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Category: Infrastructure/Networking

Broadcom Survey Surfaces Drive to Consolidate Automation Platforms

Broadcom this week published a global survey of 535 IT leaders that found more than three-quarters (78%) are moving to consolidate cloud automation tools and platforms. However, thus far, only 8% of respondents said they had completed that effort.

Overall, the survey also found more than 81% already have multiple cloud automation solutions in place, and 70% noted this issue had created significant challenges, including increasing time to automate (59%), time to report (52%) and time to remediation (52%) and higher costs (49%). In addition, 46% noted it’s more difficult to troubleshoot IT environments, while 41% noted multiple tools increase compliance risks.

IT teams also find that having multiple automation tools slows down software delivery (37%) and contributes to the over-provisioning of IT environments (32%).

Aline Gerew, head of automation for the Agile Operations Division at Broadcom, said the multiple islands of automation that have evolved on various cloud computing platforms now need to be centralized to reduce the current level of friction in the cloud. Those efforts need to start with platforms capable of unifying observability across all the platforms employed, she added.

The amount of consolidation that any IT organization can implement depends on the amount of political capital they have. In many cases, the applications and associated automation tools running in any given environment have been adopted by a department or business unit that exercises a lot of autonomy regarding IT. The issue that creates is multiple tools and platforms increase the total cost of IT. At a time when many organizations are experiencing economic headwinds, the need to reduce the total cost of IT naturally becomes a more pressing concern.

Rather than consolidate automation tools and platforms, the simpler option is to deploy an automation framework that is designed to be integrated with the various tools and platforms that are already in place, noted Gerew. That approach enables automation to be applied at much higher levels of scale to reduce costs without forcing every arm of an organization to standardize on the same tools and platform, she noted.

The survey found that, when it comes to cloud automation, the most important consideration factors are operational costs (62%), performance (53%) and operational efficiency (49%).

Responsibility for cloud automation will naturally vary from one organization to the next, but the survey noted that 67% of respondents currently have a cloud architect on staff and another 33% are planning to hire one.

That role is increasingly required as organizations find themselves managing a wide range of cloud computing environments, including software-as-a-service applications (57%), legacy applications that have been lifted into the cloud (56%), new cloud-native applications (46%) and legacy applications that have been refactored for the cloud (41%).

It’s apparent that cloud computing environments are becoming more complex with each passing day. There may come a day when artificial intelligence (AI) helps reduce the level of complexity; in the meantime, IT organizations need to find ways to streamline the management of cloud computing environments. Today, those environments are made up of applications based on a wide range of increasingly divergent architectures as the rate of application deployments exceeds the retirement of legacy apps.

September 1, 2023
Cisco Aligns With Nutanix on Hybrid Cloud Computing

Cisco and Nutanix this week announced a strategic partnership through which the two companies will collaborate to advance hybrid cloud computing.

As part of that agreement, Cisco is committing to making AHV virtual machine software from Nutanix available from the edge to the cloud, starting with the Cisco Unified Compute Systems (UCS) rack and, soon after, blade servers.

Jeremy Foster, senior vice president and general manager of Cisco Compute, said via a software-as-a-service (SaaS) platform, Cisco will be able to centrally manage virtual machines from Nutanix alongside the other major virtual machines it already supports.

In addition, the partnership will enable Cisco to apply multiple forms of artificial intelligence (AI) across a hybrid cloud computing environment, he added.

Thomas Cornely, senior vice president of product management for Nutanix, said the goal is to provide IT teams with an opportunity to consolidate infrastructure across a hybrid cloud computing environment. In effect, Nutanix and Cisco are working toward converging the control planes across their entire respective portfolios, he added.

The goal is to enable IT teams to streamline the number of platforms that need to be supported as part of an effort to reduce the total cost of IT, said Cornely.

The issue that IT organizations regularly encounter is that each platform requires hiring additional IT personnel to manage. At a time when many organizations are encountering economic headwinds, there is a natural inclination to reduce the number of platforms as the size of the IT staff is reduced. Despite advances in automation, IT labor costs still represent the largest overall segment of any IT budget.

It’s not clear how aggressively IT teams are consolidating IT infrastructure to reduce costs, but after more than a decade of continuous investments in IT, many organizations are discovering they are supporting a wide range of platforms. During a downturn, however, IT teams tend to more aggressively eliminate “snowflake” platforms that require unique skillsets to manage, noted Cornely.

