Distributed Data Management. Christoph Lofi Institut für Informationssysteme Technische Universität Braunschweig

Transcription

1 Distributed Data Management Christoph Lofi Institut für Informationssysteme Technische Universität Braunschweig

2 8.0 Content Provisioning 8.0 Content Distribution 8.1 Swarming 8.2 BitTorrent 8.3 Anonymous P2P Distributed Data Management Christoph Lofi IfIS TU Braunschweig 2

3 7 Network Models Last week, we covered three network models Random Graphs Distributed Data Management Christoph Lofi IfIS TU Braunschweig 3

4 7 Network Models Small World Graphs Distributed Data Management Christoph Lofi IfIS TU Braunschweig 4

5 7 Network Models Scale Free Graphs Distributed Data Management Christoph Lofi IfIS TU Braunschweig 5

6 7 Network Models The network structure of a peer-to-peer system influences: average necessary number of hops (path length) possibility of greedy, decentralized routing algorithms stability against random failures sensitivity against attacks redundancy of routing table entries (edges) many other properties of the system build onto this network Important measures of a network structure are: average path length clustering coefficient the degree distribution Influence the edge generation rules such that a network structure arises showing the desired properties Distributed Data Management Christoph Lofi IfIS TU Braunschweig 6

7 8.1 Swarming Sometimes large amounts of data have to be distributed over networks Software updates, video on demand, etc. Early approaches: Napster, Gnutella, Fasttrack, Kaazaa, Use P2P network to locate a node offering the requested content Download whole file from a single peer If download fails: repeat search, resume download from alternative source Distributed Data Management Christoph Lofi IfIS TU Braunschweig 7

8 8.1 Swarming Issues Poor performance due to asymmetric uplink/downlink bandwidth Most common network home network connection technology: ADSL Asynchronous Digital Subscriber Line e.g. ADSL kb/sec download, 1024 kb/sec upload No load distribution Popular files may have extremely low download speed due to congestion of the offering node Low reliability (except for small files) Connected glitches may severely hamper download Frequent re-connects and resumes necessary Distributed Data Management Christoph Lofi IfIS TU Braunschweig 8

9 8.1 Swarming Idea: Chunks Split large files into small chunks Assign hash values to chunks Identification: simple and deterministic labeling of chunks Transfer Protection: download chunk and compute hash Compare computed hash with hash provided by offering peer If comparison fails, a transfer error occurred reload chunk Original File 0x9A3C 0x7C23 0x194F 0xDE6A Distributed Data Management Christoph Lofi IfIS TU Braunschweig 9

10 8.1 Swarming Parallelization Locate the swarm of all peers hosting the required file (and thus the required chunks) Download different chunks from different sources simultaneously Utilize upload capacity of multiple sources Overall download speed may thus exceed upload capabilities of individual sources Usually, upload capacity is the bottleneck due to asymmetrical connections Sources: Destination: Distributed Data Management Christoph Lofi IfIS TU Braunschweig 10

11 8.1 Swarming So called swarming strategy Download chunks in parallel from a swarm of peers Swarming Advantages Peer failures: no loss of files, only chunks Discord unfinished chunks and download them anew No complicated resume mechanism necessary Increased throughput Download chunks in parallel from different sources Distributed Data Management Christoph Lofi IfIS TU Braunschweig 11

12 8.1 Swarming Swarming Issues Chunk selection: in which order should chunks be requested from which peer Avoid scarcity Best overall availability? Fairness: how can the protocol ensure fair usage of bandwidth Avoid free-riding: all peers should contribute to the networks Bandwidth allocation: single pairs should not be overwhelmed by request while others are idling Systems implementing swarming BitTorrent Avalanche Distributed Data Management Christoph Lofi IfIS TU Braunschweig 12

13 8.2 BitTorrent BitTorrent Torrent = big stream Author: Bram Cohen, 2001 Protocol for swarming file distribution, no search features Designed for Protocol to quickly and decentrally distribute large content Distributed Data Management Christoph Lofi IfIS TU Braunschweig 13

14 8.2 BitTorrent Implements swarming strategy for content distribution Especially suited for flash crowds, i.e. content which is high in demand for a short period of time Central components Web server for search (torrent site) Classic web server maintains list of available content (so called torrents ) Provides search functionalities Content is represented as a torrent file containing required meta data e.g. address of the tracker Tracker for peer coordination A tracker is a centralized service maintaining the peer swarms i.e. which peers have which chunks of which torrents Distributed Data Management Christoph Lofi IfIS TU Braunschweig 14

