Machine learning (ML) tasks are one of the major workloads in today's edge computing networks. Existing edge-cloud schedulers allocate the requested amounts of resources to each task, falling short of best utilizing the limited edge resources for ML tasks. This paper proposes TapFinger, a distributed scheduler for edge clusters that minimizes the total completion time of ML tasks through co-optimizing task placement and fine-grained multi-resource allocation. To learn the tasks' uncertain resource sensitivity and enable distributed scheduling, we adopt multi-agent reinforcement learning (MARL) and propose several techniques to make it efficient, including a heterogeneous graph attention network as the MARL backbone, a tailored task selection phase in the actor network, and the integration of Bayes' theorem and masking schemes. We first implement a single-task scheduling version, which schedules at most one task each time. Then we generalize to the multi-task scheduling case, in which a sequence of tasks is scheduled simultaneously. Our design can mitigate the expanded decision space and yield fast convergence to optimal scheduling solutions. Extensive experiments using synthetic and test-bed ML task traces show that TapFinger can achieve up to 54.9% reduction in the average task completion time and improve resource efficiency as compared to state-of-the-art schedulers.

翻译：机器学习任务已成为当前边缘计算网络中的主要工作负载之一。现有边缘云调度器为每个任务分配所请求的资源量，未能充分利用有限的边缘资源来优化机器学习任务。本文提出TapFinger——一种面向边缘集群的分布式调度器，通过协同优化任务部署与细粒度多资源分配，最小化机器学习任务的总完成时间。为学习任务不确定的资源敏感性并实现分布式调度，我们采用多智能体强化学习，并提出多项技术提升其效率，包括：异构图注意力网络作为多智能体强化学习主干网络、演员网络中定制的任务选择阶段，以及贝叶斯定理与掩码方案的集成。我们首先实现单任务调度版本（每次最多调度一个任务），随后推广至多任务调度场景（同时对一系列任务进行调度）。该设计可缩减决策空间规模，并快速收敛至最优调度方案。基于合成数据与实测机器学习任务轨迹的大量实验表明，与最先进的调度器相比，TapFinger可将平均任务完成时间降低最高达54.9%，同时提升资源利用效率。