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.

当今的关键应用，包括基因组分析，气候模拟，药物发现，空间观察以及计算化学和高能物理学中的数值模拟，都是数据密集的。存储性能瓶颈是限制数据密集型应用程序的性能和可扩展性的主要威胁。该项目的目的是为可以减轻存储性能瓶颈的平行存储系统进行自我优化的平行存储系统的一般框架，因此可以通过加速众多科学领域的创新过程对社会产生重大影响。结果还将通过使包括磁盘阵列，键值商店和并行/分布式文件系统在内的广泛的并行存储系统中受益，从而提高存储系统中知识的状态。更广泛的影响包括通过夏令营进行指导和培训K-12学生，并促进代表性不足的学生参与科学和工程的参与。这项研究开发了小说，理论上扎根，以及实验验证的在线检测方法，以自动消除磁盘I/O瓶颈。具体目标包括：（i）以数据流挖掘和社交网络分析理论为指导的新的在线方法，用于连续监视磁盘I/O的请求并有效地分析它们，以及（ii）自动通过BIN包装，图形着色理论和网络流程理论自动触发的自我选择技术，可以仔细地计划适应性的数据，以改善适应性的数据。使用软件仿真和原型实现的实验和验证是通过分析理论上可能的，实践中可以实现的，并试图缩小两者之间的差距来执行的。

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

Ultra-Low Latency SSDs' Impact on Overall Energy Efficiency

• 发表时间: 2020
• 作者: B. Harris;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

A Comparative Study of HDD and SSD RAIDs’ Impact on Server Energy Consumption

HDD 和 SSD RAID 对服务器能耗影响的比较研究

• DOI: 10.1109/cluster.2017.103
• 发表时间: 2017
• 期刊: 2017 IEEE International Conference on Cluster Computing (CLUSTER
• 作者: Tomes, Erica;Altiparmak, Nihat
• 通讯作者: Altiparmak, Nihat