针对大小核边缘设备的高效视频分析OA

Video analytics extracts high-value information from video streams and plays a crucial role in applications such as intelligent transportation and public safety.Although traditional cloud-based video analytics offers powerful computational capabilities,uploading massive amounts of video data results in high bandwidth consumption and network latency.Edge computing reduces network latency by processing video data near cameras;however,it still faces two major challenges.First,frame-by-frame analysis leads to redundant inference,and existing frame reuse methods cannot fully exploit local similarities in historical frames.Second,uneven core workload arises because task allocation across big LITTLE lacks real-time load awareness.To address these issues,this study proposes Vable,an efficient video analytics system for big.LITTLE edge devices.It employs a multihistorical frame,block-level frame reuse mechanism,which partitions video frames into fine-grained blocks and employs a tree-based storage structure combined with Locality-Sensitive Hashing(LSH)for similarity matching,enabling efficient cross-frame computation reuse and significantly reduces redundant inference overhead.Additionally,Vable introduces a core workload-aware list-based Directed Acyclic Graph(DAG)partitioning algorithm that dynamically allocates analysis tasks by monitoring the real-time load of big LITTLE,thus balancing computation and communication overhead while avoiding latency increases caused by load imbalance.A prototype of Vable is implemented and evaluated using two real-world datasets.Experimental results demonstrate that Vable reduces the end-to-end latency by 59.23％in UA_V1 compared to WiseFuse(the highest-latency baseline)and by 45.83％in UA_V2 compared to Faastlane,effectively lowering the end-to-end latency while maintaining high throughput.