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Empire of Colonies Self-stabilizing and Self-organizing Distributed Algorithms

机译:殖民地帝国的自稳定和自组织分布式算法

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Self-stabilization ensures automatic recovery from an arbitrary state; we define self-organization as a property of algorithms which display local attributes. More precisely, we say that an algorithm is self-organizing if (1) it converges in sublinear time and (2) reacts "fast" to topology changes. If s(n) is an upper bound on the convergence time and d(n) is an upper bound on the convergence time following a topology change, then s(n) ∈ o(n) and d(n) ∈ o(s(n)). The self-organization property can then be used for gaining, in sub-linear time, global properties and reaction to changes. We present self-stabilizing and self-organizing algorithms for many distributed algorithms, including distributed snapshot and leader election.We present a new randomized self-stabilizing distributed algorithm for cluster definition in communication graphs of bounded degree processors. These graphs reflect sensor networks deployment. The algorithm converges in O(log n) expected number of rounds, handles dynamic changes locally and is, therefore, self-organizing. Applying the clustering algorithm to specific classes of communication graphs, in O(log n) levels, using an overlay network abstraction, results in a self-stabilizing and self-organizing distributed algorithm for hierarchy definition.Given the obtained hierarchy definition, we present an algorithm for hierarchical distributed snapshot. The algorithms are based on a new basic snap-stabilizing snapshot algorithm, designed for message passing systems in which a distributed spanning tree is defined and in which processors communicate using bounded links capacity. The combination of the self-stabilizing and self-organizing distributed hierarchy construction and the snapshot algorithm form an efficient self-stabilizer transformer. Given a distributed algorithm for a specific task, we are able to convert the algorithm into a self-stabilizing algorithm for the same task with an expected convergence time of O(log~2 n) rounds.
机译:自稳定可确保从任意状态自动恢复;我们将自组织定义为显示局部属性的算法的属性。更准确地说,我们说,如果算法(1)在亚线性时间收敛,并且(2)对拓扑变化做出“快速”反应,则该算法是自组织的。如果s(n)是收敛时间的上限,而d(n)是拓扑变化后收敛时间的上限,则s(n)∈o(n)和d(n)∈o(s (n))。然后,可以将自组织属性用于在亚线性时间内获得全局属性和对变化的反应。我们为包括分布式快照和领导者选举在内的许多分布式算法提供了自稳定和自组织算法。针对有界处理器的通信图中的聚类定义,我们提出了一种新的随机自稳定分布式算法。这些图反映了传感器网络的部署。该算法收敛于O(log n)个预期的回合数,在本地处理动态变化,因此是自组织的。通过使用覆盖网络抽象将聚类算法应用于O(log n)级别的特定类别的通信图,从而获得用于层次结构定义的自稳定和自组织分布式算法。鉴于获得的层次结构定义,我们提出了一个分层分布式快照的算法。该算法基于一种新的基本快照稳定快照算法,该算法适用于消息传递系统,在该消息传递系统中定义了分布式生成树,并且其中处理器使用有界链接容量进行通信。自稳定和自组织的分布式层次结构构造与快照算法的结合形成了一个高效的自稳定变压器。给定针对特定任务的分布式算法,我们能够将其转换为针对同一任务的自稳定算法,其预期收敛时间为O(log〜2 n)次。

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