on the robustness of soft- state protocols john lui, cuhk vishal misra, columbia u. dan rubenstein,...
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![Page 1: On the Robustness of Soft- State Protocols John Lui, CUHK Vishal Misra, Columbia U. Dan Rubenstein, Columbia U](https://reader035.vdocuments.mx/reader035/viewer/2022062518/5697bf831a28abf838c86480/html5/thumbnails/1.jpg)
On the Robustness of Soft-On the Robustness of Soft-State ProtocolsState Protocols
John Lui, CUHKVishal Misra, Columbia U.
Dan Rubenstein, Columbia U.
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StateState
• To operate correctly, network protocols require that communicating nodes share state, e.g.,– Connection is “active”– The largest sequence # received was …
• Q: In networks with a lossy/unpredictable control channel, how is state information kept consistent across nodes?
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Keeping State ConsistentKeeping State Consistent
• Two very different approaches / philosophies / mantras to how the signaling is performed:– Hard-state: The “Telephony Philosophy”?– Soft-state: The “Internet Philosophy”
[Clark’89]
• The difference:– Easy to describe philosophically– Hard to define precisely
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Soft-state signalingSoft-state signaling
Signaling plane
Communication plane
Sender Receiver
• Best effort signaling• Refresh timer: state needs periodic refresh• State only removed by time-out• Failure to communicate go to safe (default) state
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Soft-state signalingSoft-state signaling
Signaling plane
Communication plane
Sender Receiver
• Best effort signaling• Refresh timer: state needs periodic refresh• State only removed by time-out• Failure to communicate go to safe (default) state
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Hard-state signalingHard-state signaling
Signaling plane
Communication plane
Sender ReceiverInstall
ack
• State is explicitly added and removed
• Assumes very reliable communication channel
• Failure to communicate special recovery procedure
removal
error
X
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So Why is Soft State Design So Why is Soft State Design “Better”?“Better”?
Some common responses:• It’s more robust
– To what? Packet loss? High delays?
• It’s better at handling really bizarre network conditions– Like what? Really high loss rates? Really high delays?
• Recovery is part of soft state’s normal operating process (no separate recovery operations needed)– So what?
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Prior work examining Soft StatePrior work examining Soft State
• [Raman,McCanne ’99]– Queueing model of SS signaling system– Showed SS/HS hybrid improves protocol
performance
• [Ji et al ’03] – Performance comparison between SS, HS,
and SS/HS hybrids– Conclusion: Hard State beats Soft State, but
hybrid SS/HS protocols are best
So Why is Soft State Design So Why is Soft State Design “Better”?“Better”?
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What’s Wrong with Traditional What’s Wrong with Traditional Performance EvaluationsPerformance Evaluations
• Tradition: “Given some network conditions, design the best protocol.”
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Input: Condition
s Protocol Parameters
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Input: Condition
sOutput:
Best Solution
What’s Wrong with Traditional What’s Wrong with Traditional Performance EvaluationsPerformance Evaluations
• Tradition: “Given some network conditions, design the best protocol.”
Protocol Parameters
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The “Traditional” ConclusionThe “Traditional” Conclusion
• For any network condition, hard state protocols can be configured for that condition to out-perform their soft state counterparts
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A more “practical” performance A more “practical” performance evaluationevaluation
• Don’t really know what the conditions will be when configuring the protocol
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Protocol Parameters
Input: Condition
sOutput: (Best?) Solution
Is Hard State best in this setting?
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Performance-Oriented View of Performance-Oriented View of Protocol Designer IntuitionProtocol Designer Intuition
• Suppose protocols are “tuned” to operate most efficiently under “normal” conditions
• Claim: HS performance worsens more rapidly than SS as conditions vary from norm
Network Condition
Perf
orm
an
ce
Normal Operating Regime
Hard State Protocol
Soft State Protocol
good
bad
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Our Comparison StudyOur Comparison Study
• We choose 3 network scenarios– DoS Attack– Correlated, Lossy Feedback Channel– Broadcast Communication Environment
• For each scenario:– Pick a HS and SS protocol used in the scenario– Choose protocol parameters (timeout lengths, #
attempts) to work well for “expected network conditions”
– Vary the network conditions– Watch how the protocol performs (w/o rechoosing
protocol parameters!!)
