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Category: IT Administration

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
New Relic Extends AI to Identify Alert Coverage Gaps

New Relic this week added additional artificial intelligence (AI) capabilities to its observability platform that make it possible to detect and resolve alert coverage gaps.

Camden Swita, a senior product manager for New Relic, said it’s not uncommon for DevOps teams to discover an issue that the existing alerts they put in place were not designed to address. New Relic is adding machine learning algorithms at no additional cost that are designed to identify anomalies indicative of those potential issues. The platform then surfaces recommended alerts that DevOps teams can apply or further customize as they see fit, he added.

Those machine learning algorithms ensure DevOps teams are tracking the right thresholds to ensure there are no blind spots that could adversely affect application performance, noted Swita.

New Relic is employing both predictive and generative AI to enable DevOps team to detect and resolve issues faster. The company has been using machine learning algorithms for predictive AI for several years and, more recently, started providing early access to a generative AI tool dubbed Grok.

The overall goal is to make the capabilities of an observability platform more accessible, Swita said. This is accomplished by surfacing issues without requiring a software engineer to craft intricate queries to interrogate metrics, logs and traces, he noted.

The AI capabilities also serve to reduce the amount of alert noise; it’s now possible to more easily understand the root cause of an issue and reduce the total number of alerts that might otherwise be sent, he added.

While there’s no doubt AI will help democratize DevOps by making it possible to write scripts and better manage workflows, it might be a while before DevOps teams trust the recommendations made. AI models typically require some time before they understand how an IT environment functions, and not every initial recommendation may be valid. In time, however, DevOps teams will find themselves increasingly supervising processes that AI models are executing.

In addition, there are competing approaches to building large language models (LLMs) that drive generative AI. For example, it’s not clear whether chaining LLMs together will be more efficient than creating a single large ‘parent’ LLM that has ‘child’ relationships with smaller LLMs, noted Swita.

In the meantime, DevOps teams would be well-advised to list tasks that are likely to be automated by AI models. Many of them will tend to be manual tasks that most software engineers don’t especially relish, so in that regard, there’s naturally a lot of interest in AI. It remains to be seen, however, whether various forms of AI might one day automate an entire DevOps workflow. In fact, most DevOps engineers are going to set a high bar for AI models that, at the very least, will be expected to perform a task as well as humans before it’s trusted completely.

August 24, 2023
IBM to Provide Hybrid Cloud Service Based on VMware Cloud

At the VMware Explore 2023 conference this week, IBM announced that it will soon provide a fully managed service based on a streamlined edition of VMware Cloud.

Brendan Kinkade, vice president for independent software vendors, technology and hybrid cloud partnerships for IBM Global Sales, said VMware is now making available editions of VMware Cloud that are both easier to deploy and manage and, just as importantly, migrate between cloud platforms and on-premises environments as IT teams see fit.

IBM is committed to using that platform to deliver a managed VMware Cloud service that will span hybrid cloud computing environments aligned to an NIST-validated framework, he added. That approach is critical because it will enable IT teams to apply VMware license credits to any instance of VMware Cloud no matter where it is deployed, noted Kinkade.

In addition, the streamlined edition of VMware Cloud doesn’t use proprietary application programming interfaces (APIs) to make it simpler to port IT environments to another cloud service or back to an on-premises IT environment whenever necessary, he added.

At the foundation of the hybrid cloud service will be an instance of a multi-tenant vCloud Director console residing on the IBM Cloud. Through the console, IT teams will be able to work with IBM to co-manage application environments.

In addition, IBM is providing a managed IBM Cloud Cyber Recovery (ICCR) Service to better secure cloud computing environments.

It’s not clear how much of IT is now being consumed as a managed service, but the COVID-19 pandemic has certainly increased that number. Many IT organizations are also finding it simpler to rely on a managed services provider (MSP) to manage infrastructure so they can focus more of their time and effort on application development, noted Kinkade.

MSPs are also in a better position to aggregate all the telemetry data needed to train artificial intelligence (AI) models that will be used to automate workflows, he added.

