Stochastic fair blue an algorithm for enforcing fairness
This presentation is the property of its rightful owner.
Sponsored Links
1 / 20

Stochastic Fair Blue An Algorithm For Enforcing Fairness PowerPoint PPT Presentation


  • 46 Views
  • Uploaded on
  • Presentation posted in: General

Stochastic Fair Blue An Algorithm For Enforcing Fairness. Wu-chang Feng (OGI/OHSU) Dilip Kandlur (IBM) Debanjan Saha (Tellium) Kang Shin (University of Michigan) April 26, 2001. Outline. Motivation Background Packet scheduling Buffer management Bloom Filters and Blue

Download Presentation

Stochastic Fair Blue An Algorithm For Enforcing Fairness

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Stochastic fair blue an algorithm for enforcing fairness

Stochastic Fair BlueAn Algorithm For Enforcing Fairness

Wu-chang Feng (OGI/OHSU)

Dilip Kandlur (IBM)

Debanjan Saha (Tellium)

Kang Shin (University of Michigan)

April 26, 2001


Outline

Outline

  • Motivation

  • Background

    • Packet scheduling

    • Buffer management

  • Bloom Filters and Blue

  • Stochastic Fair Blue

  • Conclusion


Motivation

Motivation

  • Internet relies on TCP congestion control

  • Proliferation of non-responsive applications

  • Network mechanisms

    • Packet scheduling

    • Buffer management


Packet scheduling approaches

Packet scheduling approaches

  • WFQ, W2FQ [Bennett96], Virtual Clock[Zhang90], SCFQ [Golestani94], STFQ [Goyal96]

    • per-flow packet scheduling and queue management

      + accuracy, correctness

      - overhead, partitioned buffers

  • SFQ [McKenney90], CBQ [Floyd94]

    • approximate fairness using hashes or flow classification

      + low overhead

      - misclassification

  • RED with per-active flow queuing [Suter98]

    + lower overhead than WFQ

    - overhead still O(N)


Buffer management approaches

Buffer management approaches

  • RED with penalty box [Floyd97]

    • Analyze drop history

      + no per-flow state

      - accuracy, complexity, large #s of bad flows

  • Flow RED [Lin97]

    • Examine per-flow queue occupancy

      + no per-flow state

      - accuracy vs. queue size, large #s of bad flows

  • Core-Stateless FQ [Stoica98]

    • Rate labeling at edge with priority dropping in core

      + core router overhead, correctness

      - packet overhead, coordination required, deployment


Stochastic fair blue

Stochastic Fair Blue

  • Buffer management algorithm

  • Single FIFO queue

  • Combines

    • Bloom filters

    • Blue queue management


Bloom filters

Bloom Filters

  • Used in

    • Spell checkers (by dictionary word)

    • Browser caches (by URL)

    • Web caches (by URL)

  • Apply multiple independent hash functions on input dictionary

  • Lookup or locate objects based on their hashing signature


Stochastic fair blue an algorithm for enforcing fairness

Blue

  • De-couple congestion management from queue length

  • Rely only on longer-term queue and link history

  • Salient features

    • Low packet loss

    • High link utilization

    • Low queuing delay

  • http://www.thefengs.com/wuchang/blue


Stochastic fair blue an algorithm for enforcing fairness

Blue

L Mbs

Sources

Sinks

A

B

Sinks generate

DupACKs/ECN

Rate = L Mbs

Queue drops and/or ECN marks at steady rate

Rate = Exactly what will keep sources at L Mbs


Example blue algorithm

Example Blue Algorithm

  • Single dropping/marking probability

    • Increase upon packet loss

    • Decrease when link underutilized

    • Freeze value upon changing

Upon packet loss:

if ((now - last_update) > freeze_time) then

Pmark = Pmark + delta

last_update = now

Upon link idle:

if ((now - last_update) > freeze_time) then

Pmark = Pmark - delta

last_update = now


Stochastic fair blue sfb

Stochastic Fair Blue (SFB)

  • Single FIFO queue

  • Multiple independent hash functions applied to each packet

  • Packets update multiple accounting bins

  • Blue performed on accounting bins

  • Observation

    • Non-responsive flows drive P to 1.0 in all bins

    • TCP flows have some bins with normal P

    • Pmin = 1.0 , rate-limit

    • Pmin < 1.0 , mark with probability Pmin


Stochastic fair blue an algorithm for enforcing fairness

hL-1

h0

h1

Pmin=1.0

P=1.0

0

Non-responsive

Flow

1

P=1.0

2

P=0.3

TCP Flow

P=0.2

Pmin=0.2

P=1.0

N-1

SFB

NL virtual bins out of L*N actual bins


Sfb evaluation

SFB Evaluation

  • 400 TCP flows

  • 1 non-responsive flow sending at 45 Mbs

  • Evaluation

    • 200KB, 2-level SFB with 23 bins per level (529 virtual bins)

    • 200KB RED queue

    • 400KB SFQ with 46 RED queues

100 Mbs

100 Mbs

45 Mbs

45 Mbs


Sfb evaluation1

SFB Evaluation

RED

SFQ+RED

Loss rates

Loss rates

TCP Flows = 3.07 Mbs

TCP Flows = 2.53 Mbs

Non-responsive = 10.32 Mbs

Non-responsive = 43.94 Mbs


Sfb evaluation2

SFB Evaluation

SFB

Loss rates

TCP Flows = 0.01 Mbs

Non-responsive = 44.84 Mbs


Sfb and misclassification

SFB and Misclassification

  • SFB deteriorates with increasing non-responsive flows

  • Non-responsive flows pollute bins in each level

  • Probability of misclassification

    • p = [1 - (1 - 1/N)M]L

    • Given M, optimize L and N subject to L*N=C


Sfb and misclassification1

SFB and Misclassification

4 non-responsive flows

8 non-responsive flows


Sfb with moving hash functions

SFB with Moving Hash Functions

  • SFB

    • Virtual buckets from spatial replication of bins

  • Moving hash functions

    • Virtual buckets temporally

  • Advantages

    • Handles misclassification

    • Handles reformed flows


Sfb with moving hash functions1

SFB with Moving Hash Functions


Conclusion

Conclusion

  • Stochastic Fair Blue

    • Combine Blue queue management with Bloom filters

      + no per-flow state, small amount of buffers, tunable to # of bad flows, amenable to hardware implementations

      - short-term accuracy

  • Current status

    • 2 known vendor implementations

    • Linux implementation

  • Future work

    • Hardware implementation using COTS programmable network processor


  • Login