Data-Driven Computing and Caching in 5G Networks: Architecture and Delay Analysis
Abstract
Recently, there has been increasing interest of deploying computation-intensive and rich-media applications on mobile devices, and ultra-low latency has become an important requirement to achieve high user QoE. However, conventional mobile communication systems are incapable of providing considerable communication and computation resources to support low latency. Although 5G is expected to effectively increase communication capacity, it is difficult to achieve ultra-low end-to-end delay for the ever growing number of cognitive applications. To address this issue, this article first proposes a novel network architecture using a resource cognitive engine and data engine. The resource cognitive intelligence, based on the learning of network contexts, is aimed at a global view of computing, caching, and communication resources in the network. The data cognitive intelligence, based on data analytics, is critical for the provisioning of personalized and smart services toward specific domains. Then we introduce an optimal caching strategy for the small-cell cloud and the macro-cell cloud. Experimental results demonstrate the effectiveness of the proposed caching strategy, and its latency is lower than that of the two conventional approaches, that is, the popular caching strategy and the greedy caching strategy.
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In literature, a variety of approaches which are candidates for Delay Tolerant KC Part I-2 have been proposed.
“In the literature, a variety of approaches which are candidates for this part have been proposed (see [63] of this special issue for an extensive review).”
From The IEEE 1918.1 “Tactile Internet” Standards Working Group and its Standards (2019) · cites this paper as [63]
Cites
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The IEEE 1918.1 “Tactile Internet” Standards Working Group and its Standards
Holland, O., Steinbach, E., Prasad, R. V., Liu, Q., Dawy, Z., Aijaz, A., Pappas, N., Chandra, K., Rao, V. S., Oteafy, S., et al. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2885541 · Cited by 200 · see citing works
“On the other hand, the SE will also provide intelligent caching capabilities which can be very impactful in reducing the E2E traffic load and thus reducing the data transmission delays [48].”
BackgroundBlockchain leveraged decentralized IoT ehealth framework
Uddin, M. A., Stranieri, A., Gondal, I., & Balasubramanian, V. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100159 · Cited by 116 · see citing works
- Authors
- Chen, M., Qian, Y., Hao, Y., Li, Y., & Song, J.
- Affiliations
- Huazhong University of Science and Technology (CN)
- Tsinghua University (CN)
- Venue
- IEEE Wireless Communications
- Published
- 2018-02
- Volume
- 25
- Issue
- 1
- Pages
- 70-75
- ISSN
- 1536-1284 (ISSN-L)
- OpenAlex
- W2791056945
- Cited by
- 195
- References
- 15
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 10 · 2023: 14 · 2022: 14 · 2021: 19 · 2020: 34 · 2019: 45 · 2018: 49 · 2017: 4
- FWCI
- 22.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Wireless Communications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Caching and Content Delivery
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- 5G, edge computing, caching strategies, end-to-end delay, personalized services, ultra-low latency
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