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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

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 ...

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 ...

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 ...

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 ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...