Keynote LCN 2008 - LCN Home Page · book magazine price name author last-name first-name award...
Transcript of Keynote LCN 2008 - LCN Home Page · book magazine price name author last-name first-name award...
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RaoufRaouf BoutabaBoutaba((with with ReazReaz AhmedAhmed))
CheritonCheriton School of Computer ScienceSchool of Computer ScienceUniversity of WaterlooUniversity of Waterloo
http://bcr2.uwaterloo.ca/~rboutabahttp://bcr2.uwaterloo.ca/~rboutaba
Distributed Search Revisited: Distributed Search Revisited: Resolving the Conflict of Efficiency & Flexibility
IEEE LCN October 16, 2008
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OutlineOutline
Advertisement and querying in LSDS
Existing search mechanisms
The DPM framework
DPMS
Plexus
Experimental evaluation
Conclusion
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Large Scale Distributed SystemsLarge Scale Distributed Systems
PropertiesTransient populations of autonomous nodes Content dynamismHeterogeneity in nodes’ capabilities
Representative domainsP2P content sharingService discoveryDistributed XML databases
Search requirementsEfficiencyFlexibility Robustness
CompletenessAutonomyAnonymity
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ContentContent--sharing P2P Systemssharing P2P Systems
Centralized(Napster)
Decentralized(Kazaa, Gnutella2)
KeywordsFileIDPeerID KeywordFileID
PeerID
File download
AdvertisementThe Lord of the Rings - The
Two Towers - 2002 (Extended Edition) DVDrip.AVI
QueryLord of the Ring Two Tower
Super Nodes
File download
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ServiceService DiscoveryDiscovery
AdvertisementService-type = service:printScope-list = staff, gradLocation = DC3335Color = trueLanguage = PSPaper-size = legal, A4, B5
Select & invokeserviceExecution
Client
Directory
Service
Service ID &Description
Advertisement
QueryService-type = service:printScope-list = gradPaper-size = A4
Query
Lookup
List of matching Services & handles
Reply
Resource and Service Discovery in Large Scale Distributed Systems. IEEE Communications Surveys &Tutorials, IEEE Press, Vol. 9 (4), pp. 2-30, Dec. 2007.
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Advertisement<bookstore specialty="novel">
<book style="autobiography"> <author>
<first-name>Joe</first-name> <last-name>Bob</last-name> <award>Trenton Literary </award>
</author> <price currency=CAD>12</price>
</book> <book style="textbook">
<author> <first-name>Mary</first-name> <last-name>Bob</last-name>
</author> <price>55</price>
</book><magazine>
<name>Times</name><price currency=USD>4</price>
</magazine> </bookstore>
P2PP2P DatabasesDatabases
Local Data Model
Semantic Mapping
ElementSchema Data
Indexing
P2P routing
1
2
3
4 XPathXPath QueryQuery//author[award]/bookstore/book[author/last-name=Bob]
bookstore
magazinebook
price name
author
last-name
first-name
award
price
AdvertisementAdvertisement
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BottomBottom--lineline
Query is based on partial information about the Advertisement.
Query is a “subset” of an Advertisement it should match against
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OutlineOutline
Advertisement and querying in LSDS
Existing search mechanisms
The DPM framework
DPMS
Plexus
Experimental evaluation
Conclusion
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Components of a Search MechanismComponents of a Search Mechanism
Query Semantics
Query Language Translation Routing
Schema
Static
Quasi-static
Dynamic
Static• One implicit schema• Globally known• P2P content sharing: Gnutella, KaZaA etc.Quasi-Static• Several schemas, occasionally created• Administratively scoped• Service discovery: Jini, SLP, SalutationDynamic• Heterogeneous schemas• User scoped, semantic mapping needed• PDBS: PeerDB, XP2P, RDFPeers etc.
A Survey of Distributed Search Techniques in Large Scale Distributed Systems. IEEE Surveys &Tutorials, IEEE Press, cdt. accepted 2007.
