Here’s how it works: Data needed locally can be processed locally, while unprocessed data can flow directly to wherever it is needed. True situational awareness is achieved by combining a dozen, 100 or even 1,000 data feeds into one cohesive whole.
“That’s the centralized view,” Orrin says, equating that part of the process to the cloud. “But in the cockpit of that airplane, you’re really operating a mobile edge computing platform, coordinating with back-end systems that provide the situational awareness you need to operate.”
Thanks to these technological advancements, an individual soldier can now carry a micro rack of servers in a pack or in the back of a vehicle that are able to do heavy processing, “like a micro cloud,” Orrin explains.
It’s not just the military that has leveraged these types of edge-to-cloud systems to empower heightened situational awareness and faster response times. In fact, the Federal Emergency Management Agency has similar needs.
“[First responders] get to a disaster zone and within a couple of minutes they’re operational – with all the capabilities they need for search and rescue, disaster relief, and other coordinated activities,” Orrin says. “That’s thanks to a micro cloud, with all of their key applications ready to go on a hyper-converged platform.”
This type of miniature cloud is a far cry from large commercial cloud offerings.
“On the edge, it’s not about infinite scale or elasticity, but rather the ability to dynamically spin up applications on demand, to build once, deploy everywhere, and to be able to use API-driven data models to deploy capabilities more quickly,” Orrin explains. “There’s also the ability to rapidly deploy new capabilities with existing data sets and access data more quickly.”
Inference and Innovation
Moreover, as this technology advances, new opportunities for innovation arise. Take a security application that records activity via a perpetual video feed. Now, when something changes it’s possible to capture and save only the data that the user needs, which, in turn, reduces the amount of data that has to be saved, processed and stored overall.“I need the inference, not the data,” Orrin explains. “I need to know that there was a tree there and now there isn’t or that there was a vehicle, but I don’t need to see 12 hours of raw data where nothing’s changed. If I want that sensor platform deployed for longer periods of time, I need to change the requirement for how much data I’m going to save. This is a mental shift to rely more on the intelligence in the platform to do the work rather than having to do it manually.”
By relying on technology to make real-time decisions rather than analysts’ hours, days, or weeks after the fact, agencies can accelerate the speed at which intelligence is recognized and analyzed. That’s where we are today, Orrin says. “It’s a hybrid cloud, an edge cloud, and it’s across multiple clouds,” he notes. “It’s not about having one architecture or the other. It’s about doing the right processing, at the right place, at the right time.”
Adopting a Data-Driven Architecture
To get this right, users need to think hard about the data and consider where the best place to compute, store and process it might be. “You want to be able to do the processing and intelligence gathering as close to the data acquisition as possible to reduce latency, to reduce the cost of transmission, and to reduce the cost of storage,” Orrin says.The right data — but not all the data — needs to return to the backend systems. There, staff can conduct Big Data analytics, leveraging commercial cloud scale, but then extract the intelligence from that data and deliver it to operators, who can then put that information to good use.
“You can call this ‘follow the bit,’ but it’s a data-driven architecture,” Orrin says. “It’s all about the data. This is what’s driving both the edge cloud and 5G mobile networks: the promise that I can unleash the data-centric applications and devices at the edge to gain a superior picture of the world we live and operate in.”
5G will provide increased bandwidth and speed, enabling more processing on the edge and driving the integration of processed insight on a grander scale.
“With 5G I can increase the number of sensors, because I have the bandwidth within my network, and then I can centralize different mobile edge computing platforms and have them do that edge processing and then deliver the results back to the cloud,” Orrin says. “It’s a more complex, but richer architecture.” That architecture is powered by Intel technologies. Understanding where Intel sees the future heading is one way federal chief information officers and information architects can ensure they are prepared to enter that future.
“We provide the building blocks for processing, computation, memory, networking and storage for all of these levels of systems from the edge to the cloud, so we bring together unique ecosystems to help drive solutions,” Orrin says. “We can drive integration and interoperability from the get-go. Intel is the common component for the telecom providers, the platform makers, the cloud providers: This is not just providing technology ingredients to each of those steps but helping to drive the ecosystem to collaborate and interoperate. That’s how you get solutions.”
Click here to read Government on the Edge: Part 1 – Developing an Edge-to-Cloud Model.
Mission Ready: Powering Innovation from Edge to Cloud
Intel’s new resource center provides resources to government decision-makers and helps the public sector make the most of modernization efforts. The resource center is your one-stop-shop for information on the remote workforce, securing the edge, data modernization and offering insights that help you achieve mission success.
