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How we optimized ROI for BSS modernization with cloud infrastructure

Learn how we optimized our cloud spending while meeting or exceeding critical performance requirements for our 5G/6G-ready business support system (BSS).
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Plan growing from coin money

Photo by Micheile Henderson 

In How we designed a 5G/6G-ready business support system (BSS) for telco operators, we performed a performance analysis with a 5G/6G-ready converged charging system (CCS). We compared using dedicated bare-metal against a common application platform for telecom providers.

In this article, we'll take a closer look at how to optimize the cost (meaning the charges you see on your cloud bill) versus the return on investment (in transactions per second [TPS], or the capacity observed) to calculate a cost-risk analysis of using hyperscaler infrastructure.

[ Learn why open source and 5G are a perfect partnership. ]

OpenShift cluster formation on AWS

OpenShift Virtualization extends OpenShift Container Platform to allow you to host and manage virtualized workloads on the same platform as container-based workloads. From the OpenShift Container Platform web console, you can import a virtual machine (VM), create new or clone existing VMs, perform live migrations between nodes, and more. OpenShift Virtualization can manage Linux and Windows VMs.

This solution covers hybrid workloads (VMs and containers), so we've enriched the sandbox Red Hat OpenShift (self-managed) cluster on AWS with OpenShift Virtualization Operator. To leverage nested virtualization, we had to use EC2 metal instances which are, unfortunately, more expensive.

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Screenshot of OpenShift Container Platform on AWS instances
Figure 1. OpenShift Container Platform on AWS instances (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)

Converged charging system (CCS) deployment formation

We deployed the i2i Systems CCS solution described in our previous article with the following footprint:

  • 3 (high-availability behind Kubernetes Service exposure) VMs that accommodate the CCS backend. It contains:
    • CPU cores: 16
    • Memory: 48GB
    • Total storage: 500GB
  • 3 (high-availability behind Kubernetes Service exposure) 5G charging function (CHF) cloud-native network functions (CNFs) that are integrated with the CCS backend
    • CPU cores per module: 2
    • Memory per module: 4GB
    • Attached storage: 15GB

[ Try this hands-on learning path to deploy a cluster in Red Hat OpenShift Service on AWS. ]

CCS KPI benchmarks

Our objective is to get some benchmarks on TPS and latency in response time. It is important to keep latency below a certain threshold to prevent fraud. Keeping response time below a certain threshold (<50ms) is acceptable for telecom service providers, as most prepaid offerings are measured using minutes of service given or taken.

1250TPS: 9.88ms latency

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Screenshot of 1250TPS metrics
Figure 2. 1250TPS: 9.88ms latency (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)

Figure 3. 1500TPS: 11.6ms latency

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Screenshot of 1500TPS metrics
Figure 3. 1500TPS: 11.6ms latency (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)

Figure 4. 2000TPS: 32.43ms latency

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Screenshot of 2000TPS metrics
Figure 4. 2000TPS: 32.43ms latency (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)

Observations

We achieved two-times-better performance with the exact VM and CNF specification as in our earlier benchmark.

The main reason for this performance difference is:

  • We leveraged the latest-generation Intel CPU with higher CPU clock frequencies, faster memory, and storage bus speeds available through the AWS EC2 metal instance.
  • We had higher networking throughput: 100Gbps vs. 40Gbps per network interface controller (NIC).

However, the average cost per CPU core in this test has a significant difference ($39.42 per month versus the CPU cost of an already-paid-for on-premises machine capital expense, or CAPEX) that carries the per-cluster cost to higher OPEX spending ($5.3K/month).

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Screenshot of compute cost
Figure 5. Compute cost overview (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)

A cloud approach with expensive resource usage should always get the best utilization levels for each resource allocated and paid; otherwise, cloud consumption may not be as economical as it could be. (Please see Quantitative cost comparison of on-premise and cloud infrastructure based EEG data processing for a detailed quantitative cost comparison of on-premises and cloud infrastructure usage for data-intensive application sets.)

