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Tag: IoT

  • Microsoft Gains DevOps Momentum

    Microsoft Gains DevOps Momentum

    Microsoft, at its recent Microsoft Build 2019 conference, launched a series of efforts intended to make it easier for organizations to embrace best DevOps practices, including making it possible to address both continuous integration and continuous delivery within the same YAML file using Azure Pipelines.

    Microsoft also announced it will extend Azure Pipelines to provide tighter integration between GitHub, which Microsoft acquired last year, and Kubernetes. Via that integration, Azure Pipelines can be used to automate everything from creating container images and packaging them as Helm charts to generating Kubernetes deployment artifacts and then deploying the workload.

    Continuing that Kubernetes theme, Microsoft also is launching the Kubernetes-based Event Driven Autoscaling (KEDA) project, which makes it possible to run Azure functions on top of Kubernetes clusters. Developed in collaboration with Red Hat, KEDA is available in developer preview for the Red Hat OpenShift platform based on Kubernetes.

    In addition, Microsoft has extended the Azure IoT Edge runtime to Kubernetes to enable internet of things (IoT) applications to be deployed on Kubernetes and Azure SQL DB Edge, which is packaged as a container and makes it possible to deploy a database on ARM or x86 systems deployed at the network edge.

    Also during the conference, Microsoft launched MLOps, which applies DevOps principles to deploying artificial intelligence (AI) models using the GitOps workflow; Azure SQL Database Serverless, which allows organizations to consume a database service on a per-second billing basis on the Azure cloud; and ML.net, a framework for building AI models.

    Microsoft also announced it is now making available a full Linux subsystem within Windows 10 and the ability to link IoT devices to the Azure cloud without writing code.

    Scott Guthrie, executive vice president for AI and cloud at Microsoft, told conference attendees Microsoft is squarely focused on making it easier to build applications that can be deployed in hybrid cloud computing environments anchored around the Microsoft Azure cloud.

    Microsoft historically has focused on making it much easier to consume emerging technologies at the point in time they start to appeal to the broadest number of customers. As DevOps best practices continue to find mainstream adoption, that same philosophy is being applied to cloud services. Microsoft may not have invented the concept of CI/CD, but it is working to make it easier for organizations that have struggled to implement continuous delivery, for example, to master that challenge. The same concept applies to Kubernetes, which some argue is still too complex for the average IT organization to master and deploy.

    It remains to be seen how much Microsoft’s efforts, most of which are only available as previews, will result in more applications being deployed on Azure. Because an application is built on a public cloud doesn’t always mean it will be deployed on that cloud. It does, however, increase the chances of that occurring. In fact, providing a better DevOps experience across a hybrid cloud computing environment is clearly key to Microsoft’s strategy as it seeks to gain ground on Amazon Web Services (AWS).

    — Mike Vizard

  • Nutanix Eases Development of IoT Apps

    Nutanix Eases Development of IoT Apps

    Nutanix, a vendor of hyperconverged infrastructure solutions, has released a new developer environment that allows developers to try and test IoT and AI apps in the cloud. Starting May 7, developers can sign up for a free 10-day trial of this experience.

    The developer environment is built under Nutanix’s Xi IoT Platform and gives developers access to a rapid development environment for creating internet of things and artificial intelligence apps in the cloud. The Rapid Development Environment for IoT includes features such as Xi IoT Cloud Instance, Xi IoT Sensor App and Xi IoT App Library and supports the ability to easily deploy IoT software in the cloud or at the edge, according to the company. The environment has been tuned specifically for IoT and AI, eliminating many of the configuration chores developers had to perform using other cloud-based development environments, and enables single-click deployment via a management console.

    For developers targeting production-edge environments, the new environment brings forth the ability to develop for the edge or for the cloud, bringing application portability into the development and testing process. By being able to use a common set of APIs, cloud and edge development becomes relatively seamless and deployment friction is vastly reduced, according to the company.

    Beyond ensuring compatibility across platform services and APIs, the environment enables development and production environments to use the same components, so recoding is not needed as part of the transition from dev to ops. That helps to simplify lifecycle management as well as fit agile practices better into the dev process.

    Nutanix also offers an extensive library of applications that can be used to ease development chores. The Xi IoT App Library offers  individual components (message bus services, time series databases, etc.) and full solutions that developers can use to address various business problems.

    Nutanix is framing the new delivery mechanism as a new experience to get more developers—from novice to advanced—creating AI and IoT scenarios.

    — Frank Ohlhorst

  • IoT Future-Proofing for the Long Haul

    IoT Future-Proofing for the Long Haul

    Think about this: The IoT application your DevOps team is developing right now, if successful at scale, will likely be the longest-living application in your shop. It may even outlive the tenure of many of your team members with your company. Is IoT future-proofing possible over a life cycle of 10 years or more? Yes, with some insight for the long haul borrowed from industrial and defense computing experience.

    That may sound counterintuitive for DevOps because those industries aren’t exactly known for agile processes. They’re designed around minimizing change and containing risk in mission-critical situations. IoT technology is evolving rapidly and it’s difficult to say what things may look like in a year or two, much less 10. What IoT applications have in common with industrial and defense systems is that they are often complex. Your operational technology (OT) people probably appreciate how complex their existing production environments have been. IoT applications add elements of wireless connectivity, cloud architecture, real-time performance, end-to-end security and algorithmic sophistication.

    At-scale IoT applications will require mastery of complexity, and lessons learned can help. The objective is not to avoid change but rather to “build the airplane while flying.” The core DevOps concept of continuous application release and testing needs to extend beyond just the application code. Teams will need to be detail oriented, looking at device configurations, interoperability, data integrity and how analytic insights evolve as data accumulates.

    Device Configurations

    Buying an enterprise server usually means selecting a commodity: Pick a processor model and frequency, memory size, number of Ethernet and USB ports, storage interfaces and packaging. In the cloud, selections in elastic instances are even easier. As long as the box fits and runs the desired operating system, the details of what’s inside aren’t that important. If you have to buy another one in two or three years, there are likely higher performance choices available.

    In long-lifecycle computing for IoT applications, boxes aren’t commodities. Real-time performance drives differences in processor and board architecture. If you have to buy another one in two or three years, that could be a problem. Vendors often offer industrial and defense customers revision control services, ranging from notification of changes to extended availability of specific configurations. Assuring supply sometimes means a lifetime purchase of components, perhaps as bonded inventory for a customer.