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Components of a Search MechanismComponents of a Search Mechanism
Query Language
Query Semantics
ExpressivenessSchema
Translation Routing
Static
Quasi-static
Dynamic
Exact keyword
Partial keyword
Property-value list
Complex queries
Exact keyword• DHT-based P2P content sharing• E.g., CFS, eMulePartial keyword• Unstructured P2P • E.g., Gnutella, FasttrackProperty-value list• Most service discovery protocols (SDPs)• E.g., Jini, Salutation, UPnPComplex queries• PDBSs and some SDPs• Hierarchical, relational op. and ranges• E.g., PeerDB, XP2P, SLP, Twine.
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Components of a Search MechanismComponents of a Search Mechanism
Query Language
Query Semantics
ExpressivenessSchema
Translation Routing
Static
Quasi-static
Dynamic
Exact keyword
Partial keyword
Property-value list
Complex queries
Flat
Hash
Hash-summary
Content-routing
Address-routing
Signature-routing
Flat ⇒ Content-routing• Preserve semantic info. in query for use in
routing decisions at each hop• E.g. semi-structured P2P & industrial SDPHash ⇒ Address-routing• Hashing looses semantic info. • Key to address (of target) mapping• E.g., DHT-techniques, SkipNetHash-summary ⇒ Signature-routing• Query semantic is preserved• Bloom-filter based & lossy aggregation• E.g., SSDS, NSS, DPMS, PLR
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ExamplesExamplesGIA pSearch Squid Twine &
PWSDNSS SSDS
Query Keyword Full-text/ semantic
Prefix-match
Subtree / path: XML
Keyword Subset of AV-list: XML
Trans-lation
Flat LSI Hilbert SFC
Stranding Bloom-filter
Bloom-filter
Routing R.Walk+ Cap. bias+ 1-hop idx.
CAN Chord Chord Controlled flooding
GlobalHierarchy
PeerDB XP2P L. Galanis RDFPeers PLR Humbolt
Query SQL XPath(absolute)
XPath(relative)
Partial RDF triple
Keyword SPARQL/RDF
Trans-lation
Synonym Finger-print
XML elem. Hash
RDF elem. hash
Attenuat. Bloom filter
URI-hash + flat
Routing TTL-flooding
Chord Chord Chord Hint-based
DHT+ Ctrl. flooding
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Research TrendsResearch Trends
DHT
Query String
Feature extraction
(LP)H
Address routing,Multiple lookup/search?
DPMSGlobal hierarchies with
Replication & aggregation(Signature routing)Pattern as Index
Query String
Feature extraction
BF
Signature/Content routing,Search overhead?
Rare item?
PlexusIndex clustering &
ECC based (Address routing)
Pattern as Address
Query String
Feature extraction
Pattern
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OutlineOutline
Advertisement and querying in LSDS
Existing search mechanisms
The DPM framework
DPMS
Plexus
Experimental evaluation
Conclusion
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Distributed Pattern Matching (DPM)Distributed Pattern Matching (DPM)
P2P network
Q:0100 1010 0100
Query
C:1011 0010 1010
D:0101 0100 0101 A:1001 0111 1100
B:1101 1010 0110
E:1110 1000 1010
F:0100 1011 0101
G:1000 1100 0010
B:1101 1010 0110
F:0100 1011 0101
Result
content messageLink
Distributed Pattern Matching for P2P Systems. In Proc. IEEE/IFIP Symposium on Network Operations and Management (NOMS), Vancouver (Canada), April 2006.
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Mapping Keywords to PatternsMapping Keywords to Patterns
Lord of the Ring Two Tower
The Lord of the Rings - The Two Towers - 2002 (Extended Edition)
Lord of War
the, lor, ord, of, rin, ing, ngs, two, tow, owe, wer, ers, 2002, 02, ext, xte, ten, …
lor, ord, of, the, rin, ing, two, tow, owe, wer lor, ord, of, war
Subset(match)
0 0 1 0 1 0 1 0 0 0 1 1
Not subset(mismatch)
1 0 0 0 1 0 1 0 1 0 0 0
1 0 1 1 1 0 1 1 1 0 1 1 Bloom FilterAdvertisement
Query Query
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The Big PictureThe Big PictureKeywords
Tri-grams
Tri-grams
Partial keywords
Content Sharing
Content Sharing
Service description
Property-value pairs
Property-value lista1=v1, a2=v2, …
Query in CNFa1=v1 ∩ a2=v2 ∩ …
Service Discovery
Service Discovery
Hierarchical data
Paths prefixes
Path prefixes/a1, /a1/b1, /a1/a2,…
XPath query/a1[b1] /a2[b2]…/an[bn]
P2P Databases
P2P Databases
Advertisement Bloom Filter
P2P overlay routing.
