Business Automation Forum Group

Five characteristics of cloud computing

Table of Contents
    Add a header to begin generating the table of contents

    Cloud computing's characteristics and benefits include on-demand self-service, broad network access, and scalability.

    As cloud computing services mature both commercially and technologically, companies will be more comfortable maximizing the potential benefits. Knowing what cloud computing is and what it does, however, is just as important. The National Institute of Standards and Technology (NIST) defines cloud computing as known today through five particular characteristics.

    On-demand self-service cloud 

    A manufacturing organization can provide additional computing resources as needed without going through the cloud service provider. This can be a storage space, virtual machine instances, database instances, and so on.

    Manufacturing organizations can use a web self-service portal as an interface to access their cloud accounts to see their cloud services, their usage, and also to provision and de-provision services as they need to.

    Broad network access

    Cloud computing resources are available over the network and can be accessed by diverse customer platforms. It other words, cloud services are available over a network—ideally high broadband communication link—such as the internet, or in the case of private clouds, it could be a local area network (LAN).

    Network bandwidth and latency are fundamental aspects of cloud computing and broad network access because they relate to service quality (QoS). This is particularly important for serving time-sensitive manufacturing applications.

    Multi-tenancy and resource pooling

    Cloud computing resources are designed to support a multi-tenant model. Multi-tenancy allows multiple customers to share the same applications or the same physical infrastructure while retaining privacy and security over their information. It's similar to people living in an apartment building, sharing the same building infrastructure, but they still have their apartments and privacy within that infrastructure. That is how cloud multi-tenancy works.

    Resource pooling means that multiple customers are serviced from the same physical resources. Providers' resource pool should be vast and flexible enough to service various client requirements and provide for the economy of scale. When it comes to resource pooling, resource allocation must not impact the performances of critical manufacturing applications.

    Rapid elasticity and scalability

    One of the great things about cloud computing is the ability to quickly provision resources in the cloud as manufacturing organizations need them. And then to remove them when they don't need them. Cloud computing resources can scale up or down rapidly and automatically respond to business demands in some cases. It is a crucial feature of cloud computing. The usage, capacity, and therefore cost can be scaled up or down with no additional contract or penalties.

    Elasticity is a landmark of cloud computing, and it implies that manufacturing organizations can rapidly provision and de-provision any of the cloud computing resources. Rapid provisioning and de-provisioning might apply to storage or virtual machines or custom applications.

    With cloud computing scalability, there is less capital expenditure on the cloud customer side. As the cloud customer needs additional computing resources, they can provide them as needed, and they are available right away. Scalability is more planned and gradual. For instance, scalability means that manufacturing organizations are gradually planning for more capacity, and of course, the cloud can handle that scaling up or scaling down.

    Just-in-time (JIT) service is the notion of requiring cloud elasticity either to provide more resources in the cloud or less. For example, if a manufacturing organization suddenly needs more computing power to perform some complex calculation, cloud elasticity would be a just-in-time service. On the other hand, if the manufacturing organization needs to provision human-machine interface (HMI) tags in the database for a manufacturing project, that is not a just-in-time service; it is planned ahead of time. So it is more on the scalability side than elasticity.

    Another feature available for rapid elasticity and scalability in the cloud is related to the testing of manufacturing applications. If a manufacturing organization needs, for example, a few virtual machines to test a supervisory control and data acquisition (SCADA) system before they roll it out in production, they can have it up and running in minutes instead of physically ordering and waiting for hardware to be shipped.

    In terms of the bottom line, when manufacturing organizations need to test something in the cloud, they pay for what they use as they use it. As long as they remember to de-provision it, they will no longer be paying for it. There is no capital expense here for computer resources. Manufacturing organizations are using the cloud provider's investment in cloud computing resources instead. This is useful for testing smart manufacturing solutions.

    Measured service

    Cloud computing resources usage is metered, and manufacturing organizations pay accordingly for what they have used. Resource utilization can be optimized by leveraging charge-per-use capabilities. This means that cloud resource usage—whether virtual server instances running or storage in the cloud—get monitored, measured and reported by the cloud service provider. The cost model is based on "pay for what you use"—the payment is variable based on the manufacturing organisation's actual consumption.

    How information ascends to the cloud for greater utilization        

    Modern instrumentation delivers data, but too often, it serves no purpose.

    Smart instruments have been available since the mid-1980s, when the first 4-20mA HART devices entered the market, quickly followed by Fieldbus-based devices. These digital communication technologies made it possible for instruments to provide more than just a process signal. Using digital interfaces, these devices were now able to send device status, diagnostics and other information.

    Endress+Hauser estimates that of the 40 million of its process instruments installed worldwide, 90% are digital, smart devices. These intelligent instruments provide an incredible amount of information at "the Edge" that is of immense benefit to a wide range of host systems and IIoT applications, such as maintenance management, asset management, inventory control, manufacturing execution systems (MES), enterprise resources planning (ERP), and so forth. But one major problem facing industrial plants is: How do we manage all this data?

    Because of the immense amount of data and problems in managing it, Endress+Hauser estimates that 97% of the data is not used. Instead, automation systems use the flow, pressure, temperature, level and other data needed to control the process and ignore or discard status, diagnostic and other data.

    A process plant may have thousands of smart instruments, all providing status and diagnostic data needed by IIoT software. Courtesy: Endress + Hauser

    Major instrument manufacturers are well aware of the problem. Several are now delivering solutions to acquire data from the Edge and making it available to specialized IIoT software without affecting or involving the automation system. Let's take a quick look at the concepts behind these solutions.

    Shot of Data Center With Multiple Rows of Fully Operational Server Racks. Modern Telecommunications, Cloud Computing, Artificial Intelligence, Database, Supercomputer Technology Concept.

