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On the Robustness of Soft State Protocols

On the Robustness of Soft State Protocols. B00902028 屠政皓 B00902071 龔逸軒. Reading materials . On the Robustness of Soft State Protocols Author: John C.S. Lui , Vishal Misra , Dan Rubenstein . Outline . Introduction Motivation The impact of the refresh timer value

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On the Robustness of Soft State Protocols

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  1. On the Robustness of Soft State Protocols B00902028 屠政皓 B00902071 龔逸軒

  2. Reading materials • On the Robustness of Soft State Protocols • Author: John C.S. Lui, Vishal Misra, Dan Rubenstein

  3. Outline • Introduction • Motivation • The impact of the refresh timer value • Correlated, lossy feedback channel • Broadcast flooding • Conclusion

  4. Communication protocol A procedure used by 2 interacting parties to exchange information Often maintain a modifiable “state” that is used to track the progress of communication

  5. “softness” of a protocol state Default value : A communication endpoint's state can take on several values Reverts to its default value if the endpoint receives no communication within a time period. Commonly referred to as the timeout period.

  6. “softness” of a protocol state The manner in which the timeout mechanism is used Hard state: used only as an emergency failsafe Soft state: utilize timeout mechanism under normal operating condition

  7. The principle difference the time over which the refresh timer expires the soft state protocol requires a small amount of additional time after the session officially ends for the refresh timer to expire and reset the sender to its default state.

  8. robustness The protocol's performance under a variety of network conditions is above an acceptable threshold. Need not be optimal

  9. robustness

  10. characteristics of soft state protocol Soft state protocols are less trusting of misbehaving endpoints. DOS attack

  11. characteristics of soft state protocol Soft state protocols use the timeout mechanism to create a virtual, predictable, feedback channel. Correlated lossy feedback channel

  12. characteristics of soft state protocol Soft state protocols are less likely to flood the network with signaling traffic Broadcast flooding

  13. The impact of refresh timer value Application-specific inconsistency cost. State (Re)Initialization cost. Refresh overhead. Stale state cost.

  14. Analytical model Life time: L I:initialization state V: valid state TD: tear down S: stale state Refresh every R time unit

  15. Choosing the right R The principle difference between soft and hard state protocol Refresh and teardown message has a loss probability p Number of refresh event: L/R Measuring performance by calculating cost C(R)

  16. Calculating Cost #loss refresh message: pL/R Protocol enters an inconsistent state Tradeoff between long time spent in inconsistent state ,or more refresh messages

  17. Calculating Cost A reinitialize cost when entering inconsistent state Time spent in inconsistent state: pL #(re)initialization : 1+pL/R

  18. Calculating Cost Stale state cost: Proportion to refresh time interval R For soft state protocol ,this cost is incurred every time a state expires For hard state protocol, it is incurred when a state removal message gets lost

  19. Caculating Cost

  20. Calculating Cost

  21. Fundamental hard vs soft tradoff • Optimal Refresh time interval ∝ • Cost of concern • a: refresh overhead , initialization cost • b: stale state cost • The protocol is “harder” if is larger

  22. Comparison • X-axis: loss probability • Y: cost

  23. DOS attack • Denial of service • An attacker reserves a resource , and then exiting without tearing down the connection • Life time = 0 ,channel loss probabilty = 1

  24. Comparison

  25. A Correlated, Lossy Feedback Channel • Problem • congestion collapse • Model • Initial new sessions with rate λ • Mean time complete the transfer of data is 1/μ • active session and inactive session Sever can be simultaneously host up to N sessions Channel can support N < M sessions without loss

  26. A Correlated, Lossy Feedback Channel (cont.) • Hard state protocol • Client attempts n times to deliver a message to the server to terminate the connection • soft state fail safe: a time with mean 1/μt after the client aborts, server can sense the absence of client and terminate • Soft state protocol • clients ping the server at high rate • when the server does not receive the ping for a time with mean 1/μs , it terminates • 1/μs << 1/μt

  27. A Correlated, Lossy Feedback Channel (cont.) • CTMC model • State (i, j), i: number of active sessions, j: number of inactive sessions • p(i, j) is the loss probability in (i, j), x(i, j) is the probability client fails to inform server its departure

  28. A Correlated, Lossy Feedback Channel (cont.) • CTMC for hard date protocol • CTMC for soft state protocol

  29. A Correlated, Lossy Feedback Channel (cont.) • Analysis

  30. A Correlated, Lossy Feedback Channel (cont.)

  31. A Correlated, Lossy Feedback Channel (cont.)

  32. Broadcast flooding • Problem • implosion • scenario • The server performs the broadcast as long as there exists a client interested receiving the data • Clients communicate to the server via unicast and the messages are queued at the server for processing • arrival rate of the massage that clients interested in broadcast is λ • interest lasts for a time exponentially distributed with rate μ • M/M/∞ system

  33. Broadcast flooding (cont.) • Hard state protocol • client explicitly contacts the server upon arrival and departure • server tracks the individual status of each clients to decide when to broadcast • Soft state protocol • in each time interval T, clients contact the server to continue the broadcast in the next interval • the server can inform other clients by piggybacking the information within the broadcast

  34. Broadcast flooding (cont.) • Use scalable polling operation to reduce control massages • Hard state protocol • Choosing leader and only maintain the state of the leader • Soft state protocol • To determine if any clients interested in the current data

  35. Broadcast flooding (cont.) • scalable polling operation • Each client is assigned a n-bit unique sequence • rounds numbered 0 to k • specify a m-bit quantity, m = ks < log n • in the i-th round, a client whose unique bit sequence match along the first (m-ik) bits of the sender specified quantity transmits a message • if any client transmits a message in round i, no further rounds are needed

  36. Broadcast flooding (cont.) • q is the probability that arbitrarily chosen receiver transmits on the i-th round when there are n receivers • Summing over all n receivers over all rounds get N(n, k, s) which is it traffic • soft state protocol traffic rate is • hard state protocol traffic rate is

  37. Broadcast flooding (cont.)

  38. Broadcast flooding (cont.)

  39. Conclusion • Soft state maintain an acceptable level of performance across a much wider range of network conditions than is maintained by their hard state counterparts

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