本文的目标是执行数据科学（DS）管道支持的数据处理，传输和共享跨多个资源执行贪婪进程。当前的数据科学流水线环境提供具有计算资源的各种基础设施服务，诸如通用处理器（GPP）、图形处理单元（GPU）、现场可编程门阵列（FPGA）和张量处理单元（TPU），其与平台和软件服务耦合以设计、运行和维护DS流水线。这些一刀切的解决方案将数据管道任务完全外部化。然而，一些任务可以在边缘执行，后端可以提供及时的资源，以确保ad-hoc和弹性执行环境。本文介绍了一种创新的可组合的“Just in Time Architecture”，用于为数据科学管道（JITA-4DS）配置DC和相关的资源管理技术。JITA-4DS是一个跨层的管理系统，它既了解应用程序的特性，又了解底层的基础设施，从而打破了应用程序、中间件/操作系统和硬件层之间的障碍。构建可定制的虚拟数据中心（VDC）需要这些层的垂直集成，以满足动态变化的数据科学管道的要求，如性能、可用性和能耗。因此，本文展示了一个实验模拟，专门用于运行数据科学工作负载，并确定调度JITA-4DS实现的资源分配的最佳策略。
英文摘要：
This paper targets the execution of data science (DS) pipelines supported by data processing, transmission and sharing across several resources executing greedy processes. Current data science pipelines environments provide various infrastructure services with computing resources such as general-purpose processors (GPP), Graphics Processing Units (GPUs), Field Programmable Gate Arrays (FPGAs) and Tensor Processing Unit (TPU) coupled with platform and software services to design, run and maintain DS pipelines. These one-fits-all solutions impose the complete externalization of data pipeline tasks. However, some tasks can be executed in the edge, and the backend can provide just in time resources to ensure ad-hoc and elastic execution environments.
This paper introduces an innovative composable "Just in Time Architecture" for configuring DCs for Data Science Pipelines (JITA-4DS) and associated resource management techniques. JITA-4DS is a cross-layer management system that is aware of both the application characteristics and the underlying infrastructures to break the barriers between applications, middleware/operating system, and hardware layers. Vertical integration of these layers is needed for building a customizable Virtual Data Center (VDC) to meet the dynamically changing data science pipelines' requirements such as performance, availability, and energy consumption. Accordingly, the paper shows an experimental simulation devoted to run data science workloads and determine the best strategies for scheduling the allocation of resources implemented by JITA-4DS.
InfiniLoRA: Disaggregated Multi-LoRA Serving for Large Language Models
Shanghai Jiao Tong University（上海交通大学）；Bytedance（字节跳动）；Singapore Management University（新加坡管理大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；National University of Singapore（新加坡国立大学）
Bidirectional Resource Scheduling for Disaggregated and Asynchronous RL Post-Training
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳计算机科学与技术学院）；Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）数据科学与分析 thrust）
Accelerating Disaggregated RL for Visual Generative LLMs with Diffusion-Based Parallelism and Trainer-Assisted Generation
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）；Data Science and Analytics Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）数据科学与分析学域）；Huawei Technologies Ltd.（华为技术有限公司）
Executing Bag of Distributed Tasks on Virtually Unlimited Cloud Resources
分布式任务包（BoDT）应用程序是相同且独立的任务的集合，每个任务都需要位于世界各地的一段输入数据。因此，云计算提供了一种有效的方式来执行BoT应用，因为它不仅包括多个地理上分布的数据中心，而且还允许用户仅为她实际使用的内容付费。在本文中，BoDT on the Cloud使用几乎无限的云资源。提出了一种启发式算法，以找到一个执行计划，考虑到预算约束。与其他方法相比，在相同的预算下，我们的算法能够减少总的执行时间高达50%。
Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey
Swinburne University of Technology（斯威本科技大学）；CSIRO（澳大利亚联邦科学与工业研究组织）；Engineering Institute of Technology (EIT)（工程技术学院）；South China University of Technology（华南理工大学）；University of Manitoba（曼尼托巴大学）
