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Author: Sridhar Asvathanarayanan

Best of 2021 – How to Scale Your Website to Perform on Mobile Devices
As we close out 2021, we at devopsy.kinsta.cloud wanted to highlight the most popular articles of the year. Following is the sixth in our series of the Best of 2021.

We all surf the internet mostly on our mobile devices because they are so handy and easily accessible. A vast majority of mobile device users are making online purchases and paying their utility bills using these devices. A recent study by Forrester, “The Digital Business Imperative,” showed that in a three-month period, 43% of the banking customers in the United States had used mobile phones for online banking transactions.

The growth year over year of the volume of online business transactions via mobile devices has been significant, encouraging companies to get conscious about building websites and e-commerce sites that look, feel, function and perform identically on computers as well as smart mobile devices. However, most of the time, the experience of browsing a website on a smartphone isn’t the same as doing so on a computer. The fundamental causes of this varied behavior across different platforms should be understood to be able to develop websites that scale effectively/smoothly across different devices.

Web pages are usually composed with one or more of the following components: header and footer, main content (text), images, forms, videos and tables. Devices differ in screen dimension (length x width), screen resolution (pixel density), compute power (CPU and memory) and operating system (iOS, Android, Windows etc.). These differences contribute significantly to the overall performance and rendering of web components such as images, videos and text across different devices. Another important point to note is that most mobile users are not always connected to a high-speed network; hence, web pages should be carefully designed so that they also work effectively on low bandwidth connections.

Most Troublesome Issues with Websites on Mobile Platforms

A variety of issues that pertain to the performance and scalability of websites across devices exist, and we will now discuss a few of the important ones as outlined below:
- Websites not adapting to different screen sizes automatically. Some sites are designed to format for variable screen sizes, but their elements may not auto-scale. This would result in the site automatically adjusting itself for various screen sizes, but the elements inside may look awfully large on smaller devices. Some sites may not be designed to adjust for variable screen sizes, causing the elements of such websites to look extremely small on devices with smaller screens.
- Websites with too much content for mobile devices. Some websites are loaded with too much content just to fill the empty space when opened on a desktop. Websites developed without being conscious of mobile users generally fall under this category. These sites take more time and bandwidth to load and if the pages aren’t designed appropriately for mobile devices, then part of the content may not even appear on the devices.
- Sites that take too long to load images. Websites with too many images or heavy image files are likely to take a long time to load if the images were not optimized during the design phase.
- Data in tables look very complex and take too long to load. Many websites present data in a tabular fashion (ex. A comparison of competing products, air fares from different travel booking sites, flight schedules etc.), and on mobile devices, these tables look too complicated, are difficult to comprehend and/or take too long to load.
- Websites hosting videos that don’t play on some devices. Not all video formats are supported by all mobile devices. Sometimes websites host media that require licenses or Adobe Flash and other specific players that some mobile devices may not support. This causes frustration among users and a poor overall experience with the website.
Design Your Sites to Adapt to Different Devices

All commonly encountered issues with websites on mobile devices can be addressed through proper design that takes those issues into consideration. The most highly recommended practice is the “mobile-first” approach. When working with several limitations such as screen size, bandwidth, etc., the need to concentrate on the right quantity and quality of content should be of high priority. The mobile-first approach places content as the primary object and aims at producing a design for the smallest of devices to ensure that they have only the most essential features. It addresses the design challenges for mobile devices first and then makes it easier to progressively enhance the design for other larger devices.

Here are a few best practices to consider when designing web sites that intend to scale on different devices.

Adapting to any screen size: At a minimum, a web page needs to be scaled to fit the screen size of any mobile device. Nowadays, mobile devices come with very high screen resolutions. The pixel density on mobile devices is much higher than that of desktop screens. So it is important to format a page to match the mobile screen’s width in device-independent pixels. The “meta viewport” tag included in the <head> of the HTML document addresses this requirement.

The meta viewport value as shown above helps format the entire HTML page and render the content to match any screen size.

‘Content Is King’: Web designers must remember that content should determine the design of their website and not vice versa. Websites with too many elements such as tables, forms, charts etc., become challenging when they need to scale on mobile devices. Developers end up hiding content for mobile users and as a result, the desktop version and the mobile version of the website become inconsistent. The goal of the design should be to focus on the core structure and content rather than to incorporate decorative elements on the website. As recommended by the mobile-first methodology, designers should ensure a single version of content for both desktop and mobile users. So web designers should carefully consider, craft and optimize the content so that it not only satisfies the business goals but also appeals to the mobile users. Content that doesn’t appear in the mobile version may not even need to appear in the desktop version.

