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With 5G, cloud adoption and AI driving data growth high-performing mediation platforms are essential. DigitalRoute's CTO, Demed L'Her, compares Kubernetes and traditional virtual machine based deployments, offering insights into the advantages and challenges of each.
Demed L'Her, DigitalRoute
Modernizing telecom mediation: how cloud, AI, and Kubernetes are changing the game
Telecom operators are navigating a critical infrastructure decision as 5G networks, cloud adoption, and AI-driven automation drive exponential growth in real-time event data. High-performing mediation platforms are essential to handle this surge, and cloud-native solutions are transforming traditional architectures. Kubernetes offers dynamic scalability and integrates with AI-driven automation to optimize mediation workloads, while Virtual Machines (VMs) remain a proven, stable choice for certain deployments.
This article examines the strengths and limitations of both deployment models across performance, efficiency, and reliability dimensions, providing insights to help telecom leaders make informed mediation infrastructure decisions.
As telecom networks handle increasing volumes of real-time charging data, 5G signaling, and IoT telemetry, mediation platforms must be scalable, automated, and cloud-ready. Hybrid cloud adoption, AI-driven automation, and Kubernetes-based orchestration are redefining how mediation workloads are deployed and optimized.
For years, VMs have been the standard for mediation, providing stability and isolation. However, as cloud-native architectures evolve and AI-driven workload prediction becomes mainstream, Kubernetes is emerging as a flexible and cost-efficient alternative. With cloud services enabling greater deployment flexibility and AI enhancing traffic optimization, telecom professionals must determine:
Should mediation workloads remain on VMs, or is Kubernetes the smarter choice for scalability, efficiency, and automation?
Mobile data traffic continued to expand substantially in 2024, with a year-on-year growth rate of 21%. This growth is increasingly driven by 5G networks, which are expected to account for 34% of all mobile data traffic by the end of 2024, up from 25% in 2023 (Source: Ericsson Mobility Report).
At the same time, cloud services and AI-driven automation are reshaping how telecom operators deploy and manage mediation infrastructure. AI-powered traffic prediction models are being used to preemptively adjust mediation workloads, ensuring that resources are allocated before congestion occurs. These models work seamlessly with Kubernetes' autoscaling capabilities, dynamically adjusting containerized mediation workloads to optimize resource utilization in real-time.
The Kubernetes market is projected to grow at a compound annual growth rate (CAGR) of 23.4% through 2031 (Source: EdgeDelta). This trend is particularly relevant for telecom mediation platforms, which increasingly require the dynamic scaling capabilities and resource optimization that containerized environments provide.
Telecom networks rely on mediation platforms to process vast amounts of event data efficiently. As infrastructure demands grow, deployment models must support scalability, automation, and resource optimization without compromising reliability.
While VMs have long provided a stable foundation, advances in cloud-native architectures and orchestration are enabling more dynamic mediation strategies. Kubernetes, with its built-in scalability and automation, is becoming an attractive alternative, particularly for hybrid and multi-cloud environments. However, the best deployment choice depends on workload characteristics, operational requirements, and long-term modernization goals.
With these factors in mind, performance and scalability are among the most critical considerations when evaluating deployment options. In this blog post, we take a closer look at how VMs and Kubernetes compare in their ability to meet the performance demands and scaling needs of modern data mediation systems.
VMs have long been the default deployment model due to their predictability and workload isolation.
Advantages:
Challenges:
Kubernetes enables automated scaling of mediation workloads, making it well-suited for cloud-native and hybrid deployments.
Advantages:
* While AI-driven mediation workloads can also operate on VM-based deployments, they are often limited by slower provisioning times and less efficient resource utilization. AI can still enhance VM environments through predictive scaling, anomaly detection, and traffic routing optimizations. However, Kubernetes offers a more dynamic framework where AI can fine-tune container-level resource distribution in real-time, making it the preferred choice for highly scalable, automated mediation workloads.
Challenges:
As telecom mediation evolves, Kubernetes presents a compelling alternative to VMs, offering scalability, cost efficiency, and automation. However, the right deployment model depends on workload needs:
