Effective Container Management and Scaling
Key Points
- Properly configuring and scaling Kubernetes resources during demand spikes—whether predictable (e.g., Black Friday) or unexpected (e.g., weather events)—prevents wasteful cloud spend and ensures service continuity.
- A well‑defined container management strategy is essential to avoid lost time‑to‑market, as mis‑managed resources can delay product delivery and increase operational overhead.
- The speaker outlines four key use‑case scenarios (batch jobs, open‑source projects, built‑in tools, data sovereignty) and frames them for two primary personas: developers and operations administrators.
- For batch‑job and serverless workloads, IBM Cloud Code Engine abstracts the underlying cluster, lets developers focus on business logic, and offers “scale‑to‑zero” pay‑as‑you‑go pricing, which is especially valuable for regulated industries needing strict financial controls.
- By leveraging such managed container platforms, developers can avoid the complexity of maintaining Kubernetes clusters while still benefiting from automatic scaling and cost efficiency.
Sections
- Strategic Container Management for Scaling - The speaker explains how proper resource allocation and scaling in Kubernetes—using real‑world scenarios like Black Friday spikes or unexpected weather—prevents waste, speeds time‑to‑market, and outlines four use‑case strategies tailored for developers and operations teams.
- Open‑Source Developer Use Case - It outlines how IBM Cloud Kubernetes Service addresses open‑source‑focused developers by delivering up‑to‑date CNCF features, low‑cost managed clusters, lifecycle automation, and a 99.99% SLA.
- Managed OpenShift for Regulated Edge - The speaker describes how IBM’s managed OpenShift and Cloud Satellite deliver compliant, SRE‑managed Kubernetes services to regulated industries and edge locations, addressing data‑sovereignty, latency, and skill‑gap challenges.
- Automating Secure Container Deployments - The speaker emphasizes using DevSecOps, infrastructure‑as‑code, and comprehensive observability to embed security, eliminate human error, and achieve repeatable, scalable Kubernetes deployments throughout development, testing, and production.
Full Transcript
# Effective Container Management and Scaling **Source:** [https://www.youtube.com/watch?v=iLyBEEkm5e0](https://www.youtube.com/watch?v=iLyBEEkm5e0) **Duration:** 00:11:22 ## Summary - Properly configuring and scaling Kubernetes resources during demand spikes—whether predictable (e.g., Black Friday) or unexpected (e.g., weather events)—prevents wasteful cloud spend and ensures service continuity. - A well‑defined container management strategy is essential to avoid lost time‑to‑market, as mis‑managed resources can delay product delivery and increase operational overhead. - The speaker outlines four key use‑case scenarios (batch jobs, open‑source projects, built‑in tools, data sovereignty) and frames them for two primary personas: developers and operations administrators. - For batch‑job and serverless workloads, IBM Cloud Code Engine abstracts the underlying cluster, lets developers focus on business logic, and offers “scale‑to‑zero” pay‑as‑you‑go pricing, which is especially valuable for regulated industries needing strict financial controls. - By leveraging such managed container platforms, developers can avoid the complexity of maintaining Kubernetes clusters while still benefiting from automatic scaling and cost efficiency. ## Sections - [00:00:00](https://www.youtube.com/watch?v=iLyBEEkm5e0&t=0s) **Strategic Container Management for Scaling** - The speaker explains how proper resource allocation and scaling in Kubernetes—using real‑world scenarios like Black Friday spikes or unexpected weather—prevents waste, speeds time‑to‑market, and outlines four use‑case strategies tailored for developers and operations teams. - [00:03:09](https://www.youtube.com/watch?v=iLyBEEkm5e0&t=189s) **Open‑Source Developer Use Case** - It outlines how IBM Cloud Kubernetes Service addresses open‑source‑focused developers by delivering up‑to‑date CNCF features, low‑cost managed clusters, lifecycle automation, and a 99.99% SLA. - [00:06:14](https://www.youtube.com/watch?v=iLyBEEkm5e0&t=374s) **Managed OpenShift for Regulated Edge** - The speaker describes how IBM’s managed OpenShift and Cloud Satellite deliver compliant, SRE‑managed Kubernetes services to regulated industries and edge locations, addressing data‑sovereignty, latency, and skill‑gap challenges. - [00:09:30](https://www.youtube.com/watch?v=iLyBEEkm5e0&t=570s) **Automating Secure Container Deployments** - The speaker emphasizes using DevSecOps, infrastructure‑as‑code, and comprehensive observability to embed security, eliminate human error, and achieve repeatable, scalable Kubernetes deployments throughout development, testing, and production. ## Full Transcript
So whether you're dealing with a known event like Black Friday sales
where you anticipate an increase in your resource utilization, or an unknown weather event
where you're not expecting it and a storm rolls through and that increases the resource utilization.
