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The Importance of Monitoring Containers

Kevin Goldberg

With the rise of Docker, Kubernetes, and other container technologies, the growth of microservices has skyrocketed among dev teams looking to innovate on a faster release cycle. This has enabled teams to finally realize their DevOps goals to ship and iterate quickly in a continuous delivery model. Why containers are growing in popularity is no surprise — they’re extremely easy to spin up or down, but come with an unforeseen issue.

Without the right foresight, DevOps and IT teams may lose a lot of visibility into these containers resulting in operational blind spots and even more haystacks to find the presumptive performance issue needle.

If your team is looking towards containers and microservices as an operational change in how you decide to ship your product, you can’t afford bugs or software issues affecting your performance, end-user experience, or ultimately your bottom line.

Ed Moyle, Director of Emerging Business & Technology at ISACA said it best in his blog, “Consider what happens to these issues when containers enter into the mix. Not only are all the VM issues still there, but they’re now potentially compounded. Inventories that were already difficult to keep current because of VM sprawl might now have to accommodate containers, too. For example, any given VM could contain potentially dozens of individual containers. Issues arising from unexpected migration of VM images might be made significantly worse when the containers running on them can be relocated with a few keystrokes.”

The infographic below provides more insight.

Kevin Goldberg is Content Marketing Manager at AppDynamics.

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The Importance of Monitoring Containers

Kevin Goldberg

With the rise of Docker, Kubernetes, and other container technologies, the growth of microservices has skyrocketed among dev teams looking to innovate on a faster release cycle. This has enabled teams to finally realize their DevOps goals to ship and iterate quickly in a continuous delivery model. Why containers are growing in popularity is no surprise — they’re extremely easy to spin up or down, but come with an unforeseen issue.

Without the right foresight, DevOps and IT teams may lose a lot of visibility into these containers resulting in operational blind spots and even more haystacks to find the presumptive performance issue needle.

If your team is looking towards containers and microservices as an operational change in how you decide to ship your product, you can’t afford bugs or software issues affecting your performance, end-user experience, or ultimately your bottom line.

Ed Moyle, Director of Emerging Business & Technology at ISACA said it best in his blog, “Consider what happens to these issues when containers enter into the mix. Not only are all the VM issues still there, but they’re now potentially compounded. Inventories that were already difficult to keep current because of VM sprawl might now have to accommodate containers, too. For example, any given VM could contain potentially dozens of individual containers. Issues arising from unexpected migration of VM images might be made significantly worse when the containers running on them can be relocated with a few keystrokes.”

The infographic below provides more insight.

Kevin Goldberg is Content Marketing Manager at AppDynamics.

Hot Topics

The Latest

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...