In the meantime, IT environments are also becoming more distributed as more workloads move to the network edge. Those workloads are not replacing applications running in on-premises or cloud computing environments, so the overall IT footprint will continue to increase. Given the chronic shortage of IT skills, the only way to effectively manage that IT estate will be to rely more on automation frameworks increasingly infused with AI.

Each IT organization will need to determine for itself what level of automation to embrace, but Cisco is betting most will opt to rely on a SaaS platform provided by a vendor versus building and maintaining an IT management platform themselves. Regardless, legacy approaches that depend heavily on manual processes will increasingly give way to cloud-based platforms that can more easily manage workloads wherever they happen to be deployed.

The issue now, as always, is funding the transition to new platforms to bring the total cost of IT back under control and eventually pay for themselves.

August 31, 2023
IBM Previews Generative AI Tool for Converting COBOL to Java

IBM this week announced it will preview a generative artificial intelligence (AI) tool for converting COBOL code running on mainframes into Java. The preview will be available next month during IBM’s TechXchange learning event, IBM said.

Kyle Charlet, CTO for Z software at IBM, said watsonx Code Assistant for Z makes use of large language models (LLMs) that IBM has trained to both convert and optimize Java code for z/OS running on IBM mainframes.

Rather than converting COBOL into Java code line for line, the watsonx Code Assistant for Z improves the syntax of Java code generated using object-oriented techniques, he noted.

IBM’s watsonx.ai code model, which will have knowledge of 115 coding languages, is based on 20 billion parameters that will eventually be applied to other programming languages such as PL/1, added Charlet.

That model is the same core LLM that IBM used to create Red Hat Ansible Lightspeed, a tool to generate code for an IT automation framework that is scheduled for release later this year. In the future, IBM will also most likely employ generative AI to convert REXX code into Ansible code, said Charlet.

IBM expects the final offering will combine generative AI tools with its existing Application Discovery and Delivery Intelligence (ADDI) inventory and analysis tool to streamline the modernization of legacy mainframe applications.

The goal, however, is not to move COBOL applications off the mainframe. Java applications created using watsonx Code Assistant for Z will still call the same data sources, such as an IMS database or CICS transaction processing system, as the original COBOL application.

That approach will make mainframes more accessible to a wider pool of developers that know Java, which should increase the overall pace of innovation for application development, noted Charlet.

There are billions of lines of COBOL code currently running on mainframes that, in one way or another, need to be modernized as the pool of developers that know COBOL continues to shrink. Generative AI will make it easier to quickly assess, update, validate and test code, which should accelerate code conversion efforts that otherwise would take years to complete, said Charlet.

It’s not clear how quickly organizations might move to modernize COBOL applications, but as the time and effort to do so decreases, so should the cost of modernizing legacy code. Most organizations still running z/OS on mainframes are committed to the platform, but if they can transition to Java, the overall cost of building and maintaining mainframe applications should decline.

In the meantime, it’s not likely that AI will replace developers. IBM recommends that developers further optimize the Java code generated by watsonx Code Assistant for Z.

Most developers are already using some form of AI to accelerate application development. The next wave of AI will see more widespread use of LLMs that are optimized for specific tasks and that generate more reliable results. After all, the quality of any output from an AI model is always directly related to the quality of the data used to train it. The narrower the set of vetted data, the more trustworthy the AI becomes—the opportunity for erroneous data to create AI hallucinations is a whole lot less than any general-purpose AI model could achieve with customization.

August 25, 2023
Data Management: Cloud Technology or Data Centers?

Traditionally, larger enterprises have relied on in-house server hardware equipment for their data management needs, optimized to run high-performing applications with a certain degree of assured security and reliability.

However, the high costs and technical expertise required to manage these solutions effectively often render them inaccessible to startups and emerging businesses. But, as the old saying goes, necessity is the mother of invention.

With the growing demand for cost-effective, efficient data management solutions, the tech industry has witnessed the advent of cloud technology and data center colocation rental services. These alternatives pave the way for startups and small businesses to handle large volumes of data without straining their budgets.

This article will compare and contrast cloud technology and data center services to help business leaders make well-informed decisions about their data management needs and the solutions that can help them.

Understanding Data Center Colocation Services

Data center colocation services offer an enticing solution for companies that require robust data management capabilities without incurring high capital expenditure for securing storage hardware, along with storing and powering them.