15 8.2 BitTorrent Workflow for download A user uses a torrent site to obtain a torrent file Torrent file contains content meta data The user s node connects to the responsible tracker Tracker URL and content identifiers ( info hash ) are provided by torrent file Node registers itself with the tracker and the corresponding torrent i.e. joins the swarm Node obtains a list of all pairs offering the torrent Contact some swarm peers Obtain a list of available chunks Download chunks from peers Distributed Data Management Christoph Lofi IfIS TU Braunschweig 15

16 8.2 BitTorrent Each offered file is split in chunks between 32KB and 4MB each Each chunk is identified by an 160-Bit SHA-Hash With respect to a certain torrent, each peer can fulfill one of the following roles Seeders Have all chunks of the torrent and are actively seeding (uploading) those chunks to the swarm Leechers Do not have all chunks of a torrent Download missing chunks from other leechers or seeders Upload chunks to other leecher There is no download-only role Distributed Data Management Christoph Lofi IfIS TU Braunschweig 16

17 8.2 BitTorrent Torrent File metadata structure Describes the files in the torrent URL of tracker File name File length Piece length SHA-1 hashes of pieces Allow peers to verify integrity Creation date An info hash is created from some fields of the torrent file This hash uniquely identifies a torrent Distributed Data Management Christoph Lofi IfIS TU Braunschweig 17

18 8.2 BitTorrent Fields being hashed in InfoHash Distributed Data Management Christoph Lofi IfIS TU Braunschweig 18

19 8.2 BitTorrent torrent site tracker new leecher peer other swarm peers torrent file register swarm peer list request info chunk information request chunk send chunk Distributed Data Management Christoph Lofi IfIS TU Braunschweig 19

20 8.2 BitTorrent :-) Distributed Data Management Christoph Lofi IfIS TU Braunschweig 20

21 8.2 BitTorrent Which chunk next? Priority Actives Finish active chunks Rarest First Improves availability of rare chunks Delays download of common chunks Random First Chunk Get first chunk quickly (rarest chunk probably slow to get) Endgame Mode Send requests for last chunks to all known peers End of download not stalled by slow peers Distributed Data Management Christoph Lofi IfIS TU Braunschweig 21

22 Game Theory Basic Ideas of Game Theory Game theory offers a general theory of strategic behavior Described in mathematical form Situations in which players may choose different actions to maximize their returns Situations in which strategic interactions among rational players produce outcomes with respect to the players preferences The outcomes might not have been intended by any of them Plays an important role in Modern economics Decision theory Multi-agent systems Distributed Data Management Christoph Lofi IfIS TU Braunschweig 22

23 Game Theory Early game theory tries to explain the optimal strategy in two-person interactions. von Neumann and Morgenstern, 1944 Initially: zero-sum games Expected utility hypothesis Players will rationally decide for the option with the highest expected outcome Distributed Data Management Christoph Lofi IfIS TU Braunschweig 23

24 Game Theory John Nash Works in game theory and differential geometry Non-zero-sum games Nash equilibrium 1950 Strategic equilibrium in which no player gains any advantage when changing strategies (while knowing the opponents strategy) 1994 Nobel Prize in Economics Harsanyi and Selten Incomplete information Also 1994 Nobel Prize in Economics Distributed Data Management Christoph Lofi IfIS TU Braunschweig 24

25 Game Theory Games Situations are treated as games. Rules The rules of the game state which actions and decisions are possible Player's Strategies Plan for actions in each possible situation in the game Player's Payoffs Is a players expected gain or loss when winning or loosing in a particular situation Dominant Strategy If players best strategy doesn t depend on what other players do Distributed Data Management Christoph Lofi IfIS TU Braunschweig 25

26 Game Theory Famous example: Prisoners Dilemma A and B are arrested by the police during a robbery They are interrogated in separate cells Unable to communicate with each other Following conditions are known If they both resist interrogation and proclaim their mutual innocence, they both will get off with a three year sentence for robbery If one of them confesses to the entire string of robberies and the other does not, the confessor will be rewarded with a light, one year sentence and while the other will get a severe eight year sentence If they both confess, then the judge will sentence both to a moderate four years in prison Distributed Data Management Christoph Lofi IfIS TU Braunschweig 26

