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的所有现代计算需求的主干；从视频流到文件共享；从社交媒体平台到科学计算。如今，当代数据中心工作调度程序在同一物理服务器上共同运行的应用程序之间采用了保守的资源共享策略。当前的策略是保守的，以确保满足关键潜伏期应用的严格延迟要求；但是，这种保守主义导致昂贵的计算资源的巨大充分利用，这既造成了资本和运营费用。 Harmonia提出了一个新型非常规资源策略，利用了贝叶斯优化的原则（BO），但引入了新颖的创新来BO并证明其对数据中心资源管理的有用性。 Harmonia将使用基于BO的学习模型来捕获资源分配对应用程序性能的影响，并相应地对共享资源进行分配并调整硬件/软件旋钮，以最大程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采用了一个近似精确的在线学习模型，这些模型是轻量级而不是重量级，完全准确的模型。通过Harmonia Runtime Framework以动态，高效和非侵入的方式将传入应用程序与现有应用程序合作。该项目的成果将影响和影响现代数据中心的运营，这为我们对各种工作负载的计算需求满足了包括短期延迟关键延迟至关重要的应用程序（例如，机器学习推断，网络搜索查询，微服务）和长期播放的吞吐量工作负载（例如，以贯穿的吞吐量为导向的工作负载（例如，科学模拟，科学模拟））。改善大规模数据中心和HPC系统的利用将导致节省成本和较低的碳足迹。该项目Harmonia的计划的教育和外展活动包括增强研究生课程，并引入有关``计算机系统中的概念''的新每月播客，以更好地吸引和准备高中生。所有已开发的工具，软件工件，测量的数据集将用于研究社区，以进一步增强项目成果及其影响。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来评估值得支持的。

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