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High Rate Data Delivery Program September 2002

High Rate Data Delivery Program September 2002. Intelligent and Agile Protocols and Architectures for Space and Terrestrial Networks. Funda Ergun Behnam Malakooti Case Western Reserve University NAG3-2578. Objective.

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High Rate Data Delivery Program September 2002

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  1. High Rate Data Delivery ProgramSeptember 2002 Intelligent and Agile Protocols and Architectures for Space and Terrestrial Networks Funda Ergun Behnam Malakooti Case Western Reserve University NAG3-2578

  2. Objective • Given a space, terrestrial, or hybrid network consisting of aerospace, near earth and terrestrial components, design architectures and protocols which will allow for faster, more reliable routing with Quality of Service, learning from their experience.

  3. Nature of the Problem and the Solutions • Network composed of various types of components—satellites, terrestrial routers, end hosts, mobile devices, … • Network topology may change in time • Different transmissions require different parameters • Existing protocols provide basic service, not much more

  4. Examples of Existing Service • IP, routing using BGP. Routing performed using shortest path or policy routing. Topology discovery is crude and takes time. • Label switching, MPLS. Path allocation, backup paths are supported. Main concern is reliability, main goal is to keep packet header short.

  5. Desired Routing Algorithm We would like our routing algorithm to: • Respect bandwidth, reliability, delay, cost concerns. • Be adaptable to changes in topology. • Not require much computational power from intermediate nodes. • Incorporate policy routing if necessary.

  6. Design of a routing protocol • Routing/scheduling algorithm with desired properties is intractable. • One can resort to heuristics/approximation algorithms. Issues to be dealt with: Running time with realistic data How “good” the routing is performed.

  7. Techniques Used • Rounding and scaling of data for approximation • Lagrangian relaxation • Estimating future resource allocation. Simulation results are very promising for both multicast and unicast networks: algorithms/heuristics are extremely fast, require very little computation, and route in a very “intelligent and precise” way.

  8. Simulation Results • If all of desired values stay within bounds, our algorithms find solutions that cost up to 5% more than the optimal solution in around 100msec for 50-100 node networks for unicast, up to 15% more for multicast networks. • Finding the optimal multicast routing for a 10-node network takes a full day; at 12 nodes it cannot be done. [ESZ], [ESZ2], [WEX].

  9. Protocols • Message passing and data storage requirements: most of the data is kept in the routers. • Not all nodes need to have access to all the information. Using hierarchical routing, data dissemination needs can be minimized. • Header lengths need not become very long; MPLS-like structure. • Routing data can be translated into diffserv-like class system.

  10. Intelligent Mobile Decision Makers • Developing a framework for Intelligent Mobile Decision Makers as a vehicle to achieve distributed decision making. • Developing Intelligent Mobile Decision Makers that can operate within IP. • Developing an approach for Clustering of Packets: for more effective and flexible network communication.

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