An affinity-driven clustering approach for service
discovery and composition for pervasive computing
J. Gaber and M.Bakhouya
Laboratoire SeT
Université de Technologie de Belfort-Montbéliard
(UTBM)
90010 Belfort, France
www.utbm.fr
[email protected]
OUTLINE
Context and Objectives
Related work
Self-Organization Approach to the Design of
Emergent Pervasive Services
Simulation results
Conclusion and future work
2
CONTEXT (1/2)
Ubiquitous computing (UC) and Pervasive
computing (PC), what’s the difference ?
in UC, the objective is to provide users the ability
to access services and resources all the time and
irrespective to their location.
in PC, the main objective is to provide
spontaneous services created on the fly by
mobiles that interact by ad hoc connections.
3
CONTEXT (2/2)
Two new paradigms have been proposed as
alternatives to the traditional Client/Server
paradigm (CSP) in [GAB00], [GAB06]
the Adaptive Servers/Client Paradigm
(SCP).
the Spontaneous Service Emergence
Paradigm (SEP).
4
OBJECTIVES
A Self-Organization Approach for service
discovery and composition for pervasive
applications
SDS : Service discovery is the process of
locating available nearby services.
SCS : Service composition process concentrates
in combining different available services
discovered by a SDS.
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RELATED WORK (1/5)
Service discovery
systems
Structured
systems
Indexation
Distributed
hash tables
Centralized Decentralized
systems
systems
Unstructured
systems
Flooding
Random
walk
Push Pull Parallel Agent
random cloning
walk
6
RELATED WORK (2/5)
Service discovery
systems
Structured
systems
Indexation
Distributed
hash tables
Centralized Decentralized
systems
systems
• Brokers
that maintain a
repository of published
services
• Hierarchical architecture
Unstructured
consisting of multiple
systems
repositories that
synchronize periodically
Flooding
• Random
Cannot meet the
requirements of both
walk
scalability and adaptability
simultaneously
Agent
• The risk
of bottlenecks
Push Pull Parallel
random
and thecloning
difficulty of
walk
repositories updating
7
RELATED WORK (3/5)
Service discovery
systems
Structured
systems
• Permits
to implement
a direct search
algorithm to efficiently
locate services.
Unstructured
• Global Overlay
systemsnetwork between
nodes are generally
hard to maintain.
Indexation
Distributed
hash tables
Centralized Decentralized
systems
systems
Flooding
Random
walk
Push Pull Parallel Agent
random cloning
walk
8
RELATED WORK (4/5)
•Allow nodes to enter
and leave the systems
without overheads
Service discovery
systems
• It is not possible to
guarantee the success Structured
or failure of a query with systems
a constant TTL
• The mechanism of
Indexation
dynamic TTL or
expanding ring is
proposed to overcome
this problem
Distributed
hash tables
Centralized Decentralized
• Generate large
loads
systems
systems
on the network
Unstructured
systems
Flooding
Random
walk
Push Pull Parallel Agent
random cloning
walk
9
RELATED WORK (5/5)
Service discovery
systems
• It
is difficult to
determine a priori the
number of parallel
Random walks
Structured
systems
•Agent cloning
approach can
overcome this
problem but need
Indexation
a regulation
algorithm
Distributed
hash tables
Centralized Decentralized
systems
systems
Unstructured
systems
Flooding
Random
walk
Push Pull Parallel Agent
random cloning
walk
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SELF-ORGANIZATION APPROACH
Service discovery
systems
Self-organization
systems
Affinity
networks
Structured
systems
Indexation
Unstructured
systems
Distributed Flooding
hash tables
Random
walk
Centralized Decentralized Push Pull Parallel Agent
random cloning
systems
systems
walk
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SELF-ORGANIZATION APPROACH
Objectives:
Scalability
nodes can establish relationships between
them based on their affinity
Adaptability
affinity relationships between nodes are
dynamic; the affinity values can be
adjusted at run-time to cope with changes
in the environment
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AFFINITY NETWORKS
To build affinity networks, nodes establish affinity
relationships between them based on their provided
services.
Affinity corresponds to the adequacy which two
services to bind
Adequacy could be implemented based on
keywords or objects in common describing a
capabilities provided by services.
To determine this affinity, services can be
expressively described by a language description in
order to obtain effective matches between their
capabilities (e.g., WSDL).
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Building and leaving affinity networks
let consider D(Si) a description of the service offered by an Sagent that want
to create an affinity relationship with a nearby Sagents .
Let us consider also MATSH(D(Si),D(Sj)) a function that return an affinity
measure mij which indicates if the service description of Si matches with the
service description of the agent Sj.
mij can be calculated as the ratio of keywords that are in common between
Si and Si .
If mij is above a certain threshold  , agent Si creates an affinity relationship
with the agent and Si creates an affinity relationship with Si .
An affinity relationship between Si and Si is considered valid if mij ,
otherwise, it is discarded and could be removed from the affinity relationship
set of Si .
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AFFINITY ADJUSTMENTS
The affinity between two agents is adjusted or reinforced
based on two level of satisfaction.
local satisfaction: described by services offered by
neighboring agents and resources needed to run services
(i.e. computing context)
mij(t 1)(ui.uj g(mij(t)))
global satisfaction: described by the user satisfaction (i.e.
user context)
mij(t 1)(u g  g(mij(t)))
1
g(mij(t))
1exp( mij(t))
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SIMULATION RESULTS
Simulation using NS2
A network of 100 nodes is generated randomly.
Each node provides one service of ten kinds of elementary
services that is described by a single of keyword.
Without creation of relationships
With creation of relationships
r
12
10
•Each node has no knowledge of services
provided by other nodes and the service
discovery and composition performs
poorly
•At the beginning of the simulation,
there are no relationships, and service
discovery and composition performs
poorly.
8
6
4
2
100
91
82
73
64
55
46
37
28
19
10
1
0
t
•As more simulator time elapses, nodes
create many affinity relationships with
adjustment learning that improve the
overall performance
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CONCLUSION AND FUTURE WORK
Decentralized approach for service discovery and composition
for pervasive environment is presented.
In this approach, the mechanism of establishing affinity
relationships is very simple.
Other mechanisms can be introduced to increase the rate at
which the nodes acquire the relationships that meet the
desired and required services.
Future work will address the integration of context-awareness
parameters in the equations described above together with
additional simulations with ns2.
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