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UNIVERSITEIT GENT 1 Peer-to-peer Networks : promise and trouble. Bart Dhoedt Ghent University - Faculty of Applied Sciences Department of Information Technology.

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Presentation on theme: "UNIVERSITEIT GENT 1 Peer-to-peer Networks : promise and trouble. Bart Dhoedt Ghent University - Faculty of Applied Sciences Department of Information Technology."— Presentation transcript:

1 UNIVERSITEIT GENT 1 Peer-to-peer Networks : promise and trouble. Bart Dhoedt Ghent University - Faculty of Applied Sciences Department of Information Technology (INTEC) Presentation at NORDUnet Network Conference August 24-27, Reykjavik, 2003 Tuesday, August 27, 2003. e-mail : phone : ++32 9 264 99 66

2 2 OUTLINE 1. Introduction 2. Taxonomy of P2P-systems 3. Issues in P2P-systems 4. P2P-trends 5. Concluding remarks

3 3 Defining P2P No agreement on formal definition (Possible) definition of a P2P-networks “A distributed network architecture may be called a Peer-to-Peer (P-to-P, P2P, …) network, if the participants share a part of their own hardware resources (processing power, storage capacity, network link capacity, printers, …). These shared resources are necessary to provide the Service and content offered by the network (e.g. file sharing or shared workspaces for collaboration) : They are accessible by other peers directly, without passing intermediary entities. The participants of such a network are thus resource (Service and content) providers as well as resource (Service and content) requestors (Servent-concept).” [W. Kellerer, “Dienstarchitekturen in der Telekommunikation - Evolution, Methoden und Vergleich”, Technical Report TUM-LKN-TR-9801,1998.]

4 4 Defining P2P about sharing symmetric (architectural view) creating an application-level overlay network decentralized application critical infrastructure owned by many Hardware resources Software resources disk space bandwidth 1001010 content liabilitycomputer cycles

5 5 Sharing resources ? -estimate of edge resources - available for P2P-network total number of Internet hosts : 150 M average disk capacity : 10 GB average available memory : 128 MB average processing power : 1 GFLOPS average BW : 100Kb/s 1% hosts 50% processing power 50% memory 10% disk space 25% network bandwidth 1.5 Mprocessors disk storage : 1.5 PB processing power : 1.5 PFLOPS BW/link : 25 Kb/s

6 6 Sharing resources ? What about supercomputers ? 12.3 TFLOPS 8192 processors 512 RS/6000 processing nodes 6.2 TB memory storage 160 TB disk storage 110 M$ 106 tons IBM ASCI White 1.5 PFLOPS 1.5 M processors 92 TB memory storage 1.5 PB disk storage ? M$ ? tons P2P-supercomputer > x 10 !

7 7 P2P @ edge ? How to unleash the power of the “Internet’s dark matter ?”

8 8 [] P2P popularity 2003 summer download hit parade 1. Kazaa Media Desktop2 644 777261 405 295 2. ICQ Lite 588 141 25 423 064 3. AOL Instant Messenger (AIM) 532 897 17 521 190 4. iMesh 392 703 55 145 269 5. WinZip 351 865100 741 790 6. ICQ Pro 2003a beta 332 624233 204 712 7. Spybot – Search & Destroy 232 993 2 764 380 8. Ad-aware 224 720 19 078 555 9. Morpheus 179 347114 140 262 10. DownloadAccelerator Plus 119 601 36 355 895 P2P [Last week] [Total] P2P

9 9 P2P popularity Gnutella network : up to 400 000 nodes operating world wide Napster : the early days …

10 10 Architectural view Mediated P2PPure P2PHybrid P2P Napster Audiogalaxy Early Gnutella FreeNet Gnutella FastTrack Kazaa

11 11 P2P-architectures mediatedpurehybrid data traffic P2P control traffic client-serverP2P local : client-server long distance : P2P efficiency+ efficient search + efficient control - inefficient search - BW consuming +/- scalability - control hot spot (mirrors needed ?) - BW needed grows rapidly good compromise robustness- single point of failure - easy to attack + graceful degradation + difficult to attack ? accountabilityeasydifficult

12 12 Application view According to application area content sharing instant messaging collaborative working distributed computing

13 13 P2P taxonomy content sharing distributed computing instant messaging collaborative working mediatedpurehybrid

14 14 File Sharing performance 150 M searches/day1.6 M downloads/day 10 TB data transfer/day1-2 TB data transfer/day 100 servers 15000 servers

15 15 Distributed computing performance 10 tapes/week, 350 GB  10 000 0.3 MB work units 35 GB/tape 16 hours recorded data SETI =“Search for extraterrestrial Intelligence” started in 1998 as a 2 year project (but still running) 4 M users signed up so far Radio telescope data sent to clients for digital signal analysis Nodes process data when cycles are available (works as screen saver) Using resources to allow better signal analysis

