Collaborative Research: CNS Core: Small: HARMONIA: New Methods for Colocating Multiple QoS-Sensitive Jobs

协作研究：CNS 核心：小型：HARMONIA：共置多个 QoS 敏感作业的新方法

• 批准号: 2124908
• 负责人: Samee Khan
• 金额: $ 21.89万
• 依托单位: Mississippi State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2124908&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

Data centers and high performance computing (HPC) systems are considered the backbone of all modern-day computational needs for services ranging from Web search to emails; from video streaming to file sharing; from social media platforms to scientific computing. Today, contemporary data center job schedulers employ conservative resource sharing strategies among applications co-running on the same physical server. Current strategies are conservative to ensure that tight latency requirements for latency-critical applications are met; however, this conservatism leads to huge underutilization of expensive computing resources, which incurs both capital and operational expenses. HARMONIA proposes a family of novel unconventional resource strategies leveraging the principles of Bayesian Optimization (BO), but introducing novel innovations to BO and demonstrating its usefulness toward data center resource management. HARMONIA will capture the impact of resource allocation on application performance using BO-based learning models, and partition the shared resources and adjust hardware/software knobs accordingly to maximize the performance of individual applications and the system utilization. To achieve practicality and scalability, HARMONIA employs a pool of approximately-accurate online learning models which are lightweight instead of a heavyweight, fully-accurate model. Incoming applications are placed and co-located with existing applications in a dynamic, efficient, and non-intrusive manner by the HARMONIA runtime framework. Outcomes of this project will influence and impact the operations of modern data centers, which serve our computational needs for a variety of workloads including short-running latency-critical application (e.g., machine learning inferences, web search queries, microservices) and long-running throughput-oriented workloads (e.g., scientific simulations). Improving the utilization of large-scale data centers and HPC systems will lead to better cost savings and a lower carbon footprint. Planned educational and outreach activities for the project HARMONIA include enhancing graduate coursework and introducing a new monthly podcast on ``concepts in computer systems'' to better engage and prepare high school students. All developed tools, software artifacts, measured datasets will be made available to the research community for further enhancing the project outcomes and their impact.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

数据中心和高性能计算（HPC）系统被认为是从网络搜索到电子邮件等所有现代计算需求的支柱；从视频流到文件共享；从社交媒体平台到科学计算。今天，当代数据中心作业调度器在同一物理服务器上共同运行的应用程序之间采用保守的资源共享策略。当前的策略是保守的，以确保满足延迟关键型应用程序的严格延迟要求；然而，这种保守性导致了昂贵的计算资源的大量未充分利用，从而产生了资本和运营费用。HARMONIA提出了一系列新颖的非常规资源策略，利用贝叶斯优化（BO）的原则，但将新颖的创新引入了BO，并展示了它对数据中心资源管理的有用性。HARMONIA将使用基于bo的学习模型捕捉资源分配对应用程序性能的影响，并对共享资源进行分区，并相应地调整硬件/软件按钮，以最大限度地提高单个应用程序的性能和系统利用率。为了实现实用性和可扩展性，HARMONIA采用了一组近似精确的在线学习模型，这些模型是轻量级的，而不是重量级的、完全精确的模型。传入的应用程序通过HARMONIA运行时框架以一种动态、高效和非侵入性的方式放置并与现有应用程序共存。该项目的结果将影响和影响现代数据中心的运营，这些数据中心服务于我们对各种工作负载的计算需求，包括短时间运行的延迟关键应用程序（例如，机器学习推断、web搜索查询、微服务）和长时间运行的面向吞吐量的工作负载（例如，科学模拟）。提高大型数据中心和高性能计算系统的利用率将更好地节省成本并降低碳足迹。HARMONIA计划的教育和推广活动包括加强研究生课程，并推出一个新的每月播客“计算机系统概念”，以更好地吸引和准备高中学生。所有开发的工具、软件工件、测量数据集将提供给研究界，以进一步提高项目成果及其影响。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

SLO-aware Virtual Rebalancing for Edge Stream Processing

用于边缘流处理的 SLO 感知虚拟重新平衡

• DOI: 10.1109/ic2e52221.2021.00027
• 发表时间: 2021
• 期刊: IEEE International Conference on Cloud Engineering
• 作者: Kang, Peng;Lama, Palden;Khan, Samee U.
• 通讯作者: Khan, Samee U.

IceBreaker: warming serverless functions better with heterogeneity

• DOI: 10.1145/3503222.3507750
• 发表时间: 2022-02
• 期刊: Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems
• 作者: Rohan Basu Roy;Tirthak Patel;Devesh Tiwari