    Interoperability

    Even simple IoT devices, such as wireless sensors, can present a problem on a 10-year scale. Connectivity specifications evolve, with both major changes and minor revisions. Cellular standards reach sunset as mobile subscribers leave them behind. Vendor support for older gear is often given reluctantly, frequently expensive and sometimes non-existent unless an upgrade is executed.

    Choosing interoperable frameworks with robust ecosystems will be critical for success in the long haul. Teams should be prepared with a “plan B” if a vendor is acquired and stops offering a specific configuration, or if they cease operations altogether. Devices with over-the-air (OTA) programming help keep pace with specification changes and alleviate security concerns as they arise.

    Data Integrity

    Teams won’t be able to just stop the IoT plane and work on it. Data collected on day 1 will be just as valuable as data collected on day 3650, if it’s handled correctly through IoT future-proofing tactics. Often, IoT devices connect to networks easily. The real test of an IoT application comes when data starts flowing, perhaps from thousands of devices. Is the data from an authorized device? Is all the data in the correct format? Is it time-stamped properly? Is it checked for errors and missing frames?

    Presumably, when an IoT application succeeds, the temptation to scale quickly sets in. Suppose a new group of sensors is added with the same class of information in a slightly different format. The system should abstract formats and sanitize data at the edge, so all data reaching the cloud is ready for algorithmic processing. For example, JSON is one tool that can help with abstraction. Edge computing may also use generative models to reduce data transmitted to the cloud, forwarding only out-of-bounds conditions for further analysis.

    Analytic Insights

    At the onset of an IoT project, teams have an idea of what they have set out to accomplish. Inevitably, as projects mature new insights come out of analytics. The architecture may not be optimized to deliver those insights in real-time, and changes may be necessary to speed up delivery of algorithmic results. Workflow optimization is an ongoing process for IoT teams. This is the ultimate instance of rebuilding the plane while in flight, extracting more value from an in-place system.

    The Ethos of IoT Future-Proofing

    IoT future-proofing is possible by embracing both the longevity and the complexity of the application. Shifting your team ethos from maintaining a static configuration to developing a living application architecture, helps overcome events certain to happen over a 10-year life cycle and beyond.

    — Don Dingee

  • Cisco Opens IoT Development Center

    Cisco Opens IoT Development Center

    Cisco Systems this week launched an IoT Dev Center as part of an effort to foster development of a new generation of applications that will be deployed at the network edge.

    Announced at the Cisco Live EMEA event, the IoT Dev Center provides access to sandboxes and tutorials for building highly distributed IoT applications that Cisco envisions will be deployed directly on top of Cisco routers and switches. To facilitate that effort, Cisco also launched a series of ruggedized switches and routers, which, in addition to usingCisco ASICs for processing network traffic, can be configured to include x86 processors that enable low-latency applications to be deployed at the edge.

    The Cisco IoT Dev Center also provides access to a DevNet Ecosystem Exchange, through which organizations will be able to discover IoT applications optimized for Cisco infrastructure.

    Susie Wee, senior vice president for the DevNet community at Cisco, said the IoT Dev Center is an extension of the company’s ongoing efforts to build a DevOps community around Cisco infrastructure. From Cisco’s perspective, IoT represents an instance of edge computing.

    Cisco is attempting to extend its current dominance over networking infrastructure to include both networking gear for IoT environments and two-node hyperconverged infrastructure (HCI) platforms intended to be deployed at the network edge. Each of those systems will be connected via the Cisco Application-Centric Infrastructure (ACI) software-defined network (SDN), which Cisco says enables intent-based networking when infused with advanced analytics.

    Cisco claims it already has 40,000 customers using some element of its portfolio to drive an IoT application. As that ecosystem continues to evolve, Wee said it’s apparent that both operational technology (OT) teams, which historically have been responsible for embedded systems, and enterprise IT teams will need to embrace best DevOps practices to deploy, manage and update IoT applications. While embedded systems deployed by OT teams were not frequently updated, IoT applications will take advantage of containers and other microservices-based technologies to add new functionality more frequently and, when required, update systems to remediate potential vulnerabilities. By the end of the decade, many of those applications will be connected via 5G wireless networks that will provide enough bandwidth to make continuously updating edge applications much more practical than it is today.

    Less clear going forward is the degree to which networking teams will become part of a larger DevOps teams. As more applications get deployed on switches and routers at the edge, it’s obvious networking teams will play a critical role. The issue is to what degree. In many cases, networking teams will attempt to provide developers with access to a defined set of resources they can access via a self-service portal. But there are also likely to be instances when developers will want programmatic control over network resources that can be invoked via application programming interfaces (APIs).

    Naturally, Cisco is not the only networking vendor with similar edge computing ambitions. But while most IoT initiatives remain relative nascent, the next major battle for control over the network edge is more likely be determined by the sophistication of the processes enabled as it is the core underlying infrastructure.

    — Mike Vizard

  • How DevOps Can Nurture the IoT Brownfield For Results

    How DevOps Can Nurture the IoT Brownfield For Results

    Your latest IoT project sailed through proof-of-concept but started running into problems during rollout. Fortunately, your DevOps superhumans got things running. Now, perhaps a year or more later, you’re seeing more and more issues crop up as the project grows. If this scenario sounds vaguely familiar, or you’re worried you may be headed there, you’re not alone. Welcome to the IoT brownfield.

    “Nah, can’t be. We selected and evaluated all-new hardware and software during the PoC. This has been a greenfield project from the start.” Be careful of this trap. Many IoT projects indeed start off as greenfield efforts, free of the constraints of integrating legacy hardware and software. Your team developed requirements with collaboration between IT and line-of-business folks. You introduced these new tools, and got results on a small scale. According to Cisco’s 2017 IoT survey of 1,845 IT and line-of-business decision makers, 60 percent of IoT projects stall at PoC, so you’re ahead of the game.

    Or so it would seem. Now comes the escalating commitment. Management assumes that if it worked on a small scale, it should just work when rolled out across the enterprise. Data and insight will just come pouring in, and everyone will have visibility to what’s happening everywhere in real-time. They write a big check for the rollout, and away you go.

    First Signs of Trouble

    At the first rollout project review, folks start splitting hairs over the definition of “complete success.” You rolled out X locations, Y of them are working, X-Y of them are in various states of triage. How can that be? You took the “same” configuration and scaled it. As the team investigates the non-working population, you’re finding little things that aren’t the same, calling for tweaks here and there.