Exactly how DevOps workflows will evolve as organizations rely more on external service providers to manage IT infrastructure is, naturally, a work in progress within many organizations. The main challenge is aligning a unique set of continuous delivery (CD) practices for deploying and updating applications with a provider that has already defined its own workflows.

Nevertheless, given the complexity of those environments, many organizations are not able to hire and retain the IT expertise required to manage it, so the only option may be to rely on an MSP.

Each IT organization will need to decide whether to rely on an MSP, given the total cost of a service. Many organizations often wind up having a mix of workloads managed by internal IT teams and external service providers. The one thing that is certain is that as the pace at which workloads are being deployed in the cloud increases, many internal IT teams are overwhelmed by a level of complexity that will only increase.

August 23, 2023
VMware Looks to GitOps to Drive Edge Computing

At its VMware Explore 2023 event, VMware today unveiled an orchestration tool, dubbed VMware Edge, that promises to simplify the management of edge computing services at scale.

At the same time, VMware has launched a Project Keswick initiative through which it will work with customers to define additional use cases for VMware Edge based on zero-trust principles and GitOps workflows for deploying applications. The company also unveiled a VMware Retail Edge platform based on VMware Edge and is planning to add additional offerings aimed at specific vertical industry segments.

Finally, VMware is making available a managed connectivity service through which it will provide private 4G/5G wireless services.

VMware Edge Cloud Orchestrator is based on a tool that VMware created to orchestrate the management of its secure access service edge (SASE) platform. That has now been extended to support a wider range of software running on edge computing platforms.

Sanjay Uppal, senior vice president and general manager of service provider and edge for VMware, said the goal is to streamline the management of software-defined edge computing platforms via a single console. Achieving that goal requires the ability to identify and programmatically manage workloads distributed across a mix of networks, he added.

VMware is betting that as more workloads are distributed to edge computing platforms, there will be a greater need to centralize the management of workloads running on platforms that are not always easy to physically access. Less clear, however, is to what degree those edge computing platforms will be managed by IT teams versus operational technology (OT) teams that are usually embedded within a department that manages a specific business process.

However, regardless of who manages those platforms, they will be relying more on GitOps workflows to programmatically provision infrastructure and deploy applications that will need to be regularly updated. In fact, because these platforms are often dispersed across hundreds of physical locations, the need for a pull-based orchestration tool that works asynchronously is critical, said Uppal.

In the longer term, IT teams should also expect artificial intelligence (AI) capabilities to be applied in a way that makes edge computing self-healing, he added.

No one can say exactly how many organizations have adopted GitOps to manage the provisioning of both infrastructure and the delivery of applications, but it’s conceivable that edge computing will force the issue. The tricky part will be finding a way to manage those GitOps workflows at the necessary level of scale. Most DevOps teams already struggle with implementing DevOps workflows at scale, so if more workloads are to be distributed across hundreds of edge computing platforms, there will be a much more acute need for automation to consistently deploy, secure and update applications.

Regardless of approach, however, there may come a day when the number of workloads deployed at the edge far outnumbers the number of workloads deployed in the cloud or in a data center. The challenge and the opportunity now is to get ahead of edge computing issues before they inevitably create a set of management issues that will be more difficult to address after applications have already been deployed.

August 22, 2023
VMware Looks to Streamline Multi-Cloud Computing Management

VMware today integrated its console for managing cloud instances with its VMware Cloud Foundation to streamline the deployment of its software in on-premises IT environments.

Announced at the VMware Explore 2023 conference, the latest edition of VMware Cloud will make it simpler to manage multi-cloud computing environments by combining VMware Cloud Foundation Software and VMware Cloud Services into a single platform.

VMware also announced early access to an ESXi life cycle management cloud service for VMware vSphere+ that enables IT teams to centrally manage upgrades across a distributed vCenter environment. Additional updates to vSphere in the upcoming release include doubling GPU capacity per virtual machine.