Essentially, this solution is like renting a space for your data in a specialized facility. These data centers provide a secure environment with the necessary power, cooling, and physical security for your server, storage, and networking equipment.

In opting for colocation services, companies can leverage state-of-the-art data center infrastructure without the burden of high upfront costs and ongoing maintenance responsibilities associated with owning a private data center. This also allows businesses to focus more on their core competencies rather than the intricacies of data center management.

However, data centers come with their own set of unique challenges. For instance, they often require a certain degree of technical expertise on the part of the business to manage their servers. This means hiring or training staff to handle server management, increasing the overall cost of this solution.

Additionally, data centers’ effectiveness can sometimes be influenced by physical proximity. The closer your operations are to the data center, the better the performance can be. This geographical factor can pose problems for businesses operating in multiple locations or managing data across different data centers.

The Power of Cloud Technology

In contrast to relying on data centers, cloud technology offers businesses of all kinds and sizes immense scalability and flexibility in terms of data management solutions.

As a service that delivers computing infrastructure, software, or storage as a service via a network connection, typically the Internet, cloud technology eliminates the need for physical proximity to servers. It is quickly scalable, both upwards and downwards, as per business needs, ensuring that you only pay for what you use.

Moreover, the ability of cloud technology to integrate various services can simplify business operations and enhance customer engagement. For instance, many businesses are now offering cloud-based text-to-pay systems that allow customers to pay from any device, providing ease of transaction while simultaneously gathering customer data in the cloud.

Cloud services can also provide robust disaster recovery and business continuity solutions. However, businesses often cite concerns about data security and privacy in the cloud. Although most cloud service providers implement best-in-class security protocols, the very nature of the cloud—being an off-premises solution—necessitates a high level of trust in the provider.

AI and Data Management Solutions

If your business relies heavily on AI applications, you must understand how your data management solution supports this need. AI requires high-quality, well-managed data to function effectively. Here, cloud technology often presents an edge over traditional data centers.

Many cloud service providers offer built-in tools and services designed to better facilitate AI implementation, including pre-trained machine learning models and AI platforms as a service. These resources can dramatically reduce the time, costs, and complexity that come with implementing AI, particularly for small to mid-sized businesses without extensive in-house AI expertise.

On the other hand, while data centers might not have these built-in tools, they offer robust performance capabilities for AI workloads and often provide more control over the data, which is vital for sensitive AI applications.

Keep in mind you can turn to other server-based solutions for storing data as well. For example, if your application relies on a significant amount of PDF content to store data and function effectively, hosting those files on a server such as a server-side PDF SDK will allow you to process those documents on the server via an API. This will then allow you to autoamte your document processing and convert large volumes of your PDF files.

Web Hosting Requirements

Your current web hosting requirements can also significantly impact the choice between data center services and cloud technology. Shared hosting services may be sufficient for businesses with low-traffic websites or applications, and if you don’t need to store a lot of data, but as your traffic increases and the website grows, so do your hosting requirements.

Cloud hosting solutions offer excellent scalability, allowing your website to grow with your business. As traffic surges, cloud servers can easily handle the increase, maintaining website performance and user experience.

Conversely, with a dedicated data center, you’re generally limited to the capacity of the server you rent. To increase your capacity, you may need to physically upgrade the hardware, a process that can result in downtime and potential loss of revenue.

Considering Cost Implications

The overall cost of your chosen solution will be a key factor when deciding between data center colocation services and cloud technology. Each service has unique cost structures that can vary between providers, and understanding these can significantly impact your company’s financial health and resource allocation.

With data center colocation services, the costs are generally more predictable. You pay a fixed amount based on your rented server space and the bandwidth you use. This stability can be advantageous for budgeting purposes, especially for businesses with steady data usage and processing needs. However, you may also incur additional expenses for any necessary hardware upgrades, maintenance, or increased bandwidth usage.

What’s more, there’s the indirect cost of employing or training staff to manage the servers, plus the potential costs of transportation to and from the data center, particularly for businesses not located near their rented server space. These costs can add up and should be factored into the total cost of operating within a data center.

Cloud services, conversely, operate on a pay-as-you-go model. This solution can be much more cost-effective for businesses with shifting data needs since you can readily scale up or down based on demand, ultimately only paying for the resources you actually use.