    Tossing out the trivial, over scale and time there is no such thing as an IoT greenfield project. Every IoT project with an extended life cycle becomes a brownfield, with a mix of new and old hardware and software. Rigorously controlling a configuration, à la defense or medical applications, is prohibitively expensive and unnecessary for most IoT applications. Your vendors won’t sell you the exact same hardware or software two years from now, five years from now, 10 years from now. Unless, that is, you pay them for things such as lifetime buys of electronic components or freezing open source code releases.

    Ideally, we should send DevOps teams into IoT projects. With a big tool stack acquiring data, the line-of-business types figure out more things to do as the picture develops. The whole point is gaining insight and adjusting for optimized results. Cycles of learning and security enhancements are part of the game.

    Coping with an IoT Brownfield

    Here’s the paradox: the longer your IoT application survives, the more complex this situation gets. How can you improve your chance of success by applying IoT brownfield thinking? Technical requirements vary, but some core philosophical tenets can help.

    Roll with the changes. Your team is already skilled at quickly rolling application software releases. Get more aggressive with ongoing changes at the firmware and network level. Incorporate over-the-air (OTA) technology for updating code on any wireless devices. Deploy multi-protocol gateways that can deal with revised protocol stacks (or entirely new ones) as IoT specifications continue evolving. Update security vulnerabilities relentlessly.

    Abstract everything. You’ll read tons about IoT interoperability, and what people typically mean is wire-level interoperability of protocols. Those multi-protocol gateways have one job—getting real-time data onto an IP network and into the cloud. What keeps me up at night is data-level interoperability. Use edge or fog computing for pre-processing streams so the cloud isn’t stuck sorting out various formats of the same class of data. Abstract your data using JSON or other lightweight techniques so that if a next-generation sensor outputs data in a slightly different format, pre-processing can be revised close to the source. Consider a publish-subscribe protocol for easier routing between nodes.

    Optimize the cloud. Scale can bite hard in the cloud tier. Heterogenous, distributed architectures usually win. Deploy a hybrid cloud solution where there is real-time data. Let the public cloud do what it’s good at: presentation, business intelligence and archiving. In a private cloud instance for heavier analytics lifting, use a framework such as Apache Spark to minimize data movement, with faster nodes sprinkled in for critical algorithms. Try dedicating a small processing core to each data stream for predictable deadlines, or using FPGAs in workflow optimized nodes for boosting compute performance.

    After All, It’s Just Business

    Ultimately, the evaluation of complete success of an IoT application will be up to the line-of-business decision-makers who wrote that big rollout check. A DevOps model is ideal for nurturing the IoT brownfield over scale and time, featuring ongoing strong collaboration between technologists and line-of-business types. Early in the project, reach out for some external IoT expertise until you have a few cycles of learning under your belt.

    As that Cisco 2017 IoT survey alluded to, 61 percent of practitioners believe we’re barely scratching the surface of IoT transformation for businesses. Avoiding the greenfield trap and incorporating these IoT brownfield coping strategies sets you up for the best possible chance of success in the long run.

    — Don Dingee

  • Digital Transformation Strategy? Think Cloud

    Digital Transformation Strategy? Think Cloud

    A few years back companies used innovation and digital transformation mostly to differentiate themselves and to stay competitive. The drastic growth in digital and cloud computing over the last couple of years have changed this mindset.

    Today, organizations have to be innovative and leverage the latest technologies just to sustain and stay in business. Enterprises that implement online retail business, online banking and several other online services aren’t considering those channels simply as another route to increase their revenue—they have realized that online services are fast becoming their primary revenue channel. According to Forrester’s Report, “The Digital Business Imperative,” data analyzed just for three months showed that 84 percent of U.S. banking customers had used online banking for their transactions and 43 percent of the customers had used a mobile phone for these activities.

    Organizations are working at a very fast pace reviewing and analyzing their processes to seize opportunities for digital transformation. It is important to understand that to realize digital transformation, companies may need to completely re-engineer their current processes to make use of technologies such as the internet of things (IoT), big data analytics and artificial intelligence instead of doing patchwork on existing processes to adapt to digital technologies. It is also important for senior IT executives to consider digital initiatives in tandem with their cloud strategy instead of treating them in isolation. There is absolutely no doubt that the scale, speed and massive infrastructure requirements such as compute power, storage and other associated platforms to support these technologies are best provided by the cloud. The question to ask is, “Are we going to develop and host the infrastructure we need to support digital, or are we going to leverage the constantly improving robust infrastructure and services that cloud providers are offering?”

    Organizations that plan for massive growth and transformation are usually the ones that invest considerably in technology to support innovative ideas. Advancements in the digital space help fuel innovation, but without a solid cloud strategy accompanied by agile development processes, ideas are likely to remain simply as ideas on paper and will take forever to get materialized into products or solutions that provide value. In today’s context, digital and cloud are almost inseparable due to the robust infrastructure, services and tools available on the cloud to support digital initiatives. All leading cloud providers offer competing solutions and services to help organizations move forward with their digital initiatives at a rapid pace.

    In my previous article, “Transforming with the Cloud: If Not Now, When?” I touched upon some of the key areas that organizations should be considering with regards to transforming their businesses with the cloud. I have attempted to structure this article in a similar fashion, with the focus being on the role played by cloud to support some of the key digital technologies such as artificial intelligence, big data and analytics and IoT. The idea is to provide a very high-level overview of the digital landscape and how the leading cloud providers are helping enterprises with their digital initiatives.

    Artificial Intelligence

    The digital revolution, which centered around mass production of computers and communication devices, has changed the way businesses have operated over the last several decades, resulting in constant improvements ranging from generating new ideas for products and services to innovative product designs to improving the customer experience. At present, the world is going through another, possibly even stronger wave of revolution. It is the use of artificial intelligence to perform complex cognitive tasks to solve business problems in ways that previously were either highly complicated or extremely resource-intensive. Most organizations deal with business propositions that yield small to medium value and require a high volume of human work, such as reviewing large numbers of documents such as RFPs to understand requirements and estimate costs versus returns to determine whether to proceed with those proposals. AI is proving to be the best alternative to handle cases such as these that are currently being dealt by human beings, but the volume of work involved challenges businesses on the feasibility of continuing those without AI.

