Edge computing brings various cloud services such as computing, networking and applications closer to the devices for local processing. It allows complex processing at the point where the data enters the network and eliminates round-trips involved in sending data from the edge, to the data centre where it gets processed and back to the edge again. To accomplish this, Verizon will offer 5G network and high-volume connections between users, devices and applications and will use AWS’s Wavelength service to deploy applications and process data at the edge. It will then seamlessly connect back to the AWS cloud.
The providers are piloting the project and aiming to deliver a wide range of services which depend on millisecond latencies.
New Demand, New Markets
By using AWS Wavelength and Verizon 5G Edge, the resulting edge computing solution will enable developers to build applications that can deliver enhanced user experiences like near real-time analytics for instant decision-making, improve services such as immersive game streaming, and automate robotic systems in manufacturing facilities.
The Ecosystm top 5 IoT Trends For 2020 report authored by Ecosystm Principal Advisors Francisco Maroto and Kaushik Ghatak stated that organisations are currently generating about 10% of their data outside a traditional data centre or cloud. This is predicted to dramatically increase in 2020 with IoT deployments fuelling investments in edge computing. The next year will see a sharp increase in IoT adoption as 5G rollouts gather steam, and this partnership is another indication of the increased interest in edge computing.
“As devices multiply and become smarter, 5G technology will become progressively more important to transfer data at a faster rate and drive edge computing. Real-time analytics will be performed at so many places along the network and infrastructure that IT management will be forced to rethink their distributed and enterprise computing strategies,” the report states.
Another Ecosystm report,the Top 5 Cloud Trends for 2020 report authored by Ecosystm Principal Advisors Claus Mortensen and Craig Baty mentions that “with IoT being a major part of the business case behind 5G, the number of connected devices and endpoints is set to explode in the coming years, potentially overloading an infrastructure based fully on data centres for processing the data.”
Edge computing will allow Cloud providers such as AWS to better cater to companies that need low latency, quick access to data and data processing. On the mobile side, it will allow them to push workloads to the device, reducing the backend workload and potentially enhancing data privacy.
A new high speed CPU-to-device interconnect standard, the Common Express Link (CXL) 1.0 was announced by Intel and a consortium of leading technology companies (Huawei and Cisco in the network infrastructure space, HPE and Dell EMC in the server hardware market, and Alibaba, Facebook, Google and Microsoft for the cloud services provider markets). CXL joins a crowded field of other standards already in the server link market including CAPI, NVLINK, GEN-Z and CCIX. CXL is being positioned to improve the performance of the links between FPGA and GPUs, the most common accelerators to be involved in ML-like workloads.
Of course there were some names that were absent from the launch – Arm, AMD, Nvidia, IBM, Amazon and Baidu. Each of them are members of the other standards bodies and probably are playing the waiting game.
Now let’s pause for a moment and look at the other announcement that happened at the same time. Nvidia and Mellanox announced that the two companies had reached a definitive agreement under which Nvidia will acquire Mellanox for $6.9 billion. Nvidia puts the acquisition reasons as “The data and compute intensity of modern workloads in AI, scientific computing and data analytics is growing exponentially and has put enormous performance demands on hyperscale and enterprise datacenters. While computing demand is surging, CPU performance advances are slowing as Moore’s law has ended. This has led to the adoption of accelerated computing with Nvidia GPUs and Mellanox’s intelligent networking solutions.”
So to me it seems that despite Intel working on CXL for four years, it looks like they might have been outbid by Nvidia for Mellanox. Mellanox has been around for 20 years and was the major supplier of Infiniband, a high speed interconnect that is common in high performance workloads and very well accepted by the HPC industry. (Note: Intel was also one of the founders of the Infiniband Trade Association, IBTA, before they opted to refocus on the PCI bus). With the growing need for fast links between the accelerators and the microprocessors, it would seem like Mellanox persistence had paid off and now has the market coming to it. One can’t help but think that as soon as Intel knew that Nvidia was getting Mellanox, it pushed forward with the CXL announcement – rumors that have had no response from any of the parties.
Advice for Tech Suppliers:
The two announcements are great for any vendor who is entering the AI, intense computing world using graphics and floating point arithmetic functions. We know that more digital-oriented solutions are asking for analytics based outcomes so there will be a growing demand for broader commoditized server platforms to support them. Tech suppliers should avoid backing or picking one of either the CXL or Infiniband at the moment until we see how the CXL standard evolves and how nVidia integrates Mellanox.