Choosing the Right Path for Your Organization

Unfortunately, there’s no one-size-fits-all solution for effective data management. Each business has unique needs, requirements, and considerations that necessitate a carefully tailored approach. When choosing between cloud technology and data center colocation services, business leaders must carefully consider several key factors to determine the right solution for them.

If scalability, flexibility, and AI integration rank high in your data management strategy, cloud technology presents an excellent solution with its on-demand resources and array of built-in tools. On the other hand, data center colocation services provide robust performance capabilities, greater control over data, and predictable costs—features that may appeal to businesses with more consistent data management needs.

Understanding your current web hosting requirements and the potential cost implications of each solution are also crucial elements to consider in your decision-making process. The demand for efficient, secure, and cost-effective data management solutions will only continue to grow as data storage needs expand rapidly.

August 9, 2023
Five Great DevOps Job Opportunities

devopsy.kinsta.cloud is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted to better serve our audience.

Our goal in these challenging economic times is to make it easier for DevOps professionals to advance their careers.

Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role it tends to create an opportunity for others.

The five job postings shared this week are selected based on the company looking to hire, the vertical industry segment and, naturally, the pay scale being offered.

We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration:

Indeed.com

Salesforce
San Francisco, California
DevOps Engineer
$183,310 to $183,900

SimplyHired.com

Sony Electronics
Culpeper, Virginia
DevOps Engineer
$130,000 to $155,000

CareerBuilder.com

The Navy-Marine Corp Relief Society
Arlington, Virginia
DevOps Network and Infrastructure Engineer for Azure
$120,000 to $140,000

LinkedIn

KPMG
Fort Lauderdale, Florida
DevOps Cloud Engineer-Remote
$113,000 to $242,000

Dice.com

Steam IT
Austin, Texas
DevOps Engineer
$100,000 to $120,000

August 7, 2023
Palo Alto Networks Extends CNAPP to CI/CD Platforms

Palo Alto Networks has extended the reach of its cloud-native application protection platform (CNAPP) to include the ability to secure continuous integration/continuous delivery (CI/CD) platforms.

Daniel Krivelevich, CTO for application security for Prisma Cloud at Palo Alto Networks, said this extension of the Prisma Cloud platform would make it easier to better secure software supply chains as software builds are continuously updated by application development teams.

Previously, Palo Alto Networks provided the ability to secure runtimes and secrets and scan code for vulnerabilities as it is being developed. This latest extension now adds the ability to secure the CI/CD platforms that drive DevOps workflows, he noted.

While more organizations than ever are shifting application security responsibility further left toward developers, many of them overlook the need to secure the CI/CD platform itself. Most developers are not cybersecurity experts, so the chances they will miss something that can find its way into a software build are still high. Extending the reach of Prisma Cloud to the CI/CD platform will ensure the build that forms the foundation of the code is free of vulnerabilities when it finds its way into a production environment.

That’s critical, because cybercriminals are becoming more adept at injecting malware into software supply chains in the hopes of being able to eventually compromise multiple downstream applications. In fact, this issue has prompted governments around the world to hold more organizations legally responsible for the security of the applications they build and deploy.

There are, of course, multiple approaches to securing software supply chains, but Palo Alto Networks is making a case for an integrated platform that provides a comprehensive approach to securing the entire IT environment. Rather than integrating multiple point products, a CNAPP ultimately reduces the total cost of cybersecurity on an end-to-end basis, noted Krivelevich.

It’s not clear how quickly organizations are transitioning to CNAPPs, but as that transition occurs, the overall state of application security should improve. One of the issues that has historically plagued application security is no one in an organization has been specifically tasked with ensuring it. Application development teams assumed cybersecurity professionals were allocating funds to it. Cybersecurity teams, however, tended to focus on areas they directly controlled, such as network firewalls. A CNAPP makes it simpler to deploy a cybersecurity framework in a more extensible way.

Of course, there’s still much work to be done when it comes to prioritizing vulnerability remediation efforts. The truth is developers only have so much time to allocate to developing patches, so the focus needs to be on identifying the vulnerabilities that are the most severe in terms of the amount of damage that might be inflicted. At the very least, however, a CNAPP should provide the means by which the context required to identify those vulnerabilities could be more easily shared.

One way or another, however, the number of vulnerabilities finding their way into production environments should steadily decline as more focus is put on securing software supply chains.

