CS 162: P2P Networks Computer Science Division Department of Electrical Engineering and Computer Sciences University of California, Berkeley Berkeley, CA 94720-1776

Main Challenge Find where a particular file is stored Note: problem similar to finding a particular page in web caching (see last lecture – what are the differences?) E F D E? C A B

Other Challenges Scale: up to hundred of thousands or millions of machines Dynamicity: machines can come and go any time

Napster Assume a centralized index system that maps files (songs) to machines that are alive How to find a file (song) Query the index system  return a machine that stores the required file Ideally this is the closest/least-loaded machine ftp the file Advantages: Simplicity, easy to implement sophisticated search engines on top of the index system Disadvantages: Robustness, scalability (?)

Napster: Example E m5 E m6 E? F D m1 A m2 B m3 C m4 D m5 E m6 F m4 E?

Gnutella Distribute file location Idea: broadcast the request Hot to find a file: Send request to all neighbors Neighbors recursively multicast the request Eventually a machine that has the file receives the request, and it sends back the answer Advantages: Totally decentralized, highly robust Disadvantages: Not scalable; the entire network can be swamped with requests (to alleviate this problem, each request has a TTL)

Gnutella: Example Assume: m1’s neighbors are m2 and m3; m3’s neighbors are m4 and m5;… m5 E m6 E E? F D m4 E? C A B m3 m1 m2

Two-Level Hierarchy Oct 2003 Crawl on Gnutella Current Gnutella implementation, KaZaa Leaf nodes are connected to a small number of ultrapeers (suppernodes) Query A leaf sends query to its ultrapeers If ultrapeers don’t know the answer, they flood the query to other ultrapeers More scalable: Flooding only among ultrapeers Ultrapeer nodes Leaf nodes

Skype Peer-to-peer Internet Telephony Two-level hierarchy like KaZaa login server Peer-to-peer Internet Telephony Two-level hierarchy like KaZaa Ultrapeers used mainly to route traffic between NATed end-hosts (see next slide)… … plus a login server to authenticate users ensure that names are unique across network B Messages exchanged to login server A Data traffic (Note*: probable protocol; Skype protocol is not published)

Detour: NAT (1/3) Internet Network Address Translation: Motivation: address scarcity problem in IPv4 Allow to independently allocate addresses to hosts behind NAT Two hosts behind two different NATs can have the same address 64.36.12.64 Internet 192.168.0.1 NAT box 169.32.41.10 NAT box 192.168.0.1 192.168.0.2 128.2.12.30 Same address

Detour: NAT (2/3) Main idea: use port numbers to multiplex/demultiplex connections of NATed end-hosts Map (IPaddr, Port) of a NATed host to (IPaddrNAT, PortNAT) (192.168.0.1:64.36.12.64)(1005:80) src addr dst addr src port dst port 1 192.168.0.1 5 (169.32.41.10:64.36.12.64)(78:80) 3 64.36.12.64 (64.36.12.64:192.168.0.1)(80:1005) Internet NAT box (64.36.12.64:169.32.41.10)(80:78) 4 169.32.41.10 (192.168.0.1:1005) ↔ 78 … 2 NAT Table

Detour: NAT (3/3) Limitations Skype and other P2P systems use Number of machines behind a NAT <= 64000. Why? A host outside NAT cannot initiate connection to a host behind a NAT Skype and other P2P systems use Login servers and ultrapeers to solve limitation (2) How? (Hint: ultrapeers have globally unique (Internet-routable) IP addresses)

BitTorrent (1/2) Allow fast downloads even when sources have low connectivity How does it work? Split each file into pieces (~ 256 KB each), and each piece into sub-pieces (~ 16 KB each) The loader loads one piece at a time Within one piece, the loader can load up to five sub-pieces in parallel

BitTorrent (2/2) Download consists of three phases: Start: get a piece as soon as possible Select a random piece Middle: spread all pieces as soon as possible Select rarest piece next End: avoid getting stuck with a slow source, when downloading the last sub-pieces Request in parallel the same sub-piece Cancel slowest downloads once a sub-piece has been received (For details see: http://bittorrent.com/bittorrentecon.pdf)

Distributed Hash Tables Problem: Given an ID, map to a host Challenges Scalability: hundreds of thousands or millions of machines Instability Changes in routes, congestion, availability of machines Heterogeneity Latency: 1ms to 1000ms Bandwidth: 32Kb/s to 100Mb/s Nodes stay in system from 10s to a year Trust Selfish users Malicious users

Content Addressable Network (CAN) Associate to each node and item a unique id in an d-dimensional space Properties Routing table size O(d) Guarantees that a file is found in at most d*n1/d steps, where n is the total number of nodes

CAN Example: Two Dimensional Space Space divided between nodes All nodes cover the entire space Each node covers either a square or a rectangular area of ratios 1:2 or 2:1 Example: Assume space size (8 x 8) Node n1:(1, 2) first node that joins  cover the entire space 7 6 5 4 3 n1 2 1 1 2 3 4 5 6 7

