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Category: IT as Code

Fiberplane Adds Open Source Libraries to Automatically Collect Metrics

Fiberplane today added an ability to automatically collect metrics in real-time and make them accessible either via its namesake notebook software for debugging infrastructure or an open source Grafana dashboard.

Fiberplane CEO Micha Hernandez van Leuffen said Autometrics is an open source set of libraries the company created to make it simpler for developers to see platform performance metrics using a set of tools embedded within their integrated development environment (IDE).

The goal is to reduce the cognitive load on developers; today, developers need to first determine what metrics to track, find a way to collect the appropriate data and then launch queries against those metrics to determine the root cause of an issue, said Hernandez van Leuffen.

Autometrics, in contrast, provides access to a set of common queries using the Prometheus programming language (PromQL) against data that has been collected using open source OpenTelemetry agent software, he noted.

That approach, for example, makes it simpler to track error rates, response time and latency of any function. Links to live charts based on real-time data can also be inserted into the comments section of any document a development team creates.

The Fiberplane notebook uses the same operational transformation algorithm embedded within Google Docs to enable teams to easily collaborate around a document or spreadsheet. The core platform itself is built in Rust and WebAssembly (Wasm) to make it simpler to integrate Fiberplane as a plug-in into an existing DevOps workflow.

Autometrics extends that capability in a way that enables DevOps teams to provide developers with instant access to observability data. They can then see how their code is performing on any given platform, which is critical as more responsibility for running applications continues to shift left, said Hernandez van Leuffen. Today, many developers need to wait for an IT operations team to share data collected via an observability platform that was designed to be used by ITOps rather than a developer, he added.

As observability capabilities become more federated across the entire software development life cycle, it’s critical to make sure developers and IT operations teams can see metrics being generated in real-time. Otherwise, confusion arises because teams are not looking at the same set of metrics.

Fiberplane address that issue by taking the concept of notebooks—often used by data science teams to collaborate—and applying it to DevOps workflows to reduce the amount of time it takes to resolve any issue. As IT environments become more distributed in the age of the cloud and microservices, troubleshooting issues invariably spans the responsibilities of multiple members of an IT team. In effect, a notebook makes it possible to spin up a virtual war room on demand that enables the members of an IT organization to collaborate—regardless of where they are physically located or which organization they may be employed by.

April 13, 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:

Monster.com

SnapLogic
San Mateo, California
Infrastructure Engineer
$183,310

LinkedIn

Metropolitan Transit Authority – NYC
New York, New York
Cybersecurity Officer – DevSecOp/Cloud Infrastructure
$144,500 to $191,000

Dice.com

Flexion
Remote
Azure DevOps Engineer
$140,000 to $170,000

Indeed.com

Woven Teams
Remote
DevOps Engineer
$116,000 to $147,000

SimplyHired.com

Raytheon Missile and Defense
Schriever Air Force Base, Colorado
Senior Software Engineer
$75,000 to $161,000

April 10, 2023
5 Best Practices for Using VMs on Azure Cloud
When beginning a cloud improvement/optimization project, an easy place to start is to focus on the initial low-hanging fruit of your virtual machine (VM) environments. This article takes a closer look at these steps, using a Microsoft Azure VM environment as an example.

Here are five best practices that can be easily achieved when using VMs on Azure cloud.

1. Properly Size Your Virtual Machines: To maximize performance and minimize costs, it’s important to size your VMs appropriately. You can use the Azure portal to determine the right size for your workloads and then select the right VM size based on the CPU, memory and storage requirements of your application. For example, you can use the Azure VM sizing calculator tool to get an estimate of the virtual machine size based on your workload requirements.

Let’s say you want to host a web application that requires 2 vCPUs, 8 GB of memory and 50 GB of storage. You can use the Azure VM sizing calculator tool to determine the appropriate VM size based on these requirements. The tool may suggest using a “Standard B2s” VM which has 2 vCPUs, 4 GB of memory and a maximum disk size of 32 GB.

In this case, you would need to increase the disk size to 50 GB. You can do this by adding a data disk to the VM or using a larger VM size with enough disk space. For example, you could use a “Standard B4ms” VM with 4 vCPUs, 16 GB of memory, and a maximum disk size of 64 GB.

