The document describes an open proximity model for service discovery in pervasive computing. The model defines proximity as a relationship between objects based on their physical or abstract closeness. It proposes using proximity relationships that are open, composable, evaluated distributively, and dynamic to scope service discovery. The model represents objects with properties and proximity functions that determine their relationships to other objects.

1. An Open Proximity Model for
Service Discovery in
Pervasive Computing
Victor Ramiro Cid
Profesor Guía:
Éric Tanter
Miembros de la Comisión:
Johan Fabry
Patricio Inostroza
Luciano Ahumada
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2. Contents
1. Motivation and Context
2. Open Proximity Model
3. Open Proximity Model in AmbientTalk
4. Conclusions and Perspectives
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3. Motivation and Context
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4. Pervasive Computing
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5. Pervasive Computing
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6. Service Discovery
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7. Service Discovery
Advertising
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8. Service Discovery
Advertising
Querying
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9. Service Discovery
Advertising
Querying
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10. Service Discovery
Advertising
Querying
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11. Service Discovery - Client
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12. Service Discovery - Client
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13. Service Discovery - Client
What about
the User?
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14. Service Discovery - Client
User
Overwhelmed
We want
calm!
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15. Service Discovery - Service
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16. Service Discovery - Service
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17. Service Discovery - Service
What about
the Service?
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18. Service Discovery - Service
Service
Overwhelmed
We want
calm!
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19. Motivation
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20. Motivation
Designers should divide the work into physical environment with
boundaries that demarcate their content. [Kindberg 2001]
The density of interactions with any particular entity falls off as one
moves away from it. [Satyanarayanan 2001]
“Scalability is a critical problem in pervasive computing.”
[Satyanarayanan 2001]
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21. Motivation
Designers should divide the work into physical environment with
boundaries that demarcate their content. [Kindberg 2001]
The density of interactions with any particular entity falls off as one
moves away from it. [Satyanarayanan 2001]
“Scalability is a critical problem in pervasive computing.”
+ [Satyanarayanan 2001]
An Open Proximity Model for
Scoping Service Discovery in
Pervasive Computing
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9

22. Contribution
Distributed
Systems
Software
Engineering
Networking
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23. Contribution
Distributed
Systems
Software
Engineering
Networking
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10

24. Open Proximity Model
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25. Proximity as a Concept
Proximity is defined as the state, quality,
sense, or fact of being near or next
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26. Proximity as a Concept
Proximity is defined as the state, quality,
sense, or fact of being near or next
• Taxonomy
• Physical and Abstract
• Objective and Subjective
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12

27. Proximity as a Concept
Proximity is defined as the state, quality,
sense, or fact of being near or next
• Taxonomy
• Physical and Abstract
• Objective and Subjective
12
12

28. Proximity as a Concept
Proximity is defined as the state, quality,
sense, or fact of being near or next
• Taxonomy
• Physical and Abstract
• Objective and Subjective
12
12

29. Proximity as a Concept
Proximity is defined as the state, quality,
sense, or fact of being near or next
• Taxonomy
• Physical and Abstract
• Objective and Subjective
12
12

30. Proximity relationships (PR)
• Open and Idiosyncratic
• Composable
• Distributed Evaluation
• Dynamic
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31. Proximity relationships (PR)
• Open and Idiosyncratic
• Composable
• Distributed Evaluation
• Dynamic
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13

32. Proximity relationships (PR)
• Open and Idiosyncratic
• Composable
• Distributed Evaluation
• Dynamic
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13

33. Proximity relationships (PR)
• Open and Idiosyncratic
• Composable
• Distributed Evaluation
• Dynamic
13
13

34. Proximity relationships (PR)
• Open and Idiosyncratic
• Composable
• Distributed Evaluation
• Dynamic
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13

35. Open Proximity Model in a nutshell
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36. Open Proximity Model in a nutshell
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37. Open Proximity Model in a nutshell
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38. Open Proximity Model in a nutshell
Proximity: Accept all incoming jobs from DCC in the 3rd floor
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39. Open Proximity Model in a nutshell
Proximity: Accept all incoming jobs from DCC in the 3rd floor
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40. Open Proximity Model in a nutshell
Proximity: Accept all incoming jobs from DCC in the 3rd floor
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41. Open Proximity Model in a nutshell
Proximity: Accept all incoming jobs from DCC in the 3rd floor
DCC PDA
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42. Open Proximity Model in a nutshell
Proximity: Accept all incoming jobs from DCC in the 3rd floor
DCC PDA
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43. Open Proximity Model in a nutshell
Print Job
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44. Model Components
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45. Model Components
Object
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46. Model Components
Object Properties
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47. Model Components
Object Properties Proximity Function
ƒ
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48. Model Components
Object Properties Proximity Function
+ + ƒ
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15

