Next Generation of IoT Backend Middleware for Edge and Cloud

物联网（IoT）、移动的设备和传感器网络的出现预计将导致分布式数据源数量的指数级增长，预计到2021年将有270亿台连接设备。然而，当前的移动的网络不能够支持要求低延迟或者产生可能使网络基础设施不堪重负的大量数据的下一代应用。 示例包括智能个人助理、医疗患者监测和智能交通系统。由于同样的低延迟要求，在广域云上使用服务器也不是一种选择。边缘计算是一种新兴的替代方案，它使用附近的资源以及通往云的路径上的节点，在大量地理分布的站点上提供计算和存储。在创建点附近处理数据有几个好处：它减少了广域网带宽需求，降低了依赖本地数据的计算结果的延迟，并有助于限制敏感数据传播的地理区域。尽管边缘计算应用前景广阔，但其开发仍面临诸多挑战：通信链路速度缓慢或不可靠，工作负载和网络不断变化，边缘设备往往动力不足，应用部署和共享数据存储也很困难。本项目旨在通过构建一个统一的模型，用于开发和部署在边缘和云端同时运行的应用，从而弥合这一差距。它探索了分层边缘计算设置上数据处理的新愿景，该设置由具有不同功能的数据中心组成，这些数据中心介于网络边缘和传统广域云之间。 该项目有三个主要研究方向：（1）通用的功能即服务运行时和部署环境;（2）共享分布式存储系统;（3）大数据处理和分析框架。

The advent of the Internet-of-Things (IoT), mobile devices, and sensor networks is expected to lead to an exponential increase in the number of distributed data sources, with a predicted 27 billion connected devices by 2021. Current mobile networks, however, are not able to support next generation applications that require low latency, or that produce large volumes of data that can overwhelm the network infrastructure. Examples include intelligent personal assistants, medical patient monitoring, and intelligent transportation systems. The use of servers on the wide-area cloud is also not an option due to the same low-latency requirements. Edge computing is an emerging alternative that uses nearby resources, as well as nodes on the path to the cloud, to provide computation and storage over very large number of geographically distributed sites. Processing data close to the point of creation has several benefits: it reduces wide-area network bandwidth demands, it lowers the latency to compute results that depend on local data, and it helps limit the geographic area where sensitive data propagates. Despite their promise, developing edge computing applications is challenging: communication links are slow or unreliable, workload and network are ever-changing, and edge devices are often under-powered; application deployment and shared data storage are also difficult.This project aims to bridge this gap by building a unified model for developing and deploying applications that run simultaneously on the edge and the cloud. It explores a new vision for data processing on a hierarchical edge computing setup consisting on data centers with varying capabilities that are interposed between the edge of the network and the traditional wide area cloud. The project has three main research thrusts: (1) a common Function-as-a-Service runtime and deployment environment; (2) a shared distributed storage system; and (3) a framework for big data processing and analytics.