It’s important to note that the actual VM size may vary based on your workload requirements and the specifics of your application. The Azure VM sizing calculator tool provides a helpful starting point, but you should also perform your own testing and validation to ensure that the VM size meets your needs.

2. Use Managed Disks: Managed disks provide increased reliability, scalability and security compared to traditional storage accounts. For example, you can use managed disks to automatically scale the disk size and performance as the data grows and to replicate data for disaster recovery.

For example, to create a 100 GB managed disk, follow these steps:
- In the Azure portal, go to the “Disks” section and click on “Add.”
- Select “Blank disk” as the source and specify the disk size as 100 GB.
- Choose the appropriate storage account type (Premium or Standard), replication options and disk type (HDD or SSD).
- Give the disk a name and create it in the same resource group as your VM.
- Once the disk is created, you can attach it to your VM by going to the virtual machine’s “Disks” section, clicking on “Attach,” and selecting the newly created disk. By using managed disks, you benefit from automatic disk management and replication as well as improved reliability and security compared to traditional storage accounts. Additionally, managed disks simplify the process of attaching and detaching disks to and from VMs, making it easier to manage your storage resources.
3. Enable Monitoring and Diagnostics: To monitor the health and performance of your VMs, you can use Azure Monitor which provides real-time insights into resource utilization, application performance and other key metrics. You can also configure alerts to notify you of potential issues and diagnose problems using Azure Log Analytics. For example, you can set up an alert to notify you if the CPU usage of a VM exceeds a certain threshold.

You can also use Azure Monitor to gain real-time insights into the virtual machine’s resource utilization, application performance and other key metrics by following these steps:
- In the Azure portal, go to the virtual machine’s “Monitoring” section.
- Click on “Diagnostic settings” and then click on “Add diagnostic setting.”
- Give the diagnostic setting a name, select the VM you want to monitor and choose the metrics and logs you want to collect.
- Choose the storage account you want to use to store the monitoring data and specify the retention policy for the data.
- Click “Save” to apply the changes.
By enabling monitoring and diagnostics for your virtual machines, you can proactively monitor the health and performance of your VMs and quickly identify and resolve potential issues. Additionally, you can use the collected data to generate reports, visualize trends and perform analysis to gain deeper insights into the performance of your VMs.

4. Implement Security Best Practices: To secure your virtual machines, you can use Azure Security Center to implement security best practices and monitor for potential threats. You can also use Azure Active Directory to control access to VMs and secure your identity management. For example, you can use Azure Security Center to assess the security of your VMs and implement security recommendations such as enabling network security group (NSG) rules to restrict inbound and outbound traffic.

If you have a VM that you want to connect to other resources in your Azure environment, you can use a managed virtual network to provide a secure and scalable networking solution for your VM.

Here is how to create a managed virtual network in Azure:
- In the Azure portal, go to the “Virtual network” section and click on “Add.”
- Give the virtual network a name, select the appropriate subscription and resource group and specify the address space.
- Create one or more subnets within the virtual network, and specify the subnet address range and subnet name.
- Associate the virtual network with a network security group (NSG) to control inbound and outbound traffic.
- Create a network interface for your VM and associate it with the virtual network and subnet you created.
By using a managed virtual network, you can create a secure and isolated network environment for your VM and connect it to other resources in your Azure environment. Additionally, you can use the virtual network to control and manage network traffic and implement advanced networking scenarios such as load balancing, VPN connectivity and more.

5. Keep Virtual Machines Up-to-Date: Regularly updating your VMs with the latest security patches and software updates helps ensure that they are protected against potential threats and vulnerabilities. You can use Azure Update Management to automate the update process and keep your VMs updated. For example, you can schedule regular updates for your VMs to ensure they always have the latest security patches and software updates installed.

When planning public cloud environments, two commonly used words are “progress” and “results.” Following the best practices above will help position your cloud journey for success.
April 7, 2023
Declarative Compliance With Policy-as-Code and GitOps
In recent times, the quest for greater agility, faster releases, enhanced scalability, security and performance brought forth the advent of several automation tools, technologies and frameworks. Software development has evolved considerably over the years to mitigate these challenges.

Monoliths have been split into microservices for improved scalability, maintenance and faster releases. Since DevOps is adept at bridging the gap between development and operations teams, now these two teams can work together to improve productivity and speed up release cycles.