July 27, 2023
Dynatrace Adds Generative AI Capabilities to Davis Engine

Dynatrace today revealed it has added generative artificial intelligence (AI) capabilities to its Davis engine it uses to apply machine learning algorithms to its observability platform.

Steve Tack, senior vice president for product management at Dynatrace, said that hypermodal approach to AI will allow DevOps teams to take advantage of probabilistic AI capabilities provided by Davis CoPilot that are enabled by large language models (LLMs) originally developed by OpenAI.

The generative AI capabilities effectively provide DevOps teams with a tool that surfaces workflow recommendations via a natural language interface. The recommendations are based on historical data such as sales and customer experience trends, seasonality and cloud application health. It is also able to analyze dependencies across large sets of data while retaining context accessed via the Dynatrace Grail data lakehouse and the Dynatrace Smartscape application visualization and mapping framework.

Dynatrace is now making a case for a multimodal AI platform that uses a common set of metrics, traces, logs, user behavior and topology to enable machine learning algorithms to predict anomalies and causal-AI dependencies to determine the root cause of an IT issue and then leverage generative AI to provide recommendations to resolve it. The company is forecasting IT teams will achieve 10% to 20% productivity improvements year-over-year as a result.

It’s too early to determine the full extent to which a multimodal approach to AI will transform IT management, but it’s clear that as IT operation teams become more productive, more time will be freed up for innovation, noted Tack. Many of the manual tasks that IT teams still need to perform divert time away from building and deploying additional applications, he added.

Naturally, there’s a lot of speculation concerning the impact AI will have on DevOps. Some contend it’s only a matter of time before the entire software engineering process is automated. As those advances are made, the cost of building applications will effectively drop to zero. Others argue that AI will augment DevOps teams in a way that makes it simpler to manage IT at scale using either the same number or possibly even fewer engineers.

The one thing that is certain is that AI will soon be pervasive, which will drive the adoption of automation frameworks as organizations look to operationalize the insights surfaced by AI platforms. As rich as the insights any AI model surfaces might be, they only provide real value when integrated with an automation framework, noted Tack.

Ultimately, most DevOps professionals will not want to work for organizations that don’t provide them with AI tools that make it more likely for them to be successful. Organizations that embrace AI may not have a sustainable competitive advantage when every other organization is doing the same, but those that don’t will soon be left behind. Those that succeed will benefit from managing application environments at a previously unprecedented level of scale at minimal additional cost. The challenge is determining which tasks will be automated with an eye toward planning how DevOps roles will need to evolve.

July 25, 2023
Complete Guide to AWS Savings Plans
AWS Savings Plans are a flexible and cost-effective way to save on your Amazon Web Services (AWS) usage. These plans allow you to commit to a specific amount of compute capacity over a one- or three-year term in exchange for a significant discount on your AWS usage. Savings plans can help you reduce your AWS bill by up to 72%, depending on the type of plan and your level of commitment.

Why Do You Need AWS Savings Plans?

With the growing reliance on cloud infrastructure in modern IT, it is crucial for organizations to optimize cloud computing costs without compromising on performance or scalability. AWS Savings Plans address this need by providing a flexible and cost-effective solution that allows you to commit to a certain level of usage at a discounted rate.

With AWS Savings Plans, by committing to a specific amount of compute capacity over a set time period, you can save up to 72% on your AWS usage. This can result in significant cost savings for your organization, freeing up resources to invest in other business-critical initiatives.

Another benefit of AWS Savings Plans is the flexibility they offer. Unlike Reserved Instances, which require you to specify instance type, operating system and region, Savings Plans are more flexible and can be applied across different instance types, regions and operating systems. This flexibility makes it easier for you to optimize your cloud computing costs while still meeting your business needs.

Difference Between AWS Savings Plans and Reserved Instances

AWS Savings Plans and Reserved Instances are both designed to help you save on your AWS usage, but they offer different levels of flexibility and cost savings.

Reserved Instances require you to commit to a specific instance type, operating system and region for a one- or three-year term in exchange for a discount on your AWS usage. While this can result in significant cost savings, it also limits your ability to optimize your cloud computing costs based on changing business needs.

AWS Savings Plans offer a more flexible solution that allows you to commit to a certain level of usage across different instance types, regions and operating systems. Additionally, in most scenarios, Savings Plans can provide a higher level of cost savings compared to Reserved Instances.

Types of AWS Savings Plans

There are two types of AWS Savings Plans: Compute Savings Plans and EC2 Instance Savings Plans.

