(74) As an IoT Platform, what should be the right balance of data computing between the Edge and the Cloud?
In this post, I am summarising some research I put in understanding what Edge strategy should be in an IIoT platform. I went about looking at how some of the prominent platforms of IIoT implementations deal with edge computing and how do they split the data processing burden between the cloud and the edge. Here’s what I found:
More and more enterprises are discovering that not all real time computing should be pushed to the cloud. There are multiple reasons:
- Security
- Latency/performance
- Cost — the more data is pushed to the cloud, the more you pay to the Cloud hosting providers.
They are pushing more and more computing to the edge of their networks and gravitating towards a hybrid cloud strategy.
Here are some of the broad observations:
- On an average, only 15% of the data generated by machines needs to be sent to the cloud and made available to other systems. The other 85% needs to be aggregated and analyzed to determine how it can be made valuable. If all is being pushed to the cloud, its very costly when only a fraction of it is going to be put into use.
- Some computing will always need to live at the edge, such as real-time processing, decision support, SCADA functions and more.
- 100% cloud adoption is not necessary and must only be utilized for non-real time workloads like post-processing analytics or planning.
- Risk based use cases like condition monitoring of remote devices where operators need to act fast on critical issues and mitigate the situation. It could be too risky for the data to travel back to the data center, undergo analysis and direct actions back to the device.
Cloud would suit more to support planning and trend-spotting by collecting metrics from all the remote machines and periodically sending them to the data center for aggregation and analysis.
There is another trend towards offering customers full on-premise solutions before the customers can trust the system. Here’s what the customers state:
“We will only start this journey with you if you can put all the infrastructure we need on our premises, isolated from the internet, so we can be assured of security, of governance, of adequate latency for decision making,”
Verdict: Hybrid Cloud strategy is a must have for all IIoT platforms with 60–70% of processing, transactions and data management happening on the edge.
In particular, Gartner in his magic quadrant report for IIoT believes that “By 2020, on-premises Internet of Things (IoT) platforms coupled with edge computing will account for up to 60% of industrial IoT (IIoT) analytics, up from less than 10% today.”
All big cloud providers are offering edge solutions
Edge is going to be the future is validated by all the big cloud providers who have begun offering edge solutions. Microsoft, Amazon and Google delivering edge products is a testament to the reality of Edge offerings.
- Azure IoT Edge:
Azure IoT Edge extends cloud intelligence and analytics to edge devices — Azure IoT Edge is a fully managed service that delivers cloud intelligence locally by deploying and running artificial intelligence (AI), Azure services, and custom logic directly on cross-platform IoT devices.Watch this video on Azure Stack for Edge: https://www.youtube.com/watch?v=c6EutwJHecc
- Amazon Greengrass and Snowball Edge
Here’s what the press release of Amazon Snowball Edge read:
“Amazon’s AWS cloud service announced that it is bringing the full cloud computing capabilities of its Elastic Compute Cloud (EC2) service to its Snowball Edge system, in a move that will create a powerful edge computing option for remote locations and factory floors. This new capability will give AWS users a more feature-rich local computing environment that they can leverage to cut down on processing delays, as well as to reduce the amount of data they need to transmit to the cloud for storage.”
- Google IoT Edge
Here’s what the press release for Google IoT Edge:
“Cloud IoT Edge extends Google Cloud’s powerful data processing and machine learning to billions of edge devices, such as robotic arms, wind turbines, and oil rigs, so they can act on the data from their sensors in real time and predict outcomes locally. Cloud IoT Edge can run on Android Things or Linux-based operating systems.”
Share your thoughts and comments on what you think should be the Edge strategy for an IIoT platform.
References:
- Tomorrow’s Digital Transformation Battles Will Be Fought at the Edge
- https://www.businessinsider.com/amazon-snowball-edge-aws-computing-ability-2018-7?IR=T
- https://cloud.google.com/iot-edge/
- https://www.forbes.com/sites/janakirammsv/2018/07/01/5-reasons-why-azure-iot-edge-is-industrys-most-promising-edge-computing-platform/#5afce1e73249
- https://uk.pcmag.com/tableau-desktop/116871/feature/how-the-iot-brings-billions-of-devices-closer-to-the-edge
- https://www.forbes.com/sites/forbestechcouncil/2018/08/21/three-myths-about-edge-computing/#7cbb8e873d52
- https://searchmobilecomputing.techtarget.com/opinion/How-IoT-edge-devices-fit-into-unified-endpoint-management
- https://www.linkedin.com/pulse/why-iiot-revolution-happen-edge-cloud-kudzai-manditereza/?lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_post_details%3BOSDYGuFdT3aZ0cWNDNGYlA%3D%3D
- https://www.iotcentral.io/blog/the-iot-architecture-at-the-edge
- https://www.ptc.com/-/media/Files/PDFs/IoT/Foundational-Elements-of-an-IoT-Solution.pdf?la=en&hash=A496A994897B2C7AC1AEC853DE4906E10A7AD03C
- https://internetofthingsagenda.techtarget.com/blog/IoT-Agenda/Myth-busting-Cloud-vs-edge-in-IIoT
- https://www.networkworld.com/article/3247801/internet-of-things/the-top-5-user-requirements-of-iot-edge-platforms.html
- https://wikibon.com/the-vital-role-of-edge-computing-in-the-internet-of-things/
- https://www.vanillaplus.com/2018/10/05/42404-instead-sending-data-cloud-not-send-cloud-edge/