Testing Strategies Empowering Power and Cooling Reduction in 5G Environments

As 5G adoption accelerates and cloud-native network architectures become the foundation of modern telecommunications, communication service providers (CSPs) face mounting pressure to reduce operational costs while meeting increasing demands for performance, scalability, and sustainability. This white paper examines how comprehensive testing strategies enable significant reductions in power consumption and cooling requirements through the effective implementation of Cloud-Native Network Function (CNF) autoscaling in Kubernetes-based 5G environments.

Traditional Present Mode of Operation (PMO) deployment models allocate maximum computing resources to network functions regardless of actual demand. While this approach ensures peak capacity, it results in significant resource underutilization, excessive server idling, and unnecessary power consumption. Idle servers and underutilized CPUs continue consuming substantial energy, increasing both operating expenses and environmental impact. As AI, augmented reality, virtual reality, gaming, streaming services, and other data-intensive applications continue driving network traffic growth, these inefficiencies become increasingly costly.

The paper explores how Horizontal Pod Autoscaling (HPA) and Cluster Autoscaling (CA) provide a more intelligent approach to cloud-native resource management. Horizontal Pod Autoscaling dynamically adjusts the number of application pods according to real-time workload metrics such as CPU and memory utilization, ensuring applications consume only the resources they require. Cluster Autoscaling complements this process by automatically increasing or decreasing the number of active Kubernetes worker nodes based on application demand. Together, these technologies enable telecommunications operators to optimize resource utilization, reduce idle infrastructure, lower power consumption, and minimize cooling requirements while maintaining high-quality service delivery.

Successfully implementing autoscaling, however, requires far more than enabling Kubernetes features. The white paper emphasizes that rigorous testing and validation are essential to ensure autoscaling mechanisms perform reliably across varying traffic conditions, unexpected failures, software upgrades, and production-scale deployments. Without comprehensive testing, operators risk degraded customer experience, inefficient scaling behavior, or missed power-saving opportunities.

The paper presents a structured testing framework covering every major aspect of CNF autoscaling. Core validation areas include HPA scale-out and scale-in performance, Cluster Autoscaling effectiveness, manual and time-based scaling for applications without HPA support, overload protection during sudden traffic spikes, application resiliency, power consumption consistency across software releases, and both CNF and platform in-service upgrades. Additional focus is placed on ensuring Kubernetes resource allocation remains optimized while maintaining latency, throughput, and overall Quality of Experience (QoE).

Recognizing that production environments experience failures as well as normal operations, the testing strategy also addresses resilience validation. Test scenarios include pod deletions, node failures, node drains, forced reboots, network instability, platform software upgrades, and recovery under varying traffic loads. These tests verify that cloud-native applications continue operating reliably while maintaining service continuity and minimizing customer impact. Comprehensive resiliency testing also enables early detection of application instability, platform issues, and performance regressions before production deployment.

Power efficiency measurement represents another major component of the testing methodology. Rather than evaluating performance alone, the framework correlates network traffic, Kubernetes resource utilization, CPU and memory consumption, autoscaling events, and actual power usage. This holistic approach allows operators to quantify power savings achieved through workload consolidation, worker node shutdown, efficient pod management, and optimized resource allocation. Continuous regression testing across software releases further ensures that future updates do not introduce higher energy consumption or reduced efficiency.

To support these objectives, the paper describes Keysight's Landslide platform and customer-tailored metrics framework, which provide realistic 5G traffic generation, Kubernetes telemetry collection, impairment injection, performance analysis, and power consumption reporting. The solution enables service providers to emulate real-world network conditions while collecting detailed metrics across control plane, data plane, Kubernetes clusters, and cloud infrastructure. Comprehensive reporting correlates traffic demand, autoscaling behavior, resource utilization, latency, throughput, and energy consumption, allowing operators to optimize both operational efficiency and service quality.

Beyond technical implementation, the white paper positions comprehensive CNF autoscaling testing as a strategic business capability. Properly validated autoscaling enables CSPs to reduce operational expenditures, improve infrastructure utilization, lower data center cooling requirements, enhance sustainability initiatives, increase network resilience, and support continuous software evolution without service interruption. As cloud-native 5G deployments continue expanding, automated testing becomes a foundational requirement for balancing performance, reliability, scalability, and environmental responsibility.

Ultimately, the paper concludes that cloud-native autoscaling, combined with comprehensive automated testing and validation, provides communication service providers with a practical pathway to achieving substantial power and cooling reductions while maintaining the high performance, resilience, and service quality demanded by next-generation 5G networks. By validating autoscaling behavior across real-world operational scenarios, operators can confidently deploy energy-efficient network architectures that reduce costs, improve sustainability, and support the future evolution of cloud-native telecommunications infrastructure.