Big data caching for networking: moving from cloud to edge

By Engin Zeydan, Ejder Bastug, Medhi Bennis, Manhal Abdel Kader, Ilyas Alper Karatepe, Ahmet Salih Er, Merouane Debbah. 2016

312 citations recorded by the source corpus.

In order to cope with the relentless data tsunami in 5G wireless networks, current approaches such as acquiring new spectrum, deploying more BSs, and increasing nodes in mobile packet core networks are becoming ineffective in terms of scalability, cost and flexibility. In this regard, context- aware 5G networks with edge/cloud computing and exploitation of big data analytics can yield significant gains for mobile operators. In this article, proactive content caching in 5G wireless networks is investigated in which a big-data-enabled architecture is proposed. In this practical architecture, a vast amount of data is harnessed for content popularity estimation, and strategic contents are cached at BSs to achieve higher user satisfaction and backhaul offloading. To validate the proposed solution, we consider a real-world case study where several hours worth of mobile data traffic is collected from a major telecom operator in Turkey, and big-data-enabled analysis is carried out, leveraging tools from machine learning. Based on the available information and storage capacity, numerical studies show that several gains are achieved in terms of both user satisfaction and backhaul offloading. For example, in the case of 16 BSs with 30 percent of content ratings and 13 GB storage size (78 percent of total library size), proactive caching yields 100 percent user satisfaction and offloads 98 percent of the backhaul.

Subfields: Computer Networks and Communications

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In Computer Networks and Communications

Citations in the corpus

Practical Distributed Learning: Secure Machine Learning with Communication-Efficient Local Updates (2019, background): “More and more big-data applications are moving from cloud to edge, including for machine-learning tasks (Cao et al., 2015; Mahdavinejad et al., 2018; Zeydan et al., 2016).”
Asynchronous Federated Optimization (2019, background): “At the same time, machine-learning applications are also moving from cloud to edge [3, 18, 21].”
A Survey on the Edge Computing for the Internet of Things (2018, background): “in [134] proposed a big-data-enabled architecture for proactive content caching in 5G wireless networks.”
SLSGD: Secure and Efficient Distributed On-device Machine Learning (2019, background): “e and fog computing [10,14] in various scenarios such as smart home or sensor networks. More and more big-data applications are moving from the cloud to the edge, including for machine-learning tasks [5,21,30]. Although computational power is growing, edge devices are still much weaker than the work”
A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, methodology): “Reference [164] proposes a big data-enable architecture, in which the estimation of content popularity can be obtained by the approach of big data analysis and is used to cache contents at the base stations.”
Edge computing meets millimeter-wave enabled VR: Paving the way to cutting the cord (2018, background): “Leveraging ubiquitous caching and computing at the wireless network edge will radically change the future mobile network architecture [7], and alleviate the current bottleneck for massive content delivery.”
On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration (2017, unknown): “aware network with edge cloud caching capabilities based-on big data analytics is considered in [49], while a content placement and delivery approach based on the prediction of content”
Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching (2020, unknown): “The studies in [15] and [18]–[20] investigated the architectures of collaborative edge caching in mobile networks.”
Resource Management in Fog/Edge Computing (2018, methodology): “ﬁc originating from all users at the Georgia Institute of Technology for a period of 3 months. Similarly, there is research aimed at caching data at base stations that will be employed in 5G networks [76]. To achieve this, trafﬁc is monitored to estimate content popularity using a Hadoop cluster. Ba”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, methodology): “In general, existing works such as in [98], [171], [192]–[196] have used ANNs to solve the caching problems such as cache replacement, content popularity prediction, and content request distribution prediction.”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “In general, existing works such as in [96], [107], [108], [113], and [109]–[112]”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, background): “pushing contents near to users, edge caching [229], is deemed Fig.”