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EAGER: Using Machine Learning to Increase the Operational Efficiency of Large Distributed Systems

EAGER：利用机器学习提高大型分布式系统的运营效率

基本信息

批准号：
1649087
负责人：
Evgenia Smirni
金额：
$ 30万
依托单位：
College of William and Mary
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2019-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649087&HistoricalAwards=false
关键词：
EAGER Using Machine Learning Increase

项目摘要

Large, distributed systems are nowadays ubiquitous and part of sustainable IT solutions to a broad range of customers and applications. Data centers in the private or public cloud and high performance computing systems are two examples of complex, highly distributed systems: the former are used by almost everyone on a daily basis, the latter are used by computational scientists for advancing science and engineering. High availability and reliability of these complex systems are important for the quality of user experience. Efficient management of such systems contributes to their availability and reliability, and relies on a priori knowledge of the timing of the collective demands of users and a priori knowledge of certain performance measures (e.g., usage, temperature, power) of various systems components.This project aims to provide a systematic methodology to improve the operational efficiency of complex, distributed systems by developing neural networks that can efficiently and accurately predict the incoming workload within fine and coarse time scales. Such workload prediction can dramatically improve the operational efficiency of data centers and high performance systems by driving proactive management strategies that specifically aim to enhance reliability. For datacenters, the focus is on actively reducing performance tickets that are automatically triggered by pro-actively managing virtual machine resizing and migration. For high performance computing systems the focus is on predicting hardware faults to autonomically improve the scheduler's efficiency, direct cooling, and improve performance and memory bandwidth.

如今，大型分布式系统无处不在，是面向广泛客户和应用的可持续IT解决方案的一部分。私有云或公共云中的数据中心和高性能计算系统是复杂的高度分布式系统的两个例子：前者几乎每天都被每个人使用，后者被计算科学家用于推进科学和工程。这些复杂系统的高可用性和可靠性对于用户体验的质量非常重要。对这种系统的有效管理有助于提高其可用性和可靠性，并依赖于对用户集体需求的时间安排的先验知识和对某些业绩计量的先验知识（例如，该项目旨在提供一种系统方法，通过开发神经网络来提高复杂分布式系统的运营效率，该神经网络可以在精细和粗略的时间尺度内有效准确地预测传入的工作量。这种工作负载预测可以通过推动专门旨在提高可靠性的主动管理策略来显著提高数据中心和高性能系统的运营效率。对于虚拟机管理者来说，重点是主动减少通过主动管理虚拟机迁移和迁移而自动触发的性能故障单。对于高性能计算系统，重点是预测硬件故障，以逐步提高调度器的效率，直接冷却，并提高性能和内存带宽。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Fault Site Pruning for Practical Reliability Analysis of GPGPU Applications

DOI：
10.1109/micro.2018.00066
发表时间：
2018-10
期刊：
2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)
影响因子：
0
作者：
Bin Nie;Lishan Yang;Adwait Jog;E. Smirni
通讯作者：
Bin Nie;Lishan Yang;Adwait Jog;E. Smirni

Efficient Deep Neural Network Serving: Fast and Furious