Big data caching for networking: moving from cloud to edge
Big data caching for networking: moving from cloud to edge
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DOI：
10.1109/mcom.2016.7565185
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发表时间：
2016-06
影响因子：
11.2
通讯作者：
E. Zeydan;Ejder Bastug;M. Bennis;Manhal Abdel Kader;Ilyas Alper Karatepe;Ahmet Salih Er;M. Debbah
中科院分区：
文献类型：
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作者：
E. Zeydan;Ejder Bastug;M. Bennis;Manhal Abdel Kader;Ilyas Alper Karatepe;Ahmet Salih Er;M. Debbah
为了科普5G无线网络中无情的数据海啸，当前的方法（诸如获取新频谱、部署更多BS以及增加移动的分组核心网络中的节点）在可扩展性、成本和灵活性方面变得无效。在这方面，具有边缘/云计算和利用大数据分析的上下文感知5G网络可以为移动的运营商带来显著收益。本文研究了5G无线网络中的主动内容缓存，提出了一种支持大数据的架构。在这个实际的架构中，大量的数据被用于内容流行度估计，并且战略内容被缓存在BS处以实现更高的用户满意度和回程卸载。为了验证所提出的解决方案，我们考虑了一个真实的案例研究，其中从土耳其的一家主要电信运营商收集了几个小时的移动的数据流量，并利用机器学习工具进行了大数据分析。基于可用的信息和存储容量，数值研究表明，在用户满意度和回程卸载方面实现了几个增益。例如，在16个BS的情况下，30%的内容评级和13 GB的存储大小（总库大小的78%），主动缓存产生100%的用户满意度和卸载98%的回程。
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.