The news in brief: New HPE GreenLake cloud services deliver an agile, lower cost, and consistent cloud experience everywhere.
We’re living in an as-a-service and edge-to-cloud world (to paraphrase the Material Girl). When Antonio Neri assumed leadership of the storied (at least part of the storied) Hewlett Packard, he grasped both that reality and that HPE had most of the tools to get there. A couple of acquisitions, a bolstered executive leadership team, and now the unveiling. There remain more work on the financial end for him, but I think HPE is positioned for growth in this arena.
Last year at Discover, HPE pushed the GreenLake idea on us. This year, it’s capabilities and possibilities are greatly expanded. And for my industrial / production readers–this applies as much to you as to Enterprise IT. It’s getting blurry at the Edge, apps like MES are moving to the cloud (actually, probably all have moved there), and the roles of Enterprise IT and Manufacturing IT are also blurring at the edges.
It’s a new world–and I don’t mean just post-Covid.
Following is from the release:
Hewlett Packard Enterprise today announced significant advancements to the company’s edge-to-cloud platform-as-a-service strategy, through next-generation cloud services and an accelerated delivery experience for HPE GreenLake. The new HPE GreenLake cloud services, which span container management, machine learning operations, VMs, storage, compute, data protection, and networking, help customers transform and modernize their applications and data – the majority of which live on premises, in colocation facilities, and increasingly at the edge.
“Now more than ever, given current market conditions, organizations have an urgent need to connect and leverage all of their applications and data in order to transform their businesses, support their employees, and serve their customers,” said Antonio Neri, President and CEO, Hewlett Packard Enterprise. “As we enter the next phase of the cloud market, customers require an approach that enables them to innovate and modernize all of their applications and workloads, including those at the edge and on premises. By delivering a consistent cloud experience everywhere through HPE GreenLake cloud services, and software designed to accelerate transformation, HPE is uniquely positioned to help customers harness the full power of their information, wherever it resides.”
Today, organizations are at a crossroads in their digital transformation efforts. According to IDC, despite the growth and adoption of public clouds, 70 percent of applications remain outside of the public cloud. Due to several factors, including application entanglement, data gravity, security and compliance, and unpredictable costs, organizations have struggled to move the majority of the applications that run their businesses to public clouds. Forced to support two operating models, organizations face additional costs, complexity and inefficiency, limited agility and innovation, and the inability to capitalize on information everywhere.
HPE delivers a unique approach to solving this dilemma by providing HPE GreenLake cloud services to customers in the environment of their choice – from edge to cloud – with a consistent operating model and with visibility and governance across all enterprise applications and data.
HPE GreenLake cloud services also provides customers with a superior economic model. Unlike public cloud vendors, which charge customers to get data back on premises, HPE charges no data egress fees. HPE GreenLake’s flexible as-a-service model and robust cost and compliance analytics tools allow customers to preserve cash flow, control spend, and prioritize investments that are aligned to business priorities.
“HPE GreenLake gives us 100% uptime, and the predictable pricing model is already helping us cut costs,” said Ed Hildreth, Manager of IT Distributed Systems, Mohawk Valley Health System. “Thanks to the cloud-like experience, when we needed to quickly activate additional features and resources in response to the COVID-19 pandemic, we were able to easily roll this out with no time delay. We are extremely pleased with HPE GreenLake and plan to leverage this model once again for new hospitals within our health system.”
Introducing New HPE GreenLake Cloud Services for Distributed Environments
HPE now offers cloud services for containers, machine learning operations, virtual machines, storage, compute, data protection and networking. All cloud services are accessible via a self-service point-and-click catalogue on HPE GreenLake Central, a platform where customers can learn about, price, and request a trial on each cloud service; spin up instances and clusters in a few clicks; and manage their multi-cloud estate from one place. They can all be deployed and run in the customers’ environment.
Based on pre-integrated building blocks, the new HPE GreenLake cloud services are now available in small, medium, and large configurations, delivered to customers from order to run in as few as 14 days. Partners and customers benefit from pre-configured reference architectures and pricing to speed time to consuming cloud services.
HPE GreenLake is one of the fastest-growing businesses in HPE with over 4 billion USD in total contract value
- Cloud services for Containers – These new HPE GreenLake cloud services, powered by HPE Ezmeral Container Platform, provide the flexibility to run containerized applications in data centers, colocation facilities, multiple public clouds, and at the edge.
- Cloud services for Machine Learning Operations – Through HPE GreenLake, customers can subscribe to a workload-specific solution built on the HPE Ezmeral Container Platform and HPE Ezmeral ML Ops for the entire ML lifecycle.
