resource addressable network: an adaptive peer-to-peer discovery substrate for internet-scale...

Resource Addressable Network: Resource Addressable Network: An Adaptive Peer-to-Peer Discovery An Adaptive Peer-to-Peer Discovery Substrate for Internet-Scale Service Substrate for Internet-Scale Service PlatformsPlatforms

Balasubramaneyam ManiymaranBalasubramaneyam ManiymaranPh.D. Student,Ph.D. Student,Department of Electrical & Computer Engineering,Department of Electrical & Computer Engineering,McGill UniversityMcGill University

Supervisor: Dr. Muthucumaru MaheswaranSupervisor: Dr. Muthucumaru Maheswaran

Advanced Networking Research Laboratory,The School of Computer Science,McGill University, Montreal, QC, Canada.

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IntroductionIntroduction• On-demand computingOn-demand computing (ODC) an emerging model for (ODC) an emerging model for

next generation systems.next generation systems.• Peer-to-peerPeer-to-peer (P2P) is one way of building ODC (P2P) is one way of building ODC

systems.systems.– P2P Grid, P2P CDNs, public computing utilities.P2P Grid, P2P CDNs, public computing utilities.

• To assemble ODC from P2P resource base.To assemble ODC from P2P resource base.– Need a generalized resource discovery scheme.Need a generalized resource discovery scheme.– Discover resources based on given requirements.Discover resources based on given requirements.

• Resource addressable networkResource addressable network (RAN). (RAN).– Discovers resources based on attributes and location.Discovers resources based on attributes and location.– One of the major concerns in RAN is scalability:One of the major concerns in RAN is scalability:

• Low overhead in managing overlay and information.Low overhead in managing overlay and information.• Three design concepts: fully decentralized, distributed Three design concepts: fully decentralized, distributed

knowledge, and adaptive design.knowledge, and adaptive design.


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RAN discovery substrateRAN discovery substrate

ODC ServiceODC Service

Physical ResourcesPhysical Resources

Location-based discoveryLocation-based discoveryLandmark-aided positioningLandmark-aided positioning

Profile-based discoveryProfile-based discovery

Network positioning Network positioning mechanism, assigning mechanism, assigning coordinates for each node in coordinates for each node in the network delay spacethe network delay space

Resource Addressable Resource Addressable NetworkNetwork• RAN: middle layer between services and RAN: middle layer between services and

resources.resources.• Attribute-based and location-based discovery.Attribute-based and location-based discovery.

Naming the resources Naming the resources based on their based on their attributesattributes

Profile-based namingProfile-based naming


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Network PositioningNetwork Positioning• Network positioningNetwork positioning: assigning coordinates : assigning coordinates

for the nodes in a virtual Cartesian space, for the nodes in a virtual Cartesian space, from which real network delay can be from which real network delay can be predicted. predicted.

xx

yy

(x(x11, y, y11))

(x(x22, y, y22))

ll1212

InterneInternett

Cartesian Cartesian spacespace

ll1212 ≈ √[(x ≈ √[(x11-x-x22))22+(y+(y11-y-y22))22]]


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Landmark Aided PositioningLandmark Aided Positioning• Landmark aided positioning Landmark aided positioning (LAP): the (LAP): the

network positioning scheme for RANnetwork positioning scheme for RAN• Using a set of Using a set of landmarkslandmarks..• Other nodes:Other nodes:

– Select a subset of the total landmarks and ping Select a subset of the total landmarks and ping them.them.

– Run optimization algorithm to position themselves Run optimization algorithm to position themselves to minimize the total error in distance prediction.to minimize the total error in distance prediction.

• Two phases of LAP:Two phases of LAP:– Landmark positioningLandmark positioning: : positioning the landmarks.positioning the landmarks.– Node positioningNode positioning: : positioning the normal nodes.positioning the normal nodes.