The Need for Compliance

One of the critical strategies for business success is to stay compliant since processes are always improving and evolving. Compliance ensures that your DevOps processes and practices are consistent and meet regulations or regulatory standards.

An organization with a compliance-driven DevOps culture can cut down on operational costs, improve efficiency and reduce risks considerably.

What is Declarative Compliance?

Declarative compliance is defined as the process of managing and enforcing compliance in which policies are defined using code, and the system’s desired state is declared rather than explicitly programmed. To enforce compliance policies, you don’t write scripts or programs; instead, you use a high-level language or configuration file.

Declarative Compliance provides several benefits:
- Increased efficiency
- Better managed compliance
- Minimized risk of human errors
- Enhanced security
- Faster deployment
- Better collaboration
With declarative compliance tools, the system’s state is defined as a set of policies, and the tool makes sure it matches the desired state. The tool will automatically fix anything out of compliance, bringing the system back into compliance if there’s a deviation.

Tools for Declarative Compliance

Some of the popular declarative compliance tools are:
- Open Policy Agent (OPA)
- Terraform
- Kubernetes Policy Controller
- Chef Compliance
- AWS Config
Declarative Compliance for Infrastructure and Application Deployment

Often, declarative compliance is used in infrastructure management and application deployment, where policies are written as code and enforced across multiple environments. In addition to managing compliance at scale, declarative compliance tools ensure that the system’s desired state stays consistent across multiple systems and applications over time.

With declarative compliance, organizations can manage compliance more efficiently, effectively and securely since they can define policies as code and enforce them automatically. In addition to reducing errors, downtime and security breaches, this approach enables organizations to stay compliant and maintain their system consistently.

Introduction to Policy-as-Code

A policy-as-code approach allows you to write programs that govern security, compliance and rules throughout your application’s life cycle. It entails defining and applying code to manage and automate policies. It shares the same ideas as infrastructure-as-code (IaC) and other DevOps practices like continuous integration and continuous delivery (CI/CD).

A policy enforces constraints and restrictions to prevent unauthorized access to resources such as databases, storage, services, etc. By codifying policies, you can enforce rules and constraints throughout the development life cycle to prevent non-conforming resources from being deployed by integrating these policies into the software development life cycle.

Figure 1: Policy-as-Code at work!

Why Policy-as-Code?

Typically, businesses have the required processes, tools and procedures to guarantee the deliverable’s quality. Performance, scalability, ease of use and accuracy are typical quantifiable quality metrics used to ascertain the quality.

You can examine the quality of your application using these metrics, but a manual procedure will take time and is prone to mistakes. A better alternative is to leverage policy-as-code.

The traditional policy enforcement processes have certain drawbacks. It is challenging to perceive that your policies would be entirely adhered to, i.e., they will not be broken. It is cumbersome to check against a list of policies manually.

Moreover, the traditional policy enforcement method cannot scale in today’s agile world, where organizations have been growing continuously with more and more employees, services and teams.

Here are the key benefits of policy-as-code at a glance:
- Faster and more efficient deployments
- Automated governance, approvals and policy enforcement
- More reliable and secure applications
- Centralized policy management
- Leverages version control
- Better visibility of policies
- Enhanced collaboration
How Does it Work?

Policy-as-code involves three key elements:
1. The policy itself, which contains the required code to model the decision-making process
2. The data and information about the environment, service or application
3. The query that is responsible for triggering the decision-making process
Here’s how policy-as-code works:
1. Policies are defined as code in a human-readable format
2. These policies are then translated into machine-readable code using Rego or HCL
3. The policies are integrated with the IaC pipeline
4. A policy engine evaluates the infrastructure and the application resources against these policies
5. The policy engine accepts the policies as input, processes them and then produces a query result
6. The policy engine reports violations (if any) to the respective stakeholders, such as the development or security teams
7. If violations are reported, the necessary remediation is performed automatically or manually
Types of Policies
- Security policies: Security is enforcing adequate technological controls to safeguard company assets.
- Compliance policies: Compliance policies are designed to help organizations adhere to governance standards. Typical examples include PCI DSS, GDPR, HIPPA, etc.
- Operational excellence: This encompasses policies pertaining to service degradation or interruption.
What is GitOps? How can GitOps Help With Compliance?

