Claudio Saes is a partner and telecom practice leader at Bell Labs Consulting, a group of the award-winning Nokia Bell Labs.
Oil, gas, utilities and mining companies have heavily invested in digitalization. By integrating sensors, video analytics and automated guided vehicles, these industries are achieving unmatched efficiency and productivity. These applications leverage an increasing amount of predictive and generative AI, which requires extensive cloud resources and demands high performance.
The challenge, however, is that this performance comes at a cost—both financially and in terms of infrastructure complexity. Let me explain.
In the past, enterprises typically hosted many of their workloads on a single cloud service provider. Accessing these cloud resources involved routing through the internet, often adding latency and introducing security concerns. Today, modern medium and large enterprises have adopted a hybrid multicloud strategy, with workloads running across multiple cloud service providers as well as on-premises infrastructure.
Why Enterprises Are Adopting Multicloud
I’ve been seeing enterprises increasingly adopt hybrid multicloud strategies for several compelling reasons. First, hybrid multicloud architectures offer unparalleled flexibility by distributing workloads across different cloud service providers (CSPs) and on-premises infrastructure, scaling resources based on demand and selecting the most suitable platforms for each application.
Second, multicloud strategies enable cost optimization. Enterprises can negotiate better prices by choosing between multiple providers, utilizing on-premises infrastructure for stable workloads and reserving public cloud resources for peak demand periods. With increasing application requirements and a vast amount of data, modern enterprises are shifting their cloud usage patterns—keeping sensitive data and critical operational technology control functions on-premises while leveraging public clouds for resource-intensive analytics and AI models.
Take the example of a smart city: AI analyzes historical and real-time data to identify traffic patterns, predict congestion and dynamically adjust traffic light timings to optimize flow. These applications require AI models that are frequently updated with nearly daily training, demanding significant cloud resources. The need for interconnectivity becomes even more crucial when data is processed in multiple locations, such as cloud servers in the same city or across the country.
The Role Of Data Centers In Multicloud
Data center companies—the organizations that physically host the servers comprising the “cloud”—recognized an opportunity. By interconnecting enterprise servers directly with cloud service provider servers, they could see reduced application latency, better reliability, higher performance and an added security layer for enterprise data.
However, traditional data center companies often provide connectivity only between their own data centers, while many enterprises are now connected to multiple cloud providers and data centers. This can place a significant burden on inter-data center links. As AI traffic moves from end users to data centers hosting AI models, it will generate diverse traffic flows across multiple inter-data center connections.
To manage this increasing traffic, additional transport network capacity may be needed across telecommunications providers, hyperscalers and enterprise network backbones. This trend is reshaping how data centers, cloud providers and telcos operate to support new levels of connectivity.
Network As A Service For Data Center Interconnection
To address these growing challenges, my company conducted an in-depth analysis of AI’s impact on our devices, cloud and networks. We found that network traffic generated by AI will increase significantly, driven by both consumer and enterprise applications. While consumer AI traffic is projected to be larger, enterprise AI traffic will grow at a faster rate, nearly 57% CAGR.
Telecom companies are positioned to take on this challenge. They serve as the conduit interconnecting enterprises, making them well-suited to offer the needed services. However, interconnecting every enterprise to a data center can be costly, particularly in environments where multiple data centers and cloud providers are involved. This is where the concept of network as a service (NaaS) comes into play.
Imagine if your enterprise could consume connectivity and bandwidth as needed, interconnecting dozens of data centers across your region or globally, with a pay-per-use model. Consider the example of the Black Friday shopping season: You need to increase data center capacity to handle the surge in customers, but your connectivity becomes the bottleneck. Or you want to replicate large databases between data centers, but the process takes too long. NaaS solutions are designed to provide the flexibility, scalability and efficiency required to overcome these challenges, enabling enterprises to thrive in a highly digitalized world.
Key Considerations For Industry Leaders
Before adopting NaaS for data center interconnection, businesses should evaluate several factors to ensure alignment with their operational needs and digital transformation goals. First, assess your current network infrastructure and determine how NaaS can integrate with existing systems. This includes evaluating compatibility with current applications, databases and security protocols. Effective integration is crucial as it ensures steady performance and minimizes disruptions during the transition. Additionally, organizations should consider their specific connectivity requirements, such as bandwidth, latency and security, to ensure that the NaaS solution can effectively meet these demands.
To determine if NaaS aligns with your operational needs, thoroughly analyze your digital transformation objectives. This involves identifying the flexibility and scalability required in their network resources to support growth and innovation. Also, consider whether NaaS can provide cost predictability and operational efficiency compared to traditional network models. Engaging with potential NaaS providers to understand your service offerings, support mechanisms and customization capabilities can help you decide how well a NaaS solution fits into your strategic plans. However, be cautious of common misconceptions about NaaS, such as underestimating the importance of vendor reliability or overestimating the ease of implementation; these pitfalls can lead to unexpected challenges during deployment.
When evaluating providers, make sure to establish clear criteria that reflect your unique operational needs. You might examine the provider’s track record in the industry, the comprehensiveness of service level agreements (SLAs) and their ability to offer tailored solutions that align with specific business requirements. Organizations should also inquire about the provider’s security measures, including how they handle data privacy and compliance with relevant regulations. Finally, assessing the vendor’s customer support capabilities and the availability of resources for training and implementation will be essential for ensuring a smooth transition to a NaaS model.
Conclusion
Embracing flexible solutions can bring businesses one step closer to an optimal network infrastructure. NaaS is just one strategy that can help enterprises navigate these complexities with confidence.
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