Chord: A Scalable Peertopeer Lookup Service for Internet Applications

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Chord A Scalable Peer-to-peer Lookup Service for
Internet Applications

• Robert Morris
• Ion Stoica, David Karger,
• M. Frans Kaashoek, Hari Balakrishnan
• MIT and Berkeley

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A peer-to-peer storage problem

• 1000 scattered music enthusiasts
• Willing to store and serve replicas
• How do you find the data?

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The lookup problem
N2
N1
N3
Internet
Keytitle ValueMP3 data
?
Client
Publisher
Lookup(title)
N6
N4
N5
4
Centralized lookup (Napster)
N2
N1
SetLoc(title, N4)
N3
Client
DB
N4
Publisher_at_
Lookup(title)
Keytitle ValueMP3 data
N8
N9
N7
N6
Simple, but O(N) state and a single point of
failure
5
Flooded queries (Gnutella)
N2
N1
Lookup(title)
N3
Client
N4
Publisher_at_
Keytitle ValueMP3 data
N6
N8
N7
N9
Robust, but worst case O(N) messages per lookup
6
Routed queries (Freenet, Chord, etc.)
N2
N1
N3
Client
N4
Lookup(title)
Publisher
Keytitle ValueMP3 data
N6
N8
N7
N9
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Structured Overlay Networks / DHTs
Main representativesChord, Pastry, Tapestry,
CAN, Kademlia, P-Grid, Viceroy
Set of Nodes
Keys of Nodes
Common Identifier Space
Hashing
ConnectThe nodes Smartly
Set of Values/Items
Keys of Values
Keys of Values
Hashing

Node IdentifierValue Identifier
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The Principle Of Distributed Hash Tables

• A dynamic distribution of a hash table onto a set
  of cooperating nodes

• Basic service lookup operation
• Key resolution from any node

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Routing challenges

• Define a useful key nearness metric
• Keep the hop count small
• Keep the tables small
• Stay robust despite rapid change
• Freenet emphasizes anonymity
• Chord emphasizes efficiency and simplicity

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Chord properties

• Efficient O(log(N)) messages per lookup
• N is the total number of servers
• Scalable O(log(N)) state per node
• Robust survives massive failures
• Proofs are in paper / tech report
• Assuming no malicious participants

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Chord overview

• Provides peer-to-peer hash lookup
• Lookup(key) ? IP address
• Chord does not store the data
• How does Chord route lookups?
• How does Chord maintain routing tables?

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Chord IDs

• Key identifier SHA-1(key)
• Node identifier SHA-1(IP address)
• Both are uniformly distributed
• Both exist in the same ID space
• How to map key IDs to node IDs?

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Consistent hashing Karger 97
Key 5
K5
Node 105
N105
K20
Circular 7-bit ID space
N32
N90
K80
A key is stored at its successor node with next
higher ID
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Basic lookup
N120
N10
Where is key 80?
N105
N32
N90 has K80
N90
K80
N60
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Simple lookup algorithm

• Lookup(my-id, key-id)
• n my successor
• if my-id lt n lt key-id
• call Lookup(id) on node n // next hop
• else
• return my successor // done
• Correctness depends only on successors

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Finger table allows log(N)-time lookups
½
¼
1/8
1/16
1/32
1/64
1/128
N80
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Finger i points to successor of n2i
N120
112
½
¼
1/8
1/16
1/32
1/64
1/128
N80
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Lookup with fingers

• Lookup(my-id, key-id)
• look in local finger table for
• highest node n s.t. my-id lt n lt key-id
• if n exists
• call Lookup(id) on node n // next hop
• else
• return my successor // done

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Lookups take O(log(N)) hops
N5
N10
N110
K19
N20
N99
N32
Lookup(K19)
N80
N60
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Another example

• Identifier circle
• Keys assigned to successor
• Evenly distributed keys and nodes
• Finger table logN
• ith finger points to first node that succeeds n
  by at least 2i-1

1
54
8
58
10
14
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• Find
• Map key to node
• Join, Leave, or Failure
• Update the immediate neighbors
• Successor and predecessor
• Stabilize eventually propagate the info
• Reliability
• Log(N) successors data replication

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30
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Chord Key Location

• Lookup in finger table the furthest node that
  precedes key
• Query homes in on target in O(logN) hops

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Problem

• Nodes close on ring can be far in the network.

