Tackling the Imbalance in Video Analytics Pipelines with Hierarchical Embodied Intelligence

With the proliferation of heterogeneous software-hardware infrastructures in camera deployment, video analytics pipelines (VAPs) are increasingly burdened by spatiotemporal workload imbalance, where uneven task distributions lead to latency constraint violations and degraded quality of experience (QoE). Eliminating this imbalance is challenging due to the inherent complexity of adjusting large-scale parameters and the dynamic nature of VAP runtime environments. To this end, we propose Hier-EI, a novel scheduling framework that combines the two-phase hierarchical design with embodied intelligence, which adaptively tunes system parameters to mitigate imbalance and maintain long-term service-level objective (SLO) performance for modern VAP systems. To tackle the complexity of combinatorial decision-making, we introduce a hierarchical collaboration mechanism with macro-micro coordination that transforms the exponential search space into a linear coarse-to-fine workflow. To adapt to runtime dynamics, we present an embodied feedback mechanism that employs closed-loop feedback to converge toward real-time optimal solutions as a Markov decision process. Extensive evaluations on a real-world prototype system built on KubeEdge demonstrate that Hier-EI achieves a 3.6× improvement in latency compliance, and a 67.4% reduction in P95 latency compared with state-of-the-art scheduling methods.