Topic Based Data Distribution in GSpace

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Topic Based Data Distribution in GSpace
SACC Software Architecture Component
Coordination
Giovanni Russello Department of Mathematics and
Computing Science, Eindhoven University of
Technology
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Overview SACC project
Separate functional and non-functional
specification
Weaving them into a component-based model via a
correctness preserving transformation/refinement
Specification Domain
Implementation Domain

Functionality
Components
Sound Transformation
Coordination
Platform Information
Timing Constraints
Observing the implementation and providing
feedback / hardware-related info
Implementing the component-based model, managing
distribution of process/data, distributed
scheduling
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The Gamma basic data structure the Multiset

• Set with multiple occurrence of the same element
• Absence of any constraint or hierarchy
  imposition between its elements
• Supports/enables chaotic model of execution

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Gamma Multiset Graphical Representation
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The Gamma reaction structure
max x, y ? y ? x ? y

• R condition

A action
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A possible approach Space-Based System

• The Space acts like a repository to store and
  retrieve data
• All the same characteristics as the Multiset
• A natural parallel and distributed programming
  style
• Uncoupled communication in space and time
• - CBD benefits from minimizing dependencies

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A Space-Based Java implementation JavaSpaces

• Java programming language extension
• Portability
• Free code availability

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JavaSpace examples

• entry objects present in the space
• write to insert an entry in the space
• take to retrieve and withdraw an entry from the
  space
• read to retrieve a copy of an entry present in
  the space

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Mapping the Gamma model onto JavaSpacesAn Example

• Gamma

• JavaSpaces
• x take(xTemplate)
• y take(yTemplate)
• if( x ? y )
• write(y)
• else
• write(x)
• write(y)
• fi

max x, y ? y ? x ? y
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The JavaSpaces drawbacks

• It is not distributed
• It is not Real Time
• It is expensive in terms of resources

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Single Space Implementation
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Distributed Space Implementation

• Advantages
• Scalability built into the architecture
• Reliability no single point of failure

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Our approach GSpace
Research hypothesis

• Efficiency
• Higher efficiency through differentiated
  distribution policies
• Adaptability through Separation of concerns
• Independence between system functionality and
  data distribution

joint work with Maarten van Steen
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Initial Experiment

• Separate distribution policies from storage
  mechanism
• Compare differentiated distribution policies vs.
  single policy
• Compare distributed storage vs. centralized
  storage

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GSpace Overview
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GSpace Conceptual
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GSpace APIs

• put to insert an element in the space
• get to obtain an element from the space
• producer to declare that a component produces an
  element type
• consumer to declare that a component needs such
  a type of element

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How to Map Distribution Policy with Datatype

• Distribution Policies
• Unreliable/Reliable Push/Pull
• Associate a Distribution Policy with a datatype
• Distribute policy to each host (before system
  starts)
• Controller incorporates Policies into its local
  PolicyTable

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Collaboration Diagram Unreliable Push Model
Policy - Put
Producer Component
UdpReceiver
Flush the message over the network
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Collaboration Diagram Unreliable Push Model
Policy - Get
Consumer Component
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Collaboration Diagram Reliable Pull Model Policy
- Put
Producer Component
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Collaboration Diagram Reliable Pull Model Policy
- Get
Consumer Component
9 sendBack(message)
TcpReceiver
Send the message over the network
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A Case Study Traffic Management
Framework Overview
Aggregator
Computation Unit
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A Case Study Traffic Management
Framework Implementation in GSpace
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A Case Study Traffic Management
Framework Implementation in JavaSpace
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Case Study Result GSpace Policy Setting1
Signal
Aggregator
Computation Unit
Display
1sec
Reliable Push
Reliable Push
Reliable Push
Average Time 103 millisec
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Case Study Graphic GSpace Policy Setting1
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Case Study Result GSpace Policy Setting2
Signal
Aggregator
Computation Unit
Display
1sec
Reliable Pull
Reliable Push
Reliable Push
Average Time 75.5 millisec
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Case Study Graphic GSpace Policy Setting2
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Case Study Result JavaSpace
Signal
JavaSpace
Aggregator
Computation Unit
Display
1sec
Average Time 140 millisec
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Case Study Graphic JavaSpace
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Result and comparison GSpace vs. JavaSpace

• GSpace performs well against JavaSpace despite
  the fact that is in a prototype stage
• It is distributed
• It offers support for different distribution
  policies

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Concluding Remarks Future Work

• A platform for experimentation with different
  distribution policies is available
• The early results are encouraging
• Mechanisms for providing timeliness guarantees
• Integrating the scheduling timing constraints
• Mechanisms for feedback performance information