1. UNIVERSITY OF JYVÄSKYLÄ Peer-to-Peer Algorithms and Prototypes in Jyväskylä Mikko Vapa, research student mikvapa@jyu.fi Department of Mathematical Information Technology University of Jyväskylä, Finland http://tisu.it.jyu.fi/cheesefactory Presentation for Workshop on Peer-to-Peer Networking 10.10.2005

2. UNIVERSITY OF JYVÄSKYLÄ Contents Peer-to-Peer Algorithms –Formalization of Peer-to-Peer Resource Discovery Problem –Approximation of Optimum for P2P Resource Discovery Algorithms using k-Steiner Minimum Trees –NeuroSearch – P2P Resource Discovery Using Evolutionary Neural Networks Peer-to-Peer Prototypes –Chedar Peer-to-Peer Middleware –Mobile Chedar –Peer-to-Peer Studio –Peer-to-Peer Distributed Computing –Mobile Peer-to-Peer Encounter Networks –Gasoline Price Comparison System and BlueCheese Development History and Future

3. UNIVERSITY OF JYVÄSKYLÄ Peer-to-Peer Algorithms

4. UNIVERSITY OF JYVÄSKYLÄ Formalization of P2P Resource Discovery Problem Currently, only textual definitions of P2P resource discovery problem exist: “given a resource name, find the node or nodes that manage the resource” Textual definitions are poor, because they do not precisely tell: –What kind of a graph is used for finding resources –What information is locally available to nodes taking part in the finding process  Therefore, the task of forwarding a resource query is unclear

5. UNIVERSITY OF JYVÄSKYLÄ Formalization of P2P Resource Discovery Problem Formalization can be used for: –Formalization of peer-to-peer resource discovery algorithms Breadth-First Search Highest Degree Search –Evaluating the performance of peer-to-peer resource discovery algorithms –Pointing out the information available in the P2P resource discovery problem, but which is not yet utilized by any local resource discovery algorithm Reply path forwarding Aggregating information from parallel query paths Branching factor Branching resources to discover

6. UNIVERSITY OF JYVÄSKYLÄ Approximation of Optimum for P2P Resource Discovery Algorithms using k-Steiner Minimum Trees If global information about P2P network is available, the optimum for P2P resource discovery algorithms can be approximated by solving k- Steiner Minimum Tree problem (finding the exact optimum would be a NP-complete problem)

7. UNIVERSITY OF JYVÄSKYLÄ Approximation of Optimum for P2P Resource Discovery Algorithms using k-Steiner Minimum Trees MST k-Steiner Minimum Tree Algorithm was developed for finding an approximation solution: Time Complexity: Worst-Case Approximation Ratio:

8. UNIVERSITY OF JYVÄSKYLÄ Query Path of MST k-Steiner

9. UNIVERSITY OF JYVÄSKYLÄ Efficiency MST k-Steiner Minimum Tree algorithm (Steiner) shows that current local search algorithms for peer-to-peer networks are far from optimal

10. UNIVERSITY OF JYVÄSKYLÄ Future Work of MST k-Steiner The future work of finding optimum consists of: –Getting the results published: Vapa M., Auvinen A., Tawast T., Ivanchenko Y., Vuori J., ”K-Steiner Minimum Tree Is An Upper Bound for Peer-to- Peer Resource Discovery Algorithms”, submitted to IEEE INFOCOM 2006 –Now we have all the tools available for discovering the theoretical limit of peer-to-peer technology in terms of total traffic induced on a telecommunication network in a real- world peer-to-peer network compared to client-server approach –Development of distributed k-Steiner minimum tree resource discovery algorithm using principles of proactive routing protocols such as Open Shortest Path First

11. UNIVERSITY OF JYVÄSKYLÄ NeuroSearch: P2P Resource Discovery Using Neural Networks NeuroSearch resource discovery algorithm uses neural networks and evolution to adapt its’ behavior to given environment Multiple layers enable the algorithm to express non-linear behavior With enough neurons the algorithm can universally approximate any decision function

12. UNIVERSITY OF JYVÄSKYLÄ Performance HDS is currently the best known local search algorithm for power-law distributed scenario

13. UNIVERSITY OF JYVÄSKYLÄ The Swift from Depth-First Search to Breadth-First Search NeuroSearch is close to HDS in performance, but different in nature

