EXPERT SPEAK

AI promises to accelerate advanced automation to unprecedented levels creating a surge in demand for Edge computing . A substantial part of the demand for Edge computing will be comprised of GPU clusters to provide distributed AI inference , explains Pete Hall at Ciena .

In the Middle East , countries like the UAE and Saudi Arabia are investing in Edge data centres to support their digital ambitions and AI initiatives , addressing challenges related to application latency , data sovereignty , and sustainability of information and communications technologies .

In the past two decades , the world has seen an intense process of cloudification of IT infrastructure , with an increasing number of applications moving to the public cloud . The massive scale of cloud data centres with highly flexible consumption models has enabled a compelling business model for compute and storage workloads , effectively discouraging local builds .

However , centralised data processing means longer routes between users and content , and thus higher latency experienced by users accessing this content .

To remediate this issue , service providers have turned to content delivery network architectures , deploying cache servers closer to users , particularly targeting streaming video . This approach has been effective to improve user experience for streaming services , while also offloading some the network of some heavy traffic flows .

Nonetheless , it is only effective for frequently consumed repeatable data , like popular streaming videos , and not economically viable for random workloads .

Even as data centre buildouts continue to unfold across the world , the shift towards Edge data centres presents challenges and opportunities .

Pete Hall , Regional Managing Director , EMEA , Ciena

Although content delivery networks have been the most widespread use case of Edge computing , a prominent and largely expected application of Edge computing has been its potential to accelerate automation and machine orchestration .

Machine decisions that need to be tightly synchronised require very low latency , in a level that a centralised compute infrastructure cannot deliver .

As AI promises to accelerate advanced automation in unprecedented levels , we are on the verge of a surge in Edge compute demand . And most likely , a substantial part of that Edge compute demand will be comprised of GPU clusters to provide distributed AI inference .

62 www . intelligenttechchannels . com