    AI systems try to mimic the human brain, which uses patterns to generate perceptions, and logic to drive the structured approach of analyzing a situation from a rational perspective. AI systems process large volumes of data that could come in from various sources such as sensors, online applications, textual data from social media and the like. AI processes the data using perception to analyze patterns and incorporates machine learning to utilize structured evaluation methods and rational decision-making, not only to extract pieces of meaningful information, but also to assemble such pieces of information to make valuable decisions.

    Cloud plays a significant role in enhancing the power of applications that incorporate AI. Almost all major players in the cloud business have developed AI services that use powerful cognitive engines to process structured data such as relational databases and unstructured data from NoSQL databases, sensors and other technologies that get uploaded to the cloud. The robust pattern-matching algorithms and the powerful logic component built into these cognitive engines are highly sophisticated. Data and compute power are the two most critical requirements to make these engines effective. The engines predict more accurately with larger datasets. AI applications such as image recognition, video analytics, natural language processing and speech recognition leverage machine learning using highly sophisticated neural networks that perform detection and prediction from large volumes of data. Parallel processing with the use of graphics processing units (GPUs) make these data processing and computations faster. Building and implementing such robust GPU-based parallel processing engines on-premises would be a very expensive and resource-intensive initiative. Cloud addresses this problem by providing APIs to access several machine learning services such as video analytics, speech recognition, process automation, vision detection and natural language processing. Behind these APIs are complex infrastructures that combine the power of clusters of GPU-based compute engines, neural networks and data lakes.

    Amazon’s AWS Marketplace offers machine learning and AI software solutions from several major software vendors to help AWS customers increase the efficiency and productivity of their businesses. AWS’ artificial intelligence solutions offer several pretrained services that can be utilized by developers to add intelligence to their applications via simple API calls. Advanced deep learning functionalities are provided by Amazon Lex, which chatbots could incorporate for automatic speech recognition and natural language processing abilities. Similarly, AWS provides Amazon Polly to build applications that can convert text into speech delivered in natural sounding male or female voices. Amazon Rekognition, meanwhile, uses technology to analyze billions of images, so applications can be developed to search images, detect and compare faces, objects and scenes.

    Google Cloud also offers competing products and services to support their customers’ AI initiatives. Google Cloud Natural Language provides powerful machine learning models that analyze text to provide meaningful insight such as customer sentiments about a product via conversations on social media. Google provides Cloud Speech-to-Text, a service that understands about 120 languages and converts audio to text. Similarly, Cloud Text-to-Speech can convert text to natural-sounding speech in 30 different voices and in several languages. Google also provides products such as Cloud AutoML, a suite of machine learning products, and Cloud Machine Learning (ML), a managed service that can be leveraged by developers and data scientists to build powerful machine learning models.

    Microsoft Azure provides the infrastructure, tools and services to build smart applications with data that can live in the cloud, on-premises or on edge devices. Several products and tools are available via marketplace, and Azure provides sophisticated AI services to integrate the tools to develop intelligent business applications. Powerful chatbots could be developed by integrating Azure Bot Service, language understanding and external systems such as Office 365 and Dynamics CRM to streamline and automate common activities. Another example is an automated failure detection system for assembly lines built based on Azure’s image classification system using a convolutional neural network to help improve the identification of faulty electronic components. Azure also provides Visual Studio Code Tools to build, test and deploy AI solutions. The powerful AI Toolkit for IoT Edge from Azure enables IoT devices with AI that runs locally through pre-built deep learning models. Last but not least, Azure’s Machine Learning Studio helps develop and manage predictive-analytics solutions.

    Big Data and Analytics

    With the growth of internet, cloud and social media, there has also been an exponential growth of data across the world. According to statistics on big data generation in the last five years, the average volume of data created in the world every day is about 2.3 trillion Gigabytes. When the nature of the data was more structured and organized, companies relied on data warehouses and BI applications to help make important data-driven business decisions. Traditional data warehouses were built based on relational databases that could be queried using SQL; the data could be extracted, transformed and loaded from one or more data sources via ETL jobs that ran up to several times a day. This overall approach has proved to be ineffective when it comes to handling and modifying continuous streams of real-time data flowing in from multiple dissimilar sources such as social media, IoT, the public web and relational databases. Big data analytics, which helps examine large structured and unstructured data sets, has become a major enabler for enterprises to make critical business decisions by providing insight and knowledge with data mining, predictive analytics and forecasting. As big data processing evolved, the concept of data lakes came into existence. A data lake is a centralized repository that allows the storing of structured and unstructured data as is and enables the use of a variety of tools and approaches to answer business questions.

    The ability of the cloud to scale vertically and horizontally makes it the ideal platform for big data hosting and analytics. With vertical scaling, it is possible to increase the capacity of a server by adding resources as needed by the applications. Horizontal scaling allows expanding hardware as the processing requirements increase. Hadoop, which leads the big data revolution, is designed as a distributed system so that it can take advantage of scaling. Parallel processing is an important part of its design so that the system can process several independent small tasks such as serving data stores and file systems, processing streaming data and handling queries in tandem. Cloud-based systems offer high bandwidth, enormous amounts of memory and scalable processing power to help big data applications with improved real-time processing and analysis of streaming data. Cloud undoubtedly is an obvious choice for applications running large workloads and storing enormous volumes of data. Cloud providers offer highly scalable database services coupled with tools and services to support information-management, business intelligence and analytics.

    All major cloud service providers offer competitive and comprehensive tools and approaches to process big data at the volume and velocity that your business demands, while at the same time being conscious to reduce the associated operational costs. To perform faster and efficient transfer of petabytes of data to the cloud, AWS provides solutions that employ different techniques such as network multiplexing, reduced storage footprint via data deduplication and compression and advanced data replication. AWS offers services including Amazon Elastic Compute Cloud (EC2) and Simple Storage Service (S3) to provide the elasticity needed for your data lake along with your choice of database products, ETL and reporting tools via AMS Marketplace solutions. Similarly, with Microsoft Azure HDInsight, solutions can be developed using open-source frameworks such as Hadoop, Spark and Hive to analyze streaming or historical data. To process large volumes of data, HDInsight offers a broad range of cost-effective solutions for ETL and data warehousing. Azure HDInsight offers specific cluster types such as Apache Hadoop, Apache Spark, R Server and Apache HBase, with cluster customization capabilities such as adding components, utilities and languages.