GitOps is a subset or extension of DevOps encompassing a collection of practices that can simplify deployments using Git. It is an operating model for cloud-native applications that leverages Git as a single source of truth for declarative applications and infrastructure. Using GitOps, you can implement a standardized policy-as-code approach across your CI/CD and GitOps pipelines.

The key principles of GitOps are:
- The state of the system is captured and stored in a Git repository
- The entire system is configured declaratively
- Automated deployment
You can create policies in Git and store them in a particular repository, which is essential since Git offers sophisticated change management tools like version control. Only the compliance team would have admin access to the repository.

It’s easy to enforce policies with Git, and you can apply them to any execution engine, like CI or CD. With GitOps, you can audit and document every action that affects data consumption.

You can expose system changes, optimize deployment and use a version-controlled infrastructure. By managing compliance with GitOps, you can make sure that the changes are visible, verifiable and auditable.

Figure 2: GitOps at work!

Using GitOps as the Execution Engine in a GitOps Process

There are several benefits to using GitOps as a process execution engine, including:
- Increased automation: GitOps automates software delivery, reducing manual intervention and increasing deployment accuracy and speed.
- Better transparency: GitOps records all changes to the system in a transparent and auditable way so that you can track compliance easily.
- Maintaining consistency: GitOps keeps the system in the desired state defined in the Git repository, reducing non-compliance risk.
- Fosters collaboration: All changes to the system are made through the Git repository, so GitOps encourages collaboration across teams.
Using Policy-as-Code and GitOps: Policy-Based Compliance

By using GitOps and policy-as-code, teams can manage compliance-as-code in an auditable, declarative way and enforce it via GitOps, ensuring that the desired state is always maintained. This can help your teams to codify compliance requirements as code, store them in version control and automatically enforce them through GitOps.

Organizations can improve the consistency and reliability of their systems by defining and enforcing compliance requirements using policy-as-code and managing their deployment processes with GitOps. While this can minimize compliance violations, it can also maximize efficiency.

By following policy-based compliance declaratively, businesses can define policies as code and enforce them automatically and repeatedly. By adopting such an approach to compliance management, you can better manage compliance in complex systems in a consistent, scalable, agile, transparent and collaborative manner.

For example, you can leverage policy-as-code to define policies for infrastructure provisioning and configuration management to ensure that the infrastructure complies with organizational policies and industry standards. Typical examples are defining policies for network configuration, access control and resource allocation.

Use Cases: Using Policy-as-Code and GitOps in a Production Environment

Infrastructure Provisioning

Open Policy Agent (OPA) can define policies for infrastructure resources like AWS Elastic Compute Cloud (EC2) servers. You can use GitOps to manage the deployment of infrastructure resources by storing IaC files in a Git directory. When the IaC files are changed, GitOps is adept at automatically deploying the changes while OPA checks for policy compliance.

Deployment

You can use policy-as-code tools such as OPA to create application deployment policies requiring specific security settings, while GitOps manages the application deployment by storing manifests.

Manage Kubernetes Clusters

You can leverage policy-as-code to better manage your Kubernetes clusters. So, you can write your code to define policies to manage Kubernetes resources, such as nodes, pods, clusters, etc., declaratively.

Compliance Management

You can use policy-as-code to establish and implement rules and regulations, such as those pertaining to security, to help businesses ensure that their systems comply with industry regulations and standards.

OPA can be used to set compliance policies, like restricting access to sensitive data to authorized users only. You can use GitOps to manage the deployment of compliance policies seamlessly.

Trusted Delivery: Policy-as-Code Integrated With GitOps Workflows

Trusted application delivery is a software delivery approach emphasizing security and trust in the software supply chain. This ensures that any software delivered to the clients is devoid of security flaws, malicious code and other attacks.

This approach prioritizes security and trust at the development, testing, packaging and deployment stages and integrates security and compliance checks into the GitOps pipelines. This quality check reassures clients about the product’s quality and ensures that any modifications adhere to predetermined rules and principles.

Trusted application delivery allows for quick deployment while securing apps with automated guardrails. These guardrails are implemented using policy-as-code. By enforcing these guardrails, you encourage frequent deployments while ensuring that the reliability and security of the application are not compromised.

