Next Generation of IoT Backend Middleware for Edge and Cloud

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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.