Using Overlay Networks for Proximity-based Discovery

Steven CzerwinskiAnthony Joseph

Sahara Winter RetreatJanuary 13, 2004

This Talk• Goals

– Build a decentralized, self-organizing discovery service – Describe how P2P overlay networks are leveraged– Compare against traditional approaches

• Investigating using infrastructure resources to augment client / server architectures– REAP and MINNO showed code & data migration helps– Need a way to find infrastructure resources

• Outline– Background on proximity-based discovery– Compass architecture– Experimental results

Proximity-based Discovery

• Locate a nearby service instance, according to a specified proximity metric

• Service definition– Provide specific functionality or content

• Data storage servers, computation servers – Uniquely defined by a name– Instances are inter-changeable

Web2Cell Proxy Instances

I’m in Tahoe, Locate a nearby Web2Cell Proxy

Motivation• Applications requiring discovery

• Benefits of using overlay networks– Does not rely on manual configuration or

multicast– No need for special discovery servers– Better scalability and fault tolerance

Application Area

Discovery target Publication

App-Level Multicast

Node participating in session Zhuang [2001]

Data Staging Nodes available for push-based caching

Flinn [2003]

Object Replication

Instance of content object Rhea [2003]

P2P networks Nodes to acts as gateways for joining

Castro [2002]

Protocol optimizers

Nodes to run mobile procedures Czerwinski [2001]

Server selection Nodes hosting a particular content set

Hanna [2001]

Overlays Applied to Discovery• Recast problem as object location & leverage

DOLRs– Servers = objects, Instances = object replicas

• Nodes hosting service instances…– Compute key by hashing service name– Publish: store instance information along the path to

root• Clients making queries

– Compute key by hashing service name– Query: search on path to root, returning first instance

• Proximity-based discovery arises from local convergence property– Paths to same root starting from nearby nodes quickly

converge– Overlay must use PNS (Proximity Neighbor Selection)

Example Publish and QueryIdentifier Space Network Distance Space

a45

891 6f36b2

6ad

6a3

Query

Publish

6f3

6b2

6ad

6a3

• Publish and query for service with key 6a0• Routes converge at node 6ad

a45

891 Publish

Query

Found it!

Compass Architecture• Built on Bamboo

– Proximity metric is estimated RTT• Publish messages are periodic for soft-state• Tracks fixed number of instances per service

– Memory consumption depends on number of unique services

– Lottery used for eviction– Tickets based on estimated network distance

• Publish messages are aggregated / batched– One message per publish period per service

• To break ties when fulfilling queries– Lottery used for selecting among multiple instance

entries– Tickets based on inverse estimated network distance

Strawmen Discovery Services

• Hierarchical– One discovery server per stub domain– All queries and publishes route to nearest server– Server returns matching instances in round-robin– Unfulfilled queries routed to next nearest server– Close to ideal, but requires configuration

• Random– Client uniformly chooses an instance from all

possible– Close to worst-case

AS Stub A AS Stub B AS Stub C

Discovery ServerService InstanceClients

PublishInstance

Query

Experiments• Used Modelnet to emulate wide-area topology

– Transit-stub topology generated by INET• Nodes

– 500 clients and 500 instance generators– 100 services, divided into 4 density classes (.1,.5,1,5 per AS

stub)– Emulated on cluster with 40 physical hosts

• Trials– 30 minute warm-up period followed by 1 hour of queries– Gateways are chosen in stub to speed warm-up

• Client load generators– Clients issue two queries per minute– Queries generated randomly

• Metric: Instance penalty– Distance from client to discovered instance minus client to

hierarchical’s instance

Accuracy Compared to Hierarchical

Usually within 10 ms of idealService density class

Med

ian

inst

ance

pen

alty

(ms)

All 5 per Stub1 per Stub.5 per Stub.1 per Stub

Accuracy Compared to Random

Much better than random, even for low densities

Med

ian

inst

ance

pen

alty

(ms)

Service density class

All

All 5 per Stub1 per Stub.5 per Stub.1 per Stub

Why Some Results are Suboptimal

Greatest problem is paths converge too late

• Examine path traveled by query• Categorize by its intersection with stub containing optimal instance

Perc

enta

ge w

ith su

bopt

imal

type

Never Entered Started InPassed ThroughEnded In Stayed In

Load Balancing Across Instances

Requests are distributed to service instances evenly

CD

F

Ideal load minus observed per minute per instance-5 0 5 10

0.0

0.2

0.4

0.6

0.8

1.0

Window = AllWindow = 10 minWindow = 2 min

Query Scalability

Compass can use much less powerful hosts

Que

ry m

essa

ges h

andl

ed p

er n

ode

per

min

Total queries issued per min

Conclusions• Overlay networks work well for discovery

– Median latency usually less than 10 ms from ideal

– Load is distributed evenly among service instances

– Reduces query load by 1/200th– No need for manual configuration

• Future work– Investigate larger network topology– Incorporate virtual coordinates– Integrate into REAP and MINNO research

Backup Slides

What About Security?• Security still unresolved in overlay networks• Malicious nodes could

– Drop all queries and publish messages– Mount DoS by constantly returning target as

answer to queries– Publish false instances to lure clients

• Duplicate pointers would dropping messages

• Integrating PKI would prevent false instances

Compared to Hierarchical

Compared to Random