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In this paper, we study joint batching and (task) scheduling to maximise thethroughput (i.e., the number of completed tasks) under the practicalassumptions of heterogeneous task arrivals and deadlines. The design aims tooptimise the number of batches, their starting time instants, and thetask-batch association that determines batch sizes. The joint optimisationproblem is complex due to multiple coupled variables as mentioned and numerousconstraints including heterogeneous tasks arrivals and deadlines, the causalityrequirements on multi-task execution, and limited radio resources. Underpinningthe problem is a basic tradeoff between the size of batch and waiting time fortasks in the batch to be uploaded and executed. Our approach of solving theformulated mixed-integer problem is to transform it into a convex problem viainteger relaxation method and $\ell_0$-norm approximation. This results in anefficient alternating optimization algorithm for finding a close-to-optimalsolution. In addition, we also design the optimal algorithm from leveragingspectrum holes, which are caused by fixed bandwidth allocation to devices andtheir asynchronized multi-batch task execution, to admit unscheduled tasks soas to further enhance throughput. Simulation results demonstrate that theproposed framework of joint batching and resource allocation can substantiallyenhance the throughput of multiuser edge-AI as opposed to a number of simplerbenchmarking schemes, e.g., equal-bandwidth allocation, greedy batching andsingle-batch execution.

Joint Batching and Scheduling for High-Throughput Multiuser Edge AI with Asynchronous Task Arrivals