Conclusion

Declarative compliance with policy-as-code and GitOps is a best practice for achieving greater compliance, reliability and agility in software development and deployment processes. You can simplify defining and enforcing compliance policies using declarative configuration files and automation tools. Using Git as the single source of truth for all configuration changes, an organization can achieve greater efficiency, visibility and auditability in the compliance process.
April 6, 2023
Synopsys Preps Extensions to Polaris SaaS Platform

Synopsys plans to extend the capabilities of its Polaris Software Integrity Platform for securing application development environments by adding dynamic application security testing (DAST) tools along with the ability to scan code used to provision infrastructure.

As a software-as-a-service (SaaS) platform, the Polaris Software Integrity Platform was created by combining the static application security testing (SAST) tool Synopsys gained with the acquisition of Coverity in 2014 and the software composition analysis (SCA) tool it added to its portfolio by acquiring Black Duck Software in 2017.

In 2015, Synopsys acquired the Seeker tool for analyzing the code created using interactive application security testing (IAST) tools from Quotium. The company last year acquired WhiteHat Security from NTT to add a DAST tool.

Patrick Carey, senior director for marketing strategy for Synopsys, said that the company would add the capabilities of those latter two platforms to create a comprehensive set of best-in-class security tools that are easily accessible via a SaaS platform.

At a time when more organizations than ever are moving to secure software supply chains, Carey said it’s critical to provide developers with more visibility into vulnerabilities across the entire software development life cycle (SDLC). Some organizations are opting to achieve that goal by embedding tools within the SDLC themselves, but many organizations would likely prefer to invoke a SaaS platform that they don’t have to maintain, noted Carey.

He noted that a SaaS platform makes it simpler to point a collection of integrated tools at a code repository to surface those vulnerabilities.

As DevSecOps workflows continue to evolve and mature, more responsibility for application security is shifted left toward developers. The challenge is that many developers have limited cybersecurity expertise, so it’s critical that they have access to high-quality security tools.

In addition, a SaaS platform helps reduce the total cost of achieving that goal by providing multiple capabilities within a single integrated platform, said Carey.

Naturally, most of the focus today is on reducing the number of vulnerabilities that find their way into production environments as applications are being built. However, there are already large numbers of unaddressed vulnerabilities in applications that have already been deployed. Organizations will need to find ways to address that technical debt in addition to providing developers with tools that help prevent those code vulnerabilities in the first place.

As such, there is no one central place through which application security can be managed, noted Carey. Instead, application security needs to be addressed across every phase of the SDLC, he added.

It’s not clear how quickly organizations are adopting DevSecOps best practices, but as calls for increased liability for software development increase—as outlined, for example, in the U.S. National Cybersecurity Strategy— organizations should expect DevSecOps workflows to become a requirement. Otherwise, organizations will be severely penalized for vulnerabilities that find their way into code. In effect, there is now a race to embrace DevSecOps before any proposed liability legislation becomes the law of the land.

April 6, 2023
Orgs Shift Toward Centralizing Automation Initiatives

A survey published today found that 75% of respondents recognized that automation silos hamper their ability to unify cloud operations across functions.

Conducted by CloudBolt Software, the survey polled 350 senior IT and DevOps leaders at organizations with more than 3,000 employees. On a positive note, the survey found 57% of respondents were starting to pursue a more centralized approach to automation, with a total of 88% acknowledging that having all previous and new automation centralized for the entire organization would ensure compliance, bolster security, enable business continuity, encourage reuse and reduce risk.

Rather than taking a more holistic approach, well over half of respondents (57%) reported automation efforts are limited to specific silos. On average, organizations are employing 27 different automation tools their organizations have deployed, using one or a few as they see fit, with little to no consistency across teams. More than half (55%) of respondents said they have four or more separate automation teams, with 95% noting that each of those teams operates in their own silo.

CloudBolt CTO Rick Kilcoyne said that lack of continuity clearly creates inefficiencies and redundancies that conspire to raise the total cost of IT. The challenge is that each team tends to favor one approach to automating a silo with little to no regard for a larger overall strategy to create an automation standard, he said.

