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Data at the Edge of the IoT: Computing Fast Enough for Real Time Applications Ready Now

By Cynthia S. Artin October 16, 2018

Tackling the edge of the Industrial IoT (IIoT) is a challenge being taken up by literally hundreds of IoT technology companies, platform providers, ecosystems and communities, including Linux Foundation’s EdgeX Foundry, and Cloud Native Computing Foundation (CNCF) among several others.

While clearly some progress has been made, given the growth of IIoT, adoption by large global enterprises and organizations may hinge on the ability to collect and process data at faster speeds, including low latency speeds so fast that real time, or near real time, solutions are feasible.

Real time analytics and artificial intelligence (AI) applications are coming online, enabling companies to understand when and how to maintain high value equipment, avoiding costly malfunctions and downtime.

These same applications can also ultimately make the cost of monitoring and managing equipment far less expensive, routing around manual processes requiring people, truck rolls and more.

“Continuous visibility and control result in smarter decisions for energy reduction, supply chain traceability, efficient use of resources and optimal quality control,” according to Iguazio, a company focused on developing and delivering the “Intelligent Edge” supporting Industry 4.0.

“The importance of the edge cannot be understated,” said co-founder and CTO of Iguazio, Yaron Haviv. “We’re seeing massive investments in moving compute to the edge from Microsoft, Google, Amazon and more, and while improvements in bandwidth with 5G networks at the telco network level will support edge-to-cloud-to-edge applications, cloud providers need to push more compute to the local level – to the smart city, factory or hospital.”

Iguazio, as a data processing company improving the speed at which data can be captured, analyzed and acted on, has implemented many systems across the most demanding industries, who’s businesses and value depend on real time or near-real-time applications.

Haviv took us through a retail scenario, where Iguazio is building a massive solution for a “superstore” chain, where each large store has thousands of cameras and sensors for surveillance of inventory, capture of shopper behavior, and more. “It makes no sense to send signals from thousands of endpoints to an off-site cloud,”  Yaron Haviv said, “so processing at the edge is the only way to realize full value when the store manager uses data to ensure shelf optimization and deliver an excellent experience for shoppers every day.”

Haviv said, “The traditional approach for on-premise computing has been IT centric, across building equipment, middleware, databases, and systems integration. In the past IT teams have written their own code, and managed this well for years, but when we move to IoT scenarios, enterprises can’t have a development and IT staff in every factory or location.” 

Iguazio’s approach is what Haviv calls a “set-top box that can send code artifacts into the edge and support the transition from on-prem to a scenario where we collect and manage some data locally, with very low and even ultra-low latency, while porting data to the cloud for analytics and other applications including machine learning and AI. If requirements are not urgent, more compute in the cloud using the batch processing model works fine, but where we’re seeing huge innovation is directly on-site, where hospitals, for example, can provide better care and run more efficiently, taking compliance into account as well.”

Haviv also gave the example of smart cars; “If you’re car is going to crash, you need immediate local data processing to help avoid that crash. In the same city, planners want to be able to route traffic away from accidents, or to manage congestion, and that has to happen locally to truly be real time.”

Haviv contributed to the CNCF Serverless Whitepaper v1.0 on serverless computing, a key aspect of edge computing. According to the whitepaper, “Serverless computing refers to the concept of building and running applications that do not require server management. It describes a finer-grained deployment model where applications, bundled as one or more functions, are uploaded to a platform and then executed, scaled, and billed in response to the exact demand needed at the moment.”

Serverless computing still needs servers, of course, and still requires operations engineers. It does mean that consumers of serverless computing no longer need to spend time and resources on server provisioning, maintenance, updates, scaling, and capacity planning. “Instead, all of these tasks and capabilities are handled by a serverless platform and are completely abstracted away from the developers and IT/operations teams. As a result, developers focus on writing their applications’ business logic. Operations engineers are able to elevate their focus to more business-critical tasks,” this must-read paper goes on to explain.

“We’ve designed our solution to function like the cloud,” Haviv said, “with time series, streaming, objects, files, SQL etcetera. Our customers can run real workloads on an edge device, and push data to the cloud for machine learning, to interact with a data automation layer, and more.”

Another scenario Iguazio has supported is real time video surveillance including facial recognition, and the ability to correlate data coming from multiple cameras, making it possible for law enforcement, for example, to track an individual person who may be about to commit a terrorist attack. “Only an edge solution can process data this quickly, ingesting the video, geolocation, time and movement, with logic not only in one camera but many.”

But the more widespread applications for video surveillance is happening, according to Haviv, in situations like physical retail described earlier.

