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Use Case: AlphaCo Relying on Kubernetes Containerization

Our Focus

AlphaCo, a larger company with annual revenues north of $100M. AlphaCo has the skills and capital needed to both develop and operate Kubernetes-based containerized applications.

Background Brief

AlphaCo is embarking on a new digitalization initiative to improve the services it provides to its customers. Using their substantial resources, AlphaCo plans to build a cloud-native solution based on micro-services deployed within containers. Naturally they’ll rely on Kubernetes as their orchestration layer.

In terms of the solution’s technical setup, AlphaCo has carefully considered 2 options:

Option 1: Manual Setup

Option 2: Managed Kubernetes Solution from AWS

After carefully considering both alternatives, AlphaCo chooses a ready-made solution in order to minimize the need for internal training and operations.

They look to leverage the expertise of a handful of internal team members who have been trained on Kubernetes and container tech. Meanwhile their IT department has managed to set up the necessary AWS accounts and environment.

Initial Challenges

With The conventional Cloud Solution

Despite employing several IT experts with relevant training, Kubernetes proves demanding to set up, develop on, and operate—especially in a situation in which multi-tenancy is critical.

In addition, AlphaCo struggles with the following:

  1. While certain key resources have relevant training, AlphaCo has no significant experience working with the cloud. As a result, a lot of time is spent on trial and error in effort to figure out how to actually set up the environment. Not only is this costly and difficult, but in the aftermath there remain concerns that certain aspects of the environment may not have been properly configured.


  2. Beyond the base environment, setting up a Kubernetes cluster so that it’s optimized both for resource utilization and security entails a steep learning curve. Exactly what resource allocation to assign and how to approach cluster security is still somewhat unclear.


  3. AlphaCo decides that creating deployment scripts using Helm charts is best as it will allow for smoother and more automated re-deployments down the line. This also entailed a significant initial learning curve.

Ongoing Issues

With The conventional Cloud Solution

In the aftermath of setup and configuration, AlphaCo contends with ongoing issues and challenges:

    1. AlphaCo painfully discovers that despite having successfully configured its development environment, a substantial portion of the work it’s already undertaken now needs to be performed again in order to set up their production environment. AWS provides some help on configuring accounts to handle dev-test-prod setups, but there was nonetheless a significant amount of experimentation needed to get everything working properly—especially relating to network connectivity.

    2. AlphaCo onboards their first customer onto their new application only to discover that running any more than a single customer on the cluster may not even be possible. Without true multi-tenancy, there’s simply no way to guarantee separation between customers within a single cluster. AlphaCo resorts to setting up entire additional clusters—manually assigning resources to each—for their subsequent customers.

    3. After onboarding their 4th customer, it becomes painfully obvious that AlphaCo’s 1-customer, 1-cluster workaround to their multi-tenancy issues is totally unsustainable. The company is incurring major resource burn from repeatedly setting up and allocating resources to new clusters. Costs begin to spiral out of control as there simply aren’t enough hands consistently available to keep each cluster tuned to optimal resource efficiency. This is quickly exacerbated by AlphaCo’s realization that each cluster requires a sizable setup due to their large data volumes. This meant that each was allocated substantial costly compute resources, only for them to sit idle most of the time.

With a Cloudshift21 Cloud CapsuleTM

Here’s what life would be like for AlphaCo if they instead chose a Cloud CapsuleTM from Cloudshift21 instead:

  1. They would have to endure next-to-no learning curve related to setting up their cloud environment and Kubernetes clusters. Everything works right out of the box with no configuration required.

  2. AlphaCo could painlessly move their application to their Cloud CapsuleTM as the Helm charts they’d already developed can simply run as-is.

  3. AlphaCo’s Cloud CapsuleTM automatically tunes resource utilization in the background—with no direct involvement or manual intervention from AlphaCo required. In addition, AlphaCo gains extensive built-in resource monitoring services to ensure that their developed applications are always running efficiently and can be further optimized if needed.

  4. While AlphaCo’s example doesn’t go for long enough to highlight major ongoing security problems, they very well likely would have encountered them with their manually-configured multi-cluster Kubernetes setup. Running their cloud ops within a Cloud CapsuleTM means that everything is isolated within secure Workspaces, and no configuration—with inherent room for mistakes—is needed.

  5. With each Cloud CapsuleTM Workspace fully isolated from every other Workspace, true multi-tenancy is enabled by default. So AlphaCo enjoys the simplicity and sanity of avoiding reliance on multiple Kubernetes clusters. Instead they simply isolate each customer within their own Workspace, keeping their full-stack setup immeasurably simpler and more cost-efficient. Maintenance simplifies to only that for the application itself—rather than for multiple entire clusters and every layer within each.

  6. Since each of AlphaCo’s customers is securely running within their own Workspace, and multiple Workspaces are still contained within the same Cloud CapsuleTM, resource utilization is incredibly efficient. All Workspaces share the same underlying cluster and cloud architecture. Running numerous Workspaces requires only marginally more in terms of resources than running one.

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