Sophistication at the Edge Part 2

Part 2 of how edge computing will have an increasingly important role in an enterprise’s cloud & digital transformation strategy along with the criteria that drives businesses to adopt an edge strategy and common use cases across industries.


I n part 1 we discussed how data generation rates are moving at an increasingly exponential rate which is ushering in this new phase of edge computing. We defined edge computing as,

“a method of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of data generation”

and discussed common edge technologies and architectures. Some, like Andreessen Horowitz’s Peter Levine’s think it is the “End of Cloud Computing” where others like Gartner Research’s Vice President and Distinguished Analyst Tom Bittman state

“The edge will eat the cloud. And this is perhaps as important as the cloud computing trend ever was.”

in his article aptly titled, The Edge Will Eat the Cloud. I’m not taking as drastic of a view but do want to help you gain a better understanding of the business drivers that are accelerating adoption at the edge and some challenges that may slow edge computing growth.

Pushed to the Edge

Figure 1: Don’t Jump! Developing an edge strategy isn’t as difficult as it seems.

So what drives enterprises and CxOs to the edge? Well a lot of things I’m sure, like regulation, operating in an increasingly global marketplace, talent management, growing revenue and much more but that is what drives them to the edge of crazy. We’ll leave that conversation to trained mental health professionals and focus on the top considerations that drive them to consider viable edge strategies.

Key Factors Driving Customers to the Edge

  • Performance. The need for speed at the edge is the primary driving factor for edge computing. Applications like autonomous driving that require sub 10-milisecond latency don’t allow for round trips to the data center to conduct analysis when speeding 80mph down a highway and trying to determine whether it should stop or not for an object that just entered your path on the highway.
  • Value. They say time is money and this holds true with your data. The data a company has this second might not mean as much a day, week or even month from now. Companies are finding when they combine sensor, social, and other streaming data they can make instant decisions giving their end users immediate feedback, provide that organization an advantage over their competition.
  • Security. Security and privacy are also improved with edge computing by keeping sensitive data within the device. Edge computing solutions allow organizations to protect user privacy by anonymizing, analyzing and deleting data at the source rather than transferring that identifiable information to the cloud.
  • Cost. Storage and compute costs are reduced by using edge computing’s reduced operational and data management expenses of local devices versus maintaining cloud data centers. As computations can happen on device, less data transmitted represents a reduction in your network cost also as your application doesn’t have to transmit as much data from the device of origin.

Now that we know the key factors driving businesses to adopt edge computing, to ensure we have a holistic view of edge computing we’ll discuss some of the challenges this industry is currently facing.

Key Challenges with Building on the Edge

  • Emerging Technology. While 5G is promising and there have already been large telecos to announce 5G offerings at the end of 2018 or 2020, it still is an emerging technology that needs to have the kinks worked out. I’ll save you the explanation of the advancements needed in the areas of Beamforming, Small Cells and Millimeter Waves for 5G to be successful, but if you want to find out more check out IEEEs 6min introductory video or this slightly longer video. You at least owe it to yourself to look at the cool 4G vs 5G enabled robot demo at the 12 minute and 3 second mark.
  • Cost & Complexity. Wait, I just told you cost was a benefit. Well, it can also be a detriment to advancing an edge solution. Anyone who worked on the front lines during the IoT hype cycle realizes that a complete edge solution will require an organization to work with many vendors and possibly attain consultation which can get complex and expensive. For the IoT piece alone you may need to deal with four vendors: the cloud service provider, hardware vendor of the IoT device, the teleco and consulting firm. The capital expenditures (CapEx) requirement on the IoT devices, even for a proof-of-concept, can scare off enterprises scared to dip more than a toe into edge computing and those companies struggling in defining a clear return on investment.
  • Security. I know, another benefit that is also a challenge. While the ability to secure your data and keep it on your devices and close to the source is beneficial, the influx of thousands to millions of new network enabled devices creates a larger attack vector which will keep some CIOs and their security leads up late a night.

Hopefully by now the stage has been set properly for you. You have a good understanding of some of the promises of edge computing but at the same time have set a realistic perspective about the challenges the edge computing industry are currently working to alleviate. Lets now look at how this is getting applied in the real world.

Use Cases that Sit Well at the Edge

As more devices come online through the addition of sensors and IoT devices, the wealth of data creates new use-cases where edge computing is the only solution. I’ll cover three of the more popular use-cases to give you an idea of what industries are leveraging edge computing and how.

Automotive: Autonomous Vehicles

Figure 2: Cars generate data, lots of it.

Self-driving cars are a popular use cases where edge technology is required for the technology to even exist. Mainstream cars may have up to 10 million lines of code and high-end luxury sedans can have nearly 100 million which is about 14 times more than a Boeing 787 Dreamliner jet. Peter Levine’s talk reveals modern high-end luxury cars (think this not that) have 100 CPUs in them. So we can expect autonomous driving vehicles to up the ante and become “data center on wheels.” These vehicles will run artificial intelligence against images in streaming video to determine whether to break (children) or accelerate (green light, enemies). This computation has to be done on the edge as the vehicle requires sub 10-millisecond response times which isn’t achievable by sending that data back to the cloud and await a response.