14. UNIVERSITY OF JYVÄSKYLÄ Typical Query Pattern of NeuroSearch

15. UNIVERSITY OF JYVÄSKYLÄ Future Work of NeuroSearch After two months of extensive simulations with 70 workstations, we have discovered from 23 different inputs 7 critical ones (Bias, White, PacketsNow, Sent, EnoughReplies, FromNeighborAmount and RepliesToGet), which need to be present to have good performance –Next, we are going to boost these 6 inputs by generalizing them to give more accurate information for forwarding Also, we need to discover: –What are the scalability factors of NeuroSearch in large graphs –The performance in dynamic real-world scenarios where peers are joining and leaving the network

16. UNIVERSITY OF JYVÄSKYLÄ Peer-to-Peer Prototypes

17. UNIVERSITY OF JYVÄSKYLÄ Chedar Peer-to-Peer Middleware Chedar (CHEap Distributed Architecture) is a P2P middleware for searching resources from a distributed network Resources can be i.e. computing power or files Distributed system without any central points Contains different resource discovery and topology management algorithms Implemented with Java 2 Standard Edition P2P Applications Chedar IP TCP Network Chedar TCP

18. UNIVERSITY OF JYVÄSKYLÄ Mobile Chedar Mobile Chedar is an extension of Chedar to mobile devices Bluetooth Java 2 Micro Edition implementation ready for Symbian Series 60 WLAN & Bluetooth Python implementation for Nokia 770 Linux Internet Tablet planned for autumn 2005

19. UNIVERSITY OF JYVÄSKYLÄ Peer-to-Peer Studio P2PStudio is used for measuring the performance, visualizing network topology and controlling of Chedar peer-to-peer network in an automated and centralized manner Implemented with Java 2 Standard Edition Server User Interface Chedar node Chedar node Chedar node Chedar node Peer-to-Peer Studio

20. UNIVERSITY OF JYVÄSKYLÄ Peer-to-Peer Distributed Computing Peer-to-Peer Distributed Computing (P2PDisCo) distributes computations to idling workstations Implemented on top of Chedar and deployed in Agora building The node that offers computation time has to implement Distributed interface to be able to receive start, stop and is application running signals Reading of parameters and writing of results are done for the stream offered by P2PDisCo  Any Java program reading input from files and writing output to files can be distributed

21. UNIVERSITY OF JYVÄSKYLÄ Mobile P2P Encounter Networks Information distributes over mobile device encounters (Mobile P2P is a future distribution model) –no centralized server, free communication bandwidth, no infrastructure Applications –information distribution –e.g. cheapest bulk product search (gasoline) 1.gasoline payment with mobile device 2.mobile devices communicate with each other (e.g. Bluetooth) 3.everybody tells what he/she has paid for the gasoline and gets in exchange prices of other gas stations 4.based on this information, mobile device can recommend the cheapest place to fill the tank –boosts the market based economy by giving equal information over the market situation to all participants –grocery store price service, dating service, joke service, event service, newspaper service…

22. UNIVERSITY OF JYVÄSKYLÄ Gasoline Price Comparison System Test application for verifying the feasibility of mobile peer-to-peer encounter networks using Bluetooth Uses BlueCheese mobile peer-to-peer middleware implemented by the MoPeDi student software project during autumn 2003 Implemented with C++ for Symbian Series 60 mobile devices GPCS User Interface

23. UNIVERSITY OF JYVÄSKYLÄ BlueCheese Protocol Stack

24. UNIVERSITY OF JYVÄSKYLÄ Development History and Future Research work proceeds as breakthroughs –P2PRealm network simulator speeded up the project 100x –P2PDisCo is speeding up the project another 100x when fully deployed In 2006, the publications side will strengthen significantly: currently 9 manuscripts are under peer review 2002 ----------- 2003 ----------- 2004 ----------- 2005 SoftwarePublications Chedar Data Fusion NeuroSearch BlueCheese P2PStudio NS-2 Simulator P2PRealm (100x) P2PDisCo (100x) Distributed Data Fusion NeuroSearch MP2P Co-ope- rative Learning Mobile Chedar MST k-Steiner P2PDisCo Mobile Chedar Formalization of P2P Resource Discovery Topology Management Gasoline Price Comparison System NeuroTopology