Responsive images. The design should consider small, handheld devices operating in areas with low signal strength. Large photos and complex graphics are not suitable for mobile devices operating under such conditions. Designers need to make sure that the images used in websites are optimized for different sizes of viewports and pixel densities. A recommended approach is to use “resolution switching,” with which it is possible to instruct the browser to select and use an appropriate size image file depending on the screen size of a device. Switching the image according to the resolution is accomplished by using two attributes: srcset and sizes. With these attributes (included in the code snippet shown below), the browser will use the device width to select the most suitable media condition provided in the sizes list, choose the slot size based on that condition and load the image referenced in the srcset that closely matches the chosen slot size.

For example, if a device with a viewport of 320px loads the page, the media condition (max-width: 320px) in the sizes list will be true and hence the corresponding 280px slot will be chosen. The first image listed in srcset (elephant-320w.jpg) has a width that is the closest to this slot Browsers that don’t support resolution switching will display the image listed in the src attribute as the default image. This approach not only picks the right image for your device viewport but also prevents loading unnecessarily large images that will consume significant bandwidth.

Responsive Tables: The world is getting highly data-driven. Bringing critical and time-sensitive data to people’s handheld devices gives a lot of power and freedom for decision-making to the end users. The challenge is to present data to the user in a fashion that is easy to load onto a mobile device and read. Most often data needs to be presented in the form of tables, but when data tables get too large and unwieldy they can be frustrating to interpret on a mobile device with a small screen. The screen could be much narrower than the width of the table forcing the user to have to zoom out just to fit the table on their screen, making the text appear extremely small. Likewise, the screen could be much wider than the width of the table, forcing users to zoom in to view the data, requiring constant vertical and horizontal scrolling. There are several approaches to building responsive tables, the most important of which will be mentioned below:

The table will not be treated like a table as the columns will be transposed to rows. Each column will be sized to be just as wide as the screen to prevent the need to scroll horizontally. By using appropriate coloring, (1 row of data) each row of data can be distinguished from another. In this case, for each “cell”, the CSS generated content (:before) should be used to apply the label so that each piece of data can be identified clearly.

The second approach is to display the data in two forms based on screen width: 1. chart form in narrow screens and 2. complete table form in wider screens. If the mobile user wants to click the chart to see the complete table, then the approach discussed in (a) can be used to show the data in tabular form.

The third approach is to show a mini graphic in a narrow screen to indicate the presence of a table, which upon the user’s click will allow the table to be expanded and displayed.

A video that always plays: Usually video files don’t play on mobile devices if their formats are unsupported or if they need a proprietary video player to play the content. The recommended approach is to use standard HTML5 tags for videos and animations. The video element in HTML5 can be used to load, decode and play videos on your website. Producing the video in multiple formats to suit different mobile platforms is highly recommended. It is also important to size the videos appropriately so that they play within their containers.

The example below shows the use of <video controls> in which the <source> tags let the developer specify several different types of video formats (indicated by the type element) that the browser can choose from. In this approach, the switch to the right format happens at the client-side and only one request is made to the server. This reduces network latency and lets the browser select the most appropriate video format without having to download a video first to check its format.

The videoWidth and videoHeight properties of the video element help identify the encoded size of a video. Video dimensions can be controlled using JavaScript or CSS. The max-width: 100 percent helps to size the videos to fit the screen. CSS media queries can be used to set the size based on the viewport dimensions. There are also several JavaScript libraries and plugins that can be used to maintain the aspect ratio and size of videos.

All Things Considered…

With the exponential growth in the number of mobile users, smart phones and tablets are used regularly to surf the web and perform business transactions. Online business is turning out to be the primary business channel for several businesses worldwide. Consequently, it is important for businesses to develop websites that work and scale well on mobile devices to enhance the mobile user experience and mirror the functionality and performance of websites on desktop computers and large monitors.

The ‘mobile-first” approach helps web designers create sites that operate well on small mobile devices. Web designers need to be careful about deciding the appropriate content that satisfies business requirements, while considering the devices’ technical limitations, such as screen sizes, processor speeds, memory and operating conditions (eg. poor network signal strength). Care should be taken to ensure that pictures, videos and data that are presented on mobile devices are designed to be responsive. Designers also need to be sensitive to breakpoints, touch targets, etc.

A well-designed website that works and scales well on a small device can always be progressively enhanced to work on larger devices.

Originally published in 2018.

— Sridhar Asvathanarayanan
December 22, 2021
Accelerating IoT by Switching Gears to 5G

Unprecedented breakthroughs in technology have often resulted in the emergence of disruptive technologies and outstanding innovations. A major development in one technology always fuels the growth and advancement of several other technology domains and industries that take advantage of it, resulting in a need to transform businesses to address new opportunities.