- Cloud services for Virtual Machines, Storage, and Compute – For customers who want a private cloud experience, HPE is launching HPE GreenLake cloud services for virtual machines, storage and compute. With provisioning of instances in five clicks, these easy-to-deploy services also provide visibility into usage and spend, and active capacity planning with powerful consumption analytics in the HPE GreenLake Central management platform.
- Cloud services for Data Protection – For customers looking to modernize data protection, HPE is making data backup and recovery effortless and automated for every SLA – from rapid recovery to long-term retention. These new cloud services through HPE GreenLake include secure and efficient on-premises backup and an enterprise cloud backup service, HPE Cloud Volumes Backup, which enables backup and recovery to/from the cloud without egress costs or lock-in, and with the agility to activate data for recovery, test/dev, and analytics.
- Cloud services for the Intelligent Edge – Today, more than ever, customers are looking to reduce CapEx to simplify their budget process and better predict and manage network operational costs. Aruba’s new Managed Connectivity Services, now available as cloud services through HPE GreenLake, provide the industry’s first complete Network as a Service offering, and bring cloud agility to the edge with the recently introduced Aruba ESP (Edge Services Platform).
While at the Hannover Messe Preview last week in Germany, I talked with the representatives of a German consortium with the interesting name of “it’s OWL”. Following are some thoughts from the various organizations that compose the consortium.
Intelligent production and new business models
Artificial Intelligence is of crucial importance for the competitiveness of industry. In the Leading-Edge Cluster it’s OWL six research institutes cooperate with more than 100 companies to develop practical solutions for small and medium-sized businesses. At the OWL joint stand (Hall 7, A12) over 40 exhibitors will demonstrate applications in the areas of machine diagnostics, predictive maintenance, process optimization, and robotics.
Prof. Dr. Roman Dumitrescu (Managing Director it’s OWL Clustermanagement GmbH and Director Fraunhofer IEM) explains: “Our research institutes are international leaders in the fields of machine learning, cognitive assistance systems and systems engineering. At our four universities and two Fraunhofer Institutes, 350 researchers are working on over 100 projects to make Artificial Intelligence usable for applications in industrial value creation. With it’s OWL, we bring this expert knowledge into practice. In 2020, we will launch three new strategic initiatives worth 50 million € to unlock the potential for AI in production, product development and the working world for small and medium-sized enterprises.”
In the initiative ‘AI Marketplace’ 20, research institutes and companies are developing a digital platform for Artificial Intelligence in product development. Providers, users, and experts can network and develop solutions on this platform. In the competence centre ‘AI in the working world of industrial SMEs’, 25 partners from industry and science make their knowledge of work structuring in the context of AI available to companies.
Learning machine diagnostics and ‘SmartBox’ for process optimization
The Institute for Industrial Information Technology at the OWL University of Applied Sciences and Arts will present new results for intelligent machine diagnostics at the trade fair. Using a three-phase motor, it will be illustrated how learning algorithms and information fusion can be used to reliably identify, predict, and visualize states of technical systems. Patterns and information hidden in time series signals are learned and presented to the user in an understandable way. Inaccuracies and uncertainties in individual sensors are solved by conflict-reducing information fusion. For example, motors can be used as sensors. Within a network of sensors and other data sources in production plants, motors can measure the “state of health” and analyze the causes of malfunctions via AI. This reduces scrap and saves up to 20 percent in materials.
The ‘SmartBox’ of the Fraunhofer Institute IOSB-INA is a universally applicable solution that identifies anomalies in processes in various production environments on the basis of PROFI-NET data. The solution requires no configuration and learns the process behavior.
With retrofitting solutions of the Fraunhofer Institute, companies can prepare machines and systems in their inventory for Industrie 4.0 applications without major investment expenditure. The spectrum ranges from mobile production data acquisition systems in suitcase format for studies of potential to permanently installable retrofit solutions. Intelligent sensor systems, cloud connections and machine learning methods build the basis for data analysis. This way, processes can be optimised and more transparency, control, planning, safety, and flexibility in production can be achieved.
Cognitive robotics and self-healing in autonomous systems
The Institute of Cognition and Robotics (CoR-Lab) presents a cognitive robotics system for highly flexible industrial production. The potential of model-driven software and system development for cognitive robotics is demonstrated by using the example of automated terminal assembly in switch cabinet construction. For this purpose, machine learning methods for environ- mental perception and object recognition, automated planning algorithms and model-based motion control are integrated into a robotic system. The cell operator is thereby enabled to perform different assembly tasks using reusable and combinable task blocks.