Figure from http//project-iris.net/talks/dht-to
ronto-03.ppt
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Chord node insertion
Insert node N40
Locate node
Add fingers
Update successor pointers and other nodes
fingers (max in-degree O(log2n) whp)
Time O(log2n) Stabilization protocol for
refreshing links
N40
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Yet another example
0
15
1
14
2
13
3
12
4
11
5
10
6
9
7
8
Nodes
Values
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Chord Routing (1/4)
Get(15)
0
15
1
2
14
13
3
12
4
11
5
10
6
9

• Routing table size M, where N 2M
• Routing entries log2(N)
• log2(N) hops from any node to any other node

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8
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Chord Routing (2/4)
Get(15)
0
15
1
2
14
13
3
12
4
11
5
10
6
9
7
8
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Chord Routing (3/4)
Get(15)
0
15
1
2
14
13
3
12
4
11
5
10
6
9
7
8
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Chord Routing (4/4)
Get(15)
0
15
1

• From node 1, only 3 hops to node 0 where item 15
  is stored
• For 16 nodes, the maximum is log2(16) 4 hops
  between any two nodes

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14
13
3
12
4
11
5
10
6
9
7
8
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Joining linked list insert
N25
N36
1. Lookup(36)
K30 K38
N40
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Join (2)
N25
2. N36 sets its own successor pointer
N36
K30 K38
N40
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Join (3)
N25
3. Copy keys 26..36 from N40 to N36
N36
K30
K30 K38
N40
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Join (4)
N25
4. Set N25s successor pointer
N36
K30
K30 K38
N40
Update finger pointers in the background Correct
successors produce correct lookups
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Failures might cause incorrect lookup
N120
N10
N113
N102
Lookup(90)
N85
N80
N80 doesnt know correct successor, so incorrect
lookup
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Solution successor lists

• Each node knows r immediate successors
• After failure, will know first live successor
• Correct successors guarantee correct lookups
• Guarantee is with some probability

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Choosing the successor list length

• Assume 1/2 of nodes fail
• P(successor list all dead) (1/2)r
• I.e. P(this node breaks the Chord ring)
• Depends on independent failure
• P(no broken nodes) (1 (1/2)r)N
• r 2log(N) makes prob. 1 1/N

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Lookup with fault tolerance

• Lookup(my-id, key-id)
• look in local finger table and successor-list
• for highest node n s.t. my-id lt n lt key-id
• if n exists
• call Lookup(id) on node n // next hop
• if call failed,
• remove n from finger table
• return Lookup(my-id, key-id)
• else return my successor // done

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Chord status

• Working implementation as part of CFS
• Chord library 3,000 lines of C
• Deployed in small Internet testbed
• Includes
• Correct concurrent join/fail
• Proximity-based routing for low delay
• Load control for heterogeneous nodes
• Resistance to spoofed node IDs

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Experimental overview

• Quick lookup in large systems
• Low variation in lookup costs
• Robust despite massive failure
• See paper for more results
• Experiments confirm theoretical results

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Chord lookup cost is O(log N)
Average Messages per Lookup
Number of Nodes
Constant is 1/2
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Failure experimental setup

• Start 1,000 CFS/Chord servers
• Successor list has 20 entries
• Wait until they stabilize
• Insert 1,000 key/value pairs
• Five replicas of each
• Stop X of the servers
• Immediately perform 1,000 lookups

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Massive failures have little impact
(1/2)6 is 1.6
Failed Lookups (Percent)
Failed Nodes (Percent)
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Related Work

• CAN (Ratnasamy, Francis, Handley, Karp, Shenker)
• Pastry (Rowstron, Druschel)
• Tapestry (Zhao, Kubiatowicz, Joseph)
• Chord emphasizes simplicity

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Chord Summary

• Chord provides peer-to-peer hash lookup
• Efficient O(log(n)) messages per lookup
• Robust as nodes fail and join
• Good primitive for peer-to-peer systems
• http//www.pdos.lcs.mit.edu/chord

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Join lazy finger update is OK
N2
N25
K30
N36
N40
N2 finger should now point to N36, not
N40 Lookup(K30) visits only nodes lt 30, will
undershoot
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CFS a peer-to-peer storage system

• Inspired by Napster, Gnutella, Freenet
• Separates publishing from serving
• Uses spare disk space, net capacity
• Avoids centralized mechanisms
• Delete this slide?
• Mention distributed hash lookup

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CFS architecturemove later?
Block storage Availability / replication Authentic
ation Caching Consistency Server
selection Keyword search Lookup
Dhash distributed block store
Chord

• Powerful lookup simplifies other mechanisms