Cloud-to-Edge Computing: A Unified Computational Paradigm

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The Internet of Things (IoT) involves large amounts of interconnected devices that produce and consume data. Over the last few years, the IoT has gained tremendous popularity both in academia and many industry sectors. IoT devices can be found in many contexts, such as smart homes and smart buildings, intelligent vehicles, medical devices, the industry 4.0, etc. Gartner has estimated that there will be over 20 billion IoT devices by the year 2020. Historically, IoT devices consisted mostly of simple sensors and actuators that relayed data to/from the cloud with little to no local processing. Nowadays, IoT devices are getting more and more powerful and connected, and are quickly evolving beyond their traditional role of low-level embedded devices to become similar to embedded computers. For instance, the popular Raspberry Pi Zero W device features a 1 GHz CPU, 512 MB of RAM, WiFi and Bluetooth support, for only 5 USD. As these devices become more powerful, they can run full operating systems (e.g., Linux) and applications written in high-level, programmer-friendly languages (e.g., Java, JavaScript, Python), which opens the door to running more complex and higher-level applications and services directly on these devices (i.e., edge computing). My proposed research aims at providing a comprehensive model and a unified paradigm that will allow developers to write large-scale distributed high-level edge applications in a unified and interoperable manner, and to dynamically deploy these applications and the services that they consume (e.g., data storage, communications, file systems) close to where the data is produced, and directly onto the IoT devices themselves. I believe that this can yield several benefits, and can help alleviating the limitations of a purely centralized (i.e., cloud) model, such as reducing bandwidth and infrastructure costs; reducing latency, which can have an impact on real-time data processing and decision making; and alleviating the dependence on a central point of failure. The proposed research has the potential to explore and provide novel solutions to multi-dimensional challenges in a bleeding-edge field of research (cloud-edge computing). Research outcomes are expected through the form of publications to top-tier systems conferences (e.g., ICDCS, SEC, Middleware), in addition to contributing to the training of several (7) graduate students, which will learn important research and leadership skills that will make them valuable in thriving academic and industry markets. Benefits to the Canadian industry are envisioned, given that several key players are well established in the IoT landscape and in the telecom industry (e.g., Bell, Telus, Cisco, Intel), as well as benefits to Canada itself; e.g., a distributed deployment of applications and services could be beneficial in geographically dispersed communities and remote sites, and could ensure that sensitive data is processed locally, directly on trusted devices.