That approach also creates issues whenever a member of one of those teams leaves an organization, he noted. In fact, the survey found 77% of respondents reported that continuity is at risk any time a vital member of the team leaves.

CloudBolt has been making a case for a federated automation platform that can be invoked either via application programming interfaces (APIs) or a graphical user interface (GUI) that provides access to low-code tools. The goal is to make the platform equally accessible to DevOps teams or IT administrators that tend to lack programming skills, and do so in a way that doesn’t require individual teams to replace their automation platform of choice. Instead, each of the automation platforms can be programmatically invoked via a central framework, said Kilcoyne. That approach eliminates any political debate over the potential need to replace one team’s favored automation platform with another, noted Kilcoyne.

Regardless of who is accessing the platform, most organizations would be well-advised to develop a center of excellence for automation, he added.

DevOps teams have always been committed to ruthlessly automating as many IT processes as possible, but in many cases, that has resulted in a plethora of automation tools being employed. In effect, automation may have become too much of a good thing. One approach to regaining control over those platforms is to create a platform engineering team that centralizes the management of DevOps workflows.

Regardless of approach, Kilcoyne said the most important thing for any team to do before automating an IT workflow is to first understand how to manually set it up. Too many IT teams are trying to apply automation to processes they don’t really understand with mixed results, he added.

The one thing that is certain, especially with the rise of artificial intelligence (AI), is the management of IT is going to become more automated.

April 6, 2023
Vega Cloud Commits to Reducing Cloud Costs

Vega Cloud today made a commitment to save customers at least 10% on their cloud infrastructure spending or it won’t charge for its software. Vega Cloud’s software automates cloud infrastructure management.

The Vega Cloud platform enables IT teams to connect, automate, manage and optimize new and existing networks, applications and other multi-cloud IT environments from a single console. As part of those capabilities, the company includes cost allocation and cost identification tools using a curated set of data to surface daily recommendations for cost improvements.

Vega Cloud CEO Kris Bliesner said the goal is to provide a financial operations (FinOps) capability within the context of a platform capable of acting on those recommendations that, on average, reduces cloud costs by 15% to 25%.

During uncertain economic times, IT leaders are under more pressure than ever to reduce costs. FinOps has emerged as a discipline for applying a set of best practices for specifically reducing cloud costs. In addition to reducing cloud costs, Vega Cloud is also making a case for reducing the total cost of IT by relying on a single automation framework to manage multiple clouds.

In the cloud era, many organizations have found themselves employing multiple clouds, each of which requires a separate set of tools to manage. IT teams then need to hire additional staff to manage each of those tools. As a result, the total cost of IT increases with each additional platform added to the overall IT estate.

Many organizations, of course, have allowed the number of platforms they manage to proliferate. This is because many application deployment decisions were driven by developers who can easily provision cloud infrastructure on their own. However, as concerns over the cost of cloud computing continue to rise, many organizations are revisiting how cloud infrastructure resources are used. Many of those reviews are being driven by finance teams that want cloud costs to become more predictable as cloud computing continues to account for a much larger percentage of the IT budget, noted Bliesner.

In fact, one of the challenges IT leaders have is finding a way to make sure developers and DevOps teams are more motivated to reduce costs rather than focusing entirely on how fast they can build and deploy applications, he added.

The reality is the average developer doesn’t need application build infrastructure running 24/7, so application development and testing is a great place to focus on cutting cloud costs initially, noted Bliesner. There’s also plenty of opportunity to better schedule workloads to reduce costs by relying more on lower-cost instances of cloud infrastructure, he said.

DevOps engineers, by nature, don’t want to be inefficient, so the issue comes down to providing them with the tools needed to identify resources that are being wasted, added Bliesner.

Most cloud service providers, of course, provide tools that enable IT teams to optimize spending, but Bliesner said those tools don’t go deep enough in terms of exposing issues. As a provider of an independent management framework, Vega Cloud is more committed to providing cost optimization capabilities that can be applied to multiple clouds, he added.

In general, interest in controlling cloud costs has risen sharply. Vega Cloud has been engaged with more customers researching this issue in the last four weeks than in the last three years, said Bliesner. The challenge—and the opportunity—is to not just cut cloud costs but also identify patterns that make it easier to predict them, noted Bliesner.