    Internet of Things

    IoT is the concept of connecting devices such as security sensors, surveillance cameras, smartphones, smart watches and other wearables and even household appliances such as washing machines, refrigerators and several others that have the ability to communicate and transfer data over the network without direct human interaction. This disruption in technology has not only empowered individuals and families with greater control of their household systems and appliances, but enabled organizations with data that provide greater insight into critical areas of business interest, opening the doors for innovative products, solutions and new business opportunities.

    The rate of adoption of IoT by businesses is growing exponentially as the number of connected devices continues to increase. As per Gartner’s recent study, businesses could already be using as many as 3.1 billion IoT devices today, and by 2020 this number is likely to rise to about 7.6 billion. According to an article in Forbes, IoT will have an impact in all industries, from manufacturing to logistics to health care. We are not too far from the days where nearly everything in the world will be connected. IoT is at present playing a dominant role in optimizing production, managing supply chains, tracking assets, making financial decisions and improving the customer experience. Digital transformation of the fitness industry has been remarkable. Wearable devices with built-in sensors can constantly collect data on physical activities such as distance travelled, calories burnt and sleep patterns to provide detailed analyses and insight for continuous health care. IoT applications are designed to use data from connected devices, and the sophisticated tools available in the cloud let you visualize, explore and build complex analytics. In complex IoT applications that use several devices, it is important to understand the state of the devices and to frequently communicate to the application components that leverage those devices. It is also essential to ensure secure identity and access between devices and applications.

    As with any revolution in technology, IoT also has its associated challenges. With more devices being connected to the internet, the volume of data generated is also immense, which in turn puts significant pressure on the internet. So there obviously is a need to support infrastructure that can transmit and store this data more efficiently. Due to the vast number of connected devices, and with that number constantly increasing, there is a push toward making these “edge” devices intelligent to perform some degree of processing and then send the results to the servers rather than sending massive amounts of data to central servers to perform all the processing. For example, surveillance cameras in the past would send all the videos to the central recording device, which in turn would record only when it detects motion. Consider the scenario where hundreds of high-definition surveillance cameras are sending video feeds constantly to the central server—the impact this could have on the network is unimaginable. It should also be noted that as the physical distance between the devices and the server increases, network transmission latency increases. With edge computing, the video cameras are smart enough to sense motion and they send videos to the cloud-based central recording system only whenever they detect motion. This drastically reduces the volume of data that is transmitted over the network and makes it a more efficient system. Cloud and IoT complement each other—connected devices generate huge amounts of data and cloud provides the infrastructure to store, process and analyze the data. It is also interesting to note that IoT is an important data source for big data analytics.

    Cloud IoT Core service, built by Google Cloud Platform (GCP) partners, offers connectivity to devices by providing the necessary hardware, software and solutions. These services include secure connection of devices (via identity and authentication) over industry standard protocols including MQTT and HTTP.  Cloud IoT Core also allows remote controlling of devices from the cloud by maintaining a logical configuration of the devices. Since this IoT service is serverless, there is no need for upfront software installation and the horizontal scaling of the GCP can instantly be leveraged when required. A host of tools such as Dataflow, BigQuery, Bigtable and Data Studio are available to help with data analytics and insights.

    Microsoft provides services for IoT via the Azure IoT Hub. Features offered by the Azure IoT Hub include ensuring bi-directional communication with billions of IoT devices, establishing device-specific access control, secure and scalable provisioning of devices and integration between cloud and edge to distribute intelligence across devices. Azure IoT Edge provides features for optimized performance of the edge and cloud such as ensuring the lowest latency between edge devices and the cloud. IoT Edge helps control IoT costs by reducing the volume of data transferred from the edge devices to the cloud. It leverages services such as Azure Stream Analytics to process data at the edge and transmit only the bare minimum data that is needed by the cloud for further analysis.

    Amazon provides a fleet of services such as AWS IoT Core, AWS IoT Device Management, AWS IoT Analytics and several others to support IoT initiatives. For easy deployment, programming and management, Amazon offers the FreeRTOS lightweight operating system for small edge devices. The AWS Greengrass software further enhances the power of edge devices by offering local compute, secure messaging and data caching. AWS IoT Analytics, a fully managed service, provides all the capabilities required to run complex analytics on IoT data to obtain insights and to build machine learning use cases.

    Taking Everything Into Account

    Technology is certainly a major driver in any business today and companies that struggle to integrate technology effectively will eventually find it difficult to sustain or do well in their marketplace. Connected devices, social media and massive volumes of several forms of structured and unstructured data feeds have paved the way to further leverage technology and transform businesses. This “digital transformation,” which includes use of artificial intelligence, big data analytics and IoT, is fast becoming a key requirement for organizations to be innovative and competitive. Digital has opened doors for better analytics and decision-making, leading enterprises to explore, analyze and obtain new insights and ideas on growing their businesses.

    With cloud reaching a high level of maturity with regards to services, tools and security, it has become the ideal platform for digital technologies. All leading cloud providers have comprehensive and competitive solutions, tools and associated services to address even the most complex digital transformation initiatives for their customers. Furthermore, the horizontal and vertical scaling of infrastructure offered by the cloud makes it highly suitable for the compute requirements demanded by digital technologies.

    Escaping from or ignoring the digital revolution will be disastrous to organizations as well as individuals. Humans and businesses are generating trillions of gigabytes of data every day. Whether we have realized it or not, digital is transforming our lives. It is important for organizations to get on board soon and devise a digital strategy that works together with their cloud initiatives and strategy.

    — Sridhar Asvathanarayanan

  • DevOps’ Role in Developing a Robust and Secure IoT Framework

    DevOps’ Role in Developing a Robust and Secure IoT Framework

    Creating a secure IoT infrastructure is a challenge for developers, requiring a holistic approach. It requires a dynamic framework that is capable of collecting data from a cloud back end as well as directly from the embedded hardware.

    Supporting this secure environment is another challenge, requiring the development of complex systems capable of deployment in a timely and cost-effective manner. This is where the relationship between DevOps and IoT proves invaluable.

    In this article, we will take a close look at how DevOps adds value to the development of a robust IoT framework. We also will examine ways in which DevOps can help in creating a secure and efficient framework for IoT app development.

    DevOps and IoT Framework

    DevOps, with its focus on reducing delivery cycle times, can increase the speed at which IoT systems can be integrated and function in a seamless and secure manner. Seamless integration can be achieved through a combination of secure app development and operational quality assurance checks.

