Abstract
The paper tackles the power of randomization in the context of local distributed computing by analyzing the ability to “boost” the success probability of deciding a distributed language using a Monte-Carlo algorithm. We prove that, in many cases, the ability to increase the success probability for deciding distributed languages is rather limited. This contrasts with the sequential computing setting where boosting can systematically be achieved by repeating the randomized execution.
| Original language | English |
|---|---|
| Pages (from-to) | 419-434 |
| Number of pages | 16 |
| Journal | Distributed Computing |
| Volume | 27 |
| Issue number | 6 |
| DOIs | |
| State | Published - 23 Nov 2014 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2014, Springer-Verlag Berlin Heidelberg.
Funding
A. Korman: Supported by the ANR project DISPLEXITY, and by the INRIA project GANG. M. Parter: Additional support from the Google European Fellowship in distributed computing.
| Funders | Funder number |
|---|---|
| Israel PBC | |
| Citi Foundation | |
| Agence Nationale de la Recherche | |
| United States-Israel Binational Science Foundation | 2008348 |
| Israel Science Foundation | 4/11, 894/09 |
| Israeli Centers for Research Excellence | |
| Ministry of science and technology, Israel |
Keywords
- Complexity classes
- Distributed local algorithms
- Randomized algorithms