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

寻找新的方法来管理增加的数据使用量，并提高新一波智能手机应用程序所需的服务水平是一个至关重要的问题。MACACO项目提出了一个创新的解决方案，通过专注于利用上下文和内容信息的数据卸载机制来解决这个问题。我们的直觉是，如果有可能提取和预测移动的网络用户在时间、位置和兴趣的三维空间中的行为（即，“什么”、“何时”和“何处”用户正在从网络提取数据），则有可能导出有效的数据卸载协议。这样的协议将预取所识别的数据并在较早的时间将它们缓存在网络边缘，优选地当移动的网络不太拥塞或提供更好的服务质量时。高速缓存可以直接在移动的终端以及网络的边缘节点（例如，毫微微小区或无线接入点）。基于以前在社交无线网络，机会主义通信和内容网络领域的研究工作，MACACO将解决这一领域的几个问题。第一个是获得适当的模型，用户的兴趣和他们的移动性之间的相关性。很多研究已经基于真实的世界数据轨迹表征了移动的节点的移动性，但是在这种背景下关于与用户兴趣的交互的知识仍然缺失。为了填补这一空白，MACACO提出了获取真实的世界数据集，以在上述三维空间中对移动的节点行为进行建模。解决的第二个问题是利用大规模数据跟踪和相应的模型推导出有效的数据卸载算法。首先，简单而有效的预测算法将推导出预测节点的移动性和兴趣。然后，MACACO将提供数据预取机制，既提高了移动的用户的感知服务质量，又显著地减轻了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.