自主车辆维修的选择性符号类型引导检查点和恢复

Yu Huang, K. Angstadt, Kevin Leach, Westley Weimer
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引用次数: 1

摘要

容错设计可以帮助自动驾驶汽车系统应对缺陷、环境变化和安全攻击。检查点和恢复容错技术在问题发生前保存应用程序状态的副本,并在问题发生后恢复该状态。然而,传统的检查点/恢复技术仍然有很高的开销,可能会携带受污染的数据,并且很少与修改源代码或改变数据布局的人工编写或自动修复一起操作。因此,很难应用传统的检查点/恢复技术来解决非环境缺陷、安全攻击或软件错误的问题。为了解决这些挑战,在本文中,我们提出并评估了一种选择性检查点和恢复(SCR)技术,该技术仅记录基于类型和最小符号注释的关键系统状态,以部署修复的程序。我们发现,使用源代码级的符号信息,即使在我们的评估中修改了应用程序的代码之后,也可以恢复应用程序。我们使用商品自动驾驶汽车系统评估了我们的方法,并证明它允许手动和自动软件维修,不携带受污染的数据,并且开销低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Selective Symbolic Type-Guided Checkpointing and Restoration for Autonomous Vehicle Repair
Fault tolerant design can help autonomous vehicle systems address defects, environmental changes and security attacks. Checkpoint and restoration fault tolerance techniques save a copy of an application's state before a problem occurs and restore that state afterwards. However, traditional Checkpoint/Restore techniques still admit high overhead, may carry along tainted data, and rarely operate in tandem with human-written or automated repairs that modify source code or alter data layout. Thus, it can be difficult to apply traditional Checkpoint/Restore techniques to solve the issues of non-environmental defects, security attacks or software bugs. To address such challenges, in this paper, we propose and evaluate a selective checkpoint and restore (SCR) technique that records only critical system state based on types and minimal symbolic annotations to deploy repaired programs. We found that using source-level symbolic information allows an application to be resumed even after its code is modified in our evaluation. We evaluate our approach using a commodity autonomous vehicle system and demonstrate that it admits manual and automated software repairs, does not carry tainted data, and has low overhead.
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