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LAP:: Landmark PositioningLAP:: Landmark Positioning• Each landmark calculates its coordinate Each landmark calculates its coordinate

relative to other landmarks.relative to other landmarks.• Landmark positioning involves two loops:Landmark positioning involves two loops:

– Inner loopInner loop contains the iteration for node contains the iteration for node positioning.positioning.• Mostly affects the computational complexity.Mostly affects the computational complexity.

– Outer loopOuter loop contains many node positioning phases. contains many node positioning phases.• Between each node positioning phase, nodes have to Between each node positioning phase, nodes have to

contact others to get their new coordinates contact others to get their new coordinates message message complexity.complexity.

• Simplex and Spring both found to be producing high outer Simplex and Spring both found to be producing high outer loop iterations.loop iterations.

• Introducing new algorithm called Introducing new algorithm called SpringEqSpringEq..


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• SpringEq (short for “spring in equilibrium”):SpringEq (short for “spring in equilibrium”):– Inspired from Spring; same spring system Inspired from Spring; same spring system

concept.concept.– But instead of minimizing the deformations in But instead of minimizing the deformations in

the spring, SpringEq consider the equilibrium the spring, SpringEq consider the equilibrium condition.condition.• The resultant force applied at each node is zero.The resultant force applied at each node is zero.

– A spring system at equilibrium can be modelled A spring system at equilibrium can be modelled by a set of simultaneous equations.by a set of simultaneous equations.

– SpringEq solves this simultaneous equation SpringEq solves this simultaneous equation using fast iterative process.using fast iterative process.

LAP:: Landmark PositioningLAP:: Landmark Positioning (cont…)(cont…)


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LAP:: Landmark Positioning LAP:: Landmark Positioning (cont…)(cont…)

• Random network configuration; 100 landmarks.Random network configuration; 100 landmarks.• Distance correlationDistance correlation: correlation between the ping and calculated : correlation between the ping and calculated

distance matrices.distance matrices.• Simplex – good prediction, but too many iterations; Spring – Simplex – good prediction, but too many iterations; Spring –

comparatively few iterations, but bad prediction;comparatively few iterations, but bad prediction;– SpringEq outperforms both Simplex and Spring.SpringEq outperforms both Simplex and Spring.

SimplexSimplex SimplexSimplex

SpringSpring

SpringSpringSpringEqSpringEq

SpringEqSpringEq

Distance correlation vs. ping error.Distance correlation vs. ping error.No. of iteration vs. ping error.No. of iteration vs. ping error.


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Clustered LAPClustered LAP• Non-random network errors Non-random network errors

severely impact positioning.severely impact positioning.– Data smoothing and Data smoothing and

optimization can not handle optimization can not handle this.this.

• Clustered landmark aided Clustered landmark aided positioningpositioning (CLAP): (CLAP):– CLAP assumptions:CLAP assumptions:

• Network errors are created by Network errors are created by abnormal congestion.abnormal congestion.

• Peers within the same network Peers within the same network segment can be trusted.segment can be trusted.

• Congestion does not affect Congestion does not affect pings within a network pings within a network segment.segment.

join a clusterjoin a cluster

ping others and ping others and share the values share the values

with otherswith otherscalculate calculate clustercluster

diameterdiameterfind coordinates using simple find coordinates using simple

LAP (SLAP)LAP (SLAP)

inter exchange inter exchange SLAP SLAP

coordinatescoordinatescalculate calculate clustercluster

centroidcentroidtune SLAP coordinates so tune SLAP coordinates so that it lies within cluster that it lies within cluster

diameterdiameter

CLAP

CL

AP

adju

stm

ent

adju

stm

ent

clust

er

clust

er

initi

aliza

tion

initi

aliza

tion


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CLAP PerformanceCLAP Performance

SLAPSLAP

CLAPCLAP

CLAP is relatively CLAP is relatively robustrobust

CLAP’s minimum CLAP’s minimum performance is performance is better than SLAP’s better than SLAP’s maximum maximum performance.performance.