At this juncture, cloud costs are coming down. The only issue left to be determined is whether that goal will be accomplished with precision versus a fiat that adversely impacts an organization’s ability to continue to build and deploy software.

March 30, 2023
env0 Extends Workflow Platform for Provisioning Infrastructure

env0 today announced it has added support for additional infrastructure-as-code (IaC) tools and the Microsoft Azure DevOps platform to its workflow automation and management platform.

Fresh from raising an additional $35 million in funding, env0 CEO Ohad Maislish said while Terraform remains the most widely used IaC tool, there are now more organizations using alternatives such as CloudFormation from Amazon Web Services (AWS) along with Pulumi and Terragrunt.

Other capabilities added to the env0 platform include automated drift detection, multi-tier workflows, auditing, external logging platform integration, support for plug-ins and expanded role-based access controls and OpenID Connect (OIDC) authentication.

Finally, env0 is extending its existing Terraform support to include support for remote backend, module registry, remote plan, and the ability to plan and apply pull requests. The capabilities are especially critical for organizations that are now building microservices-based applications that require more sophisticated workflows to provision infrastructure resources, noted Maislish.

Most IaC tools are designed to enable developers to address technical implementation and provisioning issues without regard for user experience, security and governance, or business goals. The env0 platform makes it possible to create infrastructure environments with guardrails in place that enable DevOps teams to centrally address governance issues, noted Maislish.

In general, there’s now a lot more focus on continuous delivery (CD) as organizations look to continuously update applications, said Maislish. That requires a more structured approach to provisioning infrastructure across teams of developers that is managed by a platform engineering team, he added. The challenge is that CD is dangerous—in the sense that mistakes can easily be made, noted Maislish.

It’s not clear whether organizations are embracing platform engineering to centralize the management of DevOps workflows, but the number of organizations that have mastered CD best practices remains low. Most organizations are adept at continuous integration (CI), but given the number of unique platforms on which applications are deployed, CD has proven more challenging to adopt. In theory, a platform engineering team should make it easier for DevOps teams to define a consistent approach to CD that could be adopted across an organization.

Regardless of the DevOps approach, the overall goal is to improve developer productivity. The problem is that in the absence of any guardrails, developers have been provisioning cloud infrastructure as best they can. Not surprisingly, cloud computing environments are rife with misconfigurations that create vulnerabilities that cybercriminals have become more adept at exploiting. The fundamental issue is not such much the cloud infrastructure that is available as much as it is the processes employed to provision the resources. In their rush to build and deploy applications, developers will sometimes ignore best practices unless guardrails force the issue.

One way or another, IT organizations are exercising more control over cloud computing environments as the security of software supply chains becomes a bigger issue. The only thing that remains to be resolved now is how to put much-needed guardrails in place in a way that adds the least amount of friction possible.

March 29, 2023
Cloud Management Issues Are Coming to a Head

A survey of 1,300 technology and data executives found that more than a decade after the initial rise of cloud computing, organizations are still struggling to manage these environments.

Conducted by Wakefield Research on behalf of NetApp, the survey found 98% of respondents reported their business has been impacted by everything from the increasing complexity of data and heightened cybersecurity risk to lack of visibility into business operations and staff burnout.

Top challenges include cybersecurity risk (45%), staff not taking full advantage of business applications (44%), increased skepticism about the cloud from leadership (44%), lack of visibility into business operations (42%), going over budget (37%) and burning out staff (31%).

A full 88% continue to encounter technical challenges, including data mobility between clouds (33%), security risks (33%) and interoperability between cloud services (30%).

The top drivers of cloud investments are scaling artificial intelligence (AI) and automation (39%), followed by driving business innovation (35%) and creating digital experiences (34%).

Organizational challenges encountered include having a clear vision for cloud strategy (32%), establishing the right level of governance (31%) and managing costs (30%). A full 84% are under pressure to show short-term return on investments (ROI) in cloud computing platforms, with 76% noting CFOs and other business leaders are skeptical of savings. Nearly half of the tech executive respondents (49%) reported that when cloud strategy discussions occur, cost concerns come up often or all the time. As a result, nearly three-fourths of executives (74%) said tiered workloads are either very or extremely important, the survey found.