Krish Prasad, senior vice president and general manager for cloud infrastructure at VMware, said the goal is to improve the overall pace of innovation; in addition to deploying workloads in on-premises IT environments, organizations are also deploying workloads on multiple cloud computing services.

VMware Cloud is being made available in five editions that can either be managed by an internal IT team, an external services provider or by VMware. In addition, a VMware Ransomware Recovery service based on VMware Cloud is also generally available. Following a breach of an on-premises IT environment, VMware Ransomware Recovery will enable organizations to run production workloads in the cloud until forensics are completed. The company also unveiled a technology preview of cybersecure storage that will integrate recovery workflows with native vSAN snapshots and add support for Google Cloud VMware Engine.

In addition, VMware is adding a managed NSX service delivered via the cloud, dubbed NSX+, that manages instances of the virtual networking software that runs on top of its virtual machine software. There is also now an NSX+ virtual private clouds (VPCs) option that isolates instances of the virtual networking platform.

Finally, VMware is adding a VMware Cloud with VMware vSAN Max, which will make 8.6 petabytes of disaggregated capacity and 3.6 million IOPS per cluster available on demand later this year.

VMware appears to be streamlining the number of platforms IT teams need to acquire to support and manage IT infrastructure at a time when more organizations are trying to navigate increasing economic headwinds.

One way or another, IT teams will need to reduce the cost of IT at a time when IT environments are becoming more complex. VMware is betting that, given its dominance in on-premises IT environments, there will be a natural inclination to deploy more of its software in the cloud to create a more homogenous IT environment that is less complex.

However, it’s not clear whether organizations are willing to pay extra for the privilege. Many organizations are now supporting multiple classes of virtual machines and associated software despite the fact that each additional platform raises the total cost of IT.

In the meantime, many IT organizations are also awaiting the outcome of Broadcom’s effort to acquire VMware before deciding how to centralize the management of multi-cloud computing environments.

August 22, 2023
Datadog Updates Aim to Improve Logging, Testing Efficiency

At its recent DASH 2023 conference, Datadog revealed Flex Logs and Intelligent Test Runner, two major new capabilities aimed at improving logging and testing efficiency for engineering teams.

Omri Sass, director of product management at Datadog, said Flex Logs introduces a new middle tier of log storage between real-time querying and cold archives. As Sass explained, this provides a cheaper option for retaining logs beyond the typical two weeks for occasional analytics and investigations. By decoupling storage from compute for reading logs, teams can independently scale each based on their needs. This enables retaining rich log data for months or years to uncover long-term trends, he said.

In addition, Sass noted that Flex Logs helps address common log analysis limitations around storage costs and rehydrating archives. The feature better aligns log pricing with specific use cases beyond just troubleshooting. According to Sass, this evolution in log storage tiers will likely shift more logs from legacy on-premises systems to its cloud platform.

The second capability, Intelligent Test Runner, leverages observability signals to optimize CI test suites. As Sass explained, comprehensive test automation in CI pipelines often increases deployment times and flakes. Intelligent Test Runner inspects code changes to determine associated tests, ignoring irrelevant ones, which helps avoid long wait times for minor changes just to exercise unaffected areas.

By tapping into existing telemetry around code commits, test health and runtimes, the feature adapts testing to each code modification. Sass explained that observability data has matured enough to enable real-time optimization like this. The end result is faster CI validation, so engineering teams can ship updates more rapidly.

Sass highlighted how Flex Logs and Intelligent Test Runner are Datadog’s attempts to address real pain points developers face around managing log data life cycles and CI speed bumps. Sass said he believes this combination of depth in core disciplines like logs and application performance monitoring (APM), along with expanding into adjacent developer workflows, is key to solving these issues through innovation.

Looking to the future, the observability space continues to evolve rapidly as emerging technologies and practices expand monitoring beyond traditional IT infrastructure. Companies handling modern workloads expect their observability platform to provide comprehensive visibility as development toolchains and architectures grow more complex, with the next generation of observability likely incorporating advanced analytics, automation and AI to help engineers tame systems at ever-increasing scale and speed.

