EDITOR ’ S QUESTION
unstructured nature of AI data , which can originate from diverse sources , including text , images and sensor data . Consequently , storage systems must be capable of managing various data types . The increasing need for real-time data processing and the growing adoption of edge computing in AI applications necessitate that data centres can dynamically move data between different storage tiers , from low-latency flash storage for active datasets to cost-effective archival storage for long-term retention .
Data locality is another critical consideration . AI workloads often perform better when located close to the data they process , minimizing latency and enhancing performance . This trend drives the development of distributed storage architectures , where data is stored across multiple physical locations closer to its point of use .
As AI workloads become more prevalent , networking infrastructure emerges as a crucial area for data centre operators to address . The substantial data volumes involved in AI training and inference necessitate networking systems capable of handling significant traffic within and between data centres and the cloud . Traditional Ethernet-based networks may struggle to meet the latency requirements of AI .
Another vital aspect of networking is ensuring high levels of redundancy and reliability . Given that AI workloads are often mission-critical , any service interruption can have severe consequences . This reality has prompted some data centre operators to adopt software-defined networking ( SDN ) solutions , which offer enhanced flexibility and control over network traffic , facilitating the management of AI workloads across distributed infrastructures and ensuring consistent performance under heavy loads .
Finally , data centre operators must be equipped to support hybrid and multi-cloud environments , as many organizations opt to run AI workloads across both
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