The evolution and development of various generations of mobile wireless technologies—mobile phones, mobile application development, eCommerce, banking, transportation, hotels, etc.—have seen a cascading impact on the growth and advancement of various applications. We are about to witness a similar multi-dimensional growth in several industries with the advent of the 5G wireless technology. With speeds that could be at least 10 times faster than 4G networks, 5G is expected to create pathbreaking opportunities for innovation and development in a variety of domains including but not limited to education, IoT, artificial intelligence (AI) and health care.

5G networks offer three major benefits, bigger channels, reduced latency and scalability, which allow the connection to a large number of sensors and devices. This combination will enable the government and other industries to introduce advanced digital initiatives involving AI, machine learning (ML), Industrial IoT (IIoT) and a variety of other data intensive applications.

This article focuses on the effect of 5G networks on IoT, and the role that DevOps could play in this mix.

Where Does 5G Fit with Regards to IoT?

At a high level, the focus areas for the five generations of mobile network technologies can be summarized as: 1G, voice; 2G, voice and texting; 3G, voice, texting and data; 4G, everything as in 3G but much faster; and 5G is expected to be everything in 4G but extremely faster.

IoT devices employ a wide range of wireless technologies such as WiFi and Bluetooth, in addition to cellular technologies such as GSM, LTE, 4G and 5G. In the field of IoT, cellular technologies such as 4G, have helped a great deal with regards to device provisioning and device management. These technologies have also improved and ensured coverage, performance, reliability and security of IoT devices and applications.

5G brings in a variety of other benefits that are not available with 4G. The speed, higher capacity and low latency of 5G will make it more ideal for simultaneously connecting a large number of devices. Further, additional enhancements made by some 5G service providers aim at expanding the frequencies for data transfer by digital cellular technologies. These features will also make 5G a major enabler in applications that use IoT where real-time performance and decision making are critical. Remote medical diagnosis and remote surgery are some examples where 5G’s ultra-low latency will help improve the virtual and augmented reality experience, making them more powerful tools in the medical industry—especially at a time of crisis like the currently prevalent COVID-19 pandemic.

The bandwidth reliability and resiliency offered by 5G will pave the way for several new applications for IIoT systems. For example, factors such as operator productivity in manufacturing, reduction in cost of quality management and enhanced quality through continuous monitoring and improvement, are some examples where IIoT can be made more effective with the use of 5G.

The volume of real-time data exchanges with IIoT devices will be greatly increased with 5G. This helps with obtaining more frequent and reliable data on several KPIs in manufacturing, worker productivity, root-cause analysis, etc. Predictive maintenance, which is turning out to be a major use case for 5G and IIoT, aims at predicting failures before they occur by leveraging AI.

The Difference that 5G Makes on IoT Data

Prior to the advent of 5G, technology required to offer a higher bandwidth and reliable network connectivity were quite limited and have further prevented from realizing the fullest potential of several edge devices. These devices can potentially generate and transmit large volumes of data that can be processed and analyzed by cloud-based applications. With 5G, there will be a sharp increase in the capture and use of valuable data from edge devices leading to greater insights and predictive analytics on IoT implementations.

5G technology offers a significant increase in transmission bandwidth, theoretically up to 10 Gbps, as opposed to 4G which only offers 100 Mbps. This increase in bandwidth will result in significant growth in the volume and quality of IoT data and a proportional growth in AI applications leveraging ML and big data. The mass volumes of data created by IoT under the influence of 5G have the power to revolutionize everything, from education to healthcare to manufacturing and even autonomous vehicles.

With 5G, there will be a surge in the volume of data generated by sensors, cameras, drones and edge IoT devices. Integration of data from different sources will lead to the development of several large data-intensive applications that help draw valuable insights regarding the domains in which they are implemented. The convergence of 5G technology, IoT and data analytics will also pave the way for innovation and several new business opportunities. IoT applications leveraging 5G technology will proliferate wearables, smart home appliances, industrial automation and also future self-driven vehicles.

All Things Considered

There is absolutely no doubt that IoT is already taking the world by storm. The power of IoT, when harnessed with the power of 5G technology, will certainly lead to several innovative approaches and open up new business opportunities. Overall, the speed, reliability and low-latency offered by 5G makes it ideal for connecting a large number of IoT devices.

IIoT devices will find a great use for 5G as they can now collect and report large volumes of real-time data from manufacturing units and sensors, helping companies obtain valuable insights and take well informed decisions. The large bandwidth supported by 5G additionally provides edge devices the ability to transmit larger volumes of data more frequently to applications that can process and use the data effectively for AI and ML.