[ See Kubernetes: Everything you need to know ]

How to improve the price/performance ratio

Cost analysis and optimization is a continuous process, especially on the cloud, where you need to monitor the cost of each solution and its components to run and deliver the best user experience and operational efficiency.

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Screenshot of namespace-level cost breakdown
Figure 6. Namespace-level cost breakdown (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)

With Kubernetes as an application platform, it is common to use namespaces as one of the isolating boundaries for tenancy and monitoring cost breakdowns.

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Screenshot of workload-level cost breakdown
Figure 7. Workload-level cost breakdown (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)

Within a namespace, a workload analysis will help contrast the cost of the application versus business metrics, such as TPS measurement.

[ Hybrid cloud and Kubernetes: A guide to successful architecture

We used Cast AI to guide us in maintaining intended performance levels for application return/revenue and lowering infrastructure and platform costs. It can automatically reduce the infrastructure and platform costs and maintain the application's intended performance levels with the optimized ratio of return/revenue.

CAST AI optimization is based on two automated strategies:

  1. Cluster auto-scaling based on workload needs versus demand, with real-time and extremely efficient rightsizing.
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    Screenshot of worker utilization levels
    Figure 8. Worker utilization levels (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)
  2. Guiding the selection and use of the most cost-effective cloud resource type (for example, spot vs. on-demand EC2 instances), effectively doing a real-time price arbitrage over hundreds of instance types.
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    Screenshot of dashboard showing areas for saving money
    Figure 9. Potential areas for savings (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)

Cast AI continuously reduces the cost of the cluster at the cluster scale and application levels.

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Screenshot of dashboard showing resource optimization
Figure 10. Continuous resource utilization optimization (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)
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Screenshot of dashboard showing cost savings
Figure 11. Savings achieved (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)

[ Learn more about cloud-native development in the eBook Kubernetes Patterns: Reusable elements for designing cloud-native applications. ]

If you don't want to implement another solution to increase cost-effectiveness, at least use AWS's native services for managing spending limits and alerts.

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An email warning about AWS spending anomalies
Figure 12. AWS spending notification (Fatih Nar, Volker Tegtmeyer, Leon Kuperman, Yasar Koc, CC BY-SA 4.0)

Summary

The BSS modernization price-over-performance benchmark tests have shown that Red Hat OpenShift Container Platform with cloud-native virtualization may deliver equivalent and sometimes even better performance for business support system (BSS) solutions with mixed container and VM workloads compared to bare metal. The tests have also shown that users can achieve better total cost of ownership and a consistent deployment experience across different infrastructure types.

This deployment example demonstrates how to optimize cost while meeting or exceeding critical performance requirements. This reference solution, developed with our partners at i2i and Cast AI, automates cost and performance optimization in real time to keep BSS solutions performing optimally from economic and performance points of view.

Details of our BSS modernization ROI optimization solution can be found in the video below.

[ Check out Red Hat's Portfolio Architecture Center for a wide variety of reference architectures you can use. ]

What to read next

Topics:   Telecom   5G   Cloud   Business  
Author’s photo

Fatih Nar

Fatih (aka The Cloudified Turk) has been involved over several years in Linux, Openstack, and Kubernetes communities, influencing development and ecosystem cultivation, including for workloads specific to telecom, media, and More about me

Author’s photo

Volker Tegtmeyer

Volker has been in the communications industry for many years. Before joining Red Hat, he worked in the telecommunications and cybersecurity industries developing various business opportunities including data networks, VOIP, IPTV, systems integration, and application and automation platforms. More about me

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Leon Kuperman

Leon is co-founder and CTO at CAST AI. Formerly Vice President of Security Products OCI at Oracle, Leon's professional experience spans across tech companies such as IBM, Truition, and HostedPCI. More about me

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Yasar Koc

Yasar Koc is an Integration Engineer with five years of experience and system administration and devops skills. He currently works as part of the System Engineering team at i2i Systems.  More about me

Navigate the shifting technology landscape. Read An architect's guide to multicloud infrastructure.

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