Manufacturing: Industry 4.0.

Figure 3: Industry 1.0 through 4.0

Industrial IoT companies face challenges turning machine data into business intelligence. Existing cloud-based technologies do not aptly solve problems of data analytics, software deployment and security for remote devices. Edge and fog computing allow industries to analyze machine-generated data and process data at the edge of the network, converting it into actionable, useful business information in ways previously not possible during the manufacturing process. As Industry 4.0 takes off, there is huge market potential here as currently 96% of devices in industrial and manufacturing environments are not yet connected to a network as previously there hasn’t been compelling edge solutions to motivate companies to do so.

Public Sector: Smart Homes, Cities, Transportation…Everything

Figure 4: Big Brother is Watching and Analyzing

As municipalities start adding IoT devices and sensors throughout their cities, the amount of data and need to compute data at the point of origin increases significantly. For an exhaustive list of ideas on how sensors, IoT devices and edge computing can come together to make a city smarter, check out this list put together by the European union, some of the examples include:

  • smart water supplies that can utilize sensors along their distribution pipes to let treatment facilities know if there are any areas water qualty has become unsafe, hopefully avoiding situations like this.
  • pollution monitoring to pinpoint gas leaks before they become the worst man-made greenhouse disaster in U.S. history.
  • smart lighting that increases energy savings, reduces maintenance costs through self reporting and reduces crime through intelligent responses to the environment (i.e. audio monitoring for gunshots and increasing brightness of all lights in the area automatically to increase visibility and awareness).
  • crime prevention through parsing real-time video feeds of camera footage to locate persons of interest and creation of crime maps that more easily identify patterns in criminal activity.
  • smart waste management that notifies the city when bins are full so they can be more efficient in cleared versus sending someone to empty a half-full bin or waiting for the next schedule data to empty a full bin that needs immediate attention.
  • event and traffic management can be done through algorithms that intelligently parse city and social media data to reorganize event locations or adjust transportation networks (buses, roads, etc) as needed

These are just a few examples of edge use cases but hopefully you get the idea around how low-latency fog and edge computing can transform an industry allowing those who adopt the technology to get unprecedented levels of insight into their business and pursue new business opportunities, some previously not possible.

Some Investment Advice

Why is investment advice in an article about edge computing? Well when putting together my thoughts before writing, the edge computing light bulb went off in my head allowing me to see Tom and Peter’s points of view about edge computing representing ending cloud as we know it today.

Figure 4: Potential Compute Ability of Edge Devices

From Figure 4, I provide an example where AT&T has enabling edge computing by providing fog and edge compute devices on a 5G enabled tower. When I break this image down, I see:

  1. small cell networks paired with base stations will allow for strong networking signals to reach areas previously not possible
  2. massive MIMO advances that will allow 5G base stations to increase their overall networking capacity or number of connected people and devices by a factor of 22 over current 4G stations
  3. advancements in edge SaaS offerings by players like IBM, Cisco, Dell, Intel, Microsoft, Arm and General Electric through pooling their resources in creation of open-source technologies. One example being the OpenFog Consortium
  4. hardware vendors focusing on creating mobile edge optimized compute devices along with the fact compute cost generally getting cheaper allowing us to more cheaply distribute compute across IoT devices and edge nodes
  5. there are 5 million towers globally that can eventually be fitted with these 10–20Gbps networks, fog and edge compute devices. In the event your unlucky and not close enough to one of the millions of towers, telecos like AT&T can send “flying cows” your way to to deploy the 5G virtual intelligent and programmable (VIP) network with drones

If widely adopted the telecos could relegate cloud service providers as we know them today to becoming specialized compute centers for the last 20% of workloads that don’t run optimally on the teleco’s edge computing platform. Combing the above points with the fact that telecos are making large investments in this space to “reinvent the cloud”, make me think now might be a great time to invest in telecos like AT&T and Verizon who have tremendous growth potential with the edge computing. They’re already benefiting from deregulation and corporate friendly taxation so this is a no brainer.

Disclaimer: I’m not a financial advisor, all financial advice provided was for entertainment purposes only.


In conclusion, I’m not saying the cloud is dead as much as I’m saying edge computing’s value proposition makes it the next final frontier for users of the cloud. The edge as the next frontier isn’t a view held only by me or cloud seeders (people looking to move the industry from the cloud to the fog ), but also by the cloud service providers. With Snowball Edge and Greengrass Amazon Web Services looks to provide customers local compute, messaging, data caching, sync, and ML inference capabilities for connected devices in a secure manner. Microsoft’s Azure cloud has similar offerings and during their IoT Edge presentation, made the statement that,

“The edge is the next wave of innovation, the next wave of how things are going to move in the future.” — Arjmand Samuel, Azure IoT Principal Program Manager

So whether you are a vendor, software provider, consultant or consumer of the cloud, you can expect to see transformations in this space over the next 5 years. It makes sense for organizations to sit down and think deeply about whether they should consider developing an edge strategy as edge computing has enormous potential to enable digital initiatives supported by IoT, but require strategic planning in advance for successful execution.

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Interested in learning more about Jamal Robinson or want to work together? Reach out to him on Twitter or through LinkedIn.



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