We will definitely see the industry witness a sharp growth in IoT, AI and ML applications relatively soon, given the fast growth of 5G and the enhancements that several service providers are already making in this technology.

April 20, 2020
Don’t Let ‘Hindsight 2020’ Be Your DevOps Strategy

The idea of DevOps originated a little over a decade ago with a desire to ensure efficiency in the overall software development and delivery process. Constant operational hurdles that have been hampering flawless and frequent delivery of applications have led organizations to think beyond and to come up with continuous improvements in their processes and tooling. Thus, DevOps has evolved into a solid practice to aid enterprises with an end-to-end feedback enabled framework that spans across various functions and stakeholders to design, develop and deliver quality software.

The IT landscape over the last few years has changed significantly. Companies are building secure applications and large volumes of workloads are being transformed to the public cloud. Nowadays, firms are actively applying AI technologies to transform their businesses more than they were just a few years ago. Some companies are still investing heavily on building their own complex AI platforms involving expensive infrastructure, but most companies have realized the value in leveraging AI as a service offered by leading cloud providers.

Digital initiatives and IoT have further increased the use of cloud-based services and the need for automated deployment and delivery of applications and services. Seamless integration of the CI/CD pipeline for on-premise or deployment to the cloud is possible only with a solid DevOps practice. For organizations that are ambitious and striving to get ahead of the game in order to accelerate the growth of their businesses, DevOps is indispensable.

To successfully implement some of the current major trends in IT, organizations are required to have a mature DevOps practice in place. In October 2019, Gartner published its predictions on technology trends for 2020. We’ll pick a few from those to discuss the crucial role that DevOps is expected to play in them, and the critical success factors for companies to be advanced forward.

Strategic Trends in IT and the Role of DevOps

Hyperautomation

Gartner’s analysis on strategic technology trends for 2020 has hyperautomation as the number one trend. Hyperautomation, which enables organizations to function as autonomous digital enterprises, will be leveraging latest technologies such as 5G networks, intelligent edge devices, advanced data analytics, expanding RPA with AI, etc. Firms aspiring to implement AI and ML on a grand scale will want to take advantage of the diverse range of complex machine learning algorithms implemented on scalable cloud infrastructure and offered as services.

Instead of recreating everything from scratch, organizations will simply upload their data to the cloud and train algorithms based on their business processes and needs. For hyperautomation initiatives to be successful, organizations need to implement end-to-end fully automated DevOps processes with the relevant tooling to specifically address the varied needs from several perspectives. Speed, agility and ensuring an error-free repeatable process will be basic requirements for hyperautomation.

On-demand deployment of transient infrastructure on the cloud, driven by infrastructure as code and configuration as code will be a primary requirement for rapid development and testing of artifacts that form components of hyperautomation. Fully automated pipelines for the creation and deployment of containers will become a core requirement not only for hyperautomation but in general for most applications. The involvement of DevOps to manage data virtualization, obfuscation and uploading data to the cloud will become more significant to increase the overall efficiency of the automated CI/CD pipelines for hyperautomation projects.

Democratization of IT

Democratization of technology is about making technology more accessible to common people. Gartner has predicted this to be another major trend for 2020. Smartphones and the internet have already played a major role on this front and have enabled people with the technology, tools and data to help make better decisions. Democratization of technology in the IT world is about enabling the IT community and it gets more and more interesting day by day.

With container technologies such as Docker Desktop that can run on a user’s laptop, a developer now has access to pre-configured container images of various operating systems and other platforms that can be pulled from container registries within seconds. Cloud services such as IaaS, SaaS and PaaS add to this by providing more independence to the development teams apart from making the entire process agile and faster. Democratization at these levels also leads to setting a different level of expectations on the overall velocity of development and delivery of applications. All these services in isolation, i.e. when not connected together through a strong DevOps process and toolset, will not produce the expected level of agility and speed.

An end-to-end DevOps process works all the way: from triggering the build of a container image as the code is built, deploying an environment on the cloud, integrating with other components, testing artifacts with automated testing and finally to producing a production-worthy artifact. When IT teams get more democratic, they will run their domain the way they perceive beneficial and produce the best possible outcome with quality and speed to make their teams and the organization successful. DevOps will be the means to achieving that goal.

Internet of Things

Edge devices are getting smarter by the day. Mass manufacturing of intelligent controller boards, such as the ESP 8266 and ESP 32, is making it extremely cheap to build edge devices with data storage and processing capabilities locally within the device itself instead of communicating with a cloud based central system for command and control. However, such intelligent devices need to be kept up-to-date on security patches, bug-fixes, firmware updates, etc. A solid DevOps framework that integrates testing of patches/fixes, and deploys tested artifacts to remote edge devices is an absolutely essential component of an organization’s IoT ecosystem.

