Multi-user Co-inference with Batch Processing Capable Edge Server

TL;DR

This paper proposes novel algorithms for energy-efficient multi-user inference offloading to GPU edge servers, optimizing batching and scheduling to minimize user energy consumption under latency constraints.

Contribution

It introduces the IP-SSA and OG algorithms for optimal task partitioning and grouping, and applies deep reinforcement learning for online task scheduling.

Findings

IP-SSA reduces up to 94.9% user energy consumption offline.

OG algorithm optimally groups tasks with different latency constraints.

DDPG-OG outperforms DDPG-IP-SSA by up to 8.92% online.

Abstract

Graphics processing units (GPUs) can improve deep neural network inference throughput via batch processing, where multiple tasks are concurrently processed. We focus on novel scenarios that the energy-constrained mobile devices offload inference tasks to an edge server with GPU. The inference task is partitioned into sub-tasks for a finer granularity of offloading and scheduling, and the user energy consumption minimization problem under inference latency constraints is investigated. To deal with the coupled offloading and scheduling introduced by concurrent batch processing, we first consider an offline problem with a constant edge inference latency and the same latency constraint. It is proven that optimizing the offloading policy of each user independently and aggregating all the same sub-tasks in one batch is optimal, and thus the independent partitioning and same sub-task aggregating (IP-SSA) algorithm is inspired. Further, the optimal grouping (OG) algorithm is proposed to optimally group tasks when the latency constraints are different. Finally, when future task arrivals cannot be precisely predicted, a deep deterministic policy gradient (DDPG) agent is trained to call OG. Experiments show that IP-SSA reduces up to 94.9\% user energy consumption in the offline setting, while DDPG-OG outperforms DDPG-IP-SSA by up to 8.92\% in the online setting.