We consider ML query processing in distributed systems where GPU-enabled
workers coordinate to execute complex queries: a computing style often seen in
applications that interact with users in support of image processing and
natural language processing. In such systems, coscheduling of GPU memory
management and task placement represents a promising opportunity. We propose
Navigator, a novel framework that unifies these functions to reduce job latency
while using resources efficiently, placing tasks where data dependencies will
be satisfied, collocating tasks from the same job (when this will not overload
the host or its GPU), and efficiently managing GPU memory. Comparison with
other state of the art schedulers shows a significant reduction in completion
times while requiring the same amount or even fewer resources. In one case,
just half the servers were needed for processing the same workload.

我们提出了 Navigator，这是一个将 GPU 内存管理和任务调度统一起来的新框架，它在保证资源利用效率的同时，将任务放置在满足数据依赖关系的位置，将来自同一作业的任务放置在一起（在不过载主机或其 GPU 的情况下），并有效地管理 GPU 内存，与其他领先的调度程序相比，完成时间明显缩短，同时所需的资源量相同甚至更少，在一个案例中，处理相同工作负载只需要一半的服务器。

导航器：用于延迟敏感机器学习工作流的分散调度器

Navigator: A Decentralized Scheduler for Latency-Sensitive ML Workflows

Cellular-Vehicle-to-Everything (C-V2X) is currently at the forefront of the
digital transformation of our society. By enabling vehicles to communicate with
each other and with the traffic environment using cellular networks, we
redefine transportation, improving road safety and transportation services,
increasing efficiency of traffic flows, and reducing environmental impact. This
paper proposes a decentralized approach for provisioning Cellular
Vehicular-to-Network (C-V2N) services, addressing the coupled problems of
service task placement and scaling of edge resources. We formalize the joint
problem and prove its complexity. We propose an approach to tackle it, linking
the two problems, employing decentralized decision-making using (i) a greedy
approach for task placement and (ii) a Deep Deterministic Policy Gradient
(DDPG) based approach for scaling. We benchmark the performance of our
approach, focusing on the scaling agent, against several State-of-the-Art (SoA)
scaling approaches via simulations using a real C-V2N traffic data set. The
results show that DDPG-based solutions outperform SoA solutions, keeping the
latency experienced by the C-V2N service below the target delay while
optimizing the use of computing resources. By conducting a complexity analysis,
we prove that DDPG-based solutions achieve runtimes in the range of
sub-milliseconds, meeting the strict latency requirements of C-V2N services.

本论文提出一种去中心化的方法，解决了服务任务分配和边缘资源扩展的紧密耦合的问题。文章通过模拟实际的 C-V2N 流量数据集，对该方法进行了基准测试，并将其性能与现有方法进行了比较，结果表明基于 DDPG 的解决方案优于现有解决方案，满足 C-V2N 服务的低延迟要求。