Fault-containing self-stabilizing algorithms

Ghosh, Sukumar; Gupta, Arobinda; Herman, Ted; Pemmaraju, Sriram V.

doi:10.1145/248052.248057

Cited by 116 publications

(101 citation statements)

References 13 publications

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“…When any change in the link status is detected, each node takes decisions based on the local knowledge in such a way that information regarding topological changes is automatically diffused across the network. Examples of these protocols include the Self-Stabilizing Shortest Path Spanning Tree (SS-SPST) [10] [11], the Energy-aware SS-SPST (SS-SPST-E) [17] and the Breadth First Spanning Tree (BFST) [8] [9] protocols. Although these protocols may reduce the volume of the control messages, they may incur higher end-to-end delay due to a slower diffusion of local changes across the network.…”

Section: Proactive Protocols With Pmls and Truls (Pp+bt)mentioning

confidence: 99%

Energy optimization for proactive unicast route maintenance in MANETs under end-to-end reliability requirements

Mukherjee¹,

Gupta²,

Varsamopoulos³

2009

Performance Evaluation

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Section: Proactive Protocols With Pmls and Truls (Pp+bt)mentioning

confidence: 99%

Energy optimization for proactive unicast route maintenance in MANETs under end-to-end reliability requirements

Mukherjee¹,

Gupta²,

Varsamopoulos³

2009

Performance Evaluation

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“…During this phase, there is no guarantee of safety. Several approaches have been introduced to offer more stringent guarantees during the stabilization phase, e.g., fault-containment [7], superstabilization [4], time-adaptivity [12], and safe convergence [9].…”

Section: Introductionmentioning

confidence: 99%

Self-stabilizing (f,g)-Alliances with Safe Convergence

Carrier

Datta

Devismes

et al. 2013

Lecture Notes in Computer Science

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Abstract. Given two functions f and g mapping nodes to non-negative integers, we give a silent self-stabilizing algorithm that computes a minimal (f, g)-alliance in an asynchronous network with unique node IDs, assuming that every node p has a degree at least g(p) and satisfies f (p) ≥ g(p). Our algorithm is safely converging in the sense that starting from any configuration, it first converges to a (not necessarily minimal) (f, g)-alliance in at most four rounds, and then continues to converge to a minimal one in at most 5n+4 additional rounds, where n is the size of the network. Our algorithm is written in the shared memory model. It is proven assuming an unfair (distributed) daemon. Its memory requirement is O(log n) bits per process, and it takes O(∆ 3 n) steps to stabilize, where ∆ is the degree of the network.

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“…Intuitively, the reason for that is that reset-based stabilization is too "twitchy": the slightest disturbance to the consistency of the system may trigger a systemwide service outage (or "hiccup") for a non-negligible amount of time, which is clearly undesirable. In response to this shortcoming, a new approach, called adaptive protocols, has recently taken the focus of attention in this research area [23,18,22,21,3]. Informally, a stabilizing system is called adaptive if its recovery time depends on the severity of the fault, measured by the number of processors whose state was corrupted.…”

Section: Introductionmentioning

confidence: 99%

“…-Recovery time, which consists of the following two measures [18,21]. Output stabilization is the time it takes until the outputs stabilize to correct values after a fault; and state stabilization, which is the time until the system completely recovers internally from a fault (meaning that it is prepared to sustain another fault).…”

Section: Introductionmentioning

confidence: 99%

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Adaptive Stabilization of Reactive Protocols

Kutten

Patt-Shamir

2004

FSTTCS 2004: Foundations of Software Technology and Theoretical Computer Science

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Abstract. A self-stabilizing distributed protocol can recover from any state-corrupting fault. A self-stabilizing protocol is called adaptive if its recovery time is proportional to the number of processors hit by the fault. General adaptive protocols are known for the special case of function computations: these are tasks that map static distributed inputs to static distributed outputs. In reactive distributed systems, input values at each node change on-line, and dynamic distributed outputs are to be generated in response in an on-line fashion. To date, only some specific reactive tasks have had an adaptive implementation. In this paper we outline the first proof that all reactive tasks admit adaptive protocols. The key ingredient of the proof is an algorithm for distributing input values in an adaptive fashion. Our algorithm is optimal, up to a constant factor, in its fault resilience, response time, and recovery time.

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Fault-containing self-stabilizing algorithms

Cited by 116 publications

References 13 publications

Energy optimization for proactive unicast route maintenance in MANETs under end-to-end reliability requirements

Energy optimization for proactive unicast route maintenance in MANETs under end-to-end reliability requirements

Self-stabilizing (f,g)-Alliances with Safe Convergence

Adaptive Stabilization of Reactive Protocols

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