27 Game Theory Prisoner Dilemma Possible outcomes A - Confession A - No Confession B - Confession 4 years each 8 years for A 1 year for B B - No Confession 1 year for A 8 years for B 3 years each Distributed Data Management Christoph Lofi IfIS TU Braunschweig 27

28 Game Theory Decision Tree of A A B Confesses B does not confesses A: Confess A: Does Not Confess A: Confess A: Does Not Confess 4 Years in Prison 8 Years in Prison 1 Year in Prison 3 Years in Prison Best strategy The dominant strategy for A is to confess No matter what B does, confessing is better choice Nash equilibrium: both A and B will confess Also, dominant strategy of B is to confess Best strategy Distributed Data Management Christoph Lofi IfIS TU Braunschweig 28

29 Game Theory A repeated game Game that the same players play more than once Differ from one-shot games because people's current actions can depend on the past behavior of other players. Cooperation is encouraged Book recommendation Thinking strategically by A.Dixit and B Nalebuff German translation: Spieltheorie für Einsteiger Distributed Data Management Christoph Lofi IfIS TU Braunschweig 29

30 8.2 BitTorrent We can employ game theory for designing a swarming protocol Swarming Game Decisions While seeding, who should get the chunks? While leeching, from whom to download chunks? Goal Available resources should be optimally used Free-riding should be prevented Distributed Data Management Christoph Lofi IfIS TU Braunschweig 30

31 8.2 BitTorrent Possible strategy in repeated games: Tit for Tat An player using this strategy will initially cooperate Player will adapt to opponent If the opponent previously was cooperative, the agent is cooperative. If not, the agent is not. Depends on four conditions Unless provoked, the agent will always cooperate If provoked, the agent will retaliate The agent is quick to forgive The agent must have a good chance of competing against the opponent more than once Get-to-know each other Distributed Data Management Christoph Lofi IfIS TU Braunschweig 31

32 8.2 BitTorrent BitTorrent uses a so-called choking mechanism for distributing chunks Base idea Prefer uploading chunks to peers which also offered chunks for download i.e. aim for bi-directional communication channels Bi-directional communication will benefit the whole swarm most Tit-for-tat Punish peers which seem to be free-riding i.e. who only download but provide no upload Distributed Data Management Christoph Lofi IfIS TU Braunschweig 32

33 8.2 BitTorrent Choking as leecher Open a bi-directional transfer to another leecher Mutually exchange missing chunks If a peer does not upload any chunks for more than a minute, choke it Temporary refuse to upload Downloading continues as usual TCP Connection is kept open No Setup costs TCP congestion control Distributed Data Management Christoph Lofi IfIS TU Braunschweig 33

34 8.2 BitTorrent Using only this choking mechanism may endanger the health of the swarm New leechers will automatically be choked because they cannot offer upload Two nodes which would have a good connection won t use it as no node takes initiative A node may be choked by all other nodes due to unlucky circumstances / network weaknesses Distributed Data Management Christoph Lofi IfIS TU Braunschweig 34

35 8.2 BitTorrent Solution: Optimistic Unchoking Randomly initiate a new connection to a currently unconnected leecher in the swarm and start uploading Take initiative Hope for a good cooperation e.g. that this new node provides a high upload rate in the bi-directional transfer Allows finding better peers Allows new peers to integrate themselves in the swarm Other peers voluntarily start uploading to them Randomly unchoke some currently choked connections Quick to forgive Helps locked-out peers to return to the swarm If a leecher is currently choked by all its peers, it initiates even more unchoking connection Anti-Snubbing Distributed Data Management Christoph Lofi IfIS TU Braunschweig 35

36 8.2 BitTorrent Open issue: upload-only choking Once a download is complete, no bi-directional transfer connections are required anymore by that peer Peer becomes a seeder Which nodes to upload to? Seeding Policy Upload to those peers with the best upload / download ratio Probable Advantages Ensures that chunks are replicated faster within the swarm Leechers that have a good upload rates are probably not being served by others But: Upload / Dowload ratio hard to determine Central bookkeeping? Bookeeping own ratio? What about cheating? Distributed Data Management Christoph Lofi IfIS TU Braunschweig 36

