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Intelligent Quality of Service Routing for Terrestrial and Space Networks

Intelligent Quality of Service Routing for Terrestrial and Space Networks. Funda Ergun Case Western Reserve University. Our Goal.

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Intelligent Quality of Service Routing for Terrestrial and Space Networks

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  1. Intelligent Quality of Service Routing for Terrestrial and Space Networks Funda Ergun Case Western Reserve University

  2. Our Goal 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 and cost-effective routing with Quality of Service, with the ability to learn from experience.

  3. Quality of Service • Network has various components—satellites, terrestrial routers, PCs, mobile devices. Topology may change in time • Different transmissions require different parameters • Existing protocols provide basic service, not much more: IP/BGP provide policy/shortest path routing. MPLS provides label switching, supports path allocation/backup paths and more intelligent path selection, thus is suitable for incorporating new algorithms. • Cost is a major issue

  4. Example Multicast Routing Network of 7 multicast, 7 non-multicast nodes and the multicast tree

  5. Unicast: Find best path between two nodes that respects constraints , minimizing overall cost. Multicast: Find best tree that goes to all multicast nodes, respecting constraints, minimizing cost. Current Internet: find shortest path for unicast. No good algorithm for multicast. Problem Model

  6. Constraints • We would like our routing to: • Respect bandwidth, reliability, delay, cost concerns. • Be adaptable to changes in topology. • Not require much computational power, allow policy routing. • Use past experience to make decisions. • Intractable!!! One can resort to heuristics/approximation algorithms. Then, must check: • Running time with realistic data • How “good” the routing is performed.

  7. Solutions and Testing • Techniques: • Rounding and scaling of data for approximation • Lagrangian relaxation • Estimating future resource allocation. • Simulation: Various types, sizes of networks for both multicast and unicast. Various types of demands considered. • Results compared to best solutions, as well as other known techniques.

  8. Test Results 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. Next Step, Concerns • Building 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. Papers F. Ergun, R. Sinha, L. Zhang. Routing with Performance Dependent Costs. Submitted to IEEE/ACM Transaction on Networks. F. Ergun, R. Sinha, L. Zhang. An Improved Algorithm for Restricted Shortest Path Routing. Information Processing Letters, 2002. F. Ergun, D. Wang, Z. Xu. Unicast and Multicast Routing with Multiple Constraints. Submitted, available as CWRU Technical Report.

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