一种可恢复互斥的自适应方法
An Adaptive Approach to Recoverable Mutual Exlcusion
摘要：
互斥是并发系统中处理冲突最常用的技术之一。传统上，互斥算法是在假设进程在获取/释放锁或执行其临界区时不会失败的情况下设计的。但是，在现实生活中确实会发生故障，可能会使锁处于不一致的状态。这就产生了\emph{recoverable mutual exclusion (RME)}的问题，它涉及到设计一个互斥算法，可以容忍故障，同时保持安全性和活性属性。任何ME算法（包括RME算法）的性能的重要度量之一是由进程产生的\emph{remote memory references (RMRs)}的数量（用于获取和释放锁以及在故障后恢复锁结构）。最著名的RME算法解决了$n$过程中的RMR的亚对数数量的问题，由$\mathcal{O}(\frac{\log n}{\log \log n})$给出，而不管系统中的故障数量。在这项工作中，我们提出了一个新的算法来解决RME问题，其RMR复杂性逐渐\emph{adapts}的故障发生在系统中的数量“最近”。在没有故障的情况下，我们的算法只生成$\mathcal{O}(1)$ RMR。此外，其RMR复杂度由$\mathcal{O}(\min\{ \sqrt{F}, \frac{\log n}{\log \log n} \})$给出，其中$F$是“最近”过去的故障总数。除了读和写指令，我们的算法使用比较和交换（\CAS{}）和提取和存储（\FAS{}）的硬件指令，这两个都是常见的在大多数现代处理器。
英文摘要：
Mutual exclusion (ME) is one of the most commonly used techniques to handle conflicts in concurrent systems. Traditionally, mutual exclusion algorithms have been designed under the assumption that a process does not fail while acquiring/releasing a lock or while executing its critical section. However, failures do occur in real life, potentially leaving the lock in an inconsistent state. This gives rise to the problem of \emph{recoverable mutual exclusion (RME)} that involves designing a mutual exclusion algorithm that can tolerate failures, while maintaining safety and liveness properties. One of the important measures of performance of any ME algorithm, including an RME algorithm, is the number of \emph{remote memory references (RMRs)} made by a process (for acquiring and releasing a lock as well as recovering the lock structure after a failure). The best known RME algorithm solves the problem for $n$ processes in sub-logarithmic number of RMRs, given by $\mathcal{O}(\frac{\log n}{\log \log n})$, irrespective of the number of failures in the system. In this work, we present a new algorithm for solving the RME problem whose RMR complexity gradually \emph{adapts} to the number of failures that have occurred in the system "recently". In the absence of failures, our algorithm generates only $\mathcal{O}(1)$ RMRs. Furthermore, its RMR complexity is given by $\mathcal{O}(\min\{ \sqrt{F}, \frac{\log n}{\log \log n} \})$ where $F$ is the total number of failures in the "recent" past. In addition to read and write instructions, our algorithm uses compare-and-swap (\CAS{}) and fetch-and-store (\FAS{}) hardware instructions, both of which are commonly available in most modern processors.
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