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Better Fault Tolerance via ApplicationEnhanced Networks

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Denial-of-service is the tricky part of QoS. accounting, detection, revocation ... DoS can happen at any interface. application must be involved. 3. Active networks ... – PowerPoint PPT presentation

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Title: Better Fault Tolerance via ApplicationEnhanced Networks


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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