Max Min Fairness

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Max Min Fairness

• How define fairness?
• Any session is entitled to as much network use
  as is any other
• .unless some sessions can use more without
  hurting others
• Other definitions
• Network usage depends on the resource consumption
  by the session
• Pay/bid for what you use

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A Simple Example

• Max-min allocation 1/3, 1/3, 1/3, 2/3

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How to Calculate Max Min Flow Share

• Fluid model
• Increase the flow until some pipe fills-up.
• Fix the bandwidth of the bottleneck flows
• Continue with the unfixed flows
• Can be done efficiently by calculating the
  bottleneck link at step 1

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Buffer management and admission control

• Simplest admission policy
• Accept packets until buffer is full (tail drop)
• However
• Tail drop is not kind to TCP flows
• RED can be used to avoid tail drop

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Reminder Hallelujah for RED

• Random early detection (RED) makes three
  improvements
• Metric is moving average of queue lengths
• small bursts pass through unharmed
• only affects sustained overloads
• Packet drop probability is a function of mean
  queue length
• prevents severe reaction to mild overload
• Can mark packets instead of dropping them
• allows sources to detect network state without
  losses
• RED improves performance of a network of
  cooperating TCP sources
• No bias against bursty sources
• Controls queue length regardless of endpoint
  cooperation

ECN
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How does it work?
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So problem is solved?

• Fairly easy to implement in hardware!
• Can work in wire-speed!
• All we need to do is set the parameters..right ?
• Turns out there is no universal good set of
  parameters
• Some studies show RED has NO advantage over tail
  drop. WHY?

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parameters

• avgQ (1-wq)avgQwqq
• Floyd-Jacobson
• Wq 0.002, not less than 0.001
• max_p 1/50,
• max_th at least twice min_th
• max_th-min_th larger than the q increase in RTT
• Future work ..

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So does it help us to surf?

• Tuning RED for Web Traffic, Christiansen et al.,
  SIGCOMM 2000
• compared to a (properly configured) FIFO queue,
  RED has a minimal effect on HTTP response times
  for offered loads up to 90 of link capacity,
• response times at loads in this range are not
  substantially effected by RED control parameters,
  
• between 90 and 100 load, RED can be carefully
  tuned to yield performance somewhat superior to
  FIFO, however, response times are quite sensitive
  to the actual RED parameter values selected, and
• in such congested networks, RED parameters that
  provide the best link utilization produce poorer
  response times.

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SPRINT study (Diot et al.)

• A parallel study, presented at NANOG 2000
• Testbed
• with CISCO routers (7500)
• with Dummynet
• used recommended RED and GRED parameters
• Heterogeneous delays (120 to 180 ms)

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Traffic characteristics

• 16 to 256 TCP connections sharing the bottleneck.
  
• Experimental traffic generated by Chariot
• long-lived TCP connections.
• more realistic traffic mix
• 90 short lived TCP connections (up to 20
  packets)
• 10 long lived TCP connections
• 1Mbps UDP in both cases

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Testbed (CISCO routers)
7500
7500
10 Megs
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Testbed (Dummynet)
7500
7500
10 Megs
Dummy net
100 Megs
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What is Dummynet?
application
dummynet
network
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Metrics observed

• Aggregate goodput through a router
• TCP and UDP loss rate
• Consecutive losses
• Queuing behavior

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Aggregate goodput (long-lived TCP)
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256 short and long lived TCP connections
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Consecutive packet losses (long lived)
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if we use optimal RED parameters
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Consecutive packet losses (realistic traffic mix)
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Queuing behavior (256 long lived connections)
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Queuing behavior (256 connections, realistic mix)
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Diots summary

• No significant difference on goodput, TCP losses
  and UDP losses.
• On consecutive losses, clear advantage to GRED
  and GRED-I.
• gentle modification solves many RED problems.
• Oscillations no clear winner. Traffic seems to
  be the determining factor.

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From the ISP standpoint ...

• Not clear there is an advantage in deploying RED,
  GRED, or GRED-I.
• Maybe GRED-I is an option if one can find a
  universal exponential dropping function.
• ECN will work with any scheme.
• Not clear the solution is in the AQM space.

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GRED-I with exponential dropping function
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buffer size
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Deficit Round Robin (DRR)

• A modification to WRR to overcome different
  packet sizes.
• No need to know the average packet size.
• Each Q is associated with a deficit counter.
• Initiated to 0.
• Holds the Qs deficit in service

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DRR

• Algorithm
• If size(HOL packet) quota deficit
• Send packet
• deficit?deficitquota-(packet size)
• Else
• Dont send packet
• deficit?deficitquota

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About Fair Queuing ...

• Not only feasible easy at the edges!
• www.agere.com (an example)
• vendors support from 64k to 200k flows
• Really fair
• everybody gets what he/she paid for
• local signaling (end host to CPE)

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fair queueing at the edge

• Core-stateless fair queueing
• WFQ is hard to do at the core
• Edge routers estimate rate and label packets
• Core routers maintain FIFO queues and drop based
  on label

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CSFQ summary

• Better than FIFO and RED
• Similar to FRED
• Not as good as DRR

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Rainbow fair queueing

• Similar to CSFQ
• Have similar performance as CSFQ
• Enable applications to mark packets and achieve
  better goodput

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Rainbow Fair Queueing (RFQ)

• Example
• A 10 Kbps B 6 Kbps C 8 Kbps
• Each layer 2 Kbps

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RFQ basic mechanism

• (1) the estimation of the flow arrival rate at
  the edge routers
• (2) the selection of the rates for each color
• (3) the assignment of colors to packets
• (4) the core router algorithm

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Rainbow Fair Queueing (RFQ)

• (1) the estimation of the flow arrival rate at
  the edge routers

• rinew arrival rate
• tik arrival time of flow I
• lik length of the kth packet of flow I
• K a constant
• Tik tik tik-1

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Rainbow Fair Queueing (RFQ)

• (2) the selection of the rates for the rates for
  each color

• ci i color average rate of packets
• N total number of colors and multiple of b
• a,b determine the block structure
• P the maximum flow rate in the network

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Rainbow Fair Queueing (RFQ) Example

• N8 ab2

c0 c1 c2 c3 c4 c5
c6 c7 P/16 P/16 P/16 P/16 P/8
P/8 P/4 P/4
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Rainbow Fair Queueing (RFQ)

• (3) the assignment of colors to packets
• Suppose the current estimate of the flow arrival
  rate is r, and j is the smallest value satisfying
  .
• Then the current packet is assigned color
  with probability .

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• (4) the core router algo.
• Conditions to decrease color
• q threshold
• Flow bw
• Positive gradient
• Hold you horses
• Conditions to increase color
• Time
• Flow below service rate

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Rainbow Fair Queueing (RFQ)

• Weighted RFQ
• wi weight for flow i
• cj wicj

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Simulations A single congested link
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Fairness flow i sends at 0.313i
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Throughput TCP flow
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Throughput UDP flows
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Control Responsiveness10Mbps 8x1M?7x1M8M
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Simulations Performance Effects of Buffer Size
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Simulations TCP Performance Under Various round
Trip Delay