The research project “AI for Autonomous Systems” of the Software Innovation Campus Paderborn aims at achieving self-healing properties of autonomous technical systems based on the principles of natural immune systems. For this purpose, anomalies must be detected at runtime and the underlying causes must be independently diagnosed. Based on the localization it is necessary to plan and implement behavioral adjustments to restore the function. In addition, the security of the systems must be guaranteed at all times and system reliability must be increased. This requires a combination of methods of artificial intelligence, machine learning and biologically inspired algorithms.
Predictive maintenance and digital twin
Within the framework of the ‘BOOST 4.0’ project, the largest European initiative for Big Data in industry, it’s OWL is working with 50 partners from 16 countries on various application scenarios for Big Data in production. it’s OWL focuses on predictive maintenance: thanks to the systematic collection and evaluation of machine data from a hydraulic press and a material conveyor system, it is possible to identify patterns in the production process in a pilot company. The Fraunhofer IEM has provided the technological and methodological basis. And successfully so: over the past two years the prediction of machine failures has been significantly improved in this specific application by means of machine learning methods. The Mean Time To Repair (MTTR) has already been reduced by more than 30 percent. The Mean Time Between Failures (MTBF) is now six times longer than before. A model of the predictive production line can be seen at the stand.
The digital twin is an important prerequisite for increasing the potential for efficiency and productivity in all phases of the machine life cycle. Companies and research institutes are working on the technical infrastructure for digital twins in an it’s OWL project. Digital descriptions and sub-models of machines, products and equipment as well as their interaction over the entire life cycle are now accessible thanks to interoperability. Requirements from the fields of energy and production technology as well as existing Industrie 4.0 standards and IT systems are taken into account. This is expected to result in potential savings of over 50 percent. At the joint stand, Lenze and Phoenix Contact will use typical machine modules to demonstrate how digital twins can be used to exchange information between components, machines, visualisations and digital services across manufacturers. Interoperability proves for the first time how the combination of data can be used to create useful information with added value for different user groups. For example, machine operators and maintenance staff can detect anomalies and receive instructions for troubleshooting.
Connect and get started – production optimization made easy
The cooperation in the Leading-Edge Cluster gives rise to new business ideas that are developed into successful start-ups. For example, Prodaso—a spin-off from Bielefeld University of Applied Sciences—has developed a simple and quickly implementable solution for the acquisition and visualization of machine and production data. The hardware can be connected to a machine in a few minutes via plug-and-play. The machine data is displayed directly in the cloud.
Prodaso has succeeded in solving a central challenge: Until now, networking machines from different manufacturers have been complex and costly. The Prodaso system can be retrofitted to all existing systems, independent of manufacturer and interface. In addition, the start- up also provides automated analysis and optimization tools. This enables companies to detect irregularities and deviations in the process flow at an early stage and to initiate appropriate measures. The company, founded in 2019, has already connected approximately 100 machines at companies in the manufacturing industry.
Top Tens and Top Twenties of the past or future year have never been my favorites. However, one can perceive trends and strain out little nuggets of gold by scanning several. Especially industrial taken broadly along with Internet of Things (IoT) and other current digital trends. I just had an interesting chat with Sean Riley, Global Director of Manufacturing and Transportation for Software AG, who released his Top Ten for 2020.
Following are his ideas interspersed with a few of my comments.
Cost Management Becomes Exceptional
As uncertainty enters the global manufacturing outlook, enterprises will become myopically focused on cost reductions. This will drive organizations to find more efficient methods of providing IT support, leveraging supplier ecosystems and simplifying value chains. [GM-much of my early work was in cost management/reduction; this is a never-ending challenge in manufacturing; however, tools continue to evolve giving us more and better solutions.]
A Blurred Line Between Products & Services
Manufacturers continue their product innovation quest and more manufacturers will begin focusing on how to deliver products as a service. The Manufacturers that have already created smart products and have elevated service levels will now begin to work out the financing considerations needed to shift from a sales based to a usage based revenue model. [GM-This is a trend most likely still in its infancy, or maybe toddler-hood; we see new examples sprouting monthly.]
Moving To Redefine Cost Models To Match Future Revenue Streams
Anticipating the shift to continual revenue streams, manufacturers will seek to shift costs to be incurred in a similar manner. This will be initially seen as a continued push to subscription based IT applications. While much progress has already been made, a larger focus will occur. [GM-I like his idea here of balancing capital versus expense budgets, continually finding the best fund source for shifting costs.]
IT Focuses on Rapid Support for Growth
The lines between business and IT users become blurred as no-code applications allow for business users to create integration services. IT professionals will leverage DevOps & Agile methodologies alongside of microservices and containers to rapidly develop applications that are able to generate incremental growth as requested by business users. This will be critical to the near term success for manufacturers, especially with economic headwinds that seem to be growing stronger. [GM-I didn’t ask about DevOps, but this idea is springing into the industrial space; cloud and software-as-a-service provide scalability both up and down for IT to balance costs and services.]