Some controller boards run light-weight versions of Linux and have the capability to additionally run a Docker engine. This helps ensure consistency through deployment of updated containers via a DevOps pipeline to the edge devices. Convergence of cloud and digital transformation initiatives, especially in IoT over the last few years, has widened the scope of DevOps. Major cloud providers are already offering several services to integrate edge devices with a full-fledged CI/CD pipeline leveraging DevOps processes and tools.

Blockchain

Application developers and IT managers spend a great deal of time either fixing bugs or recovering systems from breakdowns caused by bugs. If best practices, processes and tools are put to proper use, this wasted effort can be greatly reduced and the entire development and delivery process could be made more efficient. Blockchain demands building a strong foundation in IT and raises the need for higher quality in software development, delivery and IT operations on the whole.

Organizations have to take a closer look at their current capabilities and process maturity before embarking on a journey involving blockchain. Firms that have faced failures with blockchain can certainly vouch for the fact that the failure wasn’t caused by the concept of blockchain or the algorithms that drive it. Rather they had resulted because the rest of the IT systems weren’t reliable enough to keep up with the strict demands of the blockchain systems. DevOps and automation will certainly help improve the security, quality, reliability and repeatable deployment of applications. When the systems that feed the blockchain application go through such increments of improvement, the net effect is that the organization is getting closer to implementing a solid blockchain solution.

Success Factors

Firms are moving fast toward the cloud for IaaS and PaaS services. As DevOps, CloudOps and SecOps are converging, CIOs are becoming conscious about building organizations that encapsulate these functions under one leadership in order to set shared goals and ensure synergies across these functions. Organizations with strict compliance requirements and governance would need to involve their operations teams to lay the rules regarding using cloud services.

Application development teams and users are still dependent on several operations teams to design and implement secure cloud connectivity and to establish the rules around using cloud services. While a strong governance is absolutely essential, there is a high possibility for firms to lose their agility and get caught once again in a mess if these operational processes are not smoothen and streamlined. For the strategic initiatives on implementing the new trends to succeed, it is vital for DevOps to be able to operate without any hurdles.

Below are a few critical success factors worth considering.

Dev and Ops in a Box

Organizational structure plays a key role in determining the success of DevOps. Keeping applications development, DevOps and infrastructure operations very closely aligned with overlapping goals, KPIs and roadmaps is a key to success. Companies that operate these functions independently without proper alignment fail to establish an organization-wide DevOps culture and rarely reap the full benefits of DevOps.

Full-Stack Engineers for DevOps

It is important to develop a DevOps culture across the company. At the same time, it is equally important to bring in experts who can evangelize DevOps and take a completely fresh look at how things are done. Experienced DevOps professionals can pin-point areas for improvement, and additionally help accelerate the adoption rate of DevOps. Settling for high-quality resources that have full-stack experience is absolutely essential if you are in for a long run with DevOps. Global companies should seriously consider placing such experienced resources close to the development and operations teams. While it may be cost-effective to recruit resources offshore, that may not be such a good idea if your active development and infrastructure teams are onshore.

Trained Internal Resources

Training internal resources are extremely important in order to carry out the recommendations from experts and to make DevOps a habit. Trained resources have to be deployed immediately to work on DevOps initiatives alongside the experts. To establish synergy and increase collaboration, it is a good idea to send development and operations resources to some common training courses and sessions.

Wrapping Things Up

Research organizations are closely observing the direction in which most organizations are moving toward in order to improve their speed, efficiency, agility and their overall ability to provide services that are secure and reliable. Business strategy and technology feed each other and as a result, some key trends in technology emerge from time to time.

We looked at some of the trends that are going to be prominent in 2020 and the role that DevOps would have in implementing these technologies. Regardless of any specific technology, it is important to automate the integration and delivery of applications. DevOps processes and tooling will help a great deal in accelerating the pace of implementing new technologies and leveraging them in business applications.

The organization of IT teams and a focus on recruiting the right talents will determine the overall success of DevOps. Training internal staff would help sustain the culture of DevOps in companies and fuel further growth in this space. DevOps will not be an optional function any more. It will be an essential component of the culture, and firms that adapt themselves sooner to this culture will be the ones most successful in implementing the current and future trends in technology.

— Sridhar Asvathanarayanan

March 3, 2020
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

July 17, 2018
Transforming with the Cloud: If Not Now, When?