    Handling massive data

    As noted above, a smart instrument generates a great deal of status, diagnostic and other information.. The flowmeter can see 125 different problems. When process conditions warrant a notification, the flowmeter generates an event message.

    While the automation system is mostly interested in inflow values and alarms, IIoT software wants to know about the warnings shown and diagnostics and other data.

    Many smart instruments can provide diagnostics to indicate problems with electronics or subcomponents. For example, Proline Coriolis flowmeters can monitor oscillation damping and frequency, temperature, signal asymmetry, exciter current, carrier pipe temperature, frequency fluctuation and other parameters. Changes in these parameters indicate potential problems.

    While every instrument manufacturer's diagnostics differ, each typically monitors internal parameters, observes changes and diagnoses problems. Any further analysis must be done by IIoT maintenance software, which means status and diagnostic data must be transmitted to this software.

    In many cases, this is accomplished by the automation system, which periodically asks each instrument for the data, then stores it in an online database, such as a process historian. Maintenance management software accesses what it needs from the historian and performs its analysis.

    This type of solution presents problems. Networks can be unduly burdened with data transmissions, historians can become bloated, and there can be lags between data collection and recognition by the IIoT software.

    Data is collected only periodically because the automation system can't deal with the massive amount of status and diagnostic data from hundreds or thousands of instruments. The information is stored in a database, which must be accessed from the maintenance software, adding even more delays.

    A better solution — now being offered by several major instrument manufacturers — provides all the data available at the Edge to IIoT software via the cloud, thus bypassing the automation system completely.

    Connecting at the Edge

    The 30+ million digital instruments currently installed worldwide communicate with their automation systems via different interfaces, including Profibus, 4-20mA HART, WirelessHART, EtherNet/IP, and several others. However, many eventually connect to an Ethernet-based network, where a specialized “edge device can acquire the data."

    The edge device is programmed to extract instrument data from the network and transmit it to IIoT software in the cloud.

    An edge device can also be installed on a smaller system, such as a pumping station, that may or may not be connected to a plant's Ethernet network or instruments connected to an older, non-Ethernet system. In that case, each instrument is wired to a nearby "edge gateway" device which collects data from devices and transmits it to the cloud.

    Once the instruments are connected to an Ethernet-based network ready for IIoT connection, the appropriate edge device is selected. Various edge devices are available from instrument manufacturers to handle expected data rates.

    For example, Endress+Hauser has multiple approaches to select the right edge device for the correct quantity of instruments transmitting information to the cloud. The edge device has high-speed data acquisition at a site with hundreds of tools to push the information to the cloud. Conversely, instrument-based edge devices are offered that run at raw speed, transmitting small amounts of data to the cloud.

    All data transmission is one way from the device to the cloud. Cybersecurity is deployed within data transmission, edge devices, and cloud services connectivity.

    Living in a cloud

    Major instrument manufacturers provide software that uses data from the Edge to diagnose problems, schedule maintenance activities, analyze processes, predict issues, etc.

    For example, cloud software consists of several components:

    Instrument diagnostics: Software built into modern instruments monitors device status and process conditions and provides further analysis data. Endress+Hauser embeds "Heartbeat Technology" into its agents to provide quality and diagnostic information and perform vital functions such as condition monitoring and in-situ verification.

    During verification, various parameters' current conditions are compared with their reference values, thereby determining the device status. The technology produces a "pass" or a "fail" statement based on the tests performed by traceable and redundant internal references. The individual tests and results are automatically recorded and used to print a verification report.

    Cloud connection: Software and hardware are needed to extract data from the plant's Ethernet network or individual devices and transmit it to the cloud-based software. At Endress+Hauser, this is accomplished with Netilion Connect, which consists of edge devices that acquire the data, a cloud platform that hosts the IIoT software and a programmable application interface (API).

    The API provides a way to connect cloud-to-cloud or cloud-to-apps in a simplified manner. It enables customers to use IIoT simply and efficiently without the complexity of IT-based computer science.

    The ecosystem is based on an open-source technology platform that is common in our industry and — like other vendor's ecosystems — offers these functions:

    Analytical software: Analytics processes data generated by the technology to assess instrument health, analyze and predict problems, schedule maintenance, etc.

    Process health: Health software analyzes instrumentation at the Edge to determine if the process is getting more difficult to control, if external influences have a detrimental effect on performance, or if changes need to be made. For example, water treatment plants can monitor incoming surface water. The system monitors conductivity, pH, dissolved oxygen and other parameters and issues warnings to operators.

    Equipment documentation: Maintenance technicians need access to equipment manuals, troubleshooting instructions and other materials describing each instrument. Library software logs in all pertinent information and makes it available to technicians on request.

    Getting started

    Implementing such a system might appear challenging, but several factors help simplify the effort.

    First, few plants are exclusive to a single instrument vendor. All this instrumentation must be identified and entered into the system, including manuals, error codes, diagnostic information and other pertinent data. This is usually done during an installed base analysis.

    Fortunately, modern instruments typically are identified with bar codes or labels that can be scanned to identify the vendor and product. Then, the necessary data can easily be downloaded from the vendor's web site.

    Second, there's no need to implement a plant-wide system right away. Most vendors provide a "start-up kit" that allows a plant to try out the concept. For example, Endress+Hauser offers a free trial version for up to 15 assets with a typical plant introductory IIoT package that can connect as many as 500 instruments to its cloud software.

    Final words

    Modern instruments provide a wealth of information about their health and their monitoring process, but few plants use all that data. Today, major instrument manufacturers offer hardware and software solutions that bring all the Edge data to IIoT software for analysis and corrective actions.

    Scroll to Top