[LFX'24] Add Sedna Joint inference HPA Proposal #457
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Signed-off-by: huang qi jie <[email protected]>
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What type of PR is this?
/kind documentation
What this PR does / why we need it:
The rapid advancement of AI has led to the widespread application of deep learning models across various fields. However, the resource demands for model inference tasks can fluctuate significantly, especially during peak periods, posing a challenge to the system's computing capabilities. To address this varying load demand, we propose an elastic inference solution leveraging KubeEdge and Horizontal Pod Autoscaling (HPA) to enable dynamic scaling of inference tasks.
KubeEdge is an edge computing framework that extends Kubernetes' capabilities to edge devices, allowing applications to be deployed and managed on edge nodes. By utilizing KubeEdge, we can distribute inference tasks across different edge devices and cloud resources, achieving efficient resource utilization and task processing.
The core of collaborative inference lies in coordinating computing resources across various devices, allowing inference tasks to be dynamically allocated based on the current load. When the system detects an increase in load, the HPA mechanism automatically scales out the number of edge nodes or enhances resource configurations to meet the inference task demands. Conversely, when the load decreases, resource allocation is scaled down to reduce operational costs. This approach ensures optimal resource allocation while maintaining inference performance.
Which issue(s) this PR fixes:
Fixes #
kubeedge/kubeedge#5753