August 9, 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
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
BigPanda Extends AIOps Platform to Embrace Generative AI

BigPanda today added generative artificial intelligence (AI) capabilities to analyze incidents surfaced by its AI for IT operations (AIOps) platform.

Blair Sibille, field CTO for BigPanda, said Generative AI for Automated Incident Analysis leverages generative AI platforms based on the general-purpose large language models (LLMs) created by OpenAI.

The goal is to make it simpler for IT teams to discern the root cause of an issue by, for example, taking advantage of summaries of alerts enabled by generative AI to make it simpler for IT professionals of any skill level to understand what requires their most immediate attention, he added. Today, insights are lost in the sea of alerts that often inundate IT teams, Sibille noted.

BigPanda has been at the forefront of applying machine learning algorithms to automate IT operations management. Now, the company is incorporating external general-purpose LLMs to further augment IT teams, said Sibille.

As IT environments continue to be more complex, it’s becoming essentially impossible for IT teams to manage them at scale without relying more on automation. AI technologies take automation to another level, but they won’t eliminate the need for DevOps teams to manage IT environments, added Sibille.

BigPanda claimed generative AI capabilities will make it possible to reduce the amount of time required to resolve tickets by seven minutes each, with an accuracy rate of 95% during beta testing. Over time, those capabilities not only reduce mean-time-to-resolution but also reduce the level of toil that often conspires to burn out DevOps teams, said Sibille.

At the core of the BigPanda AIOps platform is a data lake capable of consuming data generated by multiple applications and systems. That data then provides the foundation on which machine learning algorithms are applied to surface insights.

It’s not clear how much generative AI will automate DevOps workflows, but in effect, more IT teams will be able to take advantage of having the equivalent of a DevOps engineer that they can query any time an issue arises using natural language, noted Sibille.

AI doesn’t mean that IT outages will be completely eliminated, but it does mean they will become a lot less stressful, 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 simply 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 clear is that AI will soon be pervasive. DevOps teams should start evaluating today which tasks will be automated with an eye toward planning how DevOps roles will evolve as AI advances accelerate.

July 11, 2023
Cloud Orchestration and its Impact on DevOps Teams
Cloud orchestration refers to the coordination and management of various interconnected cloud services and resources to automate and streamline complex IT processes. In simple terms, it is the process of integrating, synchronizing, and automating various cloud-based components to create a seamless and unified workflow.

Cloud orchestration aims to optimize the deployment, management, and use of resources in a cloud environment, improving efficiency, reducing costs, and ensuring that cloud services are delivered in a timely and effective manner. It is related to the concept of CloudOps (cloud operations), which uses cloud orchestration concepts to allow more efficient management of cloud resources. Cloud orchestration is a critical aspect of modern IT infrastructure management, as it enables organizations to manage and control their cloud resources more effectively.

Difference between Cloud Orchestration and Cloud Automation

Though the terms cloud orchestration and cloud automation are often used interchangeably, they have distinct differences.

Cloud automation refers to the process of automating individual tasks and processes within the cloud environment. It involves the use of scripts, templates and other tools to automate repetitive tasks, such as provisioning resources, deploying applications and configuring environments. The primary goal of cloud automation is to reduce the time and effort needed to manage cloud resources.

Cloud orchestration is a broader concept that encompasses not only the automation of tasks but also the coordination and management of the entire cloud ecosystem. It involves the integration of various cloud services, tools and resources to create a unified, streamlined workflow. Cloud orchestration ensures that all components in the cloud environment work together seamlessly, automating complex processes and managing dependencies between tasks.

In essence, cloud automation is a subset of cloud orchestration. While cloud automation focuses on automating individual tasks, cloud orchestration takes a more holistic approach, orchestrating multiple tasks and resources to achieve a higher level of automation and efficiency.

How Cloud Orchestration Enhances DevOps Practices

Increased Efficiency and Speed

One of the main benefits of cloud orchestration in a DevOps context is the ability to increase efficiency and speed in the software development and deployment process. By automating and streamlining various tasks, cloud orchestration eliminates manual processes, reduces the potential for human error and accelerates the overall development cycle.

