CSR: Large: VarSys: Managing Variability in High-Performance Computing Systems

CSR：大型：VarSys：管理高性能计算系统的可变性

• 批准号: 1565314
• 负责人: Kirk Cameron
• 金额: $ 118.98万
• 依托单位: Virginia Polytechnic Institute and State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1565314&HistoricalAwards=false
• 关键词: CSR; Large; VarSys; Managing; Variability

The usefulness of the smallest mobile system in a pocket and the largest and fastest supercomputers in datacenters around the world require unrelenting advances in systems software design. These advances make computers faster, more reliable, more secure, better able to analyze large data sets, and ultimately essential to the lives of nearly everyone on the planet.Variability can wreak havoc on the performance of large-scale computer systems that support high-performance computing and e-commerce. In high-performance computing, variability threatens U.S. competitiveness and our ability to achieve exascale performance within the cost and energy constraints of supercomputers. In e-commerce (e.g., Amazon and Wall Street trading), variability threatens profit margins by requiring greater capital expenditures to compensate for potential swings in the performance of datacenters and the cloud. System variability also impairs our capacity to separate malware from normal system activity.This project will develop techniques to increase the ability to identify and manage variability in advanced computing systems. Specifically, this project will focus on developing the VarSys software framework to control aspects of variability and ultimately improve the design and operational efficiencies of both high-performance and cloud systems. Furthermore, to highlight the broader impact of VarSys beyond computer systems design, applying variability identification and management to improve malware detection will be an important component.In addition to publishing the results and creating open source software, the project team will be hosting technical meetings to encourage broad participation in the development of variability metrics and benchmarks for advanced computer systems. The intent is to develop an ecosystem of stakeholders dedicated to progress in the emergent area of computer system software variability. In addition to training graduate students, there will also be hack-a-thons targeting undergraduate students to educate them on the future impact of variability and encourage their involvement in related research.At present, computer system variability is often viewed as noise or an unavoidable consequence of complex designs. This project isolates causes of variability and identifies conditions when variability can be managed. When complete, the resulting VarSys software will enable scientific computer systems research previously impracticable. The usefulness of the techniques to improve the designs of advanced computing systems including high-performance supercomputers, cloud datacenters, and malware detection software will be demonstrated. These advances ultimately impact the lives and livelihood of consumers as well as ensuring U.S. competitiveness in science and e-commerce.

口袋里最小的移动系统和世界各地数据中心最大、最快的超级计算机的实用性，需要系统软件设计的不懈进步。这些进步使计算机更快、更可靠、更安全，能够更好地分析大数据集，最终对地球上几乎每个人的生活都至关重要。可变性可能会对支持高性能计算和电子商务的大型计算机系统的性能造成严重破坏。在高性能计算中，可变性威胁着美国的竞争力，以及我们在超级计算机的成本和能源限制下实现亿级性能的能力。在电子商务(例如，亚马逊和华尔街交易)中，变化性通过要求更大的资本支出来补偿数据中心和云性能的潜在波动，从而威胁到利润率。系统可变性也削弱了我们将恶意软件从正常系统活动中分离出来的能力。这个项目将开发技术来提高识别和管理高级计算系统中可变性的能力。具体地说，该项目将专注于开发VarSys软件框架，以控制变异性的各个方面，并最终提高高性能和云系统的设计和运营效率。此外，为了突出VarSys在计算机系统设计之外的更广泛影响，应用变异性识别和管理来改进恶意软件检测将是一个重要组成部分。除了发布结果和创建开放源码软件外，项目组还将主办技术会议，以鼓励广泛参与高级计算机系统的变异性度量和基准的开发。其目的是建立一个利益相关者的生态系统，致力于在计算机系统软件可变性的新兴领域取得进展。除了培训研究生外，还将有针对本科生的黑客技巧，教育他们关于可变性未来的影响，并鼓励他们参与相关研究。目前，计算机系统可变性通常被视为噪音或复杂设计不可避免的后果。这个项目隔离了可变性的原因，并确定了可变性可以管理的条件。建成后，VarSys软件将使以前不切实际的科学计算机系统研究成为可能。将展示这些技术在改进包括高性能超级计算机、云数据中心和恶意软件检测软件在内的先进计算系统设计方面的有用性。这些进步最终影响到消费者的生活和生计，并确保美国在科学和电子商务方面的竞争力。

项目成果

A First Look: Using Linux Containers for Deceptive Honeypots

• DOI: 10.1145/3140368.3140371
• 发表时间: 2017-11
• 期刊: Proceedings of the 2017 Workshop on Automated Decision Making for Active Cyber Defense
• 作者: Alexander Kedrowitsch;D. Yao;G. Wang;K. Cameron

Security Optimization of Dynamic Networks with Probabilistic Graph Modeling and Linear Programming

• DOI: 10.1109/tdsc.2015.2411264
• 发表时间: 2016-07
• 期刊: IEEE Transactions on Dependable and Secure Computing
• 影响因子: 7.3
• 作者: Hussain M. J. Almohri;L. Watson;D. Yao;Xinming Ou

Quasi-Newton Stochastic Optimization Algorithm for Parameter Estimation of a Stochastic Model of the Budding Yeast Cell Cycle

• DOI: 10.1109/tcbb.2017.2773083
• 发表时间: 2019-01-01
• 期刊: IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
• 影响因子: 4.5
• 作者: Chen，Minghan;Amos，Brandon D.;Kakoti，Gisella
• 通讯作者: Kakoti，Gisella

Copula-based reliability analysis of degrading systems with dependent failures

• DOI: 10.1016/j.ress.2019.106618
• 发表时间: 2020-01-01
• 期刊: RELIABILITY ENGINEERING & SYSTEM SAFETY
• 影响因子: 8.1
• 作者: Fang, Guanqi;Pan, Rong;Hong, Yili
• 通讯作者: Hong, Yili

Toward scalable monitoring on large-scale storage for software defined cyberinfrastructure

• DOI: 10.1145/3149393.3149402
• 发表时间: 2017-11
• 期刊: Proceedings of the 2nd Joint International Workshop on Parallel Data Storage & Data Intensive Scalable Computing Systems
• 作者: A. Paul;S. Tuecke;Ryan Chard;A. Butt;K. Chard;Ian T Foster