Iguazio’s Continuous Data Platform powers IIoT applications at the edge, on-premises and in the cloud for real-time insights, leveraging AI tools as well as a variety of data services, eliminating data pipeline complexities and making intelligent security more possible to implement, including using blockchain for certain applications.

As the CNCF whitepaper points out, edge computing of the future will include:

1.     Functions-as-a-Service (FaaS), which typically provides event-driven computing. Developers run and manage application code with functions that are triggered by events or HTTP requests. Developers deploy small units of code to the FaaS, which are executed as needed as discrete actions, scaling        without the need to manage servers or any other underlying infrastructure.

2.     Backend-as-a-Service (BaaS), which are third-party API-based services that replace core subsets of functionality in an application. Because those APIs are provided as a service that auto-scales and operates transparently, this appears to the developer to be serverless.

Serverless products or platforms deliver the following benefits to developers, extending significant economic benefits while optimizing connected things, in the IoT and IIoT in these two main ways:

1.     Zero Server Ops: Serverless dramatically changes the cost model of running software applications through eliminating the overhead involved in the maintenance of server resources.

2.     No Compute Cost When Idle: One of the greatest benefits of serverless products from a consumer perspective is that there are no costs resulting from idle capacity. For example, serverless compute services do not charge for idle virtual machines or containers; in other words, there is no charge when code is not running or no meaningful work is being done. For databases, there is no charge for database engine capacity waiting idly for queries. Of course this does not include other costs such as stateful storage costs or added capabilities/functionality/feature set.

“In a smart factory,” Haviv said, “we’re seeing a lot of agile development of machine learning models as well as deployment of operational production pipelines. This facilitates detection of patterns in historical data sets making it possible to learn in more granularity about machinery, predict outcomes and make correlations with real-time fresh data. We provide an integrated platform with a variety of services, very high performance and can squeeze into a single solution what others need more computers to process.”

As data processing continues to grow and costs go up, Iguazio provides applications and integration of open source services – including AI and ML through their Nuclio platform, with serverless functions that are bundled with an applications library, marketplace, and canned solutions for speech recognition, sentiment analysis, and more.

When it comes to security, the Iguazio data engine classifies data transactions and provides fine grained policies to control access, service levels, multitenancy and data lifecycles, which the company claims enables data sharing

and governance across apps and business units without compromising security, reliability and performance.

The company has announced partnerships with many large companies, including Verizon, and are co-selling services in Asia working with Microsoft.

In Asia, Iguazio is also working with Equinix and Pickme, a ride hailing service, where the “edge” is not necessarily the car, but rather a local-cloud-connected end-point at the telco or metro level. Given that the distance in urban areas is relatively small and given less constraints on bandwidth with the advent of 5G, real-time, scalable and affordable approaches are more possible today than ever before.

“Working in fleet management in the mobility space has been fascinating,” Haviv said. “The real value is being created by aggregating data from multiple cars, with a combination of variables, and using statistics over a large observation of data points to make cities safer and more livable. We’re seeing the equivalent of Network Operations Centers (NOCs) in cities for these reasons.”

Iguazio is also working with massive campus hospitals, huge facilities which are in fact small cities. “The model here is to build a small data center within the hospital, to reduce latency and host highly sensitive data, with their own ‘mini-cloud’ for mission critical locations and applications. With edge computing, hospitals can track their very expensive equipment, while also tracking everything from ensuring medications, plasma and other supplies are kept at the right temperature and at the right level of supply, to patient and provider interactions when integrated with Electronic Medical records. Information does not need to go to a bigger cloud, but it can,” Haviv said, noting the elasticity intrinsic in hybrid or multi-cloud models, depending on the type of data, requirements for low latency, and amount of real time vs. batch processing required.

We will continue to follow and write about Iguazio’s progress, and the evolution of serverless and edge computing in the IoT and IIoT.

Finally, if you’re curious about the name Iguazio, the company’s name is derived from the Iguazu waterfalls in South America.

“Big data is typically defined as data in high Volume, Velocity and Variety,” the company’s website says. “With our passion for hiking and trekking, we were inspired by the Iguazu waterfalls in South America where the Vs are so visible. The sheer Volume of water, the extreme Velocity of it and the wide Variety of falls and cascades in the area. The magnitude of the Iguazu waterfalls reflects the challenges of dealing with huge amounts of data enterprise, which organizations need to handle and harness nowadays in order to stay competitive.”

 
The company also claims the Iguazu flows much faster than the Amazon, a nod to AWS?

The company was founded in 2014 and has offices in the USA, Singapore, the UK and Israel.




Edited by Ken Briodagh

Contributing Writer

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