Conference proceeding
Scalability analysis of distributed search in large peer-to-peer networks
2016 IEEE International Conference on Big Data (Big Data), pp 909-914
Dec 2016
Abstract
We study decentralized searches in large-scale, self-organized peer-to-peer networks and investigate the influences of network size and degree distribution (neighborhood size) on search efficiency. Experimental results show that searches are efficient and scalable in large networks, especially with large neighborhood sizes (degrees). Analysis of the data supports a proposed scalability model, in which search path length L (efficiency) is proportional to a poly-logarithmic function of network size N, with degree d m (majority neighborhood size) as the log base. The model explains 90% (R 2 ) of variances in search path lengths. Search time (search path length) predicted by the model shows great potential for efficient searches in real-scale networks of up to a billion distributed systems.
Metrics
7 Record Views
1 citations in Web of Science
3 citations in Scopus
Details
- Title
- Scalability analysis of distributed search in large peer-to-peer networks
- Creators
- Weimao Ke - Drexel University, Information ScienceJaved Mostafa - Sch. of Inf. & Libr. Sci., Univ. of North Carolina, Chapel Hill, NC, USA
- Publication Details
- 2016 IEEE International Conference on Big Data (Big Data), pp 909-914
- Publisher
- IEEE
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Scopus ID
- 2-s2.0-85015245578
- Other Identifier
- 991019170392004721