The question of whether to move to the cloud is no longer relevant for most organizations. Migrating workloads to the cloud has already become an inevitable requirement for a vast majority of companies regardless of their sizes, and if your organization isn’t doing that already, then it is simply the question of when and how you’ll consider doing so.

The agility, redundancy and elasticity with regards to scaling compute resources present a compelling need to move to the cloud sooner than later, let alone other factors such as reduced overall capital expenses associated with managing a robust on-premises infrastructure. The finance sector is usually a little behind in embracing major changes in IT operations because of their strict regulatory compliance requirements and the complexity in their application portfolio as a result of several mergers and acquisitions. These restrictions are quite common, especially with insurance companies, but surprisingly there are several major insurance companies that have made significant investments to modernize their traditional applications and migrate them to the cloud for efficiency, agility and cost reasons.

To effectively support the business that constantly strives to be highly competitive and to reduce the time-to-market, most applications development efforts are planned in an Agile fashion with short sprints that produce usable deliverables within a few weeks. When development teams go Agile, it is important that all other associated services operate in a similar manner. Without agility in infrastructure provisioning and other support services, it is simply impossible for the development teams to be successful in producing deliverables at high velocity. A commonly heard complaint from most of the application development teams is about the delays from infrastructure teams on provisioning servers. Provisioning a virtual machine (VM) takes more than three weeks in many companies, even today. This is due to a variety of factors such as work backlog, complexity in approval processes, compliance requirements, lack of automation and several others that are organization-specific. IaaS and PaaS offerings from cloud leaders such as Microsoft Azure and AWS address these issues to a great extent. Even if companies are not ready to migrate their existing applications to the cloud, they should seriously consider IaaS and PaaS from well-established cloud providers for new applications being developed.

There are several factors that could influence IT leaders’ decisions on their strategy regarding cloud and some of the areas that certainly cannot be overlooked are discussed here. The purpose of this article is to provide a general high-level overview, especially for companies that have been hesitating to migrate their workloads to the cloud. It should be noted that we are barely scratching the tip of the iceberg in this discussion.

Complexities in the Application Stack and Infrastructure

It is not uncommon for organizations to be overwhelmed by complexities and issues with their current infrastructure setup and get bogged down dealing with their day-to-day operational challenges. Due to these ongoing issues, one might develop a false notion on the actual effort involved in migrating to the cloud and may even wonder whether it is an achievable target state. It is important to map out the current infrastructure that supports the business applications and have a clear view of the complexities, dependencies and licenses that are in play. The application servers, database servers, storage arrays, enterprise messaging bus, document management systems and several other key components that work together to power your key business applications need to be taken into consideration. This will not only help decide the right applications that are suitable for the cloud, but will also help pick the right cloud provider to suit your needs. You can notice the convergence of IaaS and PaaS as it is becoming difficult to separate one from the other. Most leading providers are offering these services as a package.

It is better to categorize your applications based on their level of complexity and shape up your cloud strategy accordingly. Standalone applications that simply require a web server, application server and database server are ideal candidates for your first phase of migration efforts. You can ensure that with the right architecture on the cloud, there will be better availability, load balancing and performance and less maintenance overhead for your standalone applications.

Amazon Elastic Compute Cloud (EC2) provides availability of 99.99 percent and its auto-scaling feature allows automatic scaling of the fleet up or down depending on the needs to maximize performance and minimize cost. Similarly, Microsoft Azure offers VM Scale Sets that can be configured for your applications to scale automatically. If the requirement is to develop brand new applications for the cloud, the options are endless. As an example, .net based web applications to be hosted on Azure can be developed to be run as Docker containers.

Containers and the management of a cluster of container nodes have come a long way since their inception. Docker Swarm and Kubernetes have drastically simplified the management, load balancing and scaling of containerized applications. Containers are not only for your new applications; they also come in as a great solution for modernizing your legacy/traditional applications and migrating them to the cloud.

Storage

Identity and access control used to be prevalent challenges in the past, but cloud providers have greatly simplified identity management services such as application authentication, user management and single sign-on by providing rich features to support the information security and compliance requirements of organizations. Microsoft Azure offers Azure Active Directory and provides a few different protocols such as WS-Federation, SAML-P and OpenID Connect to authenticate the user. It also supports multi-factor authentication. For brand new applications that are developed using Visual Studio 2017, it is extremely simple to integrate the application for authentication and authorization workflows on Azure AD using the above protocols.