Other factors impacting multi-cloud strategy include environmental, social and governance (ESG) concerns (79%) and data sovereignty (74%), the survey found.

Ronen Schwartz, senior vice president and general manager for cloud volumes at NetApp, said as more organizations employ multiple clouds, total costs tend to rise. Each cloud platform requires mastering another control plane, which means someone in the IT organization must have the right skills to manage, he noted. IT leaders are attempting to exercise more control over cloud deployments, but developers, for a variety of reasons, have already deployed workloads across multiple cloud computing environments, added Schwartz. One approach to achieving that goal is to make sure data is stored on the least expensive tiered service possible for any given specific use case, he noted.

Less clear is whether an uncertain economy might push organizations to consolidate workloads on fewer cloud computing platforms. Even then, the cost of moving a workload is not trivial given the proprietary application programming interfaces (APIs) that many developers have likely invoked as DevOps built and deployed cloud applications.

Of course, there is no going back; the issue now becomes how to define a set of best practices for managing cloud computing environments in a way that minimizes costs. It turns out that cloud computing can be more costly than an on-premises IT environment simply because the infrastructure resources made available to developers are almost infinite. In the final analysis, however, that can turn out to be too much of a good thing—especially if organizations wind up spending more on IT to support these environments. Many of those environments are not rigorously managed because, for example, developers may not be inclined to spin down a development environment on a Friday because it takes too long to spin it back up the following Monday.

One way or another, however, a level of long overdue scrutiny of cloud computing costs may be on the horizon.

March 29, 2023
Enfabrica Makes Cloud Networking Performance Advances

Enfabrica Corp. today emerged from stealth at the MemCon conference to launch a class of processors for networking in cloud computing environments that are optimized for moving data between CPUs, graphical processor units (GPUs), accelerators and memory.

Enfabrica CEO Rochan Sankar said the Accelerated Compute Fabric (ACF) enables scalable, streaming, multi-terabit-per-second movement of data in a way that also reduces the total cost of cloud networking. That’s critical as the amount of data being generated by applications infused with artificial intelligence (AI) capabilities that require access to larger amounts of data continues to increase, he added.

At the same time, DevOps workflows will need to evolve as it becomes possible to compose AI fabrics of compute, memory and network resources capable of scaling to tens of thousands of nodes versus relying on top-of-rack network switches, network interface cards (NICs), PCIe switches and CPU-controlled DRAM that creates too many bottlenecks when moving large amounts of data.

Enfabrica’s first chip, the Accelerated Compute Fabric Switch (ACF-S), supports standards-based interfaces, including multi-port 800 Gigabit Ethernet networking and high-radix PCIe Gen5 and CXL 2.0+ interfaces. The company claims that Enfabrica’s ACF-S is the first data center silicon product in the industry to deliver headless memory scaling to any accelerator by incorporating CXK memory bridging to enable a single GPU rack to have direct, low-latency, uncontended access to local DDR5 DRAM at more than 50 times greater capacity than GPU-native High-Bandwidth Memory (HBM). That approach should enable customers to cut their cost of GPU compute by an estimated 50% for large language model (LLM) inferencing and 75% for deep learning recommendation model (DLRM) inferencing, the company claimed.

Simulation tests suggested ACF-enabled systems could achieve the same target inference performance using only half the number of GPUs and CPU hosts compared to the latest GPU servers.

The cost of building AI applications today is prohibitive for many organizations, but as processor advances are made, there may come a time when the cost of building and training AI models substantially decreases. Advances in networking processor technologies will, as a result, accelerate the pace at which network operations are integrated into programmable DevOps workflows versus relying on dedicated networking teams to manage top-of-rack switches.

It’s not clear yet whether cloud service providers will embrace ACF, but the chip makes it clear there is a significant opportunity to reduce the amount of networking infrastructure needed to build and deploy AI applications.

Less clear, of course, is to what degree machine learning operations (MLOps) might ultimately be folded into DevOps workflows. While developers of AI models create a unique type of software artifact, they still need to be integrated with application code running in a production environment via application programming interfaces (APIs). One way or another, the total cost of building and deploying applications infused with AI models will need to decline.

In the meantime, the networking bottlenecks that conspire to make deploying distributed computing applications difficult today may soon become overcome as latency issues are increasingly overcome.

March 28, 2023