CAN Example: Two Dimensional Space Node n2:(4, 2) joins  space is divided between n1 and n2 7 6 5 4 3 n1 n2 2 1 1 2 3 4 5 6 7

CAN Example: Two Dimensional Space Node n2:(4, 2) joins  space is divided between n1 and n2 7 6 n3 5 4 3 n1 n2 2 1 1 2 3 4 5 6 7

CAN Example: Two Dimensional Space Nodes n4:(5, 5) and n5:(6,6) join 7 6 n5 n4 n3 5 4 3 n1 n2 2 1 1 2 3 4 5 6 7

CAN Example: Two Dimensional Space Nodes: n1:(1, 2); n2:(4,2); n3:(3, 5); n4:(5,5);n5:(6,6) Items: f1:(2,3); f2:(5,1); f3:(2,1); f4:(7,5); 7 6 n5 n4 n3 5 f4 4 f1 3 n1 n2 2 f3 1 f2 1 2 3 4 5 6 7

CAN Example: Two Dimensional Space Each item is stored by the node who owns its mapping in the space 7 6 n5 n4 n3 5 f4 4 f1 3 n1 n2 2 f3 1 f2 1 2 3 4 5 6 7

CAN: Query Example Each node knows its neighbors in the d-space Forward query to the neighbor that is closest to the query id Example: assume n1 queries f4 7 6 n5 n4 n3 5 f4 4 f1 3 n1 n2 2 f3 1 f2 1 2 3 4 5 6 7

Chord Associate to each node and item a unique ID in an uni-dimensional space Properties Routing table size O(log(N)) , where N is the total number of nodes Guarantees that a file is found in O(log(N)) steps

Data Structure Assume identifier space is 0..2m Each node maintains Finger table Entry i in the finger table of n is the first node that succeeds or equals n + 2i Predecessor node An item identified by id is stored on the succesor node of id

Chord Example Assume an identifier space 0..8 Node n1:(1) joinsall entries in its finger table are initialized to itself Succ. Table i id+2i succ 0 2 1 1 3 1 2 5 1 1 7 6 2 5 3 4

Chord Example Node n2:(3) joins 1 7 6 2 5 3 4 Succ. Table i id+2i succ i id+2i succ 0 2 2 1 3 1 2 5 1 1 7 6 2 Succ. Table i id+2i succ 0 3 1 1 4 1 2 6 1 5 3 4

Chord Example Nodes n3:(0), n4:(6) join 1 7 6 2 5 3 4 Succ. Table i id+2i succ 0 1 1 1 2 2 2 4 0 Succ. Table i id+2i succ 0 2 2 1 3 6 2 5 6 1 7 Succ. Table i id+2i succ 0 7 0 1 0 0 2 2 2 6 2 Succ. Table i id+2i succ 0 3 6 1 4 6 2 6 6 5 3 4

Chord Examples Nodes: n1:(1), n2(3), n3(0), n4(6) Succ. Table Items Nodes: n1:(1), n2(3), n3(0), n4(6) Items: f1:(7), f2:(2) i id+2i succ 0 1 1 1 2 2 2 4 0 7 Succ. Table Items 1 7 i id+2i succ 0 2 2 1 3 6 2 5 6 1 Succ. Table 6 2 i id+2i succ 0 7 0 1 0 0 2 2 2 Succ. Table i id+2i succ 0 3 6 1 4 6 2 6 6 5 3 4

Query Upon receiving a query for item id, a node Check whether stores the item locally If not, forwards the query to the largest node in its successor table that does not exceed id Succ. Table Items i id+2i succ 0 1 1 1 2 2 2 4 0 7 Succ. Table Items 1 7 i id+2i succ 0 2 2 1 3 6 2 5 6 1 query(7) Succ. Table 6 2 i id+2i succ 0 7 0 1 0 0 2 2 2 Succ. Table i id+2i succ 0 3 6 1 4 6 2 6 6 5 3 4

Discussion Query can be implemented Iteratively Recursively Performance: routing in the overlay network can be more expensive than in the underlying network Because usually there is no correlation between node IDs and their locality; a query can repeatedly jump from Europe to North America, though both the initiator and the node that store the item are in Europe! Solutions: Tapestry takes care of this implicitly; CAN and Chord maintain multiple copies for each entry in their routing tables and choose the closest in terms of network distance

Discussion Robustness Security Maintain multiple copies associated to each entry in the routing tables Replicate an item on nodes with close ids in the identifier space Security Can be build on top of CAN, Chord, Tapestry, and Pastry

Discussion The key challenge of building wide area P2P systems is a scalable and robust location service Naptser: centralized solution Guarantee correctness and support approximate matching… …but neither scalable nor robust Gnutella, KaZaa Support approximate queries, scalable, and robust… …but doesn’t guarantee correctness (i.e., it may fail to locate an existing file) Distributed Hash Tables Guarantee correctness, highly scalable and robust… … but difficult to implement approximate matching