Improved Collaboration and Communication

Cloud orchestration fosters better collaboration and communication between development and operations teams by providing a unified platform for managing cloud resources. This shared platform enables teams to work together more effectively, breaking down traditional silos and ensuring that all stakeholders have visibility into the development and deployment process.

Enhanced Infrastructure and Application Management

Cloud orchestration simplifies the management of complex cloud infrastructure by providing a single, unified platform for cloud optimization—improving the management of resources, applications and services in the cloud.

This centralized management approach enables organizations to more easily monitor, control and optimize their cloud environment, ensuring that resources are used effectively and that applications are running smoothly. With cloud orchestration, organizations can more easily scale their infrastructure to meet changing demands, ensuring that they have the resources they need when they need them.

Improved Monitoring and Troubleshooting

Cloud orchestration provides improved monitoring and troubleshooting capabilities, enabling organizations to more effectively identify and address issues in their cloud environment. With comprehensive monitoring tools and dashboards, teams can gain insight into the performance of their applications, identify bottlenecks and pinpoint potential issues before they become critical.

Challenges of Integrating Cloud Orchestration With DevOps

Despite the numerous benefits of cloud orchestration, integrating it into DevOps practices can present several challenges. Some of the common challenges include:
- Complexity: Cloud orchestration involves the integration of various cloud services, tools and resources, which can be complex and time-consuming. Organizations may struggle with complexity, particularly if they lack the necessary expertise or resources to effectively implement and manage the solution.
- Resistance to change: Implementing cloud orchestration requires a change in mindset and approach to software development and deployment. As your organization migrates more systems to the cloud, some team members may be resistant to adopting new processes and tools, making it difficult to fully embrace and realize the benefits of cloud orchestration.
- Security concerns: As with any cloud-based solution, security is a critical concern when integrating cloud orchestration into DevOps practices. Organizations must ensure that their solution is secure and that they have the necessary policies and procedures in place to protect their sensitive data and resources.
- Cost and resource constraints: Implementing and managing a Cloud Orchestration solution can be resource-intensive, requiring significant investment in infrastructure, tools, and personnel. Organizations must carefully consider the costs and resources required to implement cloud orchestration and determine whether the benefits outweigh the investment.
Best Practices to Integrate Cloud Orchestration Into DevOps

To successfully integrate cloud orchestration into DevOps practices and overcome the challenges mentioned above, organizations should consider the following best practices:
- Assess your organization’s readiness: Before implementation, evaluate your organization’s readiness for change. This includes assessing your existing infrastructure, processes and tools, as well as your team’s willingness and ability to adopt new practices and technologies.
- Develop a clear strategy: Develop a clear strategy for integrating cloud orchestration into your DevOps practices. This should include defining your goals and objectives, identifying the tools and resources you will need and establishing a timeline for implementation.
- Choose the right tools and technologies: Select the appropriate tools and technologies that best meet your organization’s needs and requirements. Consider factors such as ease of integration, scalability, security and cost when evaluating potential solutions.
- Train and educate your team: Ensure that your team has the necessary knowledge and skills to effectively implement and manage cloud orchestration. This may involve providing training and education on new tools, processes and best practices, as well as fostering a culture of continuous learning and improvement.
- Monitor and measure success: Continuously monitor and measure the success of your implementation. This includes tracking key performance indicators (KPIs), such as deployment speed, resource utilization and application performance, to ensure that your solution is delivering the desired results.
Conclusion

Cloud orchestration is an essential tool for organizations looking to streamline and optimize their IT infrastructure, particularly in the context of DevOps practices. By automating and coordinating complex cloud processes, organizations can improve efficiency, speed, collaboration and overall performance.

However, successfully integrating with DevOps can be challenging, requiring a clear strategy, the right tools and technologies and a commitment to continuous improvement. By following the best practices outlined in this guide, organizations can overcome these challenges and fully realize the benefits.
July 6, 2023