16 16 Distributed computing performance computations per work unit3.1x10 12 FP-operations work unit throughput700 000/day 22x10 17 FLOP/day >25 TFLOPS ASCI White@DoE SETI@home Processing25 TFLOPS 12.3 TFLOPS Cost1 M USD110 M USD

17 17 Scaling problems Mechanisms in GNUTELLA to limit traffic Network horizon set by TTL Descriptor ID’s avoid cyclic routing PONG/QueryHIT/Push NOT flooded BUT... “1 Gnutella request would cause 90MB data traffic on Napster scale network”

18 18 Scaling answers 1. Reduce network horizon to reduce f 2. Use of reflectors = node with high BW available - mimics peer sharing all files of its “clients” 3. Use of UltraPeers = same principle as reflector, but chosen dynamically low access BW high BW access handles all PING/PONG QUERY/QUERYHIT Traffic handle ONLY download traffic

19 19 Robustness self-organization leads to power-law networks (1% of servents shows server-like behaviour …) very robust to random node failure more vulnerable to targeted attacks Simulation result for FreeNet peers [T. Hong, “Performance”, Chapter 14 in “Peer-to-peer : Harnessing the Benefits of a Disruptive Technology”, ISBN 0-596-00110-X, O’Reilly, March 2001.]

20 20 Free-riding on Gnutella Network size since Jan 2002 - only 30 % of nodes offering content - 50% of queries satisfied by 1% of servents []

21 21 Overlay mismatch Mismatch between application layer network and physical network 40% Gnutella clients belong to top 10% AS only 2-5% links within AS based on domain names based on network traffic analysis Gnutella’s clustering logic shows no/little correlation with domain name based clustering [M. Ripeanu, A. Iamnichi, I. Foster, “Mapping the Gnutella Network”, IEEE Internet Computing, January-February 2002.]

22 22 Business Models How to monetise P2P ? authors agree on “P2P business models are unclear” reality : few companies make money on P2P current situation : File sharing application sponsored by advertisement (banners) some other possibilities micropayment mechanisms indirect mechanisms (P2P will increase BW-need and hence …) tip based strategy (cf. US-model …) make “low”-quality content available to get people interested in specific content make use of end users devices to reduce cost !

23 23 Problems/issues/barriers/challenges Problems Solutions node/link transient nature robustness scalability bandwidth consumption Network discontinuities (firewalls, (dynamic) NAT) File-sharing : content redundancy Cycle-sharing : checkpointing Hybrid approach Avoid floodings (e.g. FreeNet : intelligent routing) Content/Query caching TTL Avoid routing cycles (Ab)use of port 80 Rendez-vous servers

24 24 Problems/issues/barriers/challenges Problems Solutions application redesign free-riding accountability asymmetric bandwidth in access (ADSL, HFC) inefficient overlay P2P-frameworksmicro-payment combine uplink capacity (e-donkey) Network/infrastructure aware routing Privacy/trust Anonymity Encryption techniques (e.g. FreeNet : plausible deniability for node operators) business models ? ???

25 25 P2P-trends emergence of platforms convergence between Grid-computing and P2P-technology enhance P2P-performance semantic searches (Tapestry, Content Addressable Networks …) Query/result caching

26 26 Platform emergence File sharing Application areas Distributed computing Instant Messaging Collaboration for 1 application area non-generic 1 application class 1 specific problem network interoperability ? Freenet Gnutella Groove SETI@home eDonkey offer generic services support the P2P paradigm used to build P2P applications ? ? ? ? ? ? Dedicated Application Programs and Protocols Platforms Frameworks

27 27 JXTA developed by Sun Microsystems set of 6 XML based open protocols Java API offered Applications Services Core Security Peer GroupsPeer PipesPeer Monitoring JXTA Community Services Sun JXTA Services Peer Commands JXTA Shell Sun JXTA Applications JXTA Community Applications peer establishment communication management routing indexing searching file sharing e-mail auctioning data storage []

28 28 BOINC Berkeley Open Infrastructure for Network Computing allows participants to participate to solve selected problems = “generic SETI@Home” []

29 29 Conclusions For network operators P2P applications can be very BW-consuming extremely popular (and addictive) use of inefficient strategies (broadcast, flooding, …) “tragedy of the commons” Danger for Bottlenecks overlay network has little relation to physical infrastructure symmetric relations between peers Change in user behaviour “always” online information provider AND information consumer

30 30 Conclusions For application developers People are willing to share resources for free (and even want to spend money …) make people feel they participate in a large project give some credit to users (competition) (top 10 list, eternal fame if solution is found, …) To avoid digging ones own grave avoid BW-consuming strategies include micropayment/trust mechanisms as - encouragement to participate - avoid free-riding - avoid DoS attacks People are (extremely) interested in digital content

31 31 Conclusions For application developers Hacker danger need for encryption mechanisms High performance P2P-platforms are emergent reuse of efforts reuse of user community Make sure your application has some scaling effect the more users, the more interesting to join !

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