Cache in the air: exploiting content caching and delivery techniques for 5G systems

By Xiaofei Wang, Min Chen, Tarik Taleb, A. Ksentini, Victor C. M. Leung. 2014

1,045 citations recorded by the source corpus.

The demand for rich multimedia services over mobile networks has been soaring at a tremendous pace over recent years. However, due to the centralized architecture of current cellular networks, the wireless link capacity as well as the bandwidth of the radio access networks and the backhaul network cannot practically cope with the explosive growth in mobile traffic. Recently, we have observed the emergence of promising mobile content caching and delivery techniques, by which popular contents are cached in the intermediate servers (or middleboxes, gateways, or routers) so that demands from users for the same content can be accommodated easily without duplicate transmissions from remote servers; hence, redundant traffic can be significantly eliminated. In this article, we first study techniques related to caching in current mobile networks, and discuss potential techniques for caching in 5G mobile networks, including evolved packet core network caching and radio access network caching. A novel edge caching scheme based on the concept of content-centric networking or information-centric networking is proposed. Using trace-driven simulations, we evaluate the performance of the proposed scheme and validate the various advantages of the utilization of caching content in 5G mobile networks. Furthermore, we conclude the article by exploring new relevant opportunities and challenges.

Subfields: Computer Networks and Communications

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Citations in the corpus

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2018, background): “, base stations equipped with computation/storage capability, could deal with the computation and content requests of UEs, and consequently this scheme improves the Quality-of-Service (QoS) of Mobile Network Operators (MNOs) and the Quality-ofExperience (QoE) of UEs and relieves the load of backbone”
Cache-enabled small cell networks: modeling and tradeoffs (2014, background): “Combining these concepts in the context of cellular networks is recent [9][10].”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, background): “For example, computing and caching resources can be deployed at the network edge to fulfill the demands for low latency and reduce energy consumption [7], [8].”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “In what follows, we summarize the sate-of-the-art related to applications of ML approaches in these aspects. to cache, and how to cache [20], [290].”
A Survey of Machine Learning Techniques Applied to Self-Organizing Cellular Networks (2017, background): “[253] provide a good overview of why caching is necessary in future networks, what might be the gains of caching at different locations within the network and also presents some of the current challenges encountered.”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, background): “From Content Delivery Network (CDN) [227] to caching contents in cellular networks, caching in the network have been investigated over the years to deal with soaring demand for multimedia services [228].”
Optimal geographic caching in cellular networks (2014, neutral): “Further beneﬁts and challenges from the application of caching in 5G networks are presented in the work by Wang et al [8].”
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, methodology): “, video streams) during off-peak hours, storing the data in the SBs’ memory units, and reusing them during peak traffic periods [135], [136].”
Challenges in 5G: how to empower SON with big data for enabling 5G (2014, background): “…load behavior, discover users' travel patterns, anticipate the whereabouts of mobile users, and further integrate the knowledge with preemptive traffic steering, proactive load balancing , dynamic radio resource and energy efficiency algorithms, and intelligent caching [3], as envisioned for 5G.”
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions (2017, background): “Besides that, the protocol design for caching redundancy and intra cache communication can be investigated with latency constraint [250].”
Big data caching for networking: moving from cloud to edge (2016, background): “[7] X.”
A Survey of Fog Computing: Concepts, Applications and Issues (2015, background): “It is also very interesting to redesign cache on fog node to exploit temporal locality and broader coverage to save network bandwidth and reduce delay, while there is existing work of cache on end device [57] and cache on edge router [51].”
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications (2017, background): “Moreover, deploying caching at the EPC is technically easier than at the RAN.”
Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching (2020, neutral): “The studies in [15] and [18]–[20] investigated the architectures of collaborative edge caching in mobile networks.”
A survey on 5G: The next generation of mobile communication (2015, methodology): “In caching, three decisions are prominent as: what to cache, where to cache, and how to cache? The authors [112, 29] suggested that UEs will have enough memory in the future, and they can work as a cache for any other UE, since a small amount of popular data requires to be cached.”
Design of Contract-Based Trading Mechanism for a Small-Cell Caching System (2017, background): “, video clips, raised by mobile users (MU), leading to heavy traffic pressures on the backhaul channels [12].”
Contract-Based Small-Cell Caching for Data Disseminations in Ultra-Dense Cellular Networks (2019, background): “cloud to the memories of local network nodes, forming a distributed caching system [3], [4], [5], such that the MUs can obtain data from adjacent nodes.”
Caching at the wireless edge: design aspects, challenges, and future directions (2016, background): “While the potential of local caching has been revealed by several recent investigations [1]–[5], the current article provides an overview of the state-of-the-art challenges and possible solutions of caching at the wireless edge, which differs from [6] that focused on caching at mobile core networks.”
Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities (2017, methodology): “, reusable video streams, during off-peak periods, storing this data in SBs equipped with memory units, and reusing them during peak traffic hours [2]–[4].”
Hierarchical Edge Caching in Device-to-Device Aided Mobile Networks: Modeling, Optimization, and Design (2018, background): “Thus, different from the existing schemes of single-level edge caching in either BSs [1]–[6], [8]–[15] or mobile devices [16]–[19], [21], [22] and hierarchical caching schemes such as in [23]–[33], our problem considers hierarchical edge caching in both mobile devices and BSs, and effectively…”