A Topological View of Partitioning Arguments: Reducing k-Set Agreement to Consensus

2Citations
Citations of this article
2Readers
Mendeley users who have this article in their library.
Get full text

Abstract

The objective of this paper is to understand the effect of partitioning in distributed computing models. In spite of being quite similar agreement problems, (deterministic) consensus (1-set agreement) and k-set agreement (for (Formula presented)) require surprisingly different techniques for proving impossibilities. There is a widely applicable generic theorem, however, which allows to reduce the impossibility of k-set agreement to consensus in message-passing models that allow some partitioning. In this paper, we provide the topological representation of this theorem, which reveals how partitioning is reflected in the protocol complex: It turns out that this leads to a “color splitting” of the algorithm’s decision map, which separates the sub-complexes representing the partitioned processes. We also harvest a general advantage of topological results, which allowed us to carry over our findings to shared memory systems. We first demonstrate the utility of our reduction theorem by proving that d-set agreement cannot be solved in the d-solo asynchronous read-write model even when a single process may crash, not just in the wait-free case. Moreover, our new insights into the structure of protocol complexes gave us the idea for a simple proof of the fact that no partitioning argument can provide a valid impossibility proof for wait-free set agreement in the iterated immediate snapshot model: For any set of partition-compatible runs (which do not contain runs where all processes always have a complete view), we provide a way to construct a simple algorithm that solves set agreement.

Cite

CITATION STYLE

APA

Rincon Galeana, H., Winkler, K., Schmid, U., & Rajsbaum, S. (2019). A Topological View of Partitioning Arguments: Reducing k-Set Agreement to Consensus. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11914 LNCS, pp. 307–322). Springer. https://doi.org/10.1007/978-3-030-34992-9_25

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free