Industrial Self-Service Analytics Become Mission Critical
Industrie 4.0 / Smart Manufacturing initiatives continue to receive greater amounts of investment but in the near term, manufacturers will focus on unleashing the power of the data they already have. Historians, LIMS, CMMS’ have valuable data going to and in them and enabling production engineers to leverage that data rapidly is critical. Industrial Self-Service Analytics that allow production and maintenance professionals to leverage predictive analytics without IT assistance will sought as a powerful differentiating factor. [GM-we are beginning to see some cool no-programming tools to help managers get data access more quickly.]
Industrie 4.0 / Smart Manufacturing Initiatives Continue to Draw Investment
It’s no surprise that Manufacturers will continue to invest in Industrie 4.0 as the promises are great however, the scaled returns have not been realized and won’t be realized in the near term. The difficult of implementing these initiatives has surpassed manufacturers expectations for several reasons. First, traditional OT companies were trusted to deliver exceptional, open platforms and that wasn’t delivered. Secondly, collaboration efforts between IT & OT professionals proved to be more convoluted and difficult than expected. [GM-I’m thinking these ideas became overblown and complex, and that is not a good thing; to swallow the whole enchilada causes stomach pain.]
Artificial Intelligence Enters the Mix
AI won’t allow for users to sit back and relax while AI handles all of their tasks for them but it will make an appearance in back office tasks. Freight payment auditing, invoice payment and, in some select areas, chatbots will be the initial main stream uses of AI and will be seen as not becoming an anomaly but be understood to be more mainstream this year. [GM-I think still an idea looking for a problem; however some AI ideas are finding homes a little at a time.]
3D Printing Find New Uses
While this technology has steadily crept into production lines, the push towards usage based product pricing will have the technology move into after market services. Slow moving parts will be the first target for this technology which will help to free up much needed working capital to support financial transformation. [GM-watch for better machines holding tighter tolerances making the technology more useful.]
5G & Edge Analytics Enable New Possibilities
As Industrie 4.0 is continued to be pursued, Manufacturers will implement new initiatives that could not previously be realized without the high speed data transmission promises of 5G or the ability to conduct advanced analytics at the edge where production occurs. This will also provide manufacturers with new methods to securely implement Smart Manufacturing initiatives and in new locations that were not previously feasible due to connectivity issues. [GM-5G is still pretty much a dream, but there is great potential for some day.]
Security Still Remains a Critical Focus
With the increasing rate of IoT sensors, IT-OT convergence, the usage of API’s and the interconnectivity of ecosystems ensuring data security remains a top priority for manufacturers. As more data becomes more available, the need to increase levels of security becomes ever greater. [GM-ah, yes, security–a never-ending problem.]
This announcement hits many trends and things you will eventually grow tired of hearing—partnerships, collaboration among companies, ecosystems, Kubernetes, containers, and, yes, 5G. The latter is coming. We just don’t know when and how, yet.
Wind River, a leader in delivering software for the intelligent edge, announced that it is collaborating with Dell EMC as a key hardware partner for distributed edge solutions. A combined software and hardware platform would integrate Wind River Cloud Platform, a Kubernetes-based software offering for managing edge cloud infrastructure, with Dell EMC PowerEdge server hardware. The initial target use case will be virtual RAN (vRAN) infrastructure for 5G networks.
“As telecom infrastructure continues to evolve, service providers are facing daunting challenges around deploying and managing a physically distributed, cloud native vRAN infrastructure,” said Paul Miller, vice president of Telecommunications at Wind River. “By working with Dell EMC to pre-integrate our technologies into a reference distributed cloud solution, we can cost-effectively deliver carrier grade performance, massive scalability, and rapid service instantiation to service providers as their foundation for 5G networks.”
“In a 5G world, new services and applications will not be driven by massively scaled, centralized data centers but by intelligently distributed systems built at the network edge,” said Kevin Shatzkamer, vice president of Enterprise and Service Provider Strategy and Solutions at Dell EMC. “The combination of Dell EMC and Wind River technology creates a foundation for a complete, pre-integrated distributed cloud solution that delivers unrivaled reliability and performance, massive scalability, and significant cost savings compared to conventional RAN architectures. The solution will provide CSPs with what they need to migrate to 5G vRAN and better realize a cloud computing future.”
