Causal reversibility blends reversibility and causality for concurrent systems. It indicates that an action can be undone provided that all of its consequences have been undone already, thus making it possible to bring the system back to a past consistent state. Time reversibility is instead considered in the field of stochastic processes, mostly for efficient analysis purposes. A performance model based on a continuous-time Markov chain is time reversible if its stochastic behavior remains the same when the direction of time is reversed. We bridge these two theories of reversibility by showing the conditions under which causal reversibility and time reversibility are both ensured by construction. This is done in the setting of a stochastic process calculus, which is then equipped with a variant of stochastic bisimilarity accounting for both forward and backward directions.
翻译:通过随机过程代数方法桥接因果可逆性和时间可逆性
翻译后的摘要:
因果可逆性将可逆性和因果性相结合应用于并发系统。它表明,如果一个动作的所有后果都已经被撤销,那么它就可以被撤销,因此可以将系统带回到一个过去的一致状态。时间可逆性则主要在随机过程领域中进行研究,主要用于有效的分析。如果基于连续时间马尔可夫链的性能模型在时间方向被逆转时其随机行为仍然相同,则该模型是时间可逆的。我们通过展示满足因果可逆性和时间可逆性构造条件来桥接这两种可逆性理论。这是在一个随机过程演算中完成的,然后该过程演算被装备了一个考虑正反两个方向的随机相似度变量。