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Many real-time applications (e.g., Augmented/Virtual Reality, cognitiveassistance) rely on Deep Neural Networks (DNNs) to process inference tasks.Edge computing is considered a key infrastructure to deploy such applications,as moving computation close to the data sources enables us to meet stringentlatency and throughput requirements. However, the constrained nature of edgenetworks poses several additional challenges to the management of inferenceworkloads: edge clusters can not provide unlimited processing power to DNNmodels, and often a trade-off between network and processing time should beconsidered when it comes to end-to-end delay requirements. In this paper, wefocus on the problem of scheduling inference queries on DNN models in edgenetworks at short timescales (i.e., few milliseconds). By means of simulations,we analyze several policies in the realistic network settings and workloads ofa large ISP, highlighting the need for a dynamic scheduling policy that canadapt to network conditions and workloads. We therefore design ASET, aReinforcement Learning based scheduling algorithm able to adapt its decisionsaccording to the system conditions. Our results show that ASET effectivelyprovides the best performance compared to static policies when scheduling overa distributed pool of edge resources.

Scheduling Inference Workloads on Distributed Edge Clusters with Reinforcement Learning