Paper detail

Self-Stabilizing Snapshot Objects for Asynchronous Fail-Prone Network Systems

A snapshot object simulates the behavior of an array of single-writer/multi-reader shared registers that can be read atomically. Delporte-Gallet et al. proposed two fault-tolerant algorithms for snapshot objects in asynchronous crash-prone message-passing systems. Their first algorithm is \emph{non-blocking}; it allows snapshot operations to terminate once all write operations have ceased. It uses $O(n)$ messages of $O(n ν)$ bits, where $n$ is the number of nodes and $ν$ is the number of bits it takes to represent the object. Their second algorithm allows snapshot operations to always terminate independently of write operations. It incurs $O(n^2)$ messages. The fault model of Delporte-Gallet et al. considers node crashes. We aim at the design of even more robust snapshot objects via the lenses of self-stabilization---a very strong notion of fault-tolerance. In addition to Delporte-Gallet et al.'s fault model, our self-stabilizing algorithm can recover after the occurrence of transient faults; these faults represent arbitrary violations of the assumptions according to which the system was designed to operate. We propose self-stabilizing variations of Delporte-Gallet et al.'s non-blocking algorithm and always-terminating algorithm. Our algorithms have similar communication costs to the ones by Delporte-Gallet et al. and $O(1)$ recovery time from transient faults. The main differences are that our proposal considers repeated gossiping of $O(ν)$ bit messages and deals with bounded space. We also consider an input parameter, $δ$, for which we claim an ability to balance the costs of snapshot operations. We validate our correctness proof, evaluate the performance of Delporte-Gallet et al.'s algorithms and our proposed variations and investigate the properties of $δ$ via PlanetLab experiments, where significant latency and communication costs reduction are observed.

preprint2020arXivOpen access
0citations
0reviews
0saves
Nocode
Nodataset
0institutions

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.