    The DevOps methodology is rooted in the agile philosophy, which attempts to bridge the gap between different technical roles that traditionally worked in silos within the same department in IoT app development.

    Using the right tools and practices, DevOps can help in creating a framework for IoT that can overcome challenges relating to the large-scale app deployment. It can help in infrastructure positioning and monitoring. Companies that have adopted DevOps methodology have reported a 38 percent increase in growth due to additional revenue sources.

    Let’s take a look at some of the ways the DevOps framework can create a secure and robust environment for IoT app development resulting in increased productivity and revenues.

    Application Reliability and Efficiency

    One of the main functions for DevOps is to improve application reliability and efficiency—there is less need for coding and increased focus on rule-based automated configuration of key connected components. With DevOps, the app development team can identify red flags and improve operational excellence. With monitoring tools such as Log-entries, Nagios and Icinga, the team can use DevOps processes to remove latency and develop an efficient and robust IoT infrastructure.

    DevOps comes with source code control of the software applications that execute in the data center. This means developers have access to the source code, which makes their job easier. The source code reduces the complexity caused by cloud connectivity, automation and virtualization.

    Applications deployed through DevOps recover 96 times faster from failures and reported to have 440 times faster lead time for changes. Deeper collaboration between operations, quality assurance and development teams result in an ongoing feedback that helps increase IoT application reliability.

    Security by Design

    An important component of DevOps is security by design, which helps improve the security of connected systems in a DevOps environment. In security by design, the system allows continuous security testing in real time, which helps improve the security of devices connected in a network.

    The security-by design approach also formalizes security controls and implements them at each stage of the IT operations. As a result, software developers can spend time efficiently developing security controls in a consistent way instead of manually building and patching individual scripts.

    The DevOps approach to IoT application development can also help in gaining insights into the usage and performance, which can help in further refinement of the design delivery pipelines. This will lead to improved security and quality architecture of IoT environment.

    Faster Release Cycles

    Around 21 percent of respondents in a survey of 300 senior managers had stated that DevOps integration increasingly lead to meeting application delivery deadlines. The DevOps metrics lead time is used to measure how quickly the app is deployed after starting work.

    DevOps integration with IoT will result in the following benefits:

    • Developers can work on different components of an application simultaneously, which allows faster completion of activities.
    • Quick release cycles allow IoT apps to be pushed live without much delay.
    • Fast release cycles enable the app development team to build IoT applications using separately deployable services that lead to quality app deployment.

    Continuous Integration and Continuous Delivery (CI/CD)

    DevOps framework helps overcome interoperability problem related to IoT app deployment and operations. Most systems required for IoT app integration are already running in the framework that helps in quick deployment. Businesses can choose the process to automate from staging to production using tools such as Jenkins, GitLab and TeamCity. CI/CD allows developers to respond more quickly to market demands by proactively monitoring the processes.

    The approach results in faster resolution time and release of quality IoT application. Research by CA Technologies found that organizations that adopt DevOps find 21 percent increase in software delivery and 18 percent reduction in time to market due to an efficient CI/CD pipeline.

    Infrastructure Automation

    Infrastructure automation save systems administrators and IT resources time on manual tasks and remove the possibilities for human error during IT configuration, patch management and performance tracking. By scripting almost every command, developers can significantly reduce input requirement of administrators. Increased automation also provides stability as the system is no longer prone to human error on input. Repetitive tasks are a prime candidate for automation, as these are the most likely areas where people make mistakes.

    DevOps methodology makes it possible to automate the infrastructure in multiple cloud platforms. For this purpose, developers can use tools such as Spinnaker and Terraform. Terraform is useful for defining and managing the DevOps infrastructure, while Spinnaker is more useful for managing the infrastructure after code release and deployment workflows.

    DevOps framework also helps automates allocation, de-allocation and monitoring of both cellular and cloud resources, which ensures quality of service (QoS) for applications that control connected devices.

    Effective Validation

    Using endpoint validation can help in evaluating how data flows between different components. Testing of the components in the DevOps environment is done using scripts written in Python, Go or Groovy. The infrastructure testing involves stimulating the environment to test security and other components of the IoT app.

    The app development team also can make use of infrastructure as code to test configuration and environment automation. The configuration is tested using tools such as Chef, Ansible and Puppet. In addition, virtualization tools such as Vagrant or container technology can be used to test the environment automation.

    A Comparison of IoT with and without DevOps

    IoT with DevOps

    IoT without DevOps
    1. Implementing DevOps framework to IoT app development results in 21 % reduced security risk.

    2. DevOps can lead to faster deployment of IoT apps. The updates to IoT apps can be deployed every few days, and even many times in a day.

    3. IoT apps with DevOps practices can result in shorter lead time due to the reduced complexity of the development systems.

    4. With DevOps, the IoT application development team has reported a 51% increase in productivity.

    		 1. 71 % of respondents have said that security breach is a large challenge for IoT development outside of DevOps framework.

    2. Lack of DevOps can prevent fast feedback mechanism.

    3. Without DevOps, lead times can be longer. This will result in time and cost inefficiencies.

    4. IoT app development without DevOps presents productivity issues. There are few standards for app development and deployment that results in decreased productivity of the team.

    Conclusion

    DevOps tools and practices help businesses achieve efficiency when it comes to IoT app development. DevOps improve the validation of automated processes across multiple cloud vendors in a secure environment.

    Looking forward, DevOps integration with IoT will become increasingly popular as it can reduce costs and improve revenues significantly for app developers.

    — Subramonian

  • Updating Your Skills for the Internet of Things

    Updating Your Skills for the Internet of Things

    The internet of things (IoT) is breaking rules. For example, in many cases IoT designers purposely limit the processing power and connectivity of IoT devices. Making devices with less processing power and connectivity may buck the IT trends of recent years, but IoT is changing some of the assumptions and approaches of traditional IT. IT professionals moving toward IoT will need to learn some new skills, strategies and technologies.

    IoT devices are smart (having their own embedded processor) and connected (often using wireless networks). But setting up, configuring, programming and maintaining an IoT device is very different from doing those tasks on a traditional computer. The typical IoT device is purposely configured with extremely limited processing power, memory and data communication capabilities. A device may not have a user interface other than perhaps a power button and a few LED lights. These constraints have positive effects; they make IoT devices both inexpensive to produce and downright miserly in their use of electrical energy. And IoT devices may even extract energy from the environment around them, a feat called energy harvesting. This design strategy enables devices to be located anywhere, even in remote locations where electrical power sources and connectivity may be limited.