P2P overlay routing.
Query Bloom Filter List of peers
ResultsQuery
AdvertisementThe DPMconstruct
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SolvingSolving the DPM Problemthe DPM Problem
Challenge: PM requires linear time algorithm
Solutions:DPMS:
• Signature routing
• Hierarchical indexing with index aggregation
• Goal: Find few matches in a few hops
Plexus:
• Address Routing
• Index clustering with Error Correcting Codes
• Goal: Find all in reasonable number of hops
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OutlineOutline
Advertisement and querying in LSDS
Existing search mechanisms
The DPM framework
DPMS
Plexus
Experimental evaluation
Conclusion
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3210
0 1
0Leaves
Level 1 - Index
Level 2 - Index
A B C … …
……
……
……
……
D GFE Y… …
S T… … … …
Branching factor, B = 3 Replication factor, R = 2
PatternPattern DistributionDistribution
D G
S T
Distributed Pattern Matching: A Key to Flexible and Efficient P2P Search. IEEE Journal on Selected Areas in Communications (JSAC), Vol. 25 (1), pp. 73-83, 2007.
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QueryQuery RoutingRouting
A B C … … Z
D GFE Y… …
S VU T… … … …
0 1
3210
Peer A is looking for a pattern, say advertised by Z
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QueryQuery RoutingRouting
A B C … … Z
D GFE Y… …
S VU T… … … …
0 1
3210
Peer A is looking for a pattern, say advertised by Z
Efficiency?Number of peers in a group at height, say H, where,
⎟⎟⎠
⎞⎜⎜⎝
⎛=
NNOH
loglog
is:( )NO log
( )NNNO logloglog εκ
κ
+
matches finding of cost
Upside• No Single point of failure• No Performance bottleneckDownside• Indexing overhead
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OutlineOutline
Advertisement and querying in LSDS
Existing search mechanisms
The DPM framework
DPMS
Plexus
Experimental evaluation
Conclusion
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Advertisement, P Query, Q
advSet(P) ⊂ C qSet(Q) ⊂ C
( ) ( ) φ≠∩⇒⊆ PadvSetQqSetPQ
Plexus: Index ClusteringPlexus: Index Clustering
C = set of cluster heads Error Correcting Codes
• Linear Covering code
• Cluster head ⇔ Codeword
• Generator matrix based routing
Plexus: A Scalable Peer-to-peer Protocol Enabling Efficient Subset Search. IEEE Transactions on Networking (TON), IEEE Press, To appear February 2009.
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LinearLinear Binary CodeBinary Code
C = <n, k, d> linear binary code– n: number of bits in a codeword– k: dimension → 2k codewords in code– d: minimum distance between any pair of codewords
– e.g., G24=<24, 12, 8>
Generator Matrix G,
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
=
⎥⎥⎥⎥⎥⎥
⎦
⎤
⎢⎢⎢⎢⎢⎢
⎣
⎡
=
knkk
n
n
k ggg
gggggg
g
gg
G
LL
LLLLL
LLLLL
LL
LL
L
L
21
22221
11211
2
1
2k codewords can be formed by applying XOR to any combination of these k rows.
