By applying cloud-computing technology found in massive data centers to smaller-scale IoT deployed in the field, edge computing will revolutionize embedded systems. Mouse clicks will be all that is needed to deploy new software. The wealth of data generated by IoT endpoints will be speedily analyzed and acted upon. Systems will be deployed quickly and securely managed and operated. Edge computing ultimately will enable rapid development and deployment of novel services, raising the embedded world’s innovation rate.
To understand how, look at how the cloud has transformed information technology (IT). Companies can now provision servers with a click of the mouse instead of physically installing hardware. Software infrastructure such as databases can be provisioned as simply. Big data sets are now easy to handle. Applications can be scaled up and down depending on load. Cloud computing, for the most part, has been centralized and operated by a specialized cloud service provider, such as Amazon Web Services. Enterprises are starting to explore private clouds for IT workloads, and a hybrid approach joins private and public clouds.
In the networking sphere, the idea of placing compute nodes in the access network has been around for a while. At first, people didn’t know what to do with the idea. The usually cited use was for caching. If that’s the only use, deploying a dedicated caching appliance is much simpler. Subsequently, along came Network Function Virtualization, NFV, which applies cloud technology to implement network functions. These functions can be hosted in a data center meaning in the IT cloud, or in the edge network alongside routers, in the access network, or at customer premises.
The term edge computing refers to cloud-style computing at any of these locations outside a big data center, and it’s now apparent that it can be used for IoT generally and not only for networking. Cloud management and orchestration technology can be used to provision new applications within the IoT in the same way as IT managers spin up servers in the cloud. An IOT node can access basic services like storage and databases in the same standardized way they’re accessed in the cloud. Big data sets generated by IOT nodes can be processed close by, reducing the latency between analysis and action and the amount of data that otherwise would have to flow through fat pipes to a cloud data center. In summary, edge computing is a big deal.
Various companies are developing edge computing. Symbiotic with public networks, edge computing has an industry specification group within the influential European Telecommunications Standards Institute (ETSI). ETSI states that multi-access edge computing1 “is a natural development in the evolution of mobile base stations and the convergence of IT and telecommunications networking.” The organization has published various specifications and guidelines on the topic. Various network equipment vendors have offered up their visions as well.
Cloud companies—Amazon, Google, Microsoft, Alibaba, etc.—are also developing edge computing. Having built their core businesses on data and the software for processing it, their focus is on extending these technologies to the IOT. Coming from the industrial automation side, Siemens has developed MindSphere, a cloud-based platform for IoT applications.
The starting point for most of these initiatives is to provide SDKs and APIs for applications on IoT nodes to access services hosted in the cloud, that is, in a remote data center.
The next step is for the services to run outside the data center in edge computing nodes. Ideally these nodes’ resources and those in the data center are completely fungible. Applications do not know where they reside and their location can move as needed. The applications themselves can run on an IoT node, or alongside services functions on an edge-computing node such as an IoT gateway. The latter case especially makes sense if the IoT node is constrained (meaning it is incapable of hosting services).
Amazon’s AWS Greengrass is a leading software framework for edge computing. It works in conjunction with the AWS IoT Device SDK, which enables constrained devices to interact with their environment and invoke services in the cloud or at an unconstrained device (e.g., an edge-computing node like an IoT gateway). This unconstrained device has persistent storage and runs the AWS Greengrass core, which provides the services, hosts software functions, and manages communications with devices. Importantly, the device running Greengrass can sync and interact with the cloud, providing the fungibility of resources discussed above. Microsoft’s Azure IoT Edge is similar. I personally expect companies that only offer SDKs and APIs for accessing remote services (analogs to the AWS IoT Device SDK) to implement something like AWS Greengrass, enabling them to fully harness the power of edge computing.
Although edge computing provides a consistent software view between cloud services hosted on edge-computing nodes and in data centers, the underlying hardware is fundamentally different. Cloud data centers are renown for massive scale and homogeneity. Each discrete piece of the data center is beefy (compared to an embedded system) and replicated many times. Because of how server processors are priced, individual compute nodes have two (as opposed to more) processors. Hundreds of these processors may be in a rack, tens of racks in a row, and tens of rows under the same roof, with multiple sites networked together.
Edge nodes, on the other hand, will vary in scale and often are a single hardware instance. Customer-premises equipment may be optimized for cost and contain a single processor, such as NXP’s QorIQ® Layerscape LS1043 processor. Fast, but still low power, Layerscape processors such as the LS2088 processor could reside at an access node, such as a base station. Because of this variation in scale, the ideal processor supplier offers many compatible processors at different price/performance points, as NXP does with its 64-bit ARM-based Layerscape line.
What sets apart NXP’s Layerscape-based solutions is security. The cloud has proven secure. Although hackers have attacked many web-based companies, they’ve exploited flaws in the companies’ applications and business practices and not flaws in the underlying cloud services. The IoT, however, has been a nightmare, and devices have routinely been hacked. To be successful, the edge must be at least as secure as the cloud, and, ideally, the edge can secure IOT nodes.
NXP’s platform trust architecture implemented in Layerscape processors can help secure IoT devices throughout their lifecycle—manufacturing, commissioning, operation, update, and decommissioning. We’ve discussed applying this trust architecture in the context of conventional embedded systems that are programmed during manufacture.
Importantly, NXP’s platform trust architecture helps companies automate the deployment of IoT and edge nodes. As noted above, a benefit of cloud technology to IT managers is the speed of provisioning servers owing to automation. Given the almost uncountable number of IoT devices forecasted to be deployed, manually generating and installing certificates and software is an untenable approach to provisioning. NXP’s trust platform has each Layerscape processor generate unique key pairs based on its unique ID number and other parameters. These key pairs can then be used to set up a secure connection with a cloud-based provisioning service to exchange certificates and other information required by Greengrass or a similar framework to bootstrap that framework’s security regime.
NXP urges all suppliers of IoT and cloud technology, be they focused on consumer or industrial applications, to engage with us in developing secure edge computing solutions. We’ve seen how cloud technology has revolutionized IT. Let’s bring the revolution to the embedded world. For more information please visit NXP and Amazon Web Services Launch Cooperation in IoT