Title :
BestPeer++: A Peer-to-Peer Based Large-Scale Data Processing Platform
Author :
Chen, Gang ; Hu, Tianlei ; Jiang, Dawei ; Lu, Peng ; Tan, Kian-Lee ; Vo, Hoang Tam ; Wu, Sai
Abstract :
The corporate network is often used for sharing information among the participating companies and facilitating collaboration in a certain industry sector where companies share a common interest. It can effectively help the companies to reduce their operational costs and increase the revenues. However, the inter-company data sharing and processing poses unique challenges to such a data management system including scalability, performance, throughput, and security. In this paper, we present Best Peer++, a system which delivers elastic data sharing services for corporate network applications in the cloud based on Best Peer -- a peer-to-peer (P2P) based data management platform. By integrating cloud computing, database, and P2P technologies into one system, Best Peer++ provides an economical, flexible and scalable platform for corporate network applications and delivers data sharing services to participants based on the widely accepted pay-as-you-go business model. We evaluate Best Peer++ on Amazon EC2 Cloud platform. The benchmarking results show that Best Peer++ outperforms Hadoop DB, a recently proposed large-scale data processing system, in performance when both systems are employed to handle typical corporate network workloads. The benchmarking results also demonstrate that Best Peer++ achieves near linear scalability for throughput with respect to the number of peer nodes.
Keywords :
business data processing; cloud computing; database management systems; groupware; peer-to-peer computing; security of data; BestPeer++; P2P technologies; cloud computing; collaboration; corporate network; data management system; database; information sharing; inter-company data sharing; large-scale data processing platform; pay-as-you-go business model; peer-to-peer network; scalability; security; Companies; Indexes; Peer to peer computing; Query processing; Servers;
Conference_Titel :
Data Engineering (ICDE), 2012 IEEE 28th International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4673-0042-1
DOI :
10.1109/ICDE.2012.18