Advice for Tech Users:
These two announcements reflect innovation that is generally so far away from the end user, that it can go unnoticed. However, think about how USB (Universal Serial Bus) has changed the way we connect devices to our laptops, servers and other mobile devices. The same will true for this connection as more and more data is both read and outcomes generated by the ‘accelerators’ for the way we drive our cars, digitize our factories, run our hospitals, and search the Internet. Innovation in this space just got a shot in the arm from these two announcements.
As large hardware manufacturers like Cisco, HPE, Dell and more are building specific, robust and secure infrastructure for the edge, it is believed that there will be a lot of money flowing in the IIoT Edge computing world.
The Development and implementation of Edge-Machine Learning solutions is a complex process and requires a combination of rich industry experience, knowledge of automation (PLCs, SCADAS, HMIs), electrical & mechanical engineering along with unique Edge Computing distributed system. This is used by Data Scientists to develop Machine Learning algorithms which can be utilised by IIoT applications in the manufacturing industry.
For organisations looking to implement these solutions, it is always a good idea to know more on adoption and ask for the continuation of a pilot project for more than a year.
Below are the top 5 things that one should follow to accelerate implementation of IIoT edge computing solutions in the Manufacturing industry –
1) Get help to find the needle in the haystack
With the fragmented ecosystem of IIoT vendors and companies talking about the Industrial Internet or Industry 4.0, the challenge that always appears in front of the customers is to ask for free pilots from the manufacturers.
It is not just finding the needle (IIoT best or cheaper solution) in the haystack (ecosystem), it is how this needle matches with your business and technology strategies.
I know, I am selling myself, but my recommendation to you is to get advice from independent IIoT experts.
2) Avoid OT Vendor Lock-In: We need machine data availability
Powerful Edge Analytics-Machine Learning applications require data exchange with the Programmable Logic Controllers (PLCs) of the manufacturers. By looking at the specifications we may think that it will be an easy task to extract the data from PLCs going through different ways or manufacturer’s help-guides. However, the problem is vendor lock-ins, most of the top PLC manufacturer’sdo not allow “easy” data access and extraction methods neither to the customers nor to any third parties.
It is not a question of protocols, it is a question of vendor lock-in and data availability.
Customers must seek and claim for open-source solutions to avoid vendor lock-in during the long run. The open source can better lead to the path of innovation in their manufacturing plants.
3) Edge Computing and Machine Learning: The last frontier to break between IT/OT
Edge Analytics is a key component in the integration of IT & OT and requires a knowledge of both to make it work. The lack of skills & knowledge in the IT and OT fields impact the business & operations and creates a dilemma on which department should lead the Edge Analytics projects.
Today, many cloud vendors offer IoT platforms and tools that combine the Cloud and the Edge application development, machine learning and analytics at the edge, governance, and end to end security. On the OT side, companies like Siemens have launched MindSphere, an open cloud-based IoT operating system based on the SAP HANA cloud platform.
Manufacturers should continue to deploy and develop Edge Computing – Machine Learning applications to monitor the health of their machines or to improve their asset maintenance or to monitor the quality control of their plant floor processes and shouldn’t stop because of the fear of the integration of their platform with the Public or Hybrid Cloud environment.
Edge Computing solutions help manufacturers to improve their competitiveness without the Clouds but make sure your Edge IIoT solution is ready for easy integration with the Clouds.
5) Connected Machines is the only way for new Business Models
Security is another major obstacle for the adoption of IIoT in the manufacturing industry. Manufacturers have been reluctant to open their manufacturing facilities to the Internet because of the risks of cyber-attacks.
In a fast-moving era where platforms and services require products and machines connected, every manufacturing factory should be able to tap into machine data remotely and make it available for Machine vendors. This requires every Edge Computing / Machine Learning system to be built with the capability to share data remotely via open and secure protocols/standards like MTConnect and OPC-UA.
Having machines connected is the first step to make machines smarter, to build smarter factories and to flourish new business models as Remote Equipment Monitoring.
The benefits of using Edge Computing / Machine Learning solutions are very attractive to the manufacturers because it offers minimal latency, conserve network bandwidth, improve operations reliability, offers quick decision-making ability, gather data, and process the collected data to gain insights. The ROI in such IIoT solutions is very attractive.
To get these benefits and to grace IIoT journey, manufacturers have to step-up and accept to receive tangible and innovative business value.