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Plexus: Routing TablePlexus: Routing Table
In a complete network each peer is responsible for a codewordPeer with codeword X maintains k links as follows:– Link Xi = X ⊕ gi 1 ≤ i ≤ k
Optionally X can link to:– Xk+1 = X ⊕ g1 ⊕ g2 ⊕ … ⊕ gk
– Replicate to Xk+1
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Plexus: RoutingPlexus: RoutingObservation: C is closed under ⊕ operation
tiii gggXYCYX ⊕⊕⊕⊕=⇒∈ L21
,
532 gggXY ⊕⊕⊕=
Example: Route from X to Y where,
X Y
X2g2
X3
X5
g3
g5
X23
g3
g5
X25g3
g5
X35 g2
g3
g5
g2
g2
X2 X23 X235=Y
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1g 1gBA
Plexus: Codeword AssignmentPlexus: Codeword AssignmentMapping codewords to peers in networks with less than 2k peers.
21gg 21gg
Logical
PhysicalAC
21gg 21gg
321 ggg 321 ggg 321 ggg 321 ggg 321 ggg 321 ggg
4321 gggg 4321 gggg
Xk+1
X1 X3
X X4
X2
1g2g
3g
4g
kgg −5
iig⊕
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Plexus: Multiple subnetsPlexus: Multiple subnets
S1⊂C S2⊂C Sr⊂C…
Mapping4
Routing5
r × n-bit pattern
Trigrams
Keywords
Attribute-value pair
Service description
Path prefixes
XML data/XPathPropertyextraction
Hash
1
2
n-bit pattern n-bit pattern n-bit pattern…
Segregate3
1 2 r
…
Subnet-1 Subnet-2 Subnet-r
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OutlineOutline
Advertisement and querying in LSDS
Existing search mechanisms
The DPM framework
DPMS
Plexus
Experimental evaluation
Conclusion
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Experimental SetupExperimental SetupSearch systems
Flooding• Uniform replication with avg. 120• TTL = 4Random walk• Uniform replication with avg. 120• Walker = 15DPMS• Recursive replication factor = 2• Branching factor = 4-6
beethoven
thove
7 registrationsbee, eet, eth, tho, hov, ove, ven
tho
hov
ove
Any DHT e.g.Chord
R1
R2
R3
R1 ∩ R2 ∩ R3
Plexus• No. of subnets = 7
DHT/Chord• Replica per key = 4
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Routing EfficiencyRouting Efficiency
No. of peers
%vi
site
dp
eers
5000 10000 15000 200000
4
8 PlexusDPMSDHT
No. of peers
%vi
site
dp
eers
5000 10000 15000 200000
2
4
6
8
PlexusDPMSFloodR-WalkDHT
Advertisement traffic Search traffic At % n-gram in query = 35%
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Search CompletenessSearch Completeness
% 3-gram in query
Sea
rch
com
plet
enes
s
0 25 50 75 10020
30
40
50
60
70
80
90
100
PlexusDPMSFloodR-walkDHT
No. of peers
Sea
rch
com
plet
enes
s
5000 10000 15000 2000020
40
60
80
100
PlexusDPMSFloodR-walkDHT
Search CompletenessNetwork size ≈ 20K
Search completenessAt % n-gram in query = 35%
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Fault ResilienceFault Resilience
% failed superpeers
Sea
rch
com
ple
tene
ss
0 10 20 30 40 500
20
40
60
80
100
PlexusDPMSFloodR-walkDHT
% failed superpeers
%vi
site
dsu
per
pee
rs(a
live)
0 10 20 30 40 500
0.4
0.8
1.2
1.6
2
2.4
2.8
3.2
3.6 PlexusDPMSFloodR-walkDHT
Search completenessWith failed peers
Query trafficWith failed peers
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OutlineOutline
Advertisement and querying in LSDS
Existing search mechanisms
The DPM framework
DPMS
Plexus
Experimental evaluation
Conclusions
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ConclusionsConclusionsWe have formulated DPM, a new problem, which can be used to model search in a number of LSDS applications
We have shown how P2P search, Service discovery systems and P2P databases can be mapped to DPM.
We have provided two solutions, DPMS and Plexus, which solve the DPM problem
Plexus surpasses all known search techniques in both structured and unstructured LSDSs.
We have demonstrated that it is possible to reconcileflexibility and efficiency.
We believe that DPM has great potential in many existing and emerging applications
Examples include molecular databases, fingerprinting, phonetic search, sound alike search, etc.