To address latency and availability challenges in an AI inference service processing video streams, which strategy is best?

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Deploying edge computing nodes closer to the data sources effectively addresses latency and availability challenges inherent in processing video streams for an AI inference service. By positioning computing resources at the edge of the network, where the data is generated, the time it takes to transmit data to a centralized data center is significantly reduced. This proximity minimizes latency, allowing for faster processing and response times, which is critical for real-time applications like video streaming.

Additionally, edge computing can enhance availability by providing localized processing capabilities, thereby reducing reliance on a centralized system. If the connection to the central data center is compromised, applications can continue to function at the edge with potentially less disruption.

Other strategies like migrating the workload to a cloud provider may introduce longer data transmission times, leading to increased latency, rather than resolving it. Using compression algorithms can reduce the size of the video streams but does not inherently resolve the latency associated with processing. Increasing the bandwidth between the data center and edge devices might improve data transfer rates but does not address the fundamental latency issues caused by the physical distance between the data source and processing unit.

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