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In cloud ML inference systems, batching is an essential technique to increasethroughput which helps optimize total-cost-of-ownership. Prior graph batchingcombines the individual DNN graphs into a single one, allowing multiple inputsto be concurrently executed in parallel. We observe that the coarse-grainedgraph batching becomes suboptimal in effectively handling the dynamic inferencerequest traffic, leaving significant performance left on the table. This paperproposes LazyBatching, an SLA-aware batching system that considers bothscheduling and batching in the granularity of individual graph nodes, ratherthan the entire graph for flexible batching. We show that LazyBatching canintelligently determine the set of nodes that can be efficiently batchedtogether, achieving an average 15x, 1.5x, and 5.5x improvement than graphbatching in terms of average response time, throughput, and SLA satisfaction,respectively.

LazyBatching: An SLA-aware Batching System for Cloud Machine Learning Inference