通过 rejuvenation 优化长运行应用的系统服务质量
Optimizing System Quality of Service through Rejuvenation for Long-Running Applications with Real-Time Constraints
AI总结：
本文研究了在满足实时约束条件下，通过优化 rejuvenation 时间来最大化系统可靠性、长寿命和可用性，同时最小化 rejuvenation 引起的应用延迟。
摘要：
可靠性、长寿命、可用性和截止期保证是衡量长运行安全关键实时应用服务质量的四个最重要指标。软件老化是影响长运行实时应用安全的主要因素，因为软件老化导致的性能退化和故障率增加可能导致截止期错过和灾难性后果。软件 rejuvenation 是处理软件老化问题最常用的方法之一。在本文中，我们研究了软件 rejuvenation 应该在何时发生，以使系统的可靠性、长寿命和可用性最大化，并且最小化由软件 rejuvenation 引起的应用延迟。特别是，我们正式分析了软件 rejuvenation 频率与系统可靠性、长寿命和可用性之间的关系。基于理论分析，我们开发了最大化系统可靠性、长寿命和可用性的方法，并通过模拟评估所开发的方法。此外，我们设计了 MIN-DELAY 半优先级驱动调度算法，以最小化由 rejuvenation 过程引起的应用延迟。模拟实验表明，所开发的半优先级驱动调度算法在最早截止期优先（EDF）和最短释放时间（LRT）调度算法上分别减少了 9.01% 和 14.24% 的应用延迟。
英文摘要：
Reliability, longevity, availability, and deadline guarantees are the four most important metrics to measure the QoS of long-running safety-critical real-time applications. Software aging is one of the major factors that impact the safety of long-running real-time applications as the degraded performance and increased failure rate caused by software aging can lead to deadline missing and catastrophic consequences. Software rejuvenation is one of the most commonly used approaches to handle issues caused by software aging. In this paper, we study the optimal time when software rejuvenation shall take place so that the system's reliability, longevity, and availability are maximized, and application delays caused by software rejuvenation is minimized. In particular, we formally analyze the relationships between software rejuvenation frequency and system reliability, longevity, and availability. Based on the theoretic analysis, we develop approaches to maximizing system reliability, longevity, and availability, and use simulation to evaluate the developed approaches. In addition, we design the MIN-DELAY semi-priority-driven scheduling algorithm to minimize application delays caused by rejuvenation processes. The simulation experiments show that the developed semi-priority-driven scheduling algorithm reduces application delays by 9.01% and 14.24% over the earliest deadline first (EDF) and least release time (LRT) scheduling algorithms, respectively.
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