ACM Home Page
Please provide us with feedback. Feedback
On the communication complexity of randomized broadcasting in random-like graphs
Full text PdfPdf (217 KB)
Source ACM Symposium on Parallel Algorithms and Architectures archive
Proceedings of the eighteenth annual ACM symposium on Parallelism in algorithms and architectures table of contents
Cambridge, Massachusetts, USA
SESSION: Communication networks table of contents
Pages: 148 - 157  
Year of Publication: 2006
ISBN:1-59593-452-9
Author
Robert Elsässer  University of Paderborn, Paderborn, Germany
Sponsors
ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 4,   Downloads (12 Months): 25,   Citation Count: 4
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1148109.1148135
What is a DOI?

ABSTRACT

Broadcasting algorithms have a various range of applications in different fields of computer science. In this paper we analyze the number of message transmissions generated by efficient randomized broadcasting algorithms in random-like networks. We mainly consider the classical random graph model, i.e., a graph Gp with n nodes in which any two arbitrary nodes are connected with probability p, independently. For these graphs, we present an efficient broadcasting algorithm based on the random phone call model introduced by Karp et al. [21], and show that the total number of message transmissions generated by this algorithm is bounded by an asymptotically optimal value in almost all connected random graphs. More precisely, we show that if p ≥ logδ n/n for some constant δ > 2, then we are able to broadcast any information r in a random graph Gp of size n in O(log n) steps by using at most O(n max{log log n, log n/ log d}) transmissions related to r, where d = pn denotes the expected average degree in Gp. We also show that for these kind of graphs there is a a matching lower bound on the number of transmissions generated by any efficient broadcasting algorithm which works within the limits of the random phone call model. Please note that the main result holds with probability 1-1/nΩ(1), even if n and d are unknown to the nodes of the graph.The algorithm we present in this paper is based on a simple communication model [21], is scalable, and robust. It can efficiently handle restricted communication failures and certain changes in the size of the network, and can also be extended to certain types of truncated power law graphs based on the models of [1, 2, 5]. In addition, our methods and results might be useful for further research on this field.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
 
2
A. Barabási and R. Albert. Emergence of scaling in random networks. Science, 286, 1999.
 
3
B. Bollobás. Random Graphs. Academic Press, 1985.
 
4
H. Chernoff. A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations. The Annals of Mathematical Statistics, 23:493--507, 1952.
 
5
F.R.K. Chung and L. Lu. Connected components in random graphs with given expected degre sequences. Annals of Combinatorics, 6:125--145, 2002.
 
6
F.R.K. Chung and L. Lu. The average distances in random graphs with given expected degrees. Internet Mathematics, 1:91--114, 2003.
 
7
F.R.K. Chung and L. Lu. Coupling on-line and off-line analyses for random power law graphs,. Internet Mathematics, 1:409--461, 2004.
 
8
F.R.K. Chung, L. Lu, and V. Vu. Eigenvalues of random power law graphs. Annals of Combinatorics, 7:21--33, 2003.
 
9
F.R.K. Chung, L. Lu, and V. Vu. The spectra of random graphs with given expected degrees. Proceedings of National Academy of Sciences, 100:6313--6318, 2003.
10
 
11
R. Elsässer. On randomized broadcasting in power law graphs. Manuscript, 2006.
12
 
13
R. Elsässer, B. Monien, and S. Schamberger. Load balancing of indivisible unit size tokens in dynamic and heterogeneous networks. In Proc. of ESA, pages 640--651, 2004.
 
14
P. Erdős and A. Rényi. On random graphs I. Publ. Math. Debrecen, 6:290--297, 1959.
 
15
P. Erdős and A. Rényi. On the evolution of random graphs. Publ. Math. Inst. Hungar. Acad. Sci., 5:17--61, 1960.
16
 
17
U. Feige, D. Peleg, P. Raghavan, and E. Upfal. Randomized broadcast in networks. Random Structures and Algorithms, 1(4):447--460, 1990.
 
18
 
19
E.N. Gilbert. Random graphs. The Annals of Mathematical Statistics, 30:1141--1144, 1959.
 
20
 
21
22
 
23
T. Leighton, B. Maggs, and R. Sitamaran. On the fault tolerance of some popular bounded-degree networks. In Proc. of FOCS'92, pages 542--552, 1992.
 
24
 
25
M. Newman. The structure and function of complex networks. SIAM Review, 45:167--256, 2003.
 
26