CAREER: Building Immunity to Memory Management Bugs during Production Runs

职业：在生产运行期间建立对内存管理错误的免疫力

• 批准号: 0953759
• 负责人: Feng Qin
• 金额: $ 42万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-03-15 至 2016-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0953759&HistoricalAwards=false
• 关键词: CAREER; Building; Immunity; Memory; Management

The widespread use of software in our society has created unprecedented demand on software availability. Unfortunately, memory management bugs, a major category of common software defects, severely affect system availability. Previous studies have shown that memory management bugs account for 43% of the reported software failures. According to the Vulnerability Notes Database maintained by the U.S. Computer Emergency Readiness Team, memory bugs are dominant in recent vulnerability reports. Memory bugs remain prevalent and challenging to handle during production runs mainly because of three reasons. First, memory bugs are deterministic, failing traditional fault tolerance methods. Second, memory bugs manifested during production runs are hard to reproduce off-site, making them difficult for diagnosis. Third, modern computing trends such as cloud computing and multi-core enable larger and more complicated software, causing memory bugs to slip into the field more easily.This project addresses the above challenges. The investigator employs a systematic approach for providing immunity to memory bugs during production runs. The main idea is to perform online diagnosis once a memory bug or failure is detected, then generate and apply runtime immune patches to the running program for surviving and preventing memory bug occurrences or failures caused by memory bugs. The research hinges on the following synergistic thrusts: (1) runtime support of diversified program execution environments for safely and efficiently surviving and preventing memory bug occurrences or failures, which addresses the determinism issue of memory bugs; (2) new algorithms for low-overhead bug diagnosis, which are applied on-site for easy reproduction of memory bugs and best exploitation of on-site bug/failure information; (3) operating system support for lightweight checkpointing and re-execution mechanisms for multi-threaded programson modern multi-core systems.

我们社会中软件的广泛使用已经对软件可用性产生了前所未有的需求。不幸的是，内存管理错误是常见软件缺陷的主要类别，严重影响了系统可用性。先前的研究表明，内存管理错误占报告的软件故障的43％。根据美国计算机紧急准备小组维护的漏洞注释数据库，在最近的漏洞报告中，内存错误是主导的。记忆错误在生产过程中仍然很普遍，并且主要是由于三个原因而进行的。首先，内存错误是确定性的，传统的容错方法失败。其次，在生产过程中表现出的记忆错误很难繁殖异地，因此难以诊断。第三，诸如云计算和多核等现代计算趋势可以使更大，更复杂的软件更容易滑入现场。该项目解决了上述挑战。研究人员采用一种系统的方法来在生产运行过程中提供对内存错误的免疫力。主要想法是在检测到内存错误或故障后，进行在线诊断，然后生成并将运行时免疫补丁应用于运行程序，以生存并防止记忆错误发生或由内存错误引起的失败。该研究取决于以下协同推力：（1）多元化程序执行环境的运行时支持，以安全有效地生存并防止记忆错误事件或失败，这解决了记忆错误的确定性问题； （2）用于低空错误诊断的新算法，该算法可在现场应用，以便于复制内存错误和最佳利用现场错误/故障信息； （3）对多线读取程序森的现代多核系统的轻巧检查点和重新执行机制的支持。