Databases play a vital role in the decision-making process when it comes to migrating to the cloud. Senior IT managers are usually highly concerned about the security of data while technical personnel are worried about ensuring storage, performance, recoverability and availability. Application developers and DBAs end up spending a great deal of time fixing code or other application configuration parameters even when databases are simply migrated from one server to another within the same data center. Cloud solutions address such concerns. Microsoft Azure, for instance lets you migrate your native on-premises SQL databases to Azure SQL Database Managed Instance by providing the native virtual network (VNET) support. You don’t have to change your applications as a result of this migration. The Managed Instance offered by Azure is quite promising and it is certainly a great solution for migrating large numbers of on-premises SQL Server databases to Azure. Similarly, managed database services are offered by AWS for both relational (RDS) and NoSQL (DynamoDB) databases. Amazon EC2 service is integrated with Amazon RDS and Amazon VPC to provide a complete, secure solution for computing, query processing and cloud storage including, but not limited to, a wide range high-performance web applications, DB servers and batch processing needs.

Disaster Recovery

Cloud providers offer data centers in several regions around the world. It requires a major investment for any organization, both financially and operationally, to match the redundancy and recoverability provided by major cloud providers. Even organizations that have a highly virtualized infrastructure setup have physical infrastructure for some of their mission-critical applications. The reasons for having such a setup could be argued, but the costs associated with setting up a disaster recovery environment for such infrastructure would be significant. To ensure comparable performance and capacity, the disaster recovery environment is typically a replica of the production environment that is underutilized most of the time, consuming significant investment.

Planning for setting up a disaster recovery site involves considering several factors such as, ensuring compute capacity and scalability (servers and storage), networking (bandwidth, load balancers, switches, routers), identity management (directory services, authentication, authorization), facility to host the DR site (location, floor space, power, air conditioning) and operational aspects (personnel, monitoring, alerting, break-fixing, hot-hands support). Cloud providers give you the option to choose the location of your DR site anywhere in the world. Depending on the contract you have with your cloud provider, you could either quickly deploy compute resources on your DR site on the cloud or utilize the necessary automated processes to switch over to an already running replica of your application environment on the cloud DR site.

There are numerous features that AWS offers to address each of the above-mentioned aspects that need to be considered for setting up a disaster recovery site. Amazon Machine Images (AMIs), for example, lets you preconfigure images with the operating system and your application stacks. It is also possible to configure your own AMIs to allow the recovery process to launch these images automatically in the event of a disaster, saving a significant amount of time.

Microsoft also provides highly dependable DR alternatives on Azure. You can use Azure as your secondary site for DR purposes or completely eliminate the need for data centers by picking different Azure regions as primary and secondary sites. Azure offers the Active-Passive and Active-Active topologies. In case of the active-passive topology, all user traffic flows to the primary region while preparing the secondary region to take over at any point since the database runs in both regions with a synchronization mechanism implemented between the regions. Active-active topology ensures running of cloud services and databases on both the primary and secondary regions and user traffic goes to both the regions. In the event of a failure at primary, all the user traffic will be directed to the secondary by simply disabling the DNS at the primary region. Auto-scaling features of Azure will allow the scaling up of compute resources in the secondary region as needed.

DevOps Maturity

Any discussion on migrating to the cloud would be incomplete without highlighting the significance of a solid DevOps practice to complement the cloud strategy. From a technical standpoint, companies that settled on Linux environments used to have a slight edge over those that went the Microsoft route, especially with regards to implementing open source solutions and technologies that support DevOps and cloud initiatives. This is highly arguable now as Microsoft has not only opened its gates wide for several major open source tools but also allowed for better integration of several leading open source products such as Docker, Jenkins and Puppet with ALM tools such as Microsoft TFS 2017.

Depending on the goals of an organization with regards to its cloud strategy, the level of maturity required from a DevOps perspective needs to be analyzed. DevOps can be looked at from infrastructure as well as applications development perspectives. If your goal is to use cloud services only for IaaS, it will be extremely valuable to develop strong, infrastructure-focused DevOps capability, practices, processes, tools and automation. Through proper scripting and integration with IT services management (ITSM) tools such as Service Now, the entire process—from requesting infrastructure through having them provisioned with all the patches, hardening, etc.—can be fully automated by leveraging tools such as Chef or scripting via PowerShell. Features such as metered billing and quotas that are already offered by cloud providers can be leveraged and integrated with organization units’ budgets, approvals and other processes to establish a well-managed, fully automated infrastructure provisioning process.

