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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
2
OUTLINE
Context and Objectives
Related work
Self-Organization Approach to the Design of Emergent Pervasive Services
Simulation results
Conclusion and future work
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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.
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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).
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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 discoverysystems
Structured systems
Unstructuredsystems
Flooding Random walk
Distributed hash tables
Indexation
Centralizedsystems
Decentralizedsystems
Push Pull Parallel random
walk
Agentcloning
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RELATED WORK (2/5)
Service discoverysystems
Structured systems
Unstructuredsystems
Flooding Random walk
Distributed hash tables
Indexation
Centralizedsystems
Decentralizedsystems
Push Pull Parallel random
walk
Agentcloning
• Brokers that maintain a repository of published services
• Hierarchical architecture consisting of multiple repositories that synchronize periodically
• Cannot meet the requirements of both scalability and adaptability simultaneously
• The risk of bottlenecks and the difficulty of repositories updating
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RELATED WORK (3/5)
Service discoverysystems
Structured systems
Unstructuredsystems
Flooding Random walk
Distributed hash tables
Indexation
Centralizedsystems
Decentralizedsystems
Push Pull Parallel random
walk
Agentcloning
• Permits to implement a direct search algorithm to efficiently locate services.
• Global Overlay network between nodes are generally hard to maintain.
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RELATED WORK (4/5)
Service discoverysystems
Structured systems
Unstructuredsystems
Flooding Random walk
Distributed hash tables
Indexation
Centralizedsystems
Decentralizedsystems
Push Pull Parallel random
walk
Agentcloning
•Allow nodes to enter and leave the systems without overheads
• It is not possible to guarantee the success or failure of a query with a constant TTL
• The mechanism of dynamic TTL or expanding ring is proposed to overcome this problem
• Generate large loads on the network
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RELATED WORK (5/5)
Service discoverysystems
Structured systems
Unstructuredsystems
Flooding Random walk
Distributed hash tables
Indexation
Centralizedsystems
Decentralizedsystems
Push Pull Parallel random
walk
Agentcloning
• It is difficult to determine a priori the number of parallel Random walks
•Agent cloning approach can overcome this problem but need a regulation algorithm
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SELF-ORGANIZATION APPROACH
Service discoverysystems
Structured systems
Unstructuredsystems
Flooding Random walk
Distributed hash tables
Indexation
Centralizedsystems
Decentralizedsystems
Push Pull Parallel random
walk
Agentcloning
Self-organizationsystems
Affinity networks
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SELF-ORGANIZATION APPROACH
Objectives:
Scalabilitynodes can establish relationships between them based on their affinity
Adaptabilityaffinity 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 ,
otherwise, it is discarded and could be removed from the affinity relationship set of Si .
ijm
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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)
)))((()1( tmgutm ijg
ij
))(exp(11))((
tmtmg
ijij
)))((.()1( tmguutm ijjiij
global satisfaction: described by the user satisfaction (i.e. user context)
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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.
0
2
4
6
8
10
12
1 10 19 28 37 46 55 64 73 82 91 100 t
r
Without creation of relationships
With creation of relationships
•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.
•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.
