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Probabilistic Packet Scheduling (PPS). Ming Zhang, Randy Wang, Larry Peterson, Arvind Krishnamurthy Department of Computer Science Princeton University. OS. 900. 300. P1. P2. 1000. 500. 1000. P3. P4. P5. Motivation – Lottery Scheduling.

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Probabilistic packet scheduling pps

Probabilistic Packet Scheduling (PPS)

Ming Zhang, Randy Wang, Larry Peterson, Arvind Krishnamurthy

Department of Computer Science

Princeton University


Motivation lottery scheduling

OS

900

300

P1

P2

1000

500

1000

P3

P4

P5

Motivation – Lottery Scheduling

  • OS defines currency and assigns lottery tickets to processes

  • Processes proportionally divide CPU cycles

  • A Process can make local CPU allocation decision


Proportional bandwidth allocation

S0

S3

S1

S5

S6

S4

S2

Proportional Bandwidth Allocation

  • Router defines currency in tickets/s and assigns tickets to its inputs

  • Link maintains currency exchange rate

  • Bandwidth at bottleneck is proportional to ticket share

  • Local bandwidth allocation decision and isolation

A 10Mb/s

1000t/s

2Mb/s

900t/s

1Mb/s

2000t/s

B 10Mb/s

500t/s

1Mb/s

300t/s

C 10Mb/s

1000t/s


Algorithm in brief
Algorithm in Brief

  • TCP source tags tickets on each packet

  • Each router runs a variant of RED to decide whether to drop or accept a packet

  • Relabel packets at each link based on currency exchange rate


Ticket tagging
Ticket Tagging

  • OutTktRate – t/s assigned to a TCP source

  • AvgRate - average throughput of a flow

  • Tag OutTktRate / AvgRate onto each packet

  • Tickets on packet are inversely proportional to the average throughput


Ticket based red tred
Ticket-based RED (TRED)

  • InTkt is the tickets on an incoming packet. ExpectTkt is the tickets “should” be on an incoming packet

  • ExpectTkt is computed as average tickets on all incoming packets

  • Bottlenecked flows put approximately ExpectTkt tickets on their packets

    If MinThresh < AvgQLen < MaxThresh

    compute probability p the same as in RED

    p’ = p * (ExpectTkt / InTkt)3

    drop the packet with probability p’


Exchange rate
Exchange Rate

  • A multi-hop flow may go through many routers

  • Different routers have their own currencies

  • Convert tickets between different currencies

  • Exchange rate at each link

    XRate = OutTktRate / InTktRate

  • Relabel packets according to exchange rate

    OutTkt = InTkt * XRate


Receiver based algorithm
Receiver-based Algorithm

  • Controlling bandwidth allocation at receiver

  • AckOutTktRate – t/s assigned to an output

  • Tagging and relabeling of ACKs are similar

  • Compute OutTktRate from tickets on ACKs

    OutTktRate = AckInTktRate


One hop configuration

100t/s

200t/s

1

1

3000t/s

2

2

P

Q

4.65Mb/s

26ms

30

30

One-Hop Configuration

  • Simulations are run in NS-2

  • Sender-based and receiver-based



Multi hop configuration

100t/s

200t/s

1000t/s

100t/s

200t/s

1000t/s

A1

A2

P1

11000t/s

S1

1.65Mb/s

26ms

A10

P3

P4

S2

B1

5500t/s

B2

P2

S20

B10

Multi-Hop Configuration




Multiple bottlenecks configuration

2000

S0

A

10Mb/s

3600

S8

S4

1.2Mb/s

10 Mb/s

S1

1000

10Mb/s

B

5Mb/s

S6

S7

? Mb/s

C10Mb/s

S2

1400

3Mb/s

S5

S9

1200

10Mb/s

D

S3

700

Multiple Bottlenecks Configuration




One hop configuration1

100t/s

200t/s

1

1

3000t/s

2

2

P

Q

4.65Mb/s

26ms

30

30

One-Hop Configuration



Related work
Related Work

  • WFQ, IntServ

  • DiffServ

  • CSFQ

  • User-share differentiation


Conclusion and future work
Conclusion and Future Work

  • Proportional bandwidth allocation

  • A modified RED algorithm (TRED), no per-flow state, scalable

  • Routers make local bandwidth allocation and isolation

  • Sender-based and receiver-based

  • Experiment with more realistic traffic load and complex topologies