In either of these scenarios, it's important to leverage
one of Kubernetes strength, which is the ability
to scale up your microservices within that containerized application
to ensure you're meeting the demand for either of those two scenarios.
Now, what happens if you don't set your resources properly and scale back down following that event?
That can result in a waste of spend for those cloud resources.
That's where it's very imperative to have a container management strategy to avoid these situations.
Now, you've seen videos where we talk about “What is a container?”
“What is Kubernetes?”
comparing containers to virtual machines.
In this video, we'll delve deeper into "How do I make the right decisions
in that container management platform" to avoid this?
Because we know that if you don't make these right decisions,
you as a business will lose the most critical resource, which is time to market.
So what do I mean by a "container management strategy"?
So let's talk about four use cases ranging from batch jobs, to open source projects, built-in tools, and data sovereignty.
And we're going to talk about that in the frame of two different personas,
whether you're a developer or more of an operations administrator.
So let's talk about this first use case, which are batch jobs.
Now, this may come in a number of different use cases and requirements where maybe I need to run serverless.
So think about a component of your architecture that doesn't need to run all the time.
It's just sitting there waiting for some trigger to take place and then we can run in action as a result of that.
A batch job is something again also that doesn't need to run all of the time.
It just runs maybe nightly processing of a particular job.
We could also run functions-as-a-service (FaaS) all in this platform.
So IBM Cloud Code Engine is our offering in this space.
Now the value to that developer persona is that it abstracts the underlying cluster
and it really lets them focus on delivering business innovation
because they're not standing up, deploying, running Kubernetes clusters.
Instead, they're focused on solving business challenges,
writing code across that diverse workload, whether it's batch, serverless functions,
modern Cloud Foundry -- and lets them run all of that in one particular offering.
Now the value of Code Engine is the ability to scale to zero.
So I'm only paying for resources as I have them deployed.
Now another benefit is that IBM Cloud Code Engine enables our regulated industries
by having these financial services controls.
So this use case focuses on the developer,
allows them to focus on delivering and solving their challenges, not operating clusters.
Now, the second use case also focuses on the developer is someone that's more in the open source community.
Now this is driven largely by those developers that work in the upstream communities through different CNCF projects.
It could be Kubernetes, it could be Istio.
There are a number of projects where they're contributing upstream,
so that persona wants access to the latest and greatest of these capabilities as soon as possible.
Now, another driver for that persona could be they're looking for a lower cost
to start with a Kubernetes cluster management service.
So when we think about these different use cases, IBM Cloud Kubernetes Service, or IKS for short,
is our managed service that focuses on delivering the latest and greatest from the CNCF community,
providing and great user experience from not only day-one cluster creation, but also ongoing lifecycle management.
A lot changes in this open source community,
so a trusted partner like IBM can ensure that we are performing upgrades, updates,
ensuring security, operational characteristics that are important to that developer,
again, so they can focus on delivering innovation,
not ensuring that different components from the community work well together.
Now, one of the benefits of IBM Cloud Kubernetes Service is an industry leading 99.99% financially backed SLA.
Now this is important to the developer / the line of business because it ensures
that your workload, your clusters are available, whether it's development or all the way running through production.
Now, for this third use case, we're going to start to focus on the operators,
the IT administrators of you out there that are looking for built-in tools in one place.
Now when I deploy that cluster, I want all my monitoring and logging solution running within that single cluster.
I also want to enable my development teams to have their CI/CD tooling all within the confines of that cluster as well.