49. Model Components
Object Properties Proximity Function
+ + ƒ
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50. Basic evaluation strategy
Multicast
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51. Basic evaluation strategy
Multicast
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52. Basic evaluation strategy
Multicast
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53. Basic evaluation strategy
Multicast
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54. Basic evaluation strategy
Multicast
16
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55. Basic evaluation strategy
Multicast
16
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56. Basic evaluation strategy
Multicast
16
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57. Basic evaluation strategy
Multicast
16
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58. Basic evaluation strategy
Multicast
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59. Selective evaluation strategy
Multicast
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60. Selective evaluation strategy
Multicast
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61. Selective evaluation strategy
Multicast
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62. Selective evaluation strategy
Multicast
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63. Selective evaluation strategy
Multicast
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64. Selective evaluation strategy
Multicast
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65. Selective evaluation strategy
Multicast
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66. Staged evaluation strategy
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67. Staged evaluation strategy
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68. Staged evaluation strategy
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69. Staged evaluation strategy
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70. Staged evaluation strategy
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71. Staged evaluation strategy
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72. Composite Proximity
• Compose functions as
boolean formulas (f1 && f2 ) || (f3 && (f4 || f5 ))
• Tree composite OR
• Internar nodes: Operations
AND AND
• Leaves: simpler functions
• Short-circuit evaluation f1 f2 f3 OR
• Distributed evaluation
f4 f5
• Must be done by the
programmer
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73. Open Proximity Model in AmbientTalk
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74. AmbientTalk AmbientTalk: the project
• Distributed object-oriented language (Prototype-based)
•
• Event-driven concurrency based on in 2005[Agha86]
Started actors
• • Small team: 3-6 peoplefutures
Asynchronous message sends, with non-blocking
• Interpreter
•
• Pure Java implementation
Built-in engine for service discovery of remote objects
• Extensible language • Runs on J2ME/CDC phones
• Powerful reflective and meta-programming layer
8
• Block closures, keyworded messages, interface with JVM
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75. Service Discovery in AmbientTalk
def hp := Printer.new(...);
export: hp as: PrinterServer;
Querying
Advertising
when: PrinterServer discovered: {|printer|
printer<-addJob(job);
}
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76. Extended Service Discovery in AmbientTalk
def hp := Printer.new(...);
export: hp with: s_props in:
euclidean(s_props,radius);
Querying
Advertising
when: PrinterServer discovered: {|printer|
printer<-addJob(job);
}with: c_props in: euclidean(c_props,radius);
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77. Properties Syntax
• AT objects with new semantics
(extended Mirror)
def props := properties:{
• They can capture variables
deftype PeerNode;
from their lexical scope
def name := “Smith”;
• Changed normal object def x() {gps.getX()};
behaviour through MOP def y() {gps.getY()};
};
• invokeField
• pass (serialization)
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78. Properties Syntax
• AT objects with new semantics
(extended Mirror)
def props := properties:{
• They can capture variables
deftype PeerNode;
from their lexical scope
def name := “Smith”;
• Changed normal object def x() {gps.getX()};
behaviour through MOP def y() {gps.getY()};
};
• invokeField
• pass (serialization)
24
24

79. Properties Syntax
• AT objects with new semantics
(extended Mirror)
def props := properties:{
• They can capture variables
deftype PeerNode;
from their lexical scope
def name := “Smith”;
• Changed normal object def x() {gps.getX()};
behaviour through MOP def y() {gps.getY()};
};
• invokeField
• pass (serialization)
24
24

80. Properties Syntax
• AT objects with new semantics
(extended Mirror)
def props := properties:{
• They can capture variables
deftype PeerNode;
from their lexical scope
def name := “Smith”;
• Changed normal object def x() {gps.getX()};
behaviour through MOP def y() {gps.getY()};
};
• invokeField
• pass (serialization)
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24