Organizations considering leveraging the cloud for applications development are definitely at an advantage compared to those considering development on-premise. Microsoft, for example, offers IaaS at a highly competitive price and provides Visual Studio Team Services (VSTS), a cloud version of Team Foundation Services (TFS), on Azure. The PaaS services offered by Azure are also quite extensive, along with integration with a variety of open source tools. With this combination, developers have a complete, end-to-end solution to address all their requirements without losing time in waiting for environments to be set up. DevOps can play a significant role here. With a fully integrated continuous integration (CI) framework, the developers could commit code to a managed Git repository on the cloud, and automated continuous integration can be kicked off via VSTS, which handles the build, automated testing, code quality control, building container images, automated spinning up of infrastructure needed for an environment and deploying code or containers on the environment following a fully integrated change control process and quality gates. Developers can focus fully on developing code and enjoy the freedom of being able to deploy code on a transient environment on the fly to test their code before it goes through other phases of testing. In highly competitive business environments, this provides the ability to perform multiple production deployments in a single day if need be.

Organizations with a high level of maturity in DevOps are the ones most likely to leverage cloud to its fullest potential. Similarly, those that are striving to attain this level of maturity are likely to get there sooner by moving to the cloud. Developing the expertise, building the process framework, and implementing the right DevOps tools will make it easier to solidify your cloud strategy.

Compliance & Security

Several organizations have refrained from migrating to the cloud mainly due to the strict compliance and security requirements that are mandated by the industry. Companies that are still hesitating to embrace the cloud are those that are skeptical about the security of their data on the cloud. Irrespective of the cloud provider or the strict SLAs and contractual terms that you may have with them, you are still accountable for the data that you own. So, it is extremely important to understand more about the security setup and the features offered by your cloud provider to secure your valuable data on the cloud.

As the owner of your organization’s data, you have to know where your data is going to physically reside, what kind of encryption will be applied to your data at rest and in transit, who will physically have access to your data, how your data will be segregated from other tenants’ in a multi-tenanted environment, what recovery processes exist, what the regulatory compliance certifications that the provider has obtained are and answers to many more questions. Security experts highly recommend classifying your data based on risk levels and devising a strategy accordingly. You may decide to host your most critical data on-premises and use the cloud for other types of data.

Microsoft offers a very comprehensive checklist and reports to help you assess the risk to your organization in moving to the cloud. Both AWS and Microsoft are certified by almost all major regulatory bodies with regards to data security and compliance. However, as outlined earlier, while your provider can take the responsibility to host your data in the most secure manner, you as the owner of the data will still be accountable for its integrity and security. So, this is an area that requires thorough analysis before you decide on your workloads that need to migrate to the cloud.

To Make the Long Story Short …

While savings on capital expenses is a given, this is not the prime motivator for most organizations to migrate to the cloud. As stated in the Gartner report, “Cloud Computing Primer for 2018,” the role played by cloud in this decade is way beyond simply setting expectations and providing a platform for better economic models. Digital initiatives such as artificial intelligence (AI), internet of things (IoT) and several other technological innovations and disruptions are fueled by digital platforms that cloud offers. Agility, scalability and flexibility, on the other hand, are certainly some of the key drivers that would encourage companies to migrate to the cloud. Furthermore, reducing your time to market can be better accomplished by moving your development workloads to the cloud. The combination of IaaS, PaaS and a solid DevOps practice to automate deployment of infrastructure and application stack makes cloud far more superior to running applications development on-premises. Companies that haven’t invested much in DevOps should seriously consider doing so before it is too late.

Containers, the tools and practices to orchestrate, load balance and scale microservices add a completely different dimension to how cloud services could be further leveraged. They help ensure seamless, defect-free, repeatable deployments, and with the necessary automation to spin up transient environments, it is difficult to match the benefits of using containers on a fully automated IaaS in an on-premises setup.

The auto-scaling features offered by cloud providers make cloud a far more superior alternative compared to hosting internally. The elasticity provided by cloud helps with scaling compute power, storage, load balancing and several other infrastructure services as needed, without having to make massive hardware investments.

Cloud providers offer solid disaster recovery options and support. Implementing an active-passive or active-active primary and secondary sites is a lot more straightforward and easier to set up on AWS or Azure than doing it on-premises.

Compliance and security are also important factors that could drive the cloud strategy. It is important for decision makers to evaluate the risk factors and then decide on the type of data that could reside on the cloud as opposed to staying internally.

NetworkComputing provides interesting statistics on public, private, and hybrid IaaS adoption in their article, “IaaS Cloud Adoption Trends.” The article further refers to research from Interop ITX and InformationWeek on how organizations are moving and managing workloads to the cloud. Per their findings, several companies are expected to deliver over half (67 percent) of their IT and business services through the cloud in the next year. Of all the companies they surveyed, 31 percent plan to allocate over 50 percent of their budget to cloud initiatives.

With a clear strategy, budgets, categorization of applications, prioritization and staff with the right expertise, you can be sure that your journey in moving your workloads to the cloud will be a pleasant one.

— Sridhar Asvathanarayanan

June 15, 2018