In recent years, federated learning (FL) has emerged as a promising approach
for machine learning (ML) and data science across distributed edge devices.
With the increasing popularity of FL, resource contention between multiple FL
jobs training on the same device population is increasing as well. Scheduling
edge resources among multiple FL jobs is different from GPU scheduling for
cloud ML because of the ephemeral nature and planetary scale of participating
devices as well as the overlapping resource requirements of diverse FL jobs.
Existing resource managers for FL jobs opt for random assignment of devices to
FL jobs for simplicity and scalability, which leads to poor performance. In
this paper, we present Venn, an FL resource manager, that efficiently schedules
ephemeral, heterogeneous devices among many FL jobs, with the goal of reducing
their average job completion time (JCT). Venn formulates the Intersection
Resource Scheduling (IRS) problem to identify complex resource contention among
multiple FL jobs. Then, Venn proposes a contention-aware scheduling heuristic
to minimize the average scheduling delay. Furthermore, it proposes a
resource-aware device-to-job matching heuristic that focuses on optimizing
response collection time by mitigating stragglers. Our evaluation shows that,
compared to the state-of-the-art FL resource managers, Venn improves the
average JCT by up to 1.88X.

本文介绍 Venn，一种 FL 资源管理器，旨在通过解决资源争用问题和优化调度算法来减少平均作业完成时间，并通过减少延迟来优化响应收集时间。与现有的 FL 资源管理器相比，Venn 在平均作业完成时间上提升了最多 1.88 倍。

Venn：联邦学习任务的资源管理

Venn: Resource Management Across Federated Learning Jobs

Many applications such as autonomous driving and augmented reality, require
the concurrent running of multiple deep neural networks (DNN) that poses
different levels of real-time performance requirements. However, coordinating
multiple DNN tasks with varying levels of criticality on edge GPUs remains an
area of limited study. Unlike server-level GPUs, edge GPUs are resource-limited
and lack hardware-level resource management mechanisms for avoiding resource
contention. Therefore, we propose Miriam, a contention-aware task coordination
framework for multi-DNN inference on edge GPU. Miriam consolidates two main
components, an elastic-kernel generator, and a runtime dynamic kernel
coordinator, to support mixed critical DNN inference. To evaluate Miriam, we
build a new DNN inference benchmark based on CUDA with diverse representative
DNN workloads. Experiments on two edge GPU platforms show that Miriam can
increase system throughput by 92% while only incurring less than 10\% latency
overhead for critical tasks, compared to state of art baselines.

提出了 Miriam，一种用于多个边缘 GPU 上的深度神经网络任务协调的计划，通过弹性内核生成器和运行时动态内核协调器，可以实现混合关键性 DNN 推理，并且在关键任务上只产生不到 10％的延迟开销，相比现有技术基准，系统吞吐量可提高 92％。