Variation of distance correlation with Variation of distance correlation with increasing network congestion.increasing network congestion.

Experiment with Experiment with Planetlab data.Planetlab data.


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CLAP PerformanceCLAP Performance (cont…) (cont…)

Cumulative distribution of relative distance error in the Cumulative distribution of relative distance error in the system for different amount of network congestion.system for different amount of network congestion.

SLAPSLAP

CLAPCLAP


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Location-based DiscoveryLocation-based Discovery• Finding a resource at specific coordinate/range:Finding a resource at specific coordinate/range:

– Multidimensional search – Multidimensional search – spatial data structurespatial data structure..– Chose Chose Hilbert curveHilbert curve as the data structure. as the data structure.

• Hilbert curve:Hilbert curve:

– Provides a Provides a d-d-D to D to 11-D mapping.-D mapping.– Preserving proximity.Preserving proximity.– Hierarchical Hilbert index Hierarchical Hilbert index location IDlocation ID (LID). (LID).


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Location-based Discovery Location-based Discovery (cont…)(cont…)

Hilbert mapping of the nodes in Planetlab Hilbert mapping of the nodes in Planetlab network (n = 133, approximation level = 7)network (n = 133, approximation level = 7)


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Location-based Discovery Location-based Discovery (cont…)(cont…)

• Routing table for location-based discovery.Routing table for location-based discovery.– Non-zero error in pings justifies fixed length LIDs.Non-zero error in pings justifies fixed length LIDs.– Ring pointersRing pointers ensuring connectivity; ensuring connectivity; jump pointersjump pointers

enhancing route complexity.enhancing route complexity.• Average search hop complexity = Average search hop complexity = h h (approx. level) (approx. level) O(1)O(1)..

Routing table at node with LID = 2.3.3.1.0Routing table at node with LID = 2.3.3.1.0


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Profile-based DiscoveryProfile-based Discovery• Discovery systems implements naming Discovery systems implements naming

schemes:schemes:– Label-based namingLabel-based naming (LBN): DNS, IP Address. (LBN): DNS, IP Address.

• Scalable, but not flexible.Scalable, but not flexible.– Description-based namingDescription-based naming (DBN): LDAP. (DBN): LDAP.

• Flexible, but with high overhead due to information Flexible, but with high overhead due to information maintenance, complex matching algorithms.maintenance, complex matching algorithms.

• Introducing Introducing profile based namingprofile based naming (PBN): (PBN):– Labels popular attribute-value combinations.Labels popular attribute-value combinations.

• Combines the goods of LBN and DBN.Combines the goods of LBN and DBN.• Can not discover all the attribute-value combinations.Can not discover all the attribute-value combinations.• Trading off flexibility (performance) for scalability.Trading off flexibility (performance) for scalability.


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Profile-based DiscoveryProfile-based Discovery (cont…)(cont…)

Profile 1: {Intel/AMD, ≤ 512MB} : 0.*Profile 1: {Intel/AMD, ≤ 512MB} : 0.*Profile 2: {Intel with 1GB}Profile 2: {Intel with 1GB} : 1.0 : 1.0Profile 3: {Intel/AMD, > 1GB}Profile 3: {Intel/AMD, > 1GB} : [1.1,1.2] : [1.1,1.2]

description spacedescription space

profile spaceprofile space

profilesprofiles

descriptiondescription 11

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Profile Profile IDsIDs

•Profile-based routing table is very similar to location-based routing table.Profile-based routing table is very similar to location-based routing table.


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Related WorksRelated Works• Network positioning:Network positioning:

– GNP: GNP: • Centralized implementation, fixed set of landmarks.Centralized implementation, fixed set of landmarks.

– Vivaldi: Vivaldi: • Dynamic landmarks: anybody can be a landmark.Dynamic landmarks: anybody can be a landmark.• New node disturbing others, requires RPC calls.New node disturbing others, requires RPC calls.

