University of Melbourne researchers channel IoT data flood with Oracle Cloud

The world’s compute and networking infrastructures will face a growing challenge: floods of data originating from billions of Internet of Things (IoT) devices coming online. The sources of that data include medical equipment; video cameras requiring image recognition, sensors throughout factories, airplanes, smart vehicles, and tractors; and countless consumer devices from refrigerators to light bulbs.

Computer science researchers at the University of Melbourne in Australia are tackling the problem by building FogBus2, an open source design and working implementation for a hardware and software platform to channel all that data in useful form. The goal is to manage traffic and provide compute infrastructure that can help make sense of such data.

The researchers needed to build FogBus2 on a platform that’s multicloud, widely available, and secure, and that would work in real-world implementations, not just a controlled lab environment.

Researchers chose to run FoguBus2 on Oracle Cloud Infrastructure (OCI), including Oracle Autonomous Data Warehouse and Oracle Machine Learning.

Additionally, the university is part of the Oracle Arm Accelerator program, which provides a one-year free trial to select university researchers, open source developers, industry partners, and private companies to run workloads on OCI Ampere A1 Compute and similar services.

The Arm processor offers support for virtual machines’ hundreds of cores, providing opportunity to massively scale operations within each virtual machine. The Oracle Autonomous Data Warehouse supports built-in artificial intelligence, containerized software, microservices, and edge computing.

The researchers also tapped Oracle Cloud Free Tier, which provides free services that any developer can use to experiment and find the best architecture to support applications, before deploying at a large scale.

Oracle Cloud Infrastructure helps FogBus2 deliver fast results. The Arm-based OCI services provide FogBus2 with rapid services startup time, 15% faster than services running on other architectures developers tried. Arm also delivers 15% to 20% performance increases and response times in applications including video recognition and other latency-sensitive applications that require high parallelization.

Oracle Autonomous Data Warehouse delivers scalable CPU and storage needed for IoT workloads, which generate sudden bursts of traffic. The data warehouse’s automation increased developer productivity, allowing the university to develop FogBus2 in one year.

Autonomous Data Warehouse’s native support for machine learning reduces the need for developers to build and manage ML capabilities in applications. Oracle Machine Learning allows researchers to manage data visualization within a database very quickly, with only a few clicks and lines of code and without the need to build separate back-end services.

Oracle Machine Learning allocates networking and compute resources for data analytics within the FogBus2 framework. Oracle Autonomous Database compiles network information, such as latency, data rate between devices, availability of hardware resources such as CPU and RAM utilization, and software information such as response time and which containers are running on which devices. Data is stored in flat tables, with each record just a simple row of data, to allow FogBus2’s remote logger container to append and read data quickly.

FogBus2 implements a three-tier framework: The IoT tier at the edge comprises the actual IoT devices gathering data, such as cameras and medical equipment. The second tier is edge clusters, running Raspberry Pi and NVIDIA Jetson Nano processors, with a MariaDB database. The edge clusters manage local analytics for applications requiring low latencies. And lastly, data from the edge clusters flow over a Virtual Private Network to a third tier, running in the OCI region in Melbourne, with Arm and x86 processors running Autonomous Data Warehouse and Oracle Machine Learning.