    Sensors on IoT devices can collect a mind-boggling variety of readings, such as the temperature of frozen food in a delivery truck, the volume of traffic passing through an intersection, the quality of soil in a particular location of a farmer’s field and countless other measurements. The sheer volume of data produced by IoT devices must be processed, interpreted and handled securely. At the same time that IoT is dramatically increasing data volumes, it is also creating new challenges to keep that data secure and private.

    Real-time data processing and networking enable data to be collected, processed and acted on very quickly. Based on intelligence provided by the data, an immediate, automated response may need to be triggered. For example, the temperature in the refrigerated delivery truck may need to be adjusted. The brakes in a smart car may need to be activated to avoid a collision. Or the mix of chemicals may need to be modified as a spraying machine advances through a farmer’s field, based on soil samples taken at the sprayer’s exact geographic location. The ability to process and act upon data so quickly seems at odds with the limited power and capabilities of the typical IoT device. But special operating systems and data processing strategies enable these real-time operations to be performed efficiently.

    Edge computing provides another strategy for processing the data from IoT devices. Rather than perform all heavy processing tasks in the cloud, edge computing moves some processing tasks back to the edge—where the local network meets the cloud. This means placing some sort of heavy processing capability near the IoT devices, and using an IoT gateway to “do the heavy lifting” on behalf of IoT devices. The gateway can handle all communication with the cloud, so the IoT devices themselves can use lightweight communication protocols and (safely behind a firewall) won’t require the same security capabilities (such as heavyweight encryption processing) that they would require if they were directly connected to the internet.

    New methods of data processing and analysis are used to make sense of the massive quantities of data produced by IoT. Data analytics tools such as Azure Stream Analytics, Vitria, and Statistica are geared toward IoT. Big data and cloud storage technologies, such as Hadoop and NoSQL database systems, take on new importance with IoT.  So do more traditional skills such as SQL. Machine learning and artificial intelligence technologies will also play a critical role for processing IoT data.

    IT professionals preparing to move to IoT should consider adding some of the following skills to their portfolio:

    • Working with microcontrollers, sensors and actuators.
    • Programming real-time operations on microcontrollers.
    • Embedded device power management and deployment of energy harvesting technologies.
    • Selecting, installing and configuring real time operating systems.
    • Setting up and configuring sensor networks and IoT gateways, including communication technologies geared toward these networks, such as MQTT, 6LoWPAN, Thread and Zigbee.
    • Managing cybersecurity on embedded devices and sensor networks.
    • Working with data analytics tools, advanced SQL, cloud storage and NoSQL databases, artificial intelligence and machine learning.

    CertNexus is looking for IT professionals to take the CIoTP beta exam. The Certified IoT Practitioner is meant for those seeking vendor-neutral, cross-industry, foundational knowledge on the internet of things. If you are interested in taking the CIoTP beta exam, please email info@certnexus.com.

    — Brian Wilson

  • Using Blockchain to Advance IoT

    Using Blockchain to Advance IoT

    Imagine two disruptive technologies come together to democratize transparency and connectivity in the tech world. That’s exactly what’s happening with blockchain technology, once synonymous with powering cryptocurrencies including Bitcoin, has the potential to be a disruptor in a number of verticals and the internet of things (IoT), with blockchain addressing key IoT security concerns.

    About Blockchain

    The blockchain is the transaction recording distributed computer ledger. Its major use traditionally has been in securing the transaction of cryptocurrencies over the internet. Different users have their own wallet, which serves as a node in the blockchain network connected to the internet. Transaction messages can be sent in an encrypted form, which acts as a digital signature and does not allow tracing of the user’s wallet or tampering with any data stored there. Each wallet is controlled by a public key and a private key. To initiate a transaction, the user broadcasts an encrypted message in the network using the private key and the message can be decrypted by other network members using the public key. To materialize a transaction between the users, it needs to be approved by the majority of the nodes to help detect any fraud or data-breaching attempt before finalizing the transaction. So, it is the public that is controlling transactions, unlike banks or other financial institutions.

    About IoT

    IoT is a concept of embedding everyday-use items with sensors and other electronic components for enabling connection and data exchange between these objects. It is growing at a fast pace, and internet-connected devices increasingly are being used in different industries. IoT has made life easier, with smart devices that can be controlled using mobile apps, and can even be programmed using artificial intelligence.

    To function most effectively, IoT devices generally take user information, which means there are security risks. As a result, IoT development is lagging and IoT vulnerability to cyberattacks due to the lack of a robust centralized security model is a real concern.

    Blockchain and IoT Complement Each Other

    It is evident from history that whenever two complementary technologies merge, new, dynamic products are formed. The greatest advantage provided by blockchain technology is security of data, which happens to be the most difficult challenge faced by IoT. Immutability, decentralization and transparent data-sharing are the most significant advantages of blockchain that are lacking in IoT, and the IoT community wants to integrate these features for data security. User privacy can be maintained in cases where desired by grouping and classifying anonymous individuals or groups under an über Do Not Track (uDNT) system. The smart IoT devices can use the data and information stored on blockchain efficiently for performing various tasks. Blockchain also can help in IoT scaling as it struggles for growth.

    Blockchain also benefits from being paired with IoT, as it is showing its worth in a new domain of application apart from cryptocurrencies that most would consider to be much more useful on a daily basis. Thus, the two disruptive technologies complement each other and can reap dynamic solutions for different industries when used in conjunction.

    Security of Data is Enhanced

    IoT needs a robust centralized security model to grow in acceptance and use—something blockchain technology and the cryptographic processes driving it can offer. More and more offline objects are being converted into online assets with IoT, thereby increasing the risk of cyberthreats. Businesses, therefore, need to plan strategically for the protection of not just the smartphone and desktop computer, but almost everything connected to the internet. The objects in IoT ecosystem can act analogous to the nodes in blockchain technology, thus making connected home, connected cars and other connected devices more reliable and secure with blockchain-based IoT.

    Data Analysis and Intelligent Use of Data

    By leveraging other technologies such as artificial intelligence (AI), businesses using IoT along with blockchain can uncover useful insights through data analysis and understand the consumer requirements and market trends in a better way.