Compute Savings Plans

Compute Savings Plans are the most flexible type of Savings Plans, providing a significant discount on your AWS usage across any instance type, region or operating system. This flexibility makes it easy for you to optimize your cloud computing costs based on your changing business needs.

Compute Savings Plans offer a discount of up to 66% compared to on-demand pricing, depending on the level of commitment you make. They can be used across all AWS services that are powered by Amazon EC2 instances, including Amazon EMR, AWS Lambda and Amazon ECS.

EC2 Instance Savings Plans

EC2 Instance Savings Plans are a more specific type of Savings Plan, offering a discount on your AWS usage for a specific instance type within a region. These plans provide a higher level of cost savings compared to Compute Savings Plans, with discounts of up to 72%.

EC2 Instance Savings Plans are ideal for organizations that have a predictable workload and require specific instance types within a region. However, they offer less flexibility than Compute Savings Plans, as they cannot be applied across different instance types, regions or operating systems.

AWS Savings Plans Pricing Models

AWS Savings Plans offer three different payment options to suit your organization’s budget and commitment level: All Upfront, Partial Upfront and No Upfront.

All Upfront

With the All Upfront payment option, you pay for the entire term of your Savings Plan upfront, resulting in the highest discount compared to on-demand pricing. This option is ideal for organizations that have the budget available to make a significant upfront payment in exchange for maximum cost savings.

Partial Upfront

The Partial Upfront payment option allows you to pay a portion of your Savings Plan upfront and the remaining balance on a monthly basis. This option provides a balance between upfront commitment and ongoing flexibility, with a lower discount compared to the All Upfront option.

No Upfront

With the No Upfront payment option, you pay for your Savings Plan on a monthly basis, with no upfront payment required. This option provides the lowest discount compared to on-demand pricing but offers the greatest flexibility for organizations with limited budgets or uncertain future usage requirements.

How to Monitor and Manage AWS Savings Plans

Monitoring and managing your AWS Savings Plans, as part of your overall cloud operations strategy, is critical to ensuring that you are optimizing your cloud computing costs and maximizing your savings. AWS provides several tools and services to help you monitor and manage your Savings Plans effectively:
- AWS Cost Explorer: A powerful tool that allows you to analyze your Savings Plans usage and costs, helping you identify opportunities to optimize your cloud computing costs. With Cost Explorer, you can view your Savings Plans coverage, utilization and savings, as well as forecast your future usage and costs.
- AWS Budgets: This service allows you to set custom cost and usage budgets for your organization, helping you track your Savings Plans usage and ensure that you are staying within your budget.
- Savings Plans Recommendations: This feature analyzes your past usage and recommends the optimal Savings Plans for your organization. It can help you make informed decisions about which Savings Plans to purchase and how much commitment to make.
Conclusion

AWS Savings Plans are an excellent solution for organizations looking to optimize their cloud computing costs and save on their AWS usage. With their flexibility, cost savings and easy monitoring and management options, AWS Savings Plans are a valuable tool for any organization using AWS services.

To unlock the full potential of AWS Savings Plans, it’s crucial to understand the different types of plans, their pricing models and how to effectively monitor and manage your usage. By doing so, you can maximize your savings and ensure that your organization is getting the most out of its investment in AWS.
July 21, 2023
Splunk Adds Hub to Extend Observability to Edge Computing Platforms

Splunk today launched a platform for ingesting data from sensors, industrial equipment and internet-of-things (IoT) platforms so that it can be analyzed alongside data collected from IT environments.

Announced at the company’s .conf23 conference, the Splunk Edge Hub extends the reach of the troubleshooting platform Splunk provides that increasingly involves various types of edge computing platforms.

Tom Casey, senior vice president for products and technology at Splunk, said Splunk Edge Hub is designed to be deployed in those edge computing environments and reduce the total amount of data that needs to be transferred back to an instance of the Spunk analytics platform running in a data center or in the cloud.

That approach makes it possible to remotely monitor everything from environmental conditions to identifying anomalies indicative of an outage that might be prevented. The overall goal is to unify visibility across IT and operational technology (OT) environments as these systems and applications become more integrated, said Casey.

It’s not clear how quickly that integration is occurring, given the historic cultural divide that has existed between IT and OT teams. But with the rise of digital business transformation initiatives, the data collected from edge computing platforms is increasingly being incorporated into a wide range of applications deployed at the edge, in local data centers and in the cloud.