Wind River Cloud Platform combines a fully cloud-native, Kubernetes and container-based architecture with the ability to manage a truly physically and geographically separated infrastructure for vRAN and core data center sites. Cloud Platform delivers single pane of glass, zero-touch automated management of thousands of nodes.
Dell EMC hardware delivers potent compute power, high performance and high capacity memory is well suited to low-latency applications.
A commercial implementation of the open source project StarlingX, Cloud Platform scales from a single compute node at the network edge, up to thousands of nodes in the core to meet the needs of high value applications. With deterministic low latency required by edge applications and tools that make the distributed edge manageable, Cloud Platform provides a container-based infrastructure for edge implementations in scalable solutions ready for production.
OK, the headline came from IHS Markit | Technology, an Informa Tech market analyst company. The answer from me.
One of the value adds of analyst firms is to provide market research studies. Where once I received industrial market information from just one analyst firm, now several send me updates. Helps round out information. But these are always estimates, and prone to some error. It’s a good guide though.
This research looks at Industrial Internet of Things (IIoT) nodes. It also does the analyst thing of providing some guidance on implementation. The research is interesting. The guidance requires another post on management practices, I think. However, what I’m hearing is that some executive reads about IIoT and picks an unlucky person to head up the project. A pilot project is authorized, mostly completed, and mostly forgotten.
Notes from the Report
The global IIoT business is arriving at a tipping point, with the industry reaching a connectivity milestone next year that will pave the way for market-changing events like the proliferation of cloud-based technologies. These developments will help propel annual IIOT node shipments to 224 million units in 2023, a 100 million unit increase from 124 million in 2018.
However, despite the industry’s progress, about half of all IIoT deployments are failing. All too often, these deployments are being hamstrung by planning breakdowns, including the failure to set reasonable objectives and to gather support and cooperation from critical personnel within organizations. Without addressing these issues, the global IIoT market could face major challenges in reaching its growth potential.
The connection inflection
Industrial assets have traditionally employed fieldbus for connecting to the industrial network, and while Ethernet solutions have been in place for a couple of decades, their adoption has been slow. However, after years of making progress in the market, Ethernet is set to displace Fieldbus as the primary network medium for the first time in 2020. Ethernet will account for 43 percent of IIOT node shipments next year, compared to 41 percent for Fieldbus.
“There are now more than 1 billion connected devices on factory floors around the world,” said Alex West, senior principal analyst, industrial technology, at IHS Markit | Technology. “This massive installed base is about to reach a tipping point, with Ethernet overtaking Fieldbus in 2020. The proliferation of Ethernet is enabling the transmission of larger volumes of data. This will ultimately bring in technologies like the cloud that are going to supercharge the IIOT business.”
Connecting to reduce downtime
The arrival of a faster connectivity solution will allow manufacturers to utilize cloud-based solutions to reduce downtime.
“One of the really significant challenges faced by industrial companies is unplanned downtime,” West said. “Just to quantity that challenge, it’s estimated in the automotive industry that $20,000 to $30,000 per minute is lost through unplanned downtime. New applications enabled through IIoT, maintenance and asset-health monitoring, are really helping overcome these challenges. We’ve estimated around a 30 percent average saving or reduction in unplanned downtime can be achieved through industrial IoT solutions.”
The benefits of IIoT solutions facilitated by enabled devices can be realized across the entire lifecycle of production, from product design, to monitoring inventory levels in the supply chain.
For example, Harley Davidson, a few years ago was facing business challenges in terms of fulfilling customer requirements. By improving the connectivity of its plant, the company was able to reduce the time to meet new orders filled from 21 days down to six hours.
Addressing IIoT deployment fails
While faster connectivity holds great promise for expanding the IIoT market, the reality is that current deployments are failing as often as they succeed.
“At the proof-of-concept phase, about half of IIoT projects are failing—which is acceptable for companies attempting to be agile and trial new applications,” West said. “However, there is a similar failure rate when companies move to the deployment stage. This means companies are investing enormous sums in these projects but aren’t getting the payback they expected.”
The failure of a project is defined as not meeting the customer’s expected payback. Many times, the high failure rate can be attributed to inflated expectations. A total of 50 percent of companies expect to see payback within one year, although many of these projects can take much longer to generate returns.
IHS Markit | Technology recommends manufacturers take the following steps to increase their chances of IIoT success:
- Specify the project by determining in advance which exact challenges you want IIoT to address.
- Start small, with some pilot projects of concepts to see how the technology can be utilized.
- Go right to the top, with senior-level management support for projects.
- Get the urge to converge, by ensuring support from all relevant functional groups.
- Leverage your people power, by getting staff involved with deploying the technology and encouraging them to view IIoT not as a threat, but as an augmentation to their job capabilities.