    User Interconnectivity is Enhanced

    With the transparency provided by blockchain, the users can make the best use of the interconnectivity provided by IoT to the networks of smart devices. IoT users can connect with each other in more beneficial ways and discuss their issues and queries more openly to gain different insights.

    Privacy and Integrity of Data is Increased

    With the increased amount of data, the privacy of data is gaining more importance as powerful IoT companies are tracking almost everything about the users. Lack of proper encryption and data leakage not only put individuals at risk, but also makes industrywide business models vulnerable. Using blockchain with IoT not only makes data immutable, tamper-proof, verifiable and transparent, but the data also is encrypted correctly to preserve user privacy.

    Transactions are Fast and Small

    Certain companies plan to incentivize users with small rewards for sending data, such as by paying a percentage of their electricity bill, providing points or paying the user, thereby creating a new route for earning a passive income. However, with banking fees often outweighing the transaction itself, this idea currently is not feasible. Blockchain can help to solve this problem.

    Transactions can be Automated

    Using blockchain, the services and transactions made by IoT devices can be automated without human interference. For instance, an autonomous car would be able to pay for a refill, parking fees or mechanic fees by enabling smart contracts that follow the “IFTTT (IF-this-THEN-that)” command.

    Infrastructure, Operational Costs are Reduced

    Using blockchain, the infrastructure for projects can be outsourced and the cloud side of IoT can be handled by the network. The need of any middleman and the legal and contractual costs, along with the maintenance, update, and supervision costs are cut down. This leaves ample time and money with the companies to focus on the hardware, application, and innovation side of things.

    How will Blockchain Combine with IoT?

    In the near future, a conventional, centralized model will not be able to manage the data produced by IoT, so, the decentralized approach of blockchain technology will be necessary for data management to avoid data tampering, single-point failures and third-party interference. The entire data is shared across the network like a shared ledger updated in real time.

    Comprehensive Blockchain-IoT Approach

    To face growing competition in the market, businesses that adopt the blockchain-IoT approach earlier are bound to be more successful. A suitable blockchain-IoT app development company is one that has the following characteristics:

    Domain-specific knowledge: Data intelligence needs to be applied along with the knowledge of specific domain to derive the warranted performance or result from investments.

    Development intricacy: With the growing business needs and customer expectations, businesses expect a high-performance computing infrastructure. Thus, the company should be able to deal with the business operation and data processing complexities.

    Data management on a large scale: For storing and processing large amounts of data, AI and machine learning need to be used. The company must have a hands-on experience in data handling and processing using these techniques.

    Final Thoughts

    Blockchain and IoT together can be a game-changing technology for the mobile world. Many companies will benefit from the added security and automation offered by blockchain. In the very near future, we are going to see how blockchain solutions will rule the commercial IoT world and futuristic companies will undertake the process of developing and implementing it themselves.

    — Juned Ghanchi

  • Connected Devices and IoT: How the DevOps Approach Can Help

    Connected Devices and IoT: How the DevOps Approach Can Help

    The evolution of connected devices has brought challenges associated with the complexity and managing huge volumes of data. These devices also bring the need for a huge ecosystem of continuous software deployment across numerous application delivery endpoints, including a diversity of devices, mobile, web and others.

    There exists also the opportunity to unify operations and development, thus giving rise to the DevOps environment within the product engineering and IoT space—encompassing tools, processes and people as a single unit for more efficient and coherent working.

    DevOps accelerators and services allow intelligent automation and power the industrial IoT and consumer IoT companies with the capabilities of deployment, testing, build and continuous integration.

    It enables the organizations in connected health care, telematics, networking, security and surveillance, and home and industrial automation to accelerate their product development cycle, deliver significant cost savings via release efforts and reduced outages, enable operational scalability and improve code quality. It will enable the solution and product companies to accelerate their prototype to product cycle and decrease deployment time to days from months.

    Application reliability and availability are the ultimate measures of the success of IT. DevOps applies lean and agile practices all through the software life cycle to attain faster deployment and high-quality application development. Also, DevOps alleviates the rising complexity caused due to new computing trends including mobility, cloud and virtualization via increased automation. Now, let’s dig deeper into the importance of DevOps in the world of IoT and its applications.

    DevOps in the IoT

    The most effective DevOps transformations take a holistic approach, which means taking care of the entire organization in all aspects. This implies including all the constituents of the application, including quality assurance, operations and development teams, line managers and business owners. The constituents may also extend to compliance and security teams. The cross-functional inputs for software functionality and system requirements generates an ongoing feedback loop that helps reduce complexity and promotes engagement.

    Thus, one of the advantages of DevOps is improved collaboration and communications across the company. Deeper collaboration between quality assurance, operations and development teams follows an approach in which they all with collaborate with the client for designing a customized solution to fit their requirements and needs.

    Application testing should reproduce the function of a production system environment. Then, the DevOps teams can locate dependencies, gain knowledge on the functioning of an application when it is in the live environment and, accordingly, make adjustments to the compute environment. These processes become repeatable and iterative with automation and practice, allowing for more deployment, testing and development.

    Analytics and performance monitoring come earlier in the life cycle by moving the testing forward in the process. DevOps can develop performance analytics models which can predict quality and operational problems before deployment rather than waiting to acquire post-production performance data to examine what went wrong.

    These metrics can be utilized to establish KPIs (key performance indicators) and the production environment can be measured against them. The user experience and application performance can be improved as production metrics adhere more consistently to the key performance indicators. At this stage, sharing the data with business teams allows for adjustments to be made with less stress and faster, and accelerates the feedback loop.

    Continuous integration allows for better agility and collaboration to enhance code validation, which in turn reduces risks. The automated processes make for releases of higher quality that are easier to handle once they are in production.

    Treating the infrastructure configurations as code enables DevOps teams to handle provisioning on the fly in software and this sets the stage for the eventual migration towards a completely software-defined environment. With the emergence of the IoT, DevOps will become even more important. The precision of the services and software controlling networks, devices and sensors is critical as every device becomes interconnected in a software-defined environment.

    Conclusion

    Today, DevOps has become the main destructive force in the adoption of many technologies such as blockchain, which is the technology behind cryptocurrencies such as the bitcoin wallet. Owing to its substantial impact on business outcomes, operational efficiencies and deliverables, DevOps has become the major reason behind the evolution of IoT.

    — savaramravindra