37 8.2 BitTorrent Download chunks in parallel Look for the rarest pieces Verify each chunk by checking hash, download again if hash fails Advertise received pieces to all connected peers leecher B leecher A I have! seed leecher C Distributed Data Management Christoph Lofi IfIS TU Braunschweig 37

38 8.2 BitTorrent Periodically calculate data-receiving rates Upload to (unchoke) the fastest downloaders Optimistic unchoking Periodically select a peer at random and upload to it Continuously look for the fastest partners leecher B leecher A seed leecher D leecher C Distributed Data Management Christoph Lofi IfIS TU Braunschweig 38

39 8.2 BitTorrent Remember: Bit Torrent uses centralized trackers for managing peer lists Tracker Issues Single Point of Failure and Attack Scalability PirateBay tracker nearly overloaded (>5 Mio. Peers) Solution: Decentralized Tracker Replace with DHT (Kademlia) Does not tackle distributed search Distributed Data Management Christoph Lofi IfIS TU Braunschweig 39

40 8.2 BitTorrent Kademlia DHT Tracker Each torrent is identified by its infohash All BitTorrent nodes using an compatible client may join the DHT tracker Not part of the core BitTorrent protocol Authors of client usually also provide bootstrapping nodes to the DHT tracker The DHT takes over the trackers responsibility DHT Key-Value pairs Key: info hash Value: swarm peer listing Distributed Data Management Christoph Lofi IfIS TU Braunschweig 40

41 8.2 BitTorrent Kademlia (as a generic DHT protocol) Kademlia also uses a SHA Bit addressed ring hashing data and nodes Similar to pastry, but uses a more sophisticated routing mechanism requiring less maintenance Each key-value pair is stored redundantly on multiple nodes Usually k nodes neighboring the node which is responsible for the range of the infohash Nodes storing a certain key frequently synchronize their data with the other responsible peers Peer arrivals and departures can be tolerated without data loss Distributed Data Management Christoph Lofi IfIS TU Braunschweig 41

42 8.2 BitTorrent BitTorrent DHT: a new node joins Obtain the torrent infohash e.g. from torrent file or from a magnet link Contact a bootstrap node and join the DHT tracker Take over responsibility for a certain range of torrents i.e. host some (redundant) peer listings for some torrent swarms New node announces itself i.e. contacts some nodes hosting the peer lists for the required torrent The new node is added to the respective peer listing The node obtains the full peer list No central authority / tracker is required Distributed Data Management Christoph Lofi IfIS TU Braunschweig 42

43 8.2 BitTorrent Peer Exchange (PEX) and Multi-Tracker To further increase the performance and resilience of a torrent, multiple trackers can be used Easiest case Torrent is registered with multiple trackers which are all explicitly specified in the torrent file More complex solution: Peer Exchange Start connecting to any one tracker Ask other connected peers in the swarm for additional peers and / or trackers Distributed Data Management Christoph Lofi IfIS TU Braunschweig 43

44 8.2 BitTorrent PEX Example Obtain a torrent and connect to tracker i.e. official Ubuntu torrent providing two trackers Obtain peer lists Additionally, connect to DHT tracker Obtain even more peers Peers using the same torrent but are trackerless or use DHT and another tracker Start peer exchange Again, obtain more peers i.e. receive peers not using DHT from a DHT node which is using, e.g. the openbittorrent-tracker Distributed Data Management Christoph Lofi IfIS TU Braunschweig 44

45 Client Example μtorrent 45

46 Client Example μtorrent Distributed Data Management Christoph Lofi IfIS TU Braunschweig 46

47 Client Example μtorrent 47

48 Tracker Peer cache IP, port, peer id State information Completed Downloading Clients report status periodically to tracker Returns random list 50 random leechers/seeds Client first contacts of them and more if some do not respond Distributed Data Management Christoph Lofi IfIS TU Braunschweig 48

49 Tracker Info Hashes Info Hashes Distributed Data Management Christoph Lofi IfIS TU Braunschweig 49

50 Magnet Links Recently, magnet links have become quite popular Magnet links define an URI scheme for any content located in a P2P network May encode all data necessary to identify or find content, e.g. Protocol, Name, Size, Protocol-Specific metadata, etc Distributed Data Management Christoph Lofi IfIS TU Braunschweig 50