In general, observability tools are being used to not only improve processes but identify and block suspicious activity. Of course, OT teams have been monitoring edge computing platforms for decades, but observability platforms make it feasible to interrogate logs, metrics and traces to hopefully discover issues before there is a disruption to services. The challenge, of course, is that not everyone on either the OT or IT team necessarily knows how to shape the queries required to discover the root cause of a potential issue. The Splunk approach essentially leverages the skillset of IT professionals that have mastered its platform to address that issue; something most OT professionals are not going to be able to do using their existing tools and platforms.

One way or another, DevOps teams will soon need to be able to remotely diagnose edge computing issues as the number of workloads deployed on these platforms expands. IT teams are not going to be able to travel to the physical location of every edge computing platform every time an issue arises. In many ways, the rise of edge computing will force a transition to observability tools that will enable IT teams to more proactively manage IT environments rather than responding after a metric that OT teams are monitoring has already exceeded its threshold.

In the event there is still an issue, observability platforms should also reduce mean-time-to-remediation (MTTR) as visibility into IT environments, including edge computing platforms, is improved.

It’s not likely that OT and IT teams will ever be completely unified, but as observability platforms continue to evolve, there will be much greater opportunities for collaboration.

July 17, 2023
A DevOps Guide to IoT Technology

Internet-of-things (IoT) is a term used to describe the network of physical devices—from everyday household items to sophisticated industrial tools—connected to the internet, sharing and collecting data. With the advent of cheap computer chips and the ubiquity of wireless networks, it’s possible to make anything a part of the IoT. Add to that the exponential growth in artificial intelligence and machine learning, and the IoT is a truly significant part of our lives.

The IoT allows devices to communicate and interact over the internet, and they can be remotely monitored and controlled using technologies many of us have never heard of, such as MQTT, QUIC and AMQP. The proliferation of these devices has led to an exponential increase in the amount of data we can gather, analyze and use to make more informed decisions. We’ve already seen the transformative potential of IoT in our homes, workplaces and cities, and it’s only set to increase as more and more devices come online.

At the heart of this technology is the concept of seamless connectivity and communication. However, the rapid development and deployment of IoT devices pose significant challenges. This is where the DevOps approach comes in, offering a solution to these challenges by improving efficiency and effectiveness in the development and operation of IoT systems.

Importance of DevOps in IoT Development and Deployment

In the context of IoT, DevOps is crucial. IoT devices are often deployed in the field, in homes or in other environments where physical access is difficult or impossible. As such, the ability to update and manage these devices remotely is a critical necessity. DevOps methodologies enable this remote management, providing mechanisms for continuous integration and continuous delivery (CI/CD) that make it possible to push updates to devices regularly and reliably.

Moreover, given the pace at which IoT technology is advancing, it’s crucial to be able to adapt quickly to changes and new developments. In this regard, DevOps is invaluable. By bringing together development and operations, it allows for more agile response to changes, faster issue resolution and overall better performance.

The Role of DevOps in IoT

Speeding Up IoT Development Cycles

One of the key benefits of using a DevOps approach in IoT development is the ability to speed up development cycles. With traditional development practices, the time between identifying a requirement or an issue and implementing a solution can be lengthy. In contrast, DevOps practices, such as continuous integration and continuous delivery, enable rapid prototyping, development, testing and deployment of new features and updates.

With the DevOps approach, developers and operations teams work together from the beginning of a project, collaborating closely to ensure that the software being developed is robust, reliable and meets the needs of the business. This cooperative approach, combined with automated testing and deployment processes, significantly reduces the time to market for new features and updates.

Ensuring High-Quality, Reliable IoT Applications

Another significant advantage of utilizing DevOps in IoT development is the increased quality and reliability of the applications being developed. With traditional software development methods, testing and quality assurance often occur after the development process. This can lead to issues being identified late in the cycle, which can be costly and time-consuming to fix.

On the other hand, DevOps practices emphasize continuous testing throughout the development process. This means that issues can be identified and addressed much earlier, leading to higher-quality, more reliable software. In the context of IoT, where devices may be deployed in critical applications such as health care or transportation, the importance of this cannot be overstated.

