CRII: CSR: Online Analysis of Disk I/O for Automatic Storage System Optimization

CRII：CSR：用于自动存储系统优化的磁盘 I/O 在线分析

• 批准号: 1657296
• 负责人: Nihat Altiparmak
• 金额: $ 17.5万
• 依托单位: University of Louisville Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-02-15 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1657296&HistoricalAwards=false
• 关键词: CRII; CSR; Online; Analysis; Disk

Today's critical applications, including genome analysis, climate simulations, drug discovery, space observation, and numerical simulations in computational chemistry and high-energy physics, are all data intensive in nature. Storage performance bottlenecks are major threats limiting the performance and scalability of data intensive applications. The goal of this project is to develop a general framework for self-optimizing parallel storage systems that can alleviate storage performance bottlenecks, and thus can have a considerable impact on society by accelerating the innovation process in a multitude of domains of science. The results will also advance the state of knowledge in storage systems by benefiting a wide range of parallel storage systems including disk arrays, key-value stores, and parallel/distributed file systems. Broader impacts include mentoring and training K-12 students through summer camps and promoting involvement of underrepresented students in science and engineering.This research develops novel, theoretically grounded, and experimentally validated methods for online detection and automatic elimination of disk I/O bottlenecks. Specific goals include the development of: (i) new online methods for continuously monitoring disk I/O requests and analyzing them efficiently, guided by data stream mining and social network analysis theory, and (ii) automatically-triggered self-optimization techniques guided by bin packing, graph coloring, and network flow theory, which can carefully plan an adaptive data layout to improve disk I/O performance. Experimentation and validation using software simulation and prototype implementation are performed by analyzing what is theoretically possible, what can be achieved in practice, and trying to close the gap between the two.

今天的关键应用，包括基因组分析、气候模拟、药物发现、空间观测以及计算化学和高能物理中的数值模拟，本质上都是数据密集型的。存储性能瓶颈是限制数据密集型应用程序性能和可伸缩性的主要威胁。该项目的目标是为自优化并行存储系统开发一个通用框架，以缓解存储性能瓶颈，从而通过加速众多科学领域的创新过程对社会产生相当大的影响。研究结果还将通过对包括磁盘阵列、键值存储和并行/分布式文件系统在内的广泛并行存储系统有利，推进存储系统的知识状态。更广泛的影响包括通过夏令营指导和培训K-12学生，促进代表性不足的学生参与科学和工程。本研究为在线检测和自动消除磁盘I/O瓶颈开发了新颖的、理论基础的和实验验证的方法。具体目标包括：(i)在数据流挖掘和社会网络分析理论的指导下，持续监测磁盘i /O请求并有效分析它们的新在线方法；（ii）在装箱、图着色和网络流理论的指导下，自动触发自优化技术，可以仔细规划自适应数据布局以提高磁盘i /O性能。通过软件仿真和原型实现来进行实验和验证，分析理论上可能实现的内容，实践中可以实现的内容，并试图缩小两者之间的差距。

项目成果

Monte Carlo Based Server Consolidation for Energy Efficient Cloud Data Centers

• DOI: 10.1109/cloudcom.2019.00046
• 发表时间: 2019-12
• 期刊: 2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)
• 作者: B. Harris;Nihat Altiparmak

Big Data Aware Virtual Machine Placement in Cloud Data Centers

• DOI: 10.1145/3148055.3148057
• 发表时间: 2017-12
• 期刊: Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
• 作者: Logan Hall;B. Harris;Erica Tomes;Nihat Altiparmak

Real-Time Characterization of Data Access Correlations

数据访问相关性的实时表征

• DOI: 10.1109/ispass51385.2021.00031
• 发表时间: 2021
• 期刊: 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS
• 作者: Harris, Bryan;Marzullo, Michael;Altiparmak, Nihat
• 通讯作者: Altiparmak, Nihat

Ultra-Low Latency SSDs' Impact on Overall Energy Efficiency

• 发表时间: 2020
• 作者: B. Harris;Nihat Altiparmak

Towards Adaptive Parallel Storage Systems

• DOI: 10.1109/tc.2018.2836426
• 发表时间: 2018-12
• 期刊: IEEE Transactions on Computers
• 影响因子: 3.7
• 作者: Erica Tomes;Everett Neil Rush;Nihat Altiparmak