Spotlight on Tech

Automation in end-to-end lifecycle management of hyperscale networks

By
Krishna Mohan SK
Associate Director - Product Management
Rakuten Symphony
January 21, 2025
6
minute read

Before we dive into the transformative power of AI-driven automation in the deployment and fulfillment of hyperscale networks, let's take a moment to understand what hyperscale networks are and why they are so crucial in today's digital landscape.

Hyperscale networks are the backbone of the modern Internet, designed to handle vast amounts of data and traffic with unparalleled efficiency and scalability. These networks are the infrastructure behind tech giants, telcos, ISPs and mission-critical organizations, supporting their massive network elements, edge clouds, data centers and cloud services. The term "hyperscale" refers to the ability to scale computing resources and networks up or down rapidly and efficiently to meet varying demands.

In today's digital era, hyperscale networks are crucial for sustaining the extensive range of online services and applications we depend on every day. As data usage surges exponentially, the significance of hyperscale networks will continue to rise. These support government initiatives for digital transformation and smart cities, enabling enterprises to maintain private and secure networks, and facilitating the development of smarter homes. From compute and storage solutions to social media and e-commerce, hyperscale networks provide the essential infrastructure that powers our increasingly interconnected lives.

Fig. 1: Comprehensive View of Core, Access, and Fiber Distribution Networks
Fig. 1: Comprehensive View of Core, Access, and Fiber Distribution Networks

At Rakuten Symphony, we have embraced AI to develop practical use cases, including network assistance, process mining, energy optimization, real-time RAN management, TCO management, observability, pre-emptive issue resolution, post-issue resolution, and paired automation. Rakuten AI benefits from inheriting leadership for all model implementations from Rakuten Group’s AI initiative spanning across more than 70 companies.

Decoding the Complexity of Hyperscale Networks

Hyperscale networks are designed to support thousands of network elements, servers, and large-scale web applications. Deploying and managing hyperscale networks involves numerous tasks that are critical to ensuring their efficiency, reliability, and scalability.

  • Network design and planning involves estimating the required network capacity to handle current and future workloads, and designing the network architecture, including the layout of fiber, switches, routers, cell sites, edge cloud, data centers, and other network devices. It also involves ensuring that the network can handle failures without significant downtime.
  • Infrastructure deployment includes conducting site surveys to gather data on physical conditions and creating digital twins of the physical site to simulate and plan the deployment of network infrastructure. 
  • Hardware installation including cable laying, rack installation, and hardware mounting.
  • Network configuration of setting up devices and developing reusable configuration templates for Zero-Touch Provisioning (ZTP).
  • Finally, monitoring and performance management, which entails continuously tracking network performance metrics such as latency, throughput, and packet loss, identifying unusual patterns that may indicate performance issues, and generating reports to keep administrators informed of network health.

Need for AI and Automation in Hyperscale Networks

Automation in hyperscale network deployment and fulfilment is essential for handling the immense scale and complexity of modern networks. Current market solutions often address specific use cases and necessitate the integration of multiple business systems to achieve a comprehensive approach to managing hyperscale networks. Automation not only streamlines the entire process – from initial site surveys and digital twin creation to hardware installation and network configuration – but also significantly reduces deployment times and operational costs.

AI further ensures consistency, minimizes human error, and enhances the ability to quickly adapt to changing demands. Furthermore, it enables real-time monitoring and rapid issue resolution, maintaining high availability and reliability essential for hyperscale environments. This efficiency and resilience are vital for meeting the growing demands of data-driven businesses and services.

Automated Network Site Lifecycle Management

Rakuten Site Manager leverages AI-driven capabilities to revolutionize hyperscale network deployment and fulfilment. By integrating advanced AI algorithms, it automates and optimizes every stage of the deployment process. From conducting precise site surveys and utilizing the Plan and Design studio to create accurate digital twins for simulation, and automating hardware installation and network configuration, the AI-driven design ensures efficient resource allocation, minimizes human error, and accelerates deployment timelines.  

Its service management and orchestration capabilities provide real-time monitoring and predictive analytics to pre-emptively address potential issues, ensuring high reliability and performance. This holistic, AI-powered solution significantly reduces operational costs and enhances the scalability and resilience of hyperscale networks.

One of the leading service providers in the US has leveraged Site Manager for their network simplification and transformation program, for AI-nizing the end-to-end convergence of hyperscale network lifecycle processes. This brings together their wireline and wireless network, digitizes workflows and facilitates efficient network deployment and management.  

Fig. 2: End-to-End Network Architecture: 5G, Cloud, and Fiber Connectivity to End Users
Fig. 2: End-to-End Network Architecture: 5G, Cloud, and Fiber Connectivity to End Users

In conclusion, the integration of AI-driven automation in the end-to-end lifecycle management of hyperscale networks is not just a technological advancement but a necessity for the modern-day digital infrastructure. As data demands continue to grow, embracing AI and automation will be crucial for sustaining the backbone of our interconnected world, enabling seamless digital transformation and smarter, more efficient network management.

This introduction marks the beginning of a series exploring the transformative potential of AI and automation in end-to-end Lifecycle Management of Hyperscale Networks. Over the coming weeks, we’ll dive into key innovations shaping the future of FTTx, including must-haves for design and planning, AI-driven video analytics for cost efficiency, and ML’s role in fiber route optimization. Stay tuned as we explore the next wave of advancements in network management.

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