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Better Fault Tolerance via Application-Enhanced Networks

Better Fault Tolerance via Application-Enhanced Networks. John Hartman Will Evans University of Arizona. End-to-end fault tolerance. Fault tolerance as a QoS issue a broken QoS guarantee is a fault Denial-of-service is the tricky part of QoS accounting, detection, revocation

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Better Fault Tolerance via Application-Enhanced Networks

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  1. Better Fault Tolerance via Application-Enhanced Networks John Hartman Will Evans University of Arizona

  2. End-to-end fault tolerance • Fault tolerance as a QoS issue • a broken QoS guarantee is a fault • Denial-of-service is the tricky part of QoS • accounting, detection, revocation • while minimizing resource usage • End-to-end argument • DoS can happen at any interface • application must be involved

  3. Active networks • Programmable network routers • Use active network technologies to: • perform early prevention of DoS attacks • tolerate faults (including DoS attacks) • “Application-enhanced networks”

  4. Project goals • Develop local resource management for active routers • fine-grain accounting and management • necessary for tolerating faults and attacks • Construct application-enhanced networks • application-specific fault/attack prevention and response • proofs-of-concept: distributed terrain navigation, network-resident storage

  5. Network-resident storage (NRS) • Storage system functionality in network • improve storage system performance and functionality • centralize functionality in network • storage-specific resource management and accounting • e.g. quotas, permissions, bandwidth • improve resistance to faults/attacks • e.g. early reject of invalid requests

  6. Active token service • Initial NRS effort • Token-based, network-resident synchronization • Storage service-specific policies • precedence, recovery • early prevention of DoS attacks

  7. Distributed terrain navigation C A B

  8. Fault prevention and tolerance • Network links & nodes fail • Denial of service • Prevention • Detect rogue application clients • Reject early • Tolerance • Application modifies network • Network CPU & storage compensate bandwidth/latency loss

  9. Main tactics • Sensitivity to data and network distance • Joint compression • Flexible multicast • Flow aggregation • Prediction • Data migration

  10. Use of active networks • Application driven re-routing • Compression at internal nodes • Data migration

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