– Others: NPS, PIC, big-bang simulation, PCoord.Others: NPS, PIC, big-bang simulation, PCoord.– LAP:LAP:

• Semi-dynamic landmarks.Semi-dynamic landmarks.• Low message overhead design.Low message overhead design.• CLAP improvement.CLAP improvement.• RAN infrastructure helps selecting landmarks.RAN infrastructure helps selecting landmarks.


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Related WorksRelated Works (cont…)(cont…)

• Location-based discovery:Location-based discovery:– SkipNet: proximity based on DNS names – fails outside DNS SkipNet: proximity based on DNS names – fails outside DNS

structure.structure.– Pastry, expressway of CAN: document discovery.Pastry, expressway of CAN: document discovery.– RAN:RAN:

• Proximity information is available at any resolution.Proximity information is available at any resolution.• No indirection.No indirection.• Fixing the search hop complexity.Fixing the search hop complexity.

• Attribute-based discovery:Attribute-based discovery:– Directory services: LDAP, MDS.Directory services: LDAP, MDS.– Intentional naming scheme/Twine:Intentional naming scheme/Twine:

• Document discovery for resource discovery.Document discovery for resource discovery.– RAN:RAN:

• Trading off performance for scalability.Trading off performance for scalability.• No indirection.No indirection.


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ConclusionConclusion• Expected contributions:Expected contributions:

– Architecture:Architecture:• Extending the concept of structured-document Extending the concept of structured-document

discovery to resource discovery:discovery to resource discovery:– Extracting a structure out of the unstructured Extracting a structure out of the unstructured

metric space using Hilbert curve.metric space using Hilbert curve.• First discovery structure combining attribute-based and First discovery structure combining attribute-based and

location-based discovery.location-based discovery.– Network positioning:Network positioning:

• CLAP: resilient to network congestion.CLAP: resilient to network congestion.• SpringEq: providing low message complexity.SpringEq: providing low message complexity.• Spring vs. Simplex comparison.Spring vs. Simplex comparison.


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ConclusionConclusion (cont…)(cont…)

• Expected contributionsExpected contributions (cont…) (cont…)::– Location-based discovery:Location-based discovery:

• Efficient overlay design using Hilbert indices.Efficient overlay design using Hilbert indices.• Fixing the search complexity by fixing the search Fixing the search complexity by fixing the search

resolution.resolution.– Profile-based naming:Profile-based naming:

• Trading off flexibility for scalability.Trading off flexibility for scalability.• Efficient profile-based routing overlay design.Efficient profile-based routing overlay design.• Profile-based search complexity depends on popularity Profile-based search complexity depends on popularity

distribution.distribution.


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• Roadmap to completion:Roadmap to completion:– LAP:LAP:

• Analysis of SpringEq for its convergence and stability. Analysis of SpringEq for its convergence and stability. (Sep. (Sep. 2005)2005)

– Architecture:Architecture:• The deficiencies the routing mechanism can face due to the The deficiencies the routing mechanism can face due to the

non-uniformity of metric space will be studied. non-uniformity of metric space will be studied. (Oct. 2005)(Oct. 2005)– Location-based discovery:Location-based discovery:

• A practical value for search resolution will be found based on A practical value for search resolution will be found based on errors in pings and the applications requirements. errors in pings and the applications requirements. (Nov. 2005)(Nov. 2005)

• Simulation study. Simulation study. (Mar. 2006)(Mar. 2006)– Profile-based discovery:Profile-based discovery:

• Analysis of other possible schemes that can map description Analysis of other possible schemes that can map description onto profile space. onto profile space. (May 2006)(May 2006)

• Impact of incorporating virtual profiles. Impact of incorporating virtual profiles. (July 2006)(July 2006)

Conclusion Conclusion (cont…)(cont…)


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Thank youThank you

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