Agile DevOps Needs Cloud Optimization

Ross Schibler
Opsani

An agile DevOps approach is an amalgamation of agile sprints and the integrated teamwork of a DevOps model. As Development and Operations teams integrate with agile practices and groups, production and deployment becomes more efficient. Features, updates, and fixes can be delivered weekly, even daily.

This collaborative advancement has established the practices of Continuous Integration (CI) and Continuous Delivery and Deployment (CD). As a result, agile DevOps teams now run a perfectly smooth and flawless CI/CD toolchain.

Except they don't. Why? Because in the agile DevOps framework, there is a vital piece missing; something that previous approaches to application development did well, but has since fallen by the wayside. That is, the post-delivery portion of the toolchain. Without continuous cloud optimization, the CI/CD toolchain still produces massive inefficiencies and overspend.

The Necessity of Cloud Optimization

Cloud optimization is the key to making sure you don't overprovision your app resources, and overspend on your cloud bills. Cloud apps can have a wide variety of functions, and a plethora of moving parts. Depending on your configuration, these parts can run your application for the better or worse. A finely-tuned app is a company's treasure, but an inefficiently tuned one can waste millions of dollars.

With the right tweaks to resources and parameters, overall app performance can improve and costs incurred can be significantly reduced. However, most companies aren't doing this tweaking. Research reveals that 80% of finance and IT leaders report that poor cloud financial management has negatively impacted their businesses. 69% admit to regularly overspending their cloud budget, by at least 25%.

One cause of this is friction between finance departments and application owners. While the CFO and finance teams lobby for saving as much money and as many resources as possible, application owners hate to even consider reducing resources to the applications, afraid that this will cause performance problems and even application failure.

Furthermore, optimization can be a pain to fold into a release cycle. The roadmap gets too crowded by new features and releases, or engineers might not find performance tuning and optimization all that exciting. However: the most likely reason why cloud optimization doesn't happen? Human limitations.

Human Limits in a Virtually Limitless World

Here's a hard pill to swallow: optimization — real, authentic cloud optimization and performance tuning — is too complex for the human brain.

This isn't to rain on the parade of the achievements of human civilization. We humans are capable of great things. But real cloud optimization is far too complicated for humans to perform. In the era of cloud-native microservice architectures, a simple, 5-container application can possess about 255-trillion resource and parameter permutations. This is simply too many data points for a human to try and work with.

Moreover, knowing which permutations to enact requires two distinctive types of knowledge. The first one is infrastructure knowledge, which should cover all stacks: application runtime, cache, compute, database config, job placement, memory, network, storage, thread management, and so on. The second is knowledge of the application workload itself, and its unique features and demands. It's almost impossible to find someone with true depth knowledge of both these realms.

Even if, by some miracle, you find someone with an in-depth familiarity with both types of knowledge, your next problem is the speed of everything. With the constant bombardment of new code, traffic changes, user growth, and new infrastructure options from cloud providers, there's only so much data a human brain can take.

The Solution to Cloud Optimization

Without the right approach and the right tools, true cloud optimization is never achieved. This is why the best thing most companies can do in terms of “performance tuning” is a basic analysis of cloud provider bills.

The solution? Leveraging artificial intelligence (AI) and deep reinforcement learning.

Achieving maximum efficiency for cloud applications requires making judgements and decisions that are too numerous and fast-moving for the human mind – but that are not too numerous for AI.

Deep reinforcement learning, a form of AI, utilizes neural networks based on the connections of the human brain's neurons. Properly trained and developed, these networks can represent hidden data and allow your CO tool to build a knowledge bank of different configurations, in the same way that the brain develops certain behavioral patterns.

An effective cloud optimization tool that leverages these capabilities can aggregate and monitor an entire system, paying close attention to how every shift and tweak in the settings and parameters affects app performance and cost. This processed information is then fed back to the input end of the neural network over and over again, to continuously compound insights.

Compounded insights mean that the network continuously teaches itself to become better at improving the overall efficiency of the application, examining millions of configurations to identify an optimal combination of resource and parameter settings. All the while, as the agile DevOps team continues to improve upon the application, so does the AI-powered cloud optimization tool improve the application's performance and cost utilization.

With each new iteration, the tool's predictions hone in on the optimal solution, and as improvements are constantly found, they are automatically promoted.

Cloud Optimization: The Future of Agile DevOps

With true cloud optimization, agile DevOps teams unlock cost savings, and users enjoy better app performance and user experience. Even though most cloud applications run with more cost than is necessary, such inefficiencies can be eliminated if organizations combine an agile DevOps framework and AI-driven cloud optimization approaches. Cloud apps may be extremely complex, dynamic, and fast-moving, but that does not mean they can't be hyper-efficient, too.