51 Magnet Links Example magnet link for BitTorrent magnet:?xt=urn:btih:3e16157f0879eb43e9e51f45 d485feff90a77283 &dn=ubuntu lts+x32 &tr=http%3a%2f%2ftracker.openbittorrent.com %2Fannounce Display Name exact Topic BitTorrent InfoHash InfoHash TRacker URL Other protocols might include search keywords, web sources, bootstrap nodes, etc Distributed Data Management Christoph Lofi IfIS TU Braunschweig 51

52 8.3 Anonymous P2P BitTorrent is provides absolutely no anonymity Peer list of a torrent contain all participating nodes IP address, port, sometimes upload/download ratio, etc. Thus, it is very easy to identify all people downloading / uploading to a torrent User behavior can be tracked quite easily by simply introducing a spy node into the swarm Privacy implication Also, no pure downloading is feasible in BitTorrent as download-only nodes will be choked Possibly legal implication for copyrighted content Distributed Data Management Christoph Lofi IfIS TU Braunschweig 52

53 8.3 Anonymous P2P Real anonymity is hard to archive Nodes need to communicate and contact each other Identities (addresses must be known) However, the number of nodes knowing a nodes identity can be limited to only trusted nodes So-called dark nets Base idea Only connect to a few trusted friend nodes Never communicate directly with a non-friend Friends forward any message anonymously to their friends If network is designed correctly, most parts should be reachable via friend-of-a-friend routing Distributed Data Management Christoph Lofi IfIS TU Braunschweig 53

54 8.3 Anonymous P2P Two notable systems Freenet: Pure P2P network using small worldproperties and anonymous routing OneSwarm: BitTorrent extension based on friend-to-friend-routing Friend-To-Friend Routing e.g. B passes a message from A to C C does not know that message originated from A A does not know that B passes message to C A requester B C provider Distributed Data Management Christoph Lofi IfIS TU Braunschweig 54

55 8.3 Anonymous P2P If the request s time-to-live expires or a node does not have neighbors to send the file to, a backtracking request failed message is sent If the request is successful, the file is sent back via the routing nodes and each node saves the file and adds the sending node s address to its local routing table i.e., frequently requested files are replicated If the routing table is full, the least recently used (LRU) entry is evicted Distributed Data Management Christoph Lofi IfIS TU Braunschweig 55

56 8.3 Anonymous P2P If the request s time-to-live expires or a node does not have neighbors to send the file to, a backtracking request failed message is sent If the request is successful, the file is sent back via the routing nodes and each node saves the file and adds the sending node s address to its local routing table i.e., frequently requested files are replicated If the routing table is full, the least recently used (LRU) entry is evicted Distributed Data Management Christoph Lofi IfIS TU Braunschweig 56

57 8.3 Anonymous P2P Example of Freenet Routing B s routing table Key Pointer D s routing table 6 C Key Pointer 15 D 9 F? key=9 A 9 F 9? 9 B 9? 9? Sorry! 9 1 E D 9? 9 F key = 9 C s routing table empty C E Distributed Data Management Christoph Lofi IfIS TU Braunschweig 57

58 Content Provisioning Swarming & BitTorrent Segment a file into multiple chunks Download chunks from multiple peers in parallel Seeder and leecher peers form a swarm Increased throughput Faster dissemination of new content i.e. for countering flash crowds Main question: which chunks should be downloaded / uploaded to best benefit the whole swarm? Prevent free-riding Discourage parasitic behavior Reward cooperation Distributed Data Management Christoph Lofi IfIS TU Braunschweig 58

59 Content Provisioning Solution: use concepts from game theory Tit-for-tat strategy Encourages strong bi-directional links among leecher Choking If a node in a bidirectional pipe is not cooperative (provides upload bandwidth), choke it by refusing further uploads to that peer Optimistic Unchoke Randomly unchoke some choked connections Take initiative and voluntarily upload to an unconnected node May discover better and more reliable partners Distributed Data Management Christoph Lofi IfIS TU Braunschweig 59

60 Next Lecture Load Balancing & Data Durability Data caching, replication, etc. P2P and Databases Building database link systems on top of P2P Toward cloud storage Distributed Data Management Christoph Lofi IfIS TU Braunschweig 60