CSR: Small: Bringing Predictable Low Latency and Strong Consistency to Data Center Services

CSR：小：为数据中心服务带来可预测的低延迟和强一致性

• 批准号: 1217597
• 负责人: Steven Gribble
• 金额: $ 45万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217597&HistoricalAwards=false
• 关键词: CSR; Small; Bringing; Predictable; Low

To provide responsive services to end-users, cloud platforms are designed for low latency. Each layer of the stack, including local storage, distributed file systems, the communication layer, and coordination services, operates with a few milliseconds latency on average. However, a typical user request flows through dozens of services. As a result, the latency tail is as important to responsiveness as average latency: if 1 in 100 RPC requests experiences 50ms latency, then as the complexity of servicing an individual user request grows, so do does the likelihood that the latency of the user's request will be "dragged down" by an outlier.We are exploring techniques for constructing data center services that provide low latency tails. Hard drives can introduce high latency when seeking, whereas SSDs provide uniform access times even for random workloads. Distributed storage systems like GFS or BigTable provide good average latency, but at scale, "rare" events like tablet splitting become common. We are designing techniques to reduce the latency impact of these rare events and that exploit redundancy to provide a fast path even if they occur. OSs and transport protocols are another source of latency noise; we are applying techniques from soft real-time systems and QoS-oriented networks to condition these layers as well. By bringing down tail latency, we will also make protocols that provide strong data consistency more practical.As part of our work, we are incorporating these topics into the undergraduate and graduate curriculum, including a graduate course on operating systems.

为了向最终用户提供响应式服务，云平台被设计为低延迟。堆栈的每一层，包括本地存储、分布式文件系统、通信层和协调服务，平均运行延迟为几毫秒。然而，一个典型的用户请求会流经几十个服务。因此，延迟尾部对于响应性和平均延迟一样重要：如果100个RPC请求中有1个经历50 ms的延迟，那么随着服务单个用户请求的复杂性增加，用户请求的延迟被异常值“拖低”的可能性也会增加。我们正在探索构建提供低延迟尾部的数据中心服务的技术。硬盘驱动器在查找时可能会引入高延迟，而SSD即使对于随机工作负载也可以提供统一的访问时间。像GFS或BigTable这样的分布式存储系统提供了良好的平均延迟，但在大规模下，像平板电脑分裂这样的“罕见”事件变得很常见。 我们正在设计技术来减少这些罕见事件的延迟影响，并利用冗余来提供快速路径，即使它们发生。操作系统和传输协议是延迟噪声的另一个来源;我们正在应用软实时系统和面向QoS的网络技术来调节这些层。 通过降低尾部延迟，我们还将使提供强大数据一致性的协议更加实用。作为我们工作的一部分，我们正在将这些主题纳入本科生和研究生课程，包括操作系统研究生课程。