,the Royal Society of Canada, Canadian Academy of Engineering,and Engineering Institute of Canada.He is named in the current Clarivate Analytics list of“Highly Cited Researchers”.
  • Supported by:
    National Key Research and Development Program of China under(2019YFB2101901);National Key Research and Development Program of China under(2018YFC0809803);National Natural Science Foundation of China under(61702364)

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摘要：

随着移动社交网络的快速发展，移动点对点通信（Peer-to-Peer，P2P）和移动边缘计算（Mobile Edge Computing，MEC）在减少流量负载、提高蜂窝网络的计算能力方面做出了相应贡献。然而，P2P和MEC的发展很大程度上忽略了社交网络的稳定性，这也与用户之间的社交关联有关。网络稳定性在提高流量卸载服务的效率和可靠性方面起着至关重要的作用。在本文中，我们将网络边缘节点和其覆盖下的P2P用户称为一个移动P2P社交网络，并引入了自适应的锚定(k,r)-core问题，以维护移动P2P网络群组的稳定性。它旨在自适应地为每个移动P2P网络选中并留存住一组对于网络的整体稳定起至关重要作用的关键用户，并为其分配一定的资源，从而使网络群组能够有最大数量的用户留存下来，并最小化流量负载。我们将这些被选中的关键用户称为锚点。为了解决这个问题，我们设计了一个“端–边–云”协同架构来实现资源的自适应分配。此外，我们还提出了一个基于相似度的锚点选取算法。我们基于一个真实的大规模移动P2P数据集进行了多重实验，其结果证明了我们提出的方法的有效性和可行性。

Abstract:

Abstract—As the rapid growth of mobile social networks, mobile peer-to-peer (P2P) communications and mobile edge computing (MEC) have been developed to reduce the traffic load and improve the computation capacity of cellular networks.However, the stability of social network is largely ignored in the advances of P2P and MEC, which is related to the social relations between users.It plays a vital role in improving the efficiency and reliability of traffic offloading service.In this paper, we integrate an edge node and the nearby P2P users as a mobile P2P social network and introduce the problem of adaptive anchored(k,r)-core to maintain the stability of multiple mobile P2P networks.It aims to adaptively select and retain a set of critical users for each network,whose participation is critical to overall stability of the network,and allocate certain resource for them so that the maximum number of users of all networks will remain engaged and the traffic of cellular network can be minimized.We called the retained users as anchor vertices.To address it, we devise a peer-edge-cloud framework to achieve the adaptive allocation of resources.We also develop a similarity based onion layers anchored (k,r)-core (S-OLAK) algorithm to explore the anchor vertices.Experimental results based on a real large-scale mobile P2P data set demonstrate the effectiveness of our method.

边缘支持的移动P2P社交网络中为提高流量卸载服务可靠性的锚点选择

随着移动社交网络的快速发展，移动点对点通信（Peer-to-Peer，P2P）和移动边缘计算（Mobile Edge Computing，MEC）在减少流量负载、提高蜂窝网络的计算能力方面做出了相应贡献。然而，P2P和MEC的发展很大程度上忽略了社交网络的稳定性，这也与用户之间的社交关联有关。网络稳定性在提高流量卸载服务的效率和可靠性方面起着至关重要的作用。在本文中，我们将网络边缘节点和其覆盖下的P2P用户称为一个移动P2P社交网络，并引入了自适应的锚定(k,r)-core问题，以维护移动P2P网络群组的稳定性。它旨在自适应地为每个移动P2P网络选中并留存住一组对于网络的整体稳定起至关重要作用的关键用户，并为其分配一定的资源，从而使网络群组能够有最大数量的用户留存下来，并最小化流量负载。我们将这些被选中的关键用户称为锚点。为了解决这个问题，我们设计了一个“端–边–云”协同架构来实现资源的自适应分配。此外，我们还提出了一个基于相似度的锚点选取算法。我们基于一个真实的大规模移动P2P数据集进行了多重实验，其结果证明了我们提出的方法的有效性和可行性。

Key words: traffic offloading service, mobile edge computing, anchored k-core problem, adaptive allocation

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