When latency, network bandwidth and security/privacy challenges are critical, fog computing bridges the continuum between cloud and the operational edge to move compute-network-storage and decision making closer to where the data is produced.
Cloud, Fog and Edge
Fog computing is a system-level architecture that leverages and extends edge capabilities to enable Industrial IoT, AI and autonomous applications to work as intended. It brings compute, control, communications and storage capabilities closer to where data is generated, working through tiered fog nodes to allow computation to seamlessly and dynamically flow through the continuum of end point devices to the cloud. In general, fog architectures are used for complex environments that need fog capabilities to distribute, orchestrate, manage and secure resources and services across networks in real-time.
Connext DDS offers a data-centric approach that supports data communications in fog environments. It secures end-to-end communications, delivers high performance connectivity with peer-to-peer data sharing, provides fault-tolerance with backup publishers and no single point of failure, and scales across the edge to cloud continuum with highly tunable quality of service settings.
Connext DDS provides:
- Resilience: Fault tolerance protecting against downtime
- Security: Data secured end-to-end and access managed per user and application
- Performance: Low-latency and high throughput data communications
- Scale: Edge to data/control center, widely shared data and applications integration
- Interoperability: Open standards providing software modularity, supporting interchange and portability
Connext DDS and Fog Computing to Optimize Wind Energy Production
Consider an offshore wind farm with 100 very large wind turbines, each with a nacelle the size of a city bus and blades nearing 100 meters long. Layers of compute and pervasive data communications are needed to implement this system. Inside each turbine, Connext DDS shares data between analytics and control applications running across multiple compute nodes. Numerous sensors gather data on the state of the machine and its subsystems, and control signals activate actuators to manage the turbine operation.
Architecturally above the turbines sits a databus and various applications that manage maintenance and operations of the entire turbine farm. The fog infrastructure processes data and handles most decisions locally. For example, Turbine #4 on the northern end of the farm may detect a sudden change in wind direction or speed and quickly alert turbines downwind to change direction or blade pitch to compensate. In doing so, the power output of the entire farm is optimized for local conditions and harm to the turbines is prevented.
The fog layer works with the cloud for maximum efficiency, optimizing communications latency and bandwidth costs. Selected data from the farm is passed back and forth with a cloud-based control center that manages multiple wind farms. Applications in the control center provide dashboard level information for human supervisors, connect with outside services like weather, integrate business systems, and provide long-term prognostics and analytics.