Dipanjan Chakraborty

Anupam Joshi

CSEEUniversity of

Maryland Baltimore County

Anamika:

Distributed Service Discovery and Composition Architecture for Pervasive

Environments

Service

I am Wireless LAN enabled!!Blender!!

I have GPS service!!

Do you have MP3 songs?

Service Discovery

I am looking fora printer!!

Are you a Toaster ??

Definitions• “Service”

– Hardware or software entity residing on any device or platform

• Has distinct functional description• Can be utilized by other services/clients

• “Service Discovery”– Process of discovering the availability of a service in

the neighborhood

• “Service Composition”– Integration and execution of multiple services in the

planned order to satisfy a request

Ad hoc Environment

• Network formed by multiple heterogeneous nodes in the reachable vicinity of one another

• Some nodes are mobile, some are not

• Environment around a device changes dynamically

• Services exist on those devices

Issues of Service Discovery in an Ad hoc Environment

• Discovery Architecture• Registry-based/centralized/semi-centralized

• In Ad hoc Environment– Global request broadcasting

– Global Advertisement and caching

• Discovery method• Unique identifier/Interfaces/attributes

• Language/network independence

• Scalability

Issues of Service Composition in Ad hoc Environments

• Services are distributed in the Environment

• Efficient Service Discovery

• Composition needs to be done in a de-centralized manner

• Fault tolerance and graceful recovery

• Solution should efficiently utilize node/service topology

General Architecture

Network Layer (DSDV/AODV/CSGR etc)

Service IntegrationLayer

Application Layer

Broker Arbitration and Delegation

Service ExecutionLayer

Fault Recovery Module

Service Discovery Layer (Bluetooth SDP, Salutation-lite etc)

Planner

Anamika: System Components

Anamika: Network Manager

• Communication between Bluetooth peers done over RFCOMM

• Connect-transmit-disconnect mode of operation

• Segmentation and reassembly of Anamika messages

• Implementation done on IBM’s Bluedrekar transport driver

Anamika: Service Discovery

• Peer-to-peer service discovery (Group-based Service Discovery)

• Dynamic caching of discovered services in peers

• Semantic description based service matching (using DAML-S and DReggie Ontology)

• Service Discovery also provides invocation information

GSD Protocol Summary

• GSD= Group-based Service Discovery

• Peer-to-peer caching of service advertisements– No global advertisements– No global request broadcast

• Describe services semantically in DARPA Agent Markup Language (DAML)– Enhance service matching mechanism based on

semantic description

GSD Protocol Summary• Class/subClass hierarchy of DAML used to classify

services to different groups based on functionality• Intelligently forward requests to appropriate nodes

– Prevent request flooding• Efficient in terms of bandwidth usage and discovering a

service in a MANET

Group-based Request Routing

S1 (G1)

S4,G1,G2,G3

N4: S1 (G1)

N1: S4

N5: S2 (G2)

N3: S3 (G3)

N2

Source

Advertisement

Service Request

N6: S6 (G2)

Service Composition Techniques

• “Request Processor” uses DAML-S to model Composition Knowledge

• Dynamic Broker Selection Technique– No assumption about the platform of the broker/central

entity– Broker Arbitration and Delegation

• Source of the request starts a process which decides the broker platform

– Parameters based on current processor usage, memory capability, longevity, services available in its vicinity etc

Dynamic Broker Selection Technique (contd)– Broker discovers *all* the required services– Fault tolerance

• Source-monitored fault-tolerance– Assumption: Source remains ‘alive’ all the time

• Periodic ‘checkpoints’ being sent to the source

• Source issues a new composition request in case of failure

Service Composition Techniques• Distributed Brokering Technique

– Broker Arbitration and Delegation• Requester is responsible to determine the ‘first’ broker

– Parameters to select a broker are similar to the ‘dynamic Broker selection’ mechanism

» More emphasis on services that are needed ‘immediately’

– ‘first’ broker not responsible for the whole composition

• Composes only ‘as much’ as it can• ‘radius’ of composition is small

– ‘first’ broker selects another broker when it has completed the ‘partial’ composition

Distributed Brokering Technique (contd.)– Fault Recovery

• Similar to the one used in ‘dynamic entity selection’ mechanism

– Each broker keeps the client informed about the partial state of composition and execution

– Client issues a new composition request with the subset that is remaining

Results

• Simulation carried in Glomosim simulator

• 25 to 100 nodes

• Movement pattern=random way-point

• Radio Range of each node=31 meters

Results

Effect of Advertisement Diameter on Request Diameter

0

1

2

3

4

5

6

7

1 2 3 4 5 6

Advertisement Diameter

Avg

. R

equ

est

Dia

met

er

Avg. Request Diameter

Results

Group-based Selective Forwarding vs. Broadcasting w.r.t No. of Requests (Static Topology)

0

10

20

30

40

50

60

1 2 3 4

Number of Advertisement Hops

Av

g. S

erv

ice

Re

qu

es

ts P

roc

es

se

d b

y a

No

de

Broadcasting Requests to all Neighbors

Selective Service group-based Forwarding

Results

Group-based Selective Forwarding vs. Broadcasting w.r.t No. of Requests (Mobility=30(1,4))

0

10

20

30

40

50

60

70

80

1 2 3 4

Advertisement Diameter

Avg

. N

o.

of

Req

ues

ts P

roce

ssed

/N

od

e

AbsoluteForwarding ofRequestsGroup-basedSelectiveForwarding

ResultsGroup-based Selective Forwarding vs. Broadcasting w.r.t No. of Requests (Mobility=3(1,4))

0

10

20

30

40

50

60

70

80

1 2 3 4

Advertisement Diameter

Av

g. N

o. o

f B

roa

dc

as

ts p

er

No

de

Complete Broadcasting

Group-based Selective Forwarding

Results

Effect of Response Time on Number of Services needed for a Composite Process

0

5

10

15

20

25

30

35

40

45

1 2 3 4 5

Number of Services

Ave

rag

e R

esp

on

se T

ime

(Sec

on

ds)

Average Response Time to Discover allservices

Future Work

• Simulation of the whole composition architecture

• Implementation of a pro-active service discovery and composition architecture

• Mathematical modeling of the discovery and composition process