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Why Cloud Consumers Need “Objective” Application Performance Management

Jim Young

The long anticipated rise of cloud computing is finally taking hold, with analysts reporting more investment in public clouds than private clouds, and suggesting that half of all production applications will be running on public clouds in three or four years.

The allure of public clouds springs from advantages like improved service scalability, reduced operational costs, and an increased focus on business goals and strategies instead of the technology needed to pursue them. However, there is a cost to that flexibility and economy, in reduced visibility of application and infrastructure health. Without direct control over the cloud infrastructure itself, traditional application performance management (APM) tools may prove impractical to deploy and manage.

I recently read a story about a war of words between a leading platform as a service vendor and a disgruntled customer, who discovered that they weren’t actually getting the amount of virtual computing capacity that they had been told they were getting.

Putting aside the customer’s justifiable indignation at not getting the resources that they believed they were paying for, the real story for a cloud consumer here (or an APM Product Manager) is that the tools they were using to monitor their workloads didn’t really provide them with a complete story. Then, when the continued mystery warranted a deeper-dive tool, it appears that they were pressured or influenced into purchasing a particular cloud APM tool because of a relationship between that tool vendor and the PaaS provider.

This suggests (and logic supports) that customers are better off using objective APM tools when monitoring workloads on public clouds, whether those workloads are running on a Platform as a Service (PaaS) solution like Heroku, or an Infrastructure as a Service (IaaS) solution like Amazon or Rackspace.

We generally espouse such a practice to help a customer maintain a posture of portability, so they can nimbly move workloads around to different cloud platforms, yet maintain continuity in their real-time and historical view of application health, without having to train their eyes on a new health dashboard whenever they move their workloads. We can employ the slightly suspicious sounding argument that a customer should not necessarily rely on his service provider for monitoring tools, since that provider has a vested interest in painting a rosy picture. Even in the presence of SLAs, a cloud tenant with no access to the infrastructure is somewhat at the mercy of his provider for performance reporting. An APM solution that the customer can deploy and configure himself provides a level of “checks and balances” oversight.

It can be impractical for customers to deploy legacy monitoring tools when moving to public clouds, so there is a need for a solution that can be deployed within those public clouds, in their own little sphere of control where their application VMs reside. By adopting an elastic and scalable ­yet small and easy to deploy architecture, as well as the ability to embed additional monitoring technology into base VM images, this solution enables robust APM, even when users can only deploy simple Linux VMs to someone else's cloud.

Jim Young is Information Development Manager, IBM Cloud and Smarter Infrastructure

Related Links:

www.ibm.com

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Why Cloud Consumers Need “Objective” Application Performance Management

Jim Young

The long anticipated rise of cloud computing is finally taking hold, with analysts reporting more investment in public clouds than private clouds, and suggesting that half of all production applications will be running on public clouds in three or four years.

The allure of public clouds springs from advantages like improved service scalability, reduced operational costs, and an increased focus on business goals and strategies instead of the technology needed to pursue them. However, there is a cost to that flexibility and economy, in reduced visibility of application and infrastructure health. Without direct control over the cloud infrastructure itself, traditional application performance management (APM) tools may prove impractical to deploy and manage.

I recently read a story about a war of words between a leading platform as a service vendor and a disgruntled customer, who discovered that they weren’t actually getting the amount of virtual computing capacity that they had been told they were getting.

Putting aside the customer’s justifiable indignation at not getting the resources that they believed they were paying for, the real story for a cloud consumer here (or an APM Product Manager) is that the tools they were using to monitor their workloads didn’t really provide them with a complete story. Then, when the continued mystery warranted a deeper-dive tool, it appears that they were pressured or influenced into purchasing a particular cloud APM tool because of a relationship between that tool vendor and the PaaS provider.

This suggests (and logic supports) that customers are better off using objective APM tools when monitoring workloads on public clouds, whether those workloads are running on a Platform as a Service (PaaS) solution like Heroku, or an Infrastructure as a Service (IaaS) solution like Amazon or Rackspace.

We generally espouse such a practice to help a customer maintain a posture of portability, so they can nimbly move workloads around to different cloud platforms, yet maintain continuity in their real-time and historical view of application health, without having to train their eyes on a new health dashboard whenever they move their workloads. We can employ the slightly suspicious sounding argument that a customer should not necessarily rely on his service provider for monitoring tools, since that provider has a vested interest in painting a rosy picture. Even in the presence of SLAs, a cloud tenant with no access to the infrastructure is somewhat at the mercy of his provider for performance reporting. An APM solution that the customer can deploy and configure himself provides a level of “checks and balances” oversight.

It can be impractical for customers to deploy legacy monitoring tools when moving to public clouds, so there is a need for a solution that can be deployed within those public clouds, in their own little sphere of control where their application VMs reside. By adopting an elastic and scalable ­yet small and easy to deploy architecture, as well as the ability to embed additional monitoring technology into base VM images, this solution enables robust APM, even when users can only deploy simple Linux VMs to someone else's cloud.

Jim Young is Information Development Manager, IBM Cloud and Smarter Infrastructure

Related Links:

www.ibm.com

Hot Topics

The Latest

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

Virtual Private Networks became a cornerstone of enterprise security at a time when corporate infrastructure looked very different from today ... For years, this model worked well. But the architecture behind VPNs assumed a centralized corporate environment—one where the network itself was the hub of activity. In a cloud — first world, that assumption no longer holds ...

Website outages get resolved just as fast in August as they do in November. I went looking for the opposite: the summer slowdown everyone assumes is there once the people who fix things are away. It isn't in the data we collected, covering 1.8 million confirmed outages across tens of thousands of websites ...

This year, many of the cloud infrastructure contracts signed in the early days of the AI boom will come up for renewal. As the year goes on, I anticipate we'll see a significant amount of cloud vendor swapouts and multi-cloud adoption, and the reason isn't just GPU depreciation. It's because they're tired of their current cloud providers ...

There's a moment the many observability teams have experienced days into bringing a new service into production: you realize that the vendor's claims of "intelligent" behavior included a large serving of hype. Their dashboards look nice until they don't, the failure modes are a black box, and no one on the team can confidently explain why the system did what it did at 2 am. Agentic AI is about to force every Ops team to relive that moment at web-scale until they start treating these systems as the dependencies they actually are ...