CLOUD VIDEO PIPELINE / ELASTIC COMPUTE

The platform stays up. Processing pools scale with the work.

CPU planning, GPU media processing, and CPU containers calling the VLM API — each pool scales on its own.

Architecture demo · not live monitoring

Always-on control plane · ECS / Fargate Pools can scale to 0. The platform stays online.
AWS Batch adds and removes EC2 instances from queued job demandPlatform scaler → ECS adds and removes containers from backlog and API quota
0 → N machines
Batch probe queue
Now → machines
InS3 file list, video indexes
WorkProbe format / duration; split shards and jobs
OutShard ranges → register media jobs
0 → N machines
Batch media queue
Now → machines
InNeeded byte ranges from S3 source video
WorkDecode → CV / proxy encode → upload
OutS3 media artifacts, CV facts, gate decisions
0 → N containers
SQS model-task queue
Now → containers
InSelected video windows, prompts
OutActivity / events / counts → validated commit
Queues carry task IDs only; workers read and write video on S3.CPU / GPU are EC2 instances; the API pool is Fargate containers.
Close-out check→
Ready event→
In progressAdded on demandReclaimed after finishCounts and animation are illustrative; instance counts need benchmarks.
Click a pool or component to see inputs, outputs, and duties

Scale-up waits for machines to start. Scale-down waits for in-flight work to finish. The number after “→” is demand. Batch schedules from actual job resources.