How artificial intelligence is disrupting cloud networking

There has been a lot of discussion about how artificial intelligence will accelerate the evolution of cloud platforms and enable a new generation of AI-driven tools to manage cloud environments. But AI could upend another aspect of the cloud: networking. As more and more AI workloads move into the cloud, the ability to provide better cloud networking solutions will become a key priority. Here’s why, and what the future of cloud networking might look like in the age of artificial intelligence.

The Impact of Artificial Intelligence on Cloud Networks

The reason artificial intelligence will place new demands on cloud networks is simple: to work well at scale, artificial intelligence works Loads will demand unprecedented levels of performance from cloud networks.

In many cases, the data that AI workloads need to access will reside on remote servers located in the same cloud platform as the workload or in a different cloud.

Cloud networks will provide critical links connecting AI workloads and data. In many cases, the amount of data will be huge, so training a simple AI model may also require a large amount of information, while the model needs to access the data with low latency. Therefore, networks will need to be able to support very high bandwidths at very high performance levels.

Are cloud networks ready for artificial intelligence?

Not only can artificial intelligence provide stable network connection power, but it is not the only cloud workload that requires excellent network performance. The ability to provide low-latency, high-bandwidth networks has long been important for use cases such as cloud desktops and video streaming.

Cloud service providers have also long-term provided solutions to help meet these network performance needs. All major clouds offer "direct connect" network services that can significantly improve network speeds and reliability, especially when moving data between clouds in a multi-cloud architecture, or as part of a hybrid cloud model between private data centers and public When moving data between clouds.

However, for artificial intelligence workloads with truly special network performance needs, direct connection to the service may not be enough. Workloads may also require optimization at the hardware level in the form of solutions such as data processing units (DPUs), which help handle network traffic ultra-efficiently. In fact, vendors like Google are already investing in this space, launching a cloud platform tailored for generative AI. It shows that a company known primarily for selling video cards also recognizes that unlocking the full potential of artificial intelligence also requires innovation in network hardware.

Future Prospects of Cloud Network

Currently, how will cloud providers, hardware suppliers and artificial intelligence developers cope with the challenges that artificial intelligence brings to the cloud network field? The specific challenges remain to be seen. Overall, however, we may see the following changes:

Greater use of direct connect: In the past, cloud direct connect services tended to be limited to companies with complex cloud architectures and high-performance requirements used by large enterprises. But among smaller organizations looking to take full advantage of cloud-based AI workflows, direct connections may become more common.

Higher Egress Costs: Since cloud providers typically charge “egress” fees when data moves out of the network, AI workloads running in the cloud may increase the costs for enterprises to egress. Pay network fees. Going forward, the ability to predict and manage egress charges triggered by AI workloads will be an important part of cloud cost optimization.

Network consumption fluctuations: Some artificial intelligence workloads will consume large amounts of cloud network resources, but only temporarily. For example, they may need to move large amounts of data while training, but scale back network usage after training is complete. This means that the ability to adapt to large fluctuations in network consumption may become another important component of cloud network performance management.

Summary

If you want to make full use of the cloud to help carry artificial intelligence workloads, you need to optimize your cloud network strategy, which requires leveraging advanced network services and hardware while adjusting Cloud cost optimization and network performance management strategies.

The solutions available to help achieve these goals are still evolving, but for any enterprise looking to deploy AI workloads in the cloud, this is a space to watch closely.