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CAREER: Enabling Predictable Performance in Cloud Computing

职业：在云计算中实现可预测的性能

基本信息

批准号：
1750109
负责人：
Anshul Gandhi
金额：
$ 40.03万
依托单位：
SUNY at Stony Brook
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1750109&HistoricalAwards=false
关键词：
CAREER Enabling Predictable Performance Cloud

项目摘要

Cloud computing allows tenants, such as Netflix and Expedia to economically rent compute and storage resources from providers. To enable low resource prices, providers consolidate multiple tenants onto a single physical server. However, this sharing of physical resources among tenants often leads to contention, resulting in unpredictable performance. Worse, tenants cannot observe resource contention due to the opaque nature of cloud computing. This project will develop novel performance models to estimate resource contention in opaque cloud deployments. These models will then be leveraged to develop solutions for cloud tenants that mitigate performance variation, thus enabling predictable performance in clouds.To realize predictable performance, the project will proceed along two integrated fronts. On the theoretical front, the project will develop uncertainty-aware stochastic performance models. These models will then be integrated with control-theoretic and machine learning techniques to infer, at runtime, the unobservable model parameters in a cloud environment. On the systems front, armed with the uncertainty-aware models, the project will develop solutions, including task schedulers and resource managers, that alleviate application performance variation. The solutions will be designed to dynamically detect and diagnose performance interference. All models and solutions will be experimentally evaluated in public and private clouds.The interdisciplinary nature of the project provides unique opportunities for integrated education and outreach. The primary benefit of the project will be increasing cloud adoption and promoting its broader impact on energy efficiency. To facilitate this goal, the project will develop open-source solutions for platforms such as OpenStack. The project will advance interdisciplinary education by developing performance analysis lectures and modules that will be integrated with existing courses taught in the departments of Computer Science and Applied Mathematics and Statistics, and the College of Business. Outreach activities will focus on creating research opportunities for local area high school students.All data produced as a result of this project, including models, software solutions, publications, and courseware, will be made publicly available at the project repository: http://www.pace.cs.stonybrook.edu/predictable-clouds.html. The project data will be maintained and made available for at least 10 years, and even longer, if needed. Data will be stored and hosted on local web servers, and will also be replicated on external public web servers, such as those provided by github, which offer long-term durability and reliability.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.

云计算允许Netflix和Expedia等租户从提供商那里经济地租用计算和存储资源。为了实现低资源价格，提供商将多个租户整合到单个物理服务器上。然而，这种在租户之间共享物理资源的方式常常会导致争用，从而导致不可预测的性能。更糟糕的是，由于云计算的不透明性，租户无法观察到资源争用。该项目将开发新的性能模型来估计不透明云部署中的资源争用。这些模型将被用来为云租户开发解决方案，以减少性能差异，从而实现云计算中的可预测性能。为了实现可预测的性能，该项目将沿着沿着两个方面进行。在理论方面，该项目将开发具有不确定性的随机性能模型。然后，这些模型将与控制理论和机器学习技术集成，以在运行时推断云环境中不可观察的模型参数。在系统方面，利用不确定性感知模型，该项目将开发解决方案，包括任务调度器和资源管理器，以减轻应用程序性能变化。这些解决方案旨在动态检测和诊断性能干扰。所有模型和解决方案都将在公共云和私有云上进行实验性评估。该项目的跨学科性质为综合教育和推广提供了独特的机会。该项目的主要好处将是增加云的采用，并促进其对能源效率的更广泛影响。为了实现这一目标，该项目将为OpenStack等平台开发开源解决方案。该项目将通过开发性能分析讲座和模块来推进跨学科教育，这些讲座和模块将与计算机科学、应用数学和统计学系以及商学院的现有课程相结合。外联活动将侧重于为当地高中生创造研究机会。该项目产生的所有数据，包括模型、软件解决方案、出版物和课件，将在项目库http://www.pace.cs.stonybrook.edu/predictable-clouds.html上公开。项目数据将保存和提供至少10年，必要时甚至更长时间。数据将存储和托管在本地Web服务器上，也将复制到外部公共Web服务器上，例如由github提供的服务器，这些服务器具有长期的耐用性和可靠性。该奖项反映了NSF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

期刊论文数量（18）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

B-MEG: Bottlenecked-Microservices Extraction Using Graph Neural Networks

DOI：
10.1145/3491204.3527494
发表时间：
2022-07
期刊：
Companion of the 2022 ACM/SPEC International Conference on Performance Engineering
影响因子：
0
作者：
Gagan Somashekar;Anurag Dutt;R. Vaddavalli;Sai Bhargav Varanasi;Anshul Gandhi
通讯作者：
Gagan Somashekar;Anurag Dutt;R. Vaddavalli;Sai Bhargav Varanasi;Anshul Gandhi

Scavenger: A Black-Box Batch Workload Resource Manager for Improving Utilization in Cloud Environments