81. Proximity Function Syntax
• They are not sent through the network
• They can capture variables from their lexical scope
• Special mapping of formal parameters based on name convention
• The formal parameters are looked up in the target properties set
def euclidean(props, radius){
{ |x,y,name|
def d := ((props.x - x).expt(2) +
(props.y - y).expt(2)).sqrt();
(d < radius).and: { (props.name <=> name) != 0 };
};
};
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82. Proximity Function Syntax
• They are not sent through the network
• They can capture variables from their lexical scope
• Special mapping of formal parameters based on name convention
• The formal parameters are looked up in the target properties set
def euclidean(props, radius){
{ |x,y,name|
def d := ((props.x - x).expt(2) +
(props.y - y).expt(2)).sqrt();
(d < radius).and: { (props.name <=> name) != 0 };
};
};
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83. Proximity Function Syntax
• Composite to represent composition of functions
• Basic functions is a Functor
• Composite nodes are Boolean operations And/Or
• Evaluation is bottom-up, optimizing boolean cases (Order matters!)
def euclidean(props, radius){
def f := Functor.new(
{|x,y| ((props.x - x).expt(2) +
(props.y - y).expt(2)).sqrt() < radius)});
def g := Functor.new({|name|
(props.name <=> name) != 0 });
def composite := And.new(f,g);
composite;
};
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84. Proximity Function Syntax
• Composite to represent composition of functions
• Basic functions is a Functor
• Composite nodes are Boolean operations And/Or
• Evaluation is bottom-up, optimizing boolean cases (Order matters!)
def euclidean(props, radius){
def f := Functor.new(
{|x,y| ((props.x - x).expt(2) +
(props.y - y).expt(2)).sqrt() < radius)});
def g := Functor.new({|name|
(props.name <=> name) != 0 });
def composite := And.new(f,g);
composite;
};
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85. Proximity Function Syntax
• Composite to represent composition of functions
• Basic functions is a Functor
• Composite nodes are Boolean operations And/Or
• Evaluation is bottom-up, optimizing boolean cases (Order matters!)
def euclidean(props, radius){
def f := Functor.new(
{|x,y| ((props.x - x).expt(2) +
(props.y - y).expt(2)).sqrt() < radius)});
def g := Functor.new({|name|
(props.name <=> name) != 0 });
def composite := And.new(f,g);
composite;
};
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86. Proximity Function Syntax
• Composite to represent composition of functions
• Basic functions is a Functor
• Composite nodes are Boolean operations And/Or
• Evaluation is bottom-up, optimizing boolean cases (Order matters!)
def euclidean(props, radius){
def f := Functor.new(
{|x,y| ((props.x - x).expt(2) +
(props.y - y).expt(2)).sqrt() < radius)});
def g := Functor.new({|name|
(props.name <=> name) != 0 });
def composite := And.new(f,g);
composite;
};
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87. Proximity Function Syntax
• Composite to represent composition of functions
• Basic functions is a Functor
• Composite nodes are Boolean operations And/Or
• Evaluation is bottom-up, optimizing boolean cases (Order matters!)
def euclidean(props, radius){
composite: {|x,y| ((props.x - x).expt(2) +
(props.y - y).expt(2)).sqrt() < radius)}
and: {|name| (props.name <=> name) != 0 };
};
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88. Proximity Function Syntax
• Composite to represent composition of functions
• Basic functions is a Functor
• Composite nodes are Boolean operations And/Or
• Evaluation is bottom-up, optimizing boolean cases (Order matters!)
Syntac
tic
Sugar
def euclidean(props, radius){
composite: {|x,y| ((props.x - x).expt(2) +
(props.y - y).expt(2)).sqrt() < radius)}
and: {|name| (props.name <=> name) != 0 };
};
27
27

89. Proximity Function Syntax
• Composite to represent composition of functions
• Basic functions is a Functor
• Composite nodes are Boolean operations And/Or
• Evaluation is bottom-up, optimizing boolean cases (Order matters!)
Syntac
tic
Sugar
def euclidean(props, radius){
composite: {|x,y| ((props.x - x).expt(2) +
(props.y - y).expt(2)).sqrt() < radius)}
and: {|name| (props.name <=> name) != 0 };
};
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90. Experiment Setup
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91. Experiment Setup
8 nodes
56 connections
15 nodes
210 connections
5 nodes
20 connections
Connections ≈ O(n ) 2
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92. Major Results
• Network layer used for service discovery in AmbientTalk:
• Does not meet all requirements needed to extend the service discovery
• Problems with:
• Measure data collision
• Define transmission velocity and retransmission rates
• Use of Java scheduler to measure time and not real scale time
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93. Major Results
AT2 OPM
400.000
300.000
Data sent (KB)
200.000
100.000
0
2 5 10 15 20 25 30 35 40 45 50
Number of Nodes
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94. Major Results
BES SES StES
4.000
3.000
Sent Data (KB)
2.000
1.000
0
0 10 100 1000 10000
Properties array size
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95. Conclusions and Perspectives
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96. Conclusions
1. Proximity Taxonomy and Proximity Relationships
2. Open Proximity Model
2.1.Three evaluation strategies proposed
2.2.trade-off between simplicity and efficiency
3. Open Proximity Model in AmbientTalk
3.1.Use of reflection and meta-programming facilities is very helpful
3.2.AmbientTalk’s network layer fails to meet several networks
requirements
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97. Perspectives
1. Improvements on OPM in AmbientTalk
2. A new communication layer for AmbientTalk
3. Publish-Suscribe Service Discovery
4. Context awareness
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98. 35
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99. GRACIAS!!!
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