通过适应性和大规模功能集成实现关键任务操作系统的可靠性

H. Wedde, J. Lind, A. Eiss
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引用次数: 7

摘要

作为DRAGON SLAYER项目的一部分,自适应和可靠的分布式文件系统MELODY已经出现,用于支持不可预测环境中的硬实时应用程序。在MELODY中,任务的时间临界性及其对最新文件信息的敏感性明确用于新颖,灵活的任务调度算法和文件复制管理策略,具有动态文件复制和文件副本的重新定位以及提供不同最近的文件版本。与更简单的模型相比,实现的适应性远远超过了额外的开销,并且增强了文件访问的可靠性和实时响应性。模型的发展和各阶段的实验分析都是以增量方式进行的。为了处理由于需求冲突和动态权衡(例如实时响应与可靠性)而导致的问题的复杂性,这是必要的。作为下一个增量模型的扩展,也是本文的主要贡献,在重新定义任务和资源调度的角色和顺序之后,开发了一系列任务和资源调度的集成策略:使用周期性和动态模型(及其组合)在固定的时间间隔内调用任务调度程序。所有这些策略都非常令人信服地与“经典”模型进行了比较,在“经典”模型中,任务调度器只在分配完资源后调度任务。对结果进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Achieving dependability in mission-critical operating systems through adaptability and large-scale functional integration
As part of the DRAGON SLAYER project, the adaptive and reliable distributed file system MELODY has emerged for supporting hard real-time applications in unpredictable environments. In MELODY, the time criticality of tasks and their sensitivity with respect to the latest file information are explicitly used for novel, flexible task scheduling algorithms and file replication management policies, featuring dynamic file replication and relocation of file copies as well as offering file versions of varying recency. The achieved adaptability far outweighs the additional overhead in comparison to simpler models, and enhances both reliability and real-time responsiveness for file access. Both the development of the model and the experimental analysis at the various stages were done in an incremental manner. This was necessary in order to cope with the complexity of the problems resulting from conflicting requirements and dynamic trade-offs (e.g. real-time responsiveness vs. reliability). As the next incremental model extension, and as the major contribution of this paper, a series of integration policies are developed for task and resource scheduling, after redefining the role and order of task and resource scheduling: the periodic and dynamic models (and combinations thereof) are used to invoke the task scheduler for a fixed interval of time. All of these policies compare very convincingly against the "classical" model where the task scheduler only schedules tasks after their resources have been allocated. The results are discussed.
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