Самостабилизация: Концепция устойчивости к сбоям в распределенных системах.

Concept of fault tolerance

Self stabilization is a concept of fault tolerance in distributed systems. Given any initial state, a self stabilizing distributed system will end up in a correct state in a finite number of execution steps. At first glance, the guarantee of self stabilization may seem less promising than that of the more traditional fault tolerance of algorithms, that aim to guarantee that the system always remains in a correct state under certain kinds of state transitions. However, that traditional fault tolerance cannot always be achieved. For example, it cannot be achieved when the system is started in an incorrect state or is corrupted by an intruder. Moreover, because of their complexity, it is very hard to debug and to analyze distributed systems. Hence, it is very hard to prevent a distributed system from reaching an incorrect state. Indeed, some forms of self stabilization are incorporated into many modern computer and telecommunications networks, since it gives them the ability to cope with faults that were not foreseen in the design of the algorithm. Many years after the seminal paper of Edsger Dijkstra in 1974, this concept remains important as it presents an important foundation for self managing computer systems and fault tolerant systems. As a result, Dijkstra's paper received the 2002 ACM PODC Influential Paper Award, one of the highest recognitions in the distributed computing community. Moreover, after Dijkstra's death, the award was renamed and is now called the Dijkstra Award.

E. W. Dijkstra in 1974 presented the concept of self stabilization, prompting further research in this area. His demonstration involved the presentation of self stabilizing mutual exclusion algorithms. It also showed the first self stabilizing algorithms that did not rely on strong assumptions on the system. Some previous protocols used in practice did actually stabilize, but only assuming the existence of a clock that was global to the system, and assuming a known upper bound on the duration of each system transition. It was only ten years later when Leslie Lamport pointed out the importance of Dijkstra's work at a 1983 conference called Symposium on Principles of Distributed Computing that researchers directed their attention to this elegant fault tolerance concept. In his talk, Lamport stated:<blockquote>I regard this as Dijkstra's most brilliant work at least, his most brilliant published paper. It's almost completely unknown. I regard it to be a milestone in work on fault tolerance I regard self stabilization to be a very important concept in fault tolerance and to be a very fertile field for research.

A distributed algorithm is self stabilizing if, starting from an arbitrary state, it is guaranteed to converge to a legitimate state and remain in a legitimate set of states thereafter. A state is legitimate if, starting from this state, the algorithm satisfies its specification. The property of self stabilization enables a distributed algorithm to recover from a transient fault regardless of its nature. Moreover, a self stabilizing algorithm does not have to be initialized as it eventually starts to behave correctly regardless of its initial state. Dijkstra's paper, which introduces the concept of self stabilization, presents an example in the context of a "token ring"—a network of computers ordered in a circle. Here, each computer or processor can "see" the whole state of one processor that immediately precedes it and that this state may imply that the processor "has a token" or it "does not have a token." were often very difficult and time consuming, such a behavior was considered desirable. (The method described in the paper cited above collects a huge amount of information from the whole network to one place; after that, it attempts to determine whether the collected global state is correct; even that determination alone can be a hard task).

The time complexity of a self stabilizing algorithm is measured in (asynchronous) rounds or cycles. A round is the shortest execution trace in which each processor executes at least one step. Similarly, a cycle is the shortest execution trace in which each processor executes at least one complete iteration of its repeatedly executed list of commands. To measure the output stabilization time, a subset of the state variables is defined to be externally visible (the output). Certain states of outputs are defined to be correct (legitimate). The set of the outputs of all the components of the system is said to have stabilized at the time that it starts to be correct, provided it stays correct indefinitely, unless additional faults occur. The output stabilization time is the time (the number of (asynchronous) rounds) until the output stabilizes.

An extension of the concept of self stabilization is that of superstabilization. The intent here is to cope with dynamic distributed systems that undergo topological changes. In classical self stabilization theory, arbitrary changes are viewed as errors where no guarantees are given until the system has stabilized again. With superstabilizing systems, there is a passage predicate that is always satisfied while the system's topology is reconfigured.