On Obtaining Global Information in a Peer-to-Peer Fully Distributed Environment (Research Note)

Authors:
Márk Jelasity;Mike Preuß
Affiliations:
-;-
Venue:
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
Year:
2002

Citing 4
Cited 4

Epidemic algorithms for replicated database maintenance

PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
Storage management and caching in PAST, a large-scale, persistent peer-to-peer storage utility

SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Maintaining Connectivity in a Scalable and Robust Distributed Environment

CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
A scalable and robust framework for distributed applications

CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02

Peer counting and sampling in overlay networks: random walk methods

Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing
Active and passive techniques for group size estimation in large-scale and dynamic distributed systems

Journal of Systems and Software
EA-Powered Basin Number Estimation by Means of Preservation and Exploration

Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Managing RFID events in large-scale distributed RFID infrastructures

Information Technology and Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

Networking solutions which do not depend on central services and where the components posses only partial information are robust and scalable but obtaining global information like e.g. the size of the network raises serious problems, especially in the case of very large systems. We consider a specific type of fully distributed peer-to-peer (P2P) environment with many interesting existing and potential applications. We suggest solutions for estimating network size and detecting partitioning, and we give estimations for the time complexity of global search in this environment. Our methods rely only on locally available (but continuously refreshed) partial information.