Mobile edge computing (MEC) within 5G networks brings the power of cloud computing, storage, and analysis closer to the end user. The increased speeds and reduced delay enable novel applications such as connected vehicles, large-scale IoT, video streaming, and industry robotics. Machine Learning (ML) is leveraged within mobile edge computing to predict changes in demand based on cultural events, natural disasters, or daily commute patterns, and it prepares the network by automatically scaling up network resources as needed. Together, mobile edge computing and ML enable seamless automation of network management to reduce operational costs and enhance user experience. In this paper, we discuss the state of the art for ML within mobile edge computing and the advances needed in automating adaptive resource allocation, mobility modeling, security, and energy efficiency for 5G networks.

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We present an algorithm, Shared-State Sampling (S 3 ), for the problem of detecting large flows in high-speed networks. While devised with different principles in mind, S 3 turns out to be a generalization of two existing algorithms tackling the same problem: Sample-and-Hold and Multistage Filters. S 3 is found to outperform its predecessors, with the advantage of smoothly adapting to the memory technology available, to the extent of allowing a partial implementation in DRAM. S 3 exhibits mild tradeoffs between the different metrics of interest, which greatly benefits the scalability of the approach. The problem of detecting frequent items in streams appears in other areas. We also compare our algorithm with proposals appearing in the context of databases and regarded superior to the aforementioned. Our analysis and experimental results show that, among those evaluated, S 3 is the most attractive and scalable solution to the problem in the context of high-speed network measurements.