Network complexity is increasing, making network control and orchestration a
challenging task. The proliferation of network information and tools for data
analytics can provide an important insight into resource provisioning and
optimisation. The network knowledge incorporated in software defined networking
can facilitate the knowledge driven control, leveraging the network
programmability. We present Seer: a flexible, highly configurable data
analytics platform for network intelligence based on software defined
networking and big data principles. Seer combines a computational engine with a
distributed messaging system to provide a scalable, fault tolerant and
real-time platform for knowledge extraction. Our first prototype uses Apache
Spark for streaming analytics and open network operating system (ONOS)
controller to program a network in real-time. The first application we
developed aims to predict the mobility pattern of mobile devices inside a smart
city environment.

Seer 是使用软件定义网络和大数据原理构建的灵活高可配置的网络智能数据平台。其可实现基于数据流量学习的预测，用于智能城市环境中移动设备的移动模式预测。

Seer：数据分析赋能软件定义网络

Seer: Empowering Software Defined Networking with Data Analytics

Massive Machine-Type Communications (MTC) over cellular networks is expected
to be an integral part of wireless "Smart City" applications. The Long Term
Evolution (LTE)/LTE-Advanced (LTE-A) technology is a major candidate for
provisioning of MTC applications. However, due to the diverse characteristics
of payload size, transmission periodicity, power efficiency, and quality of
service (QoS) requirement, MTC poses huge challenges to LTE/LTE-A technologies.
In particular, efficient management of massive random access is one of the most
critical challenges. In case of massive random access attempts, the probability
of preamble collision drastically increases, thus the performance of LTE/LTE-A
random access degrades sharply. In this context, this article reviews the
current state-of-the-art proposals to control massive random access of MTC
devices in LTE/LTE-A networks. The proposals are compared in terms of five
major metrics, namely, access delay, access success rate, power efficiency, QoS
guarantee, and the effect on Human-Type Communications (HTC). To this end, we
propose a novel collision resolution random access model for massive MTC over
LTE/LTE-A. Our proposed model basically resolves the preamble collisions
instead of avoidance, and targets to manage massive and bursty access attempts.
Simulations of our proposed model show huge improvements in random access
success rate compared to the standard slotted-Aloha-based models. The new model
can also coexist with existing LTE/LTE-A Medium Access Control (MAC) protocol,
and ensure high reliability and time-efficient network access.

本篇论文旨在回顾控制 LTE/LTE-A 网络中的大规模机器类型通信 (Massive Machine-Type Communications，MTC) 设备的随机接入的现状和最新提案，提出一种碰撞解决方案的随机接入模型来解决泊松分布访问竞争问题，并相比于标准的基于分时 Aloha 协议的模型，在整体访问成功率和功率效率上取得了显著改进。