MACACO: Mobile context-Adaptive CAching for COntent-centric networking

寻找新方法来管理不断增加的数据使用量并提高新一波智能手机应用程序所需的服务水平是一个重要问题。 MACACO 项目针对这一问题提出了一种创新的解决方案，重点关注利用上下文和内容信息的数据卸载机制。我们的直觉是，如果能够提取和预测移动网络用户在时间、位置和兴趣三个维度空间中的行为（即“什么”、“何时”和“何处”用户从网络中提取数据），就有可能导出有效的数据卸载协议。此类协议将预取已识别的数据，并在较早的时间将它们缓存在网络边缘，最好是在移动网络不太拥塞或提供更好的服务质量时。缓存可以直接在移动终端以及网络的边缘节点（例如毫微微蜂窝基站或无线接入点）处完成。基于之前在社交无线网络、机会通信和内容网络领域的研究工作，MACACO 将解决该领域的几个问题。第一个是为用户兴趣与其移动性之间的相关性导出适当的模型。许多研究已经根据现实世界的数据轨迹描述了移动节点的移动性，但仍然缺乏关于在这种情况下与用户兴趣交互的知识。为了填补这一空白，MACACO 提出获取现实世界数据集来对上述三维空间中的移动节点行为进行建模。解决的第二个问题是利用大规模数据轨迹和相应模型来推导有效的数据卸载算法。首先，将推导出简单有效的预测算法来预测节点的移动性和兴趣。然后，MACACO 将提供数据预取机制，既提高移动用户感知的服务质量，又显着减轻蜂窝网络的峰值带宽需求。概念验证将通过位于法国、瑞士和英国的联合测试平台进行展示。

Finding new ways to manage the increased data usage and to improve the level of service required by the new wave of smartphones applications is an essential issue. The MACACO project proposes an innovative solution to this problem by focusing on data offloading mechanisms that take advantage of context and content information. Our intuition is that if it is possible to extract and forecast the behaviour of mobile network users in the three dimensional space of time, location and interest (i.e. 'what', 'when' and 'where' users are pulling data from the network), it is possible to derive efficient data offloading protocols. Such protocols would pre-fetch the identified data and cache them at the network edge at an earlier time, preferably when the mobile network is less congested, or offers better quality of service. Caching can be done directly at the mobile terminals, as well as at the edge nodes of the network (e.g., femtocells or wireless access points). Building on previous research efforts in the fields of social wireless networking, opportunistic communications and content networking, MACACO will address several issues in this space. The first one is to derive appropriate models for the correlation between user interests and their mobility. Lots of studies have characterised mobile nodes mobility based on real world data traces, but knowledge about the interactions with user interests in this context is still missing. To fill this gap, MACACO proposes to acquire real world data sets to model mobile node behaviour in the aforementioned three-dimensional space. The second issue addressed is the derivation of efficient data-offloading algorithms leveraging the large-scale data traces and corresponding models. Firstly, simple and efficient prediction algorithms will be derived to forecast the node's mobility and interests. Then, MACACO will provide data pre-fetching mechanisms that both improves the perceived quality of service of the mobile user andnoticeably offloads peak bandwidth demands at the cellular network. A proof of concept will be exhibited though a federated testbed located in France, Switzerland and in the UK.