We have Red Hat OpenShift on IBM Cloud, which is our offering that provides managed OpenShift.
Now earlier we talked about Kubernetes.
OpenShift is much more than Kubernetes.
It brings the security, the hardening, the enterprise-grade scale of Kubernetes,
plus all of the value of built-in monitoring, logging, operator hub, code-ready workspaces,
all of this into one value add package solution
that enables those teams to then focus on delivering that business innovation.
Now, with our managed OpenShift, we're very focused also on the cluster creation process,
whether you point and click through the UI, which is lovely, but really you're going to automate that going forward.
We also provide lifecycle management so that operator doesn't have to know when updates are taking place.
We're going to provide that lifecycle management for them.
Again, moving up that line of responsibility,
enabling that operator to focus on what's important to their business challenges.
We also have the financial services controls for our managed OpenShift
that really enables you in the regulated industries to bring that workflow to cloud,
because we're doing so as a trusted partner with our managed OpenShift offering.
Now our last use case is again, focus on the operator.
Now this is around data sovereignty.
When we think about use cases where not everything can run in public cloud.
Now, this is for a number of reasons.
It could be things like latency concerns or I need to run that application at the edge.
Maybe I want to modernize my application in place before moving it out to the cloud.
So all of these challenges will help the operator determine how to run that workload.
Now, let's remember distributed cloud
This is essentially-- sometimes referred to as local cloud as a service --it's about
bringing fully SRE-managed services outside the confines of a cloud provider's infrastructure.
So in our case, IBM is managing these offerings on infrastructure that we don't own.
This is running in your data center. It could be running in retail locations.
It could be running in manufacturing.
Think about do you have resources and skills to run Kubernetes clusters in a manufacturing plant?
Most likely not.
So by having an IBM managed service running there,
it allows you to focus on the business application that you need to run in that plant.
IBM Cloud Satellite is our distributed cloud offering that allows you to bring those PaaS Services, Platform-as-a-Service,
to run in your location of choice,
helping you accelerate your business challenges by running managed services anywhere you need them.
This is the ultimate in flexibility
when we think about closing out a container management strategy, how and where do I need to run those workloads,
and using IBM Cloud Satellite, bringing a fully managed OpenShift offering to your infrastructure of choice
helps you accelerate your business initiatives.
Now that we've talked about the decision points and container management strategy
and also realizing it's not one size fits all.
All of your workloads or your components of a given
microservices architecture will not fit within a single solution.
So the end game is really thinking about how we accelerate that,
understanding that our workloads are not solved with just container management.
We need a broader ecosystem of solutions to really empower and create and engaging experience for our users.
So we're going to talk about AI automation and observability
and how they tie in to your containerized workload because
it's really applicable to both personas across all four of these use cases.
First, let's talk about AI.
This is a means to create a more engaging experience with your customer base.
How do we ensure that we are using the data that we have resources to and creating targeted campaigns,
making sure that chatbots are responding with more intelligent responses,
whether that's using watsonx, or OpenShift AI, all of these things make your applications run smarter.
Second thing is automation.
Now we really want to minimize human error. We want to eliminate risk.
We want to ensure we've got repeatability from those different environments.
Now we've heard DevSecOps, this is moving security left in that entire lifecycle process that we work on.
So how do we automate that,
because developers, they don't want to learn security controls.
We want it built into the process so they can adhere to them without learning anything new.
This is also going to save the line of business time and money by catching those vulnerabilities earlier in this process.
Also, infrastructure-as-code ensures that when we're creating clusters
and resources, we do so in a pre described, defined, repeatable manner
to eliminate human error from setting those up from our development,
test, QA -- ultimately into those production environments.
We also touched on...
Kubernetes requires new skills and resources to run efficiently.
And this is very true in the observability area where we need insight and recommendations
through the entire stack, whether it's down at the infrastructure layer
or all the way at the top level of our containerized applications.
Getting the insights to how are they performing.
When do we need to scale those resources?
How do we know that we are meeting our customer demand?
So all of these things across all four of the use cases
really highlights that we're moving beyond just a container management strategy.
We're enhancing our containerized applications to make sure that they run smarter and create a better user experience.
Thank you for watching.
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