Self-Routing in Pervasive Computing Environments using Smart Messages

1
Self-Routing in Pervasive Computing
Environments using Smart Messages

• Anitha Ellappan
• Poonam Hajgude
• Santosh Patil
• University of Central Florida

Group 4
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Networks of Embedded Systems
Incorporating intelligence in devices and
providing them network connectivity

Linux Camera
Linux Car

• Functionally heterogeneous nodes
• Very large scale
• Ad hoc topologies
• Dynamic network configurations
• Limited a priori knowledge about network
  resources

3
Programmability Challenge

• Traditional message passing distributed computing
  does not work for networks of embedded systems
• unknown and volatile network configurations
• end-to-end data transfer may hardly complete
  (i.e., all or nothing semantics is not
  appropriate)
• fixed address naming and routing (e.g., IP) are
  too rigid
• Solution Cooperative Computing using Smart
  Messages
• More flexible naming and routing are needed
• applications interested in content/services, not
  individual nodes
• different applications have different routing
  requirements

4
Outline

• Motivation
• Smart Messages Overview
• Self-Routing Mechanism
• content-based migration
• application scenarios
• Evaluation
• SM prototype
• simulations
• Conclusions Future Work

5
Smart Messages at a Glance

• Distributed computing using execution migration
• Applications composed of one or multiple Smart
  Messages
• Smart Message (SM)
• composed of code, data, and execution state
• executes on nodes of interest named by properties
• Cooperative Nodes
• execution environment (Virtual Machine)
• content-based memory (Tag Space)
• Self-Routing
• routing performed at application-level
• applications can change routing during execution

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Application example
EZCab - An application for locating and booking
free cabs in densely crowded traffic
environments.
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Smart Messages

• Migratory execution units consisting of code and
  data section (Bricks) and a light weight
  execution state.
• Goal
• To reduce the support required from nodes by
  placing parts of intelligence in SMs
• This provides flexibility and obviates the
  difficulty of reprogramming the network for new
  application

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Node Architecture System Support provided by
nodes
Execution is non-preemptive, but time bounded
Admission prevents excessive use of resources
sm1
SM arrival
Virtual Machine
Admission Manager
SM migration
sm2

SM Ready Queue

• Two types of tags
• application tags (Temp)
• I/O Tags (Permanent)
• Tags used by SMs for
• routing
• data exchange
• synchronization
• I/O access

tag1
tag2

Tag Space
Identifier Name of tag Data Application
specific
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Smart Messages Migration
migrate(Taxi)
Taxi
Taxi
1
2
3
4
sys_migrate(2)
sys_migrate(3)
sys_migrate(4)

• Two level migration
• migrate()
• embeds routing algorithm
• migrates application to next node of interest
• names nodes in terms of arbitrary conditions
• on tag names and tag values
• sys_migrate()
• one hop migration
• used to implement migrate

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Self-Routing

• Smart Messages carry the routing and execute it
  at each node
• Smart Messages control their routing
• select routing algorithm (migrate primitive)
• from multiple library implementations
• implement a new one
• change routing algorithm during execution
• in response to adverse network conditions
• according to applications requirements

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Dynamic Change of Routing (1)
Dense network Low mobility Proactive routing
Sparse network High mobility On-demand routing
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Dynamic Change of Routing (2)
space-bound on-demand routing to reach the nodes
of interest
geographical routing to reach circle
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Smart Messages Routing Algorithms

• Goal Evaluate the potential of SMs to implement
  different content-based routing algorithms
• on-demand content-based routing (similar to AODV
  Perkins 99)
• greedy geographical routing (similar to GPSR
  Karp 00)
• proactive routing using Bloom filters (similar to
  Probabilistic Routing Rhea 02)
• rendez-vous routing (combining on-demand and
  proactive routing)
• e.g., geographic dissemination limited flooding
• advantage improves the response time for
  applications while avoiding global dissemination
  and large scale flooding

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Evaluation Strategies (1)

• Implementation
• SM prototype over Sun Java KVM on HP iPAQs
• small scale network (8 nodes)
• evaluated the effects of code caching

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Evaluation Strategies (2)

• Simulation
• SM simulator
• large scale network (256 nodes)
• evaluated the effects of best routing selection
  and dynamic change of routing
• Metrics
• completion time user-observed response time for
  an application
• total number of bytes sent total amount of
  traffic generated by an application
• also indicates the energy and bandwidth consumed
  by an application

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On-Demand Routing vs. Geographical On-Demand
Routing

• 3 nodes of interest located in the corners
• have to be visited in clockwise order
• application has knowledge about these nodes
  regions
• vary the radius from 100m to 700m

starting node
node of interest
other node
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Conclusions

• Self-Routing provides high flexibility for SM
  applications
• choose the routing
• implement their own routing
• change the routing dynamically
• Self-Routing has performance benefits
• improved response time for applications
• significant energy and bandwidth savings in the
  network

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Future Work

• Spatial Programming with Smart Messages
• programming model for networks of embedded
  systems
• network resources accessed transparently using
  space, tag spatial references
• a node referenced by space, tag is reached
  through a combination of geographical and
  content-based routing
• Design and implement real world applications
  using Smart Messages and self-routing

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References

• Cristian Borcea, Chalermek Intanagonwiwat,
  Akhiles Saxena, Liviu Iftode, Self-Routing in
  Pervasive Computing Environments using Smart
  Messages, First IEEE Conference on Pervasive
  Computing and Communications, 2003.
• Cristian Borcea, Deepa Iyer, Porlin Kang,
  Akhilesh Saxena, and Liviu Iftode, Cooperative
  Computing for Distributed Embedded Systems 22nd
  International Conference on Distributed Computing
  Systems, July 2002.
• Banavar G, Beck J, Gluzberg E, Munson J, Sussman
  JB, Zukowski D, Challenges An Application Model
  for Pervasive Computing. Sixth ACM
  MOBICOM,Boston,2000.

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Thank you