On the Economic Value of Mobile Caching

Recent growth in user demand for mobile data has strained mobile network infrastructure. One possible solution is to use mobile (i.e., moving) devices to supplement existing infrastructure according to users’ needs at different times and locations. For instance, vehicles can be used as communication relays or computation points. However, it is unclear how much value these devices add relative to their deployment costs: they may, for instance, interfere with existing network infrastructure, limiting the potential benefits. We take the first step towards quantifying the value of this supplemental infrastructure by examining the use case of mobile caches. We consider a network operator using both mobile (e.g., vehicular) and stationary (small cell) caches, and find the optimal amount of both types of caches under time- and location-varying user demands, as a function of the cache prices. In doing so, we account for interference between users’ connections to the different caches, which requires solving a non-convex optimization problem. We show that there exists a threshold price above which no vehicular caches are purchased. Moreover, as the network operator’s budget increases, vehicular caching yields little additional value beyond that provided by small cell caches. These results may help network operators and cache providers find conditions under which vehicles add value to existing networks.

[1]  Konstantinos Poularakis,et al.  Approximation Algorithms for Mobile Data Caching in Small Cell Networks , 2014, IEEE Transactions on Communications.

[2]  Lada A. Adamic,et al.  Zipf's law and the Internet , 2002, Glottometrics.

[3]  Ying Cui,et al.  Analysis and Optimization of Caching and Multicasting in Large-Scale Cache-Enabled Heterogeneous Wireless Networks , 2017, IEEE Transactions on Wireless Communications.

[4]  Dong Ku Kim,et al.  VehiCaching: Embracing User Request on Vehicle Route with Proactive Data Transportation , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[5]  P. Tseng Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization , 2001 .

[6]  Shunliang Zhang,et al.  Economic analysis of cache location in mobile network , 2013, 2013 IEEE Wireless Communications and Networking Conference (WCNC).

[7]  Srihari Nelakuditi,et al.  If WiFi APs Could Move: A Measurement Study , 2018, IEEE Transactions on Mobile Computing.

[8]  Giuseppe Caire,et al.  Wireless Device-to-Device Caching Networks: Basic Principles and System Performance , 2013, IEEE Journal on Selected Areas in Communications.

[9]  Pierre Coucheney,et al.  Impact of Competition Between ISPs on the Net Neutrality Debate , 2013, IEEE Transactions on Network and Service Management.

[10]  Zheng Chen,et al.  D2D caching vs. small cell caching: Where to cache content in a wireless network? , 2016, 2016 IEEE 17th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[11]  Walid Saad,et al.  Breaking the Economic Barrier of Caching in Cellular Networks: Incentives and Contracts , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[12]  Sajal K. Das,et al.  Incentive Mechanisms for Participatory Sensing , 2015, ACM Trans. Sens. Networks.

[13]  Jing Zhao,et al.  Roadcast: A Popularity Aware Content Sharing Scheme in VANETs , 2009, 2009 29th IEEE International Conference on Distributed Computing Systems.

[14]  Qiang Ni,et al.  Drone-Aided Communication as a Key Enabler for 5G and Resilient Public Safety Networks , 2018, IEEE Communications Magazine.

[15]  Jeffrey G. Andrews,et al.  A Primer on Cellular Network Analysis Using Stochastic Geometry , 2016, ArXiv.

[16]  Wei Yu,et al.  Optimizing User Association and Spectrum Allocation in HetNets: A Utility Perspective , 2014, IEEE Journal on Selected Areas in Communications.

[17]  Lingyang Song,et al.  Roadside Unit Caching: Auction-Based Storage Allocation for Multiple Content Providers , 2017, IEEE Transactions on Wireless Communications.

[18]  Luigi Vigneri Vehicles as a mobile Cloud: Modelling, optimization and performance analysis , 2017 .

[19]  Xuemin Shen,et al.  Incentive Mechanism for Cached-Enabled Small Cell Sharing: A Stackelberg Game Approach , 2017, GLOBECOM 2017 - 2017 IEEE Global Communications Conference.

[20]  Khaled Ben Letaief,et al.  Mobility-aware caching for content-centric wireless networks: modeling and methodology , 2016, IEEE Communications Magazine.

[21]  Marco Di Renzo,et al.  Average Rate of Downlink Heterogeneous Cellular Networks over Generalized Fading Channels: A Stochastic Geometry Approach , 2013, IEEE Transactions on Communications.

[22]  Hervé Rivano,et al.  Centrally Controlled Mass Data Offloading Using Vehicular Traffic , 2017, IEEE Transactions on Network and Service Management.

[23]  Weisong Shi,et al.  A Mobility-Aware Vehicular Caching Scheme in Content Centric Networks: Model and Optimization , 2019, IEEE Transactions on Vehicular Technology.

[24]  Tommy Svensson,et al.  Moving cells: a promising solution to boost performance for vehicular users , 2013, IEEE Communications Magazine.

[25]  Lijun Chen,et al.  Demand shaping in cellular networks , 2017, 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[26]  Dong Ku Kim,et al.  Delay-Tolerable Contents Offloading via Vehicular Caching Overlaid with Cellular Networks , 2017, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..

[27]  Xiang Cheng,et al.  In-Vehicle Caching (IV-Cache) Via Dynamic Distributed Storage Relay (D$^2$SR) in Vehicular Networks , 2019, IEEE Transactions on Vehicular Technology.

[28]  Mehdi Bennis,et al.  Cache-enabled small cell networks: modeling and tradeoffs , 2014, EURASIP Journal on Wireless Communications and Networking.

[29]  Yan Zhang,et al.  Cooperative Content Caching in 5G Networks with Mobile Edge Computing , 2018, IEEE Wireless Communications.

[30]  Mehdi Bennis,et al.  Living on the edge: The role of proactive caching in 5G wireless networks , 2014, IEEE Communications Magazine.