Enabling Scalability and Flexibility in IoT Systems

Finally, DevOps plays a crucial role in enabling the scalability and flexibility of IoT systems. As the number of IoT devices continues to grow exponentially, the ability to manage and monitor these devices efficiently is becoming increasingly important. DevOps practices, such as infrastructure as code (IaC) and the use of containers and microservices, provide the tools necessary to scale and adapt IoT systems as needed.

With IaC, the infrastructure required to run an IoT application is defined and managed as code, making it easy to replicate and scale. Containers and microservices, on the other hand, allow applications to be broken down into smaller, independent components that can be scaled individually, providing a high level of flexibility and efficiency.

Implementing DevOps in IoT Development

Integrating CI/CD in IoT Application Development

Continuous integration/continuous delivery (CI/CD) is a fundamental DevOps practice that plays a vital role in IoT development. It involves automating the processes of software delivery and infrastructure changes. With CI, developers frequently merge their code changes into a central repository where automated builds and tests are run. CD takes this a step further by automating the release of these changes to the production environment.

For IoT, integrating CI/CD can help maintain the quality of software, increase efficiency, and speed up the process of delivering software updates or new features to devices. It also ensures that any changes made can be easily rolled back, reducing the impact of any potential issues.

Applying IaC for IoT Infrastructure Management

Infrastructure-as-code (IaC) is another key DevOps practice that is highly relevant to IoT. IaC is the process of managing and provisioning computing infrastructure through machine-readable definition files rather than physical hardware configuration or interactive configuration tools.

In the context of IoT, IaC can simplify the process of setting up and managing the complex infrastructure required for IoT applications. With IaC, developers can quickly and reliably replicate infrastructure setups, making it easier to scale IoT applications as the number of connected devices grows.

Using Containers and Microservices in IoT Application Architecture

Containers and microservices are two more DevOps practices that can significantly benefit IoT development. A container is a standalone executable package that includes everything needed to run an application, including the code, runtime, system tools, system libraries and settings. Microservices is an architectural style that structures an application as a collection of small, loosely coupled services.

In the context of IoT, using containers and microservices can greatly simplify the process of developing, testing and deploying IoT applications. Moreover, they can enhance the scalability and flexibility of IoT systems, making it easier to adapt to changing requirements and scale as the number of connected devices grows.

Challenges of Implementing DevOps for IoT and How to Overcome Them

Dealing with Device and Platform Diversity

One of the main challenges in implementing DevOps for IoT is dealing with the diversity of devices and platforms. IoT devices can range from simple sensors to complex machines, each with its own capabilities, requirements and operating systems. Moreover, these devices may communicate using different protocols and have different connectivity requirements.

The key to addressing this diversity is to adopt a platform-agnostic approach. This involves developing software that can run on any device, regardless of its hardware or operating system. Containerization can help achieve this by providing a consistent and reproducible environment for running applications.

Ensuring IoT Security and Privacy

Security and privacy are significant concerns in IoT, and they can be particularly challenging in a DevOps context. The increased frequency of updates and changes that come with the DevOps approach can potentially introduce new vulnerabilities. Moreover, the interconnected nature of IoT devices means that a security breach can have far-reaching consequences.

To address these challenges, it’s essential to integrate security practices into the DevOps process. This is often referred to as DevSecOps. This approach involves continuously monitoring and testing for security vulnerabilities throughout the development process rather than treating security as an afterthought.

Managing IoT Data Volume and Complexity

The sheer volume and complexity of data generated by IoT devices can also pose a challenge in a DevOps context. Traditional data management and analysis tools may not be able to handle this volume and complexity.

To tackle this, it’s important to utilize modern data management solutions that can handle large volumes of data. Additionally, employing advanced analytics and machine learning techniques can help make sense of complex data and extract valuable insights.

Coordinating IoT System Updates and Maintenance

Finally, coordinating updates and maintenance for a large number of IoT devices can be a significant challenge. This is particularly true in a DevOps context, where updates and changes are frequent.

To address this, it’s crucial to have a robust system for managing and tracking updates. Automated deployment tools can be invaluable in this regard, allowing updates to be pushed out to devices in a controlled and coordinated manner.

Conclusion

In conclusion, implementing DevOps in IoT development can bring significant benefits, including faster development cycles, higher-quality, reliable applications and improved scalability and flexibility. However, it also presents unique challenges that need to be effectively addressed. By understanding and properly implementing the principles and practices of DevOps, we can harness the full potential of IoT technology and drive forward innovation.

July 14, 2023