CSR: SHF: SMALL: Efficient, Low-Latency Networked Storage

CSR：SHF：小型：高效、低延迟的网络存储

• 批准号: 1320071
• 负责人: Christopher Stewart
• 金额: $ 40万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-10-01 至 2017-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320071&HistoricalAwards=false
• 关键词: CSR; SHF; SMALL; Efficient; Low

From key-value stores to distributed file systems to distributed databases, networked storage underpins modern Internet services. Networked storage allows programmers to separate logic and data, enables high throughput scale out, and takes advantage of increasingly fast datacenter networks. However, in the big data era, networked storage faces a new challenge: The amount of data accessed per user request is growing rapidly; outpacing processor speeds and DRAM capacity. Increasingly, user-perceived response times are dominated by the slowest storage accesses, i.e., the 99th percentile tails. Networked storage is notorious for fat tailed response times.We are developing networked storage systems that are 1) always fast and 2) cost efficient. A key approach is to understand and selectively use replication for predictability (or cloning). In this approach, clients issue redundant storage accesses against independent hardware resources. The first to respond provides the result. Replication for predictability reduces client-perceived variability, leading to always fast response times. Our implementations are especially cost effective at scale. To lower costs, we study the root causes of slow response times, saving resources by focusing on common causes. We also trade quality---e.g., slightly degraded search engine results--- for lower hardware costs when appropriate. For broader impact, the PI will work to transfer the technology to national and local companies.

从键值存储到分布式文件系统再到分布式数据库，网络存储支撑着现代互联网服务。网络存储允许程序员分离逻辑和数据，实现高吞吐量横向扩展，并利用日益快速的数据中心网络。 然而，在大数据时代，网络存储面临着新的挑战：每个用户请求访问的数据量正在迅速增长，超过了处理器速度和DRAM容量。越来越多地，用户感知的响应时间由最慢的存储访问支配，即，第99百分位的尾巴。 网络存储因响应时间的厚尾而臭名昭著。我们正在开发的网络存储系统1）始终快速，2）具有成本效益。 一个关键的方法是理解和选择性地使用复制的可预测性（或克隆）。 在这种方法中，客户端对独立的硬件资源发出冗余存储访问。第一个响应的人提供结果。 可预测性复制减少了客户感知的可变性，从而始终实现快速响应。 我们的实施在规模上特别具有成本效益。 为了降低成本，我们研究了响应时间缓慢的根本原因，通过关注常见原因来节省资源。 我们也交易质量-例如，稍微降级的搜索引擎结果-在适当的时候降低硬件成本。 为了更广泛的影响，PI将努力将技术转让给国家和地方公司。

项目成果

Scaling Analytics through Approximate and Distributed Computing

通过近似和分布式计算扩展分析

• 发表时间: 2017
• 期刊: OSU Masters Thesis
• 作者: Chakrabarti, Aniket

Obtaining and Managing Answer Quality for Online Data-Intensive Services

获取和管理在线数据密集型服务的答案质量

• 发表时间: 2017
• 期刊: ACM Transactions on Modeling and Performance Evaluation of Computing Systems
• 影响因子: 0.6
• 作者: Kelley, Jaimie;Stewart, Christopher;Morris, Nathaniel;Tiwari, Devesh;He, Yuxiong;Elnikety, Sameh
• 通讯作者: Elnikety, Sameh

Resource Allocation using Adaptive Characterization of Online, Data-Intensive Workloads

• 发表时间: 2017
• 作者: Jaimie Kelley