Ross Schibler is CEO and Co-Founder of Opsani

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Agile DevOps Needs Cloud Optimization

Ross Schibler
Opsani

An agile DevOps approach is an amalgamation of agile sprints and the integrated teamwork of a DevOps model. As Development and Operations teams integrate with agile practices and groups, production and deployment becomes more efficient. Features, updates, and fixes can be delivered weekly, even daily.

This collaborative advancement has established the practices of Continuous Integration (CI) and Continuous Delivery and Deployment (CD). As a result, agile DevOps teams now run a perfectly smooth and flawless CI/CD toolchain.

Except they don't. Why? Because in the agile DevOps framework, there is a vital piece missing; something that previous approaches to application development did well, but has since fallen by the wayside. That is, the post-delivery portion of the toolchain. Without continuous cloud optimization, the CI/CD toolchain still produces massive inefficiencies and overspend.

The Necessity of Cloud Optimization

Cloud optimization is the key to making sure you don't overprovision your app resources, and overspend on your cloud bills. Cloud apps can have a wide variety of functions, and a plethora of moving parts. Depending on your configuration, these parts can run your application for the better or worse. A finely-tuned app is a company's treasure, but an inefficiently tuned one can waste millions of dollars.

With the right tweaks to resources and parameters, overall app performance can improve and costs incurred can be significantly reduced. However, most companies aren't doing this tweaking. Research reveals that 80% of finance and IT leaders report that poor cloud financial management has negatively impacted their businesses. 69% admit to regularly overspending their cloud budget, by at least 25%.

One cause of this is friction between finance departments and application owners. While the CFO and finance teams lobby for saving as much money and as many resources as possible, application owners hate to even consider reducing resources to the applications, afraid that this will cause performance problems and even application failure.

Furthermore, optimization can be a pain to fold into a release cycle. The roadmap gets too crowded by new features and releases, or engineers might not find performance tuning and optimization all that exciting. However: the most likely reason why cloud optimization doesn't happen? Human limitations.

Human Limits in a Virtually Limitless World

Here's a hard pill to swallow: optimization — real, authentic cloud optimization and performance tuning — is too complex for the human brain.

This isn't to rain on the parade of the achievements of human civilization. We humans are capable of great things. But real cloud optimization is far too complicated for humans to perform. In the era of cloud-native microservice architectures, a simple, 5-container application can possess about 255-trillion resource and parameter permutations. This is simply too many data points for a human to try and work with.

Moreover, knowing which permutations to enact requires two distinctive types of knowledge. The first one is infrastructure knowledge, which should cover all stacks: application runtime, cache, compute, database config, job placement, memory, network, storage, thread management, and so on. The second is knowledge of the application workload itself, and its unique features and demands. It's almost impossible to find someone with true depth knowledge of both these realms.

Even if, by some miracle, you find someone with an in-depth familiarity with both types of knowledge, your next problem is the speed of everything. With the constant bombardment of new code, traffic changes, user growth, and new infrastructure options from cloud providers, there's only so much data a human brain can take.

The Solution to Cloud Optimization

Without the right approach and the right tools, true cloud optimization is never achieved. This is why the best thing most companies can do in terms of “performance tuning” is a basic analysis of cloud provider bills.

The solution? Leveraging artificial intelligence (AI) and deep reinforcement learning.

Achieving maximum efficiency for cloud applications requires making judgements and decisions that are too numerous and fast-moving for the human mind – but that are not too numerous for AI.

Deep reinforcement learning, a form of AI, utilizes neural networks based on the connections of the human brain's neurons. Properly trained and developed, these networks can represent hidden data and allow your CO tool to build a knowledge bank of different configurations, in the same way that the brain develops certain behavioral patterns.

An effective cloud optimization tool that leverages these capabilities can aggregate and monitor an entire system, paying close attention to how every shift and tweak in the settings and parameters affects app performance and cost. This processed information is then fed back to the input end of the neural network over and over again, to continuously compound insights.

Compounded insights mean that the network continuously teaches itself to become better at improving the overall efficiency of the application, examining millions of configurations to identify an optimal combination of resource and parameter settings. All the while, as the agile DevOps team continues to improve upon the application, so does the AI-powered cloud optimization tool improve the application's performance and cost utilization.

With each new iteration, the tool's predictions hone in on the optimal solution, and as improvements are constantly found, they are automatically promoted.

Cloud Optimization: The Future of Agile DevOps

With true cloud optimization, agile DevOps teams unlock cost savings, and users enjoy better app performance and user experience. Even though most cloud applications run with more cost than is necessary, such inefficiencies can be eliminated if organizations combine an agile DevOps framework and AI-driven cloud optimization approaches. Cloud apps may be extremely complex, dynamic, and fast-moving, but that does not mean they can't be hyper-efficient, too.

Ross Schibler is CEO and Co-Founder of Opsani

Related Links

Hot Topics

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Keys to Building a Partner Ecosystem That Scales

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

AI Deepfakes: Rethinking Trust in the Workplace

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

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Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...