CNRL logo

Computer Networking Research Laboratory

Dept. of Electrical & Computer Engineering, Colorado State University

Collaborative Peer-to-Peer (P2P) Systems

Introduction

Resource-rich computing devices, decreasing communication cost, and Web 2.0 technologies are fundamentally changing the way we communicate, learn, socialize, and collaborate. With these changes, we envision P2P systems that play an even greater role in collaborative applications. P2P computing fits naturally to this new era of user-driven, distributed applications utilizing resource-rich edge devices. Thus, there is a tremendous opportunity to create value by combining societal trends with P2P systems. Peer collaboration has expanded beyond its conventional applications wherein files or processor cycles are shared by peers to perform similar tasks. Emerging collaborative P2P systems look for diverse peers that could bring in unique capabilities to a community thereby empowering it to engage in greater tasks that cannot be accomplished by individual peers, yet are beneficial to all the peers. Collaborative P2P systems are applicable in a wide variety of contexts such as:

  • Distributed Collaborative Adaptive Sensing (DCAS) systems such as Collaborative Adaptive Sensing of the Atmosphere (CASA)
  • P2P clouds - Community based cloud computing
  • Mobile and Social P2P networks
  • Global Environment for Network Innovations (GENI) - Discovery, aggregation, and utilization of heterogeneous and distributed resources

So far we have develop a community-aware caching scheme for structured P2P systems, analyzed the characteristics of multi-attribute resources and queries from real-world systems, developed a model to generate random nodes with statistical properties, analyzed performance of existing resource discovery schemes, and developed architecture for large-scale and multi-attribute resource discovery.

Project Team

Sponsor

This research is supported in part by the Engineering Research Center program of the National Science Foundation under NSF award number 0313747.

Publications

  1. H. M. N. D. Bandara and A. P. Jayasumana, “Community-Based Caching for Enhanced Lookup Performance in P2P Systems,” IEEE Transactions on Parallel and Distributed Systems, 2012, doi: 10.1109/TPDS.2012.270, To appear.
  2. H. M. N. D. Bandara, A. P. Jayasumana, and M. Zink, “Radar Networking in Collaborative Adaptive Sensing of Atmosphere: State of the Art and Research Challenges,” In Proc. IEEE Globecom workshop on Radar and Sonar Networks, Dec. 2012, To appear.
  3. H.M.N. Dilum Bandara and Anura P. Jayasumana, “Collaborative Applications over Peer-to-Peer Systems - Challenges and Solutions,” Peer-to-Peer Networking and Applications, Springer, 2012, To appear. Preliminary version.
  4. H.M.N. Dilum Bandara and Anura P. Jayasumana, “Resource and Query Aware, Peer-to-Peer-Based Multi-Attribute Resource Discovery,” In Proc. 37th IEEE Conf. on Local Comp uter Networks (LCN '12), Oct. 2012, To appear.
  5. Panho Lee, Anura P. Jayasumana, H.M.N. Dilum Bandara, Sanghun Lim, and V. Chandrasekar, “A Peer-to-Peer Collaboration Framework for Multi-Sensor Data Fusion,” Journal of Network and Computer Application, vol. 35. no. 2, May 2012, pp. 1052-1066.
  6. H.M.N. Dilum Bandara and Anura P. Jayasumana, "Evaluation of P2P Resource Discovery Architectures Using Real-Life Multi-Attribute Resource and Query Characteristics,” In Proc. IEEE Consumer Communications and Networking Conf. (CCNC '12), Jan. 2012.
  7. H.M.N. Dilum Bandara and Anura P. Jayasumana, "On Characteristics and Modeling of P2P Resources with Correlated Static and Dynamic Attributes," In Proc. IEEE GLOBECOM '11, Dec. 2011.
  8. H.M.N. Dilum Bandara and Anura P. Jayasumana, "Characteristics of Multi-Attribute Resources/Queries and Implications on P2P Resource Discovery," In Proc 9th ACS/IEEE Int. Conf. On Computer Systems And Applications (AICCSA 2011), Sharm El-Sheikh, Egypt, Dec. 2011.
  9. H.M.N. Dilum Bandara and Anura P. Jayasumana, "Exploiting Communities for Enhancing Lookup Performance in Structured P2P Systems," In proc. IEEE Int. Conf. on Communications (ICC '11), Kyoto, Japan, June 2011.
  10. H. M. N. Dilum Bandara and Anura P. Jayasumana, "Distributed Multi-Sensor Data Fusion Over Named Data Networks," Under review.
  11. H. M. N. D. Bandara, “Enhancing collaborative peer-to-peer systems using resource aggregation and caching: A multi-attribute resource and query aware approach,” PhD Dissertation, Colorado State University, Fall 2012.

  Posters/Presentations

  1. H. M. N. D. Bandara and A. P. Jayasumana, "Globally Distributed Datacenters: A Collaborative Peer-to-Peer Approach," 2nd Annual Front Range High Performance Computing Symposium, Fort Collins, CO, Aug. 2012. Slides
  2. File Sharing to Resource Sharing – Evolution of P2P Networking presented by Anura P. Jayasumana at IEEE Consumer Communications and Networking Conf. Tutorials (CCNC '12), Jan. 2012. Also at:
    • Int'l Conf. on Collaboration Technologies and Systems (CTS '12), May 2012.
  3. H.M.N. Dilum Bandara, and Anura P. Jayasumana, "Collaborative P2P Systems for Distributed Data Fusion & Beyond," CS Graduate Student Research Symposium, Colorado State University, Fort Collins, CO, USA, Apr. 12, 2010. Abstract
  4. P. Lee, S. Lim, H.M.N. D. Bandara, S. Doshi, A. P. Jayasumana, V. Chandrasekar, and J. Kurose, "D6: Application Aware Overlay Network Based Data Fusion Framework for Distributed Collaborative Adaptive Sensing," CASA Student Poster Session, University of Massachusetts, Amherst, May 2009.
  5. T. Banka, P. Lee, H.M.N. D. Bandara, and A. P. Jayasumana, “An application-aware overlay networks architecture,” Front Range Architecture Compilers Tools and Languages Workshop - Spring 2009 (FRACTAL 2009), Colorado State University, Fort Collins, CO, USA, Apr. 25, 2009.

 Simulators, Utilities, and Datasets

Simulators

  • Simulators for comparing performance of P2P resource discovery solutions
  • Resource and query aware P2P-based resource discovery
  • Community caching
  • NDN for DCAS

Utilities

  • ResQue - Multi-attribute resource and range query generator. Generates random computing nodes with multiple static and dynamic attributes over a given time period and multi-attribute range queries.

Datasets