Work

Randomized Local Network Computing

Year: 2015

Type: conference-paper

Abstract: In this paper, we carry on investigating the line of research questioning the power of randomization for the design of distributed algorithms. In their seminal paper, Naor and Stockmeyer [STOC 1993] established that, in the context of network computing, in which all nodes execute the same algorithm in parallel, any construction task that can be solved locally by a randomized Monte-Carlo algorithm can also be solved locally by a deterministic algorithm. This result however holds in a specific context. In particular, it holds only for distributed tasks whose solutions can be locally checked by a deterministic algorithm. In this paper, we extend the result of Naor and Stockmeyer to a wider class of tasks. Specifically, we prove that the same derandomization result holds for every task whose solutions can be locally checked using a 2-sided error randomized Monte-Carlo algorithm. This extension finds applications to, e.g., the design of lower bounds for construction tasks which tolerate that some nodes compute incorrect values. In a nutshell, we show that randomization does not help for solving such resilient tasks.

Source: ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)

DOI: 10.1145/2755573.2755596

Authors: Laurent Feuilloley, Pierre Fraigniaud

Institutions: École Normale Supérieure Paris-Saclay, Aalto University, Centre National de la Recherche Scientifique, Laboratoire d'Informatique Algorithmique: Fondements et Applications, Université Paris Diderot

Language: English

Topic: Complexity and Algorithms in Graphs

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Distributed Computing : computability and complexity