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A Generic Signaling Protocol ModelA Generic Signaling Protocol Model
• L = Lifetime that a “state” should exist
• R = Refresh interval
• T = Timeout interval (e.g., 3R for SS many protocols)
• p = Channel loss probability
• K1 , K2 , etc. = Various Costs (described later)
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Refresh CostRefresh Cost
Signaling plane
Communication plane
Sender Receiver
Cost = 3K1 Cost = K1 Total Cost ~
L/R K1 Cost = 2K1
Cost to keep state consistent
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(Re)Initialization Cost(Re)Initialization Cost
Signaling plane
Communication plane
Sender Receiver
# of drops ~ pL/R, Cost = K2 pL/R
p Cost to recover from accidental timeout
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Stale state costStale state cost
Signaling plane
Communication plane
Sender Receiver
Stale state lifetime ~ R, Cost = K3 pR
p
State Removal Signal
Cost of enacting an actual timeout
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Total CostTotal Cost
C(R) = K2 p L/R+ K1 L/R + K3 p R
E[C(R)] = K2 p E[L]/R+ K1 E[L]/R + K3 p R What is the optimal What is the optimal R R to minimize to minimize
total cost?total cost? K2 K1 >> K3 , R
K2 K1 << K3 , R
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Optimal Optimal RR implications implications
• K2 ,K1 large Performance emphasis– Fewer refresh pings, bad to tear down state
accidentally
• K3 large Robustness emphasis– Bad to miss tearing down state
• Higher R, “Harder” the protocol, Lower R, “Softer” the protocol
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Cost ComparisonCost Comparison
Results match
previous robustness
intuition
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Resource Blocking (DoS) AttacksResource Blocking (DoS) Attacks
• Good Traffic: uses and releases resource
• Attacker: doesn’t release resource until timeout
Hard state more susceptible to attacks
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Correlated, Lossy Feedback ChannelCorrelated, Lossy Feedback Channel
• Client connects to a server• If loss rate from server too high, client
chooses to disconnect– Soft State: receiver stops sending refresh
messages– Hard State: receiver tries to push a
“disconnect” message through the lossy channel
• Channel losses (in both directions) are equal
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The Hard-State DilemmaThe Hard-State DilemmaSTOP!
STOP!
STOP!
Feedback loop: Inability to terminate induces greater losses, making it more difficult to
terminate
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Results of Markov Model FormulationResults of Markov Model Formulation
As session expected lifetime (1/μ) decreases,
HS zombie sessions grow
large
Soft State has many fewer
zombie sessions
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Robust Multicast FeedbackRobust Multicast Feedback
• Scenario: sender broadcasts transmission as long as some receiver listening
• Q: How does sender know if a receiver is listening?
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Hard State ApproachHard State Approach
• Each “interested” receiver explicitly notifies sender of join and leave
S
R
R
R
I’m interested
I’m interested
I’m interested
I’m no longer interested
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Soft State ApproachSoft State Approach
• Some receiver must ping sender about interest within time period T or broadcast stops
• receiver pings randomly delayed and broadcast so other receivers can suppress their pings
• propagation delays can induce multiple pings per interval
T T T T
S
RR
R
X X X X X
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Optimized VersionsOptimized Versions
• Prefix-matching methods [Bolot’93] can be used to reduce receiver communication costs– Hard-state: used to choose a leader– Soft-sate: used to reduce feedback
rate
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Heavy Arrival Rate ComparisonHeavy Arrival Rate Comparison
= arrival rate of
interested clients
Soft State designs exhibit better scalability with large for both versions of polling protocols
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Heavy Departure Rate ComparisonHeavy Departure Rate Comparison
μ = departure
rate of interested
clients
Soft State designs exhibit better scalability with large μ for both versions of polling protocols
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ConclusionsConclusions
• Hard state protocols can often outperform soft state protocols when network conditions are known
• What makes soft state “better” design is its ability to provide “acceptable” performance over a larger variety of network conditions