CSR: Medium: Eidetic Systems

CSR：媒介：Eidetic Systems

• 批准号: 1513718
• 负责人: Jason Flinn
• 金额: $ 70万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-10-01 至 2019-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513718&HistoricalAwards=false
• 关键词: CSR; Medium; Eidetic; Systems

The vast majority of state produced by a typical computer is generated, consumed, and then lost forever. Lost state includes process address spaces, deleted files, interprocess communication, and input received from the network. With lost state comes lost value: users cannot recover detailed information about past computations that would be useful for auditing, forensics, debugging, error tracking, and many other purposes.To solve this problem, this project is developing eidetic computer systems that can recall, on demand, any past state that existed on a computer, including all versions of all files, transient application state, and network communication. Further, eidetic computer systems can explain the provenance of current and past state at byte granularity: for example, they can answer questions such as "Where did this data come from and what steps were used to derive the data?" or "What state or outputs did this data influence?"The proposed work is deploying several important system features that utilize eidetic systems: (1) Total recall: a computer system can recover not just any prior data saved to disk (as in versioning file systems), but also any prior application state, transient output, or computation state. (2) Complete provenance: for any data object, the system can provide the history of how that data was produced, including inter-process channels and the computations that transformed the data. (3) Rich queries: users can reason over the entire history of execution to detect anomalies, recover workflows, and improve productivity. (4) Replay-structured storage: network usage can be reduced by deterministically regenerating file data at endpoints, and storage usage can be reduced by opportunistically deduplicating logs of non-deterministic inputs rather than file data. (5) Privacy-preserving replay: provenance can enable comprehensive deletion policies in which all values derived from sensitive data are identified and redacted.

典型计算机产生的绝大多数状态都会被生成、消耗，然后永远丢失。 丢失状态包括进程地址空间、已删除的文件、进程间通信以及从网络接收的输入。 失去状态就意味着失去价值：用户无法恢复有关过去计算的详细信息，而这些信息对审计、取证、调试、错误跟踪和许多其他目的有用。为了解决这个问题，该项目正在开发清晰的计算机系统，可以根据需要调用计算机上存在的任何过去状态，包括所有文件的所有版本、瞬时应用程序状态和网络通信。 此外，清晰的计算机系统可以以字节粒度解释当前和过去状态的来源：例如，它们可以回答诸如“这些数据来自哪里以及使用哪些步骤来导出数据？”之类的问题。或“此数据影响什么状态或输出？”拟议的工作是部署利用清晰系统的几个重要系统功能：（1）完全召回：计算机系统不仅可以恢复任何先前保存到磁盘的数据（如版本控制文件系统），还可以恢复任何先前的应用程序状态、瞬态输出或计算状态。 （2）完整的来源：对于任何数据对象，系统都可以提供该数据如何产生的历史记录，包括进程间通道和转换数据的计算。 (3) 丰富的查询：用户可以对整个执行历史进行推理，以检测异常、恢复工作流程并提高生产力。 (4)重放结构存储：可以通过在端点确定性地重新生成文件数据来减少网络使用量，并且可以通过机会性地对非确定性输入而不是文件数据的日志进行重复数据删除来减少存储使用量。 (5) 隐私保护重放：出处可以实现全面的删除策略，其中从敏感数据派生的所有值都被识别和编辑。