Distributed Virtual-Time Scheduling in Rings (DVSR)

1
Distributed Virtual-Time Scheduling in Rings
(DVSR)

• Chun-Hung Chen
• 2004.04.30
• National Taipei University of Technology

2
Outlines

• RPR Recall
• Problems in RPR
• Ring Ingress Aggregated with Spatial Reuse
  Fairness (RIAS)
• Distributed Virtual-Time Scheduling in Rings
  (DVSR)
• Simulation Results
• Conclusions

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RPR Recall

• RPR stands for Resilient Packet Ring, which is in
  IEEE 802.17 Draft State
• Dual rings structure with Destination strip
  mechanism

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• Traffic is classified in three classes
• Class A (A0 or A1), Class B (CIR or EIR), Class C
• When congested, the station will compute its
  approximation fair rate by
• Dividing the available bandwidth between all
  upstream stations that are currently sending
  frames through this station
• Using its own current add rate

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• Two operation mode
• Conservative Mode
• Congested station will wait a FRTT to send a new
  fair rate if it is still in congestion
• Aggressive Mode
• Congested station sends new fair rate in every
  100µs if it is still in congestion

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Problems in RPR

• Single Rate Controller
• Per-destination rate controller is optional
• Permanent Oscillation With Unbalanced
  Constant-Rate Traffic Inputs
• Unbalanced traffic will trigger severe and
  permanent oscillations
• Computed add_rate or Capacity/Active_Stations do
  not reflect the true situation
• Throughput Loss
• Utilization degrades due to oscillation
• AM CM
• Convergence
• Slow convergence time

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• Transit traffic has priority over ingress
  station traffic
• Each node measures my_rate of ingress traffic
• If a node is congested
• send my_rate upstream
• upstream nodes throttle to my_rate

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The Problem with Darwin

• my_rate is NOT the ring-wide fair rate
• Example of permanent oscillation and throughput
  degradation in Darwin

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Modeling RPR Oscillations (Analytical and
Simulation Results)
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RIAS

• Ring Ingress Aggregated with Spatial Reuse
  Fairness
• Define the level of traffic granularity for
  fairness determination at a link as an
  ingress-aggregated (IA) flow
• Ensure maximal spatial reuse subject to the first
  constraint
• Steps of RIAS
• Allocate bandwidth on each link locally fair
  according to an ingress aggregated granularity
  (IA traffic)
• Refine bandwidth allocation for each IA flow
  according to its egress point and bottlenecks
• Reclaim unused bandwidth fairly by iterating
• Highly Similar to Max-Min Flow Control

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Comparison

• Proportional Fair Allocation
• Penalizes flows farther away from the destination
• Important for TCP in the Internet (rate decrease
  with RTT)
• Fairness with Ingress-Egress flow granularity
• Incorrectly rewards nodes for spreading out
  traffic to many destination versus all to hub node

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Illustration of RIAS Fair (1/3)
1/4
1/4
1/4
1/4

• Parking Lot
• 4 flows each receive rate ¼

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Illustration of RIAS Fair (2/3)
1/4
3/4
1/4
1/4
1/4

• Parallel Parking Lot
• Each flow receives rate ¼ on downstream link
• Left 1-hop flow fully reclaims excess bandwidth
  (RIAS)

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Illustration of RIAS Fair (3/3)
1/2
1/4
1/4
1/4
1/4
1/4
1/2
3/4

• Upstream Parallel Parking Lot
• Key points
• Flow granularity for fairness
• Spatial reuse

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Proportional Fair

• Proportional fairness
• Penalizes flows farther away from the hub
• Important for TCP in the Internet (rate decreases
  with RTT)
• TCP/GigE approximates this in the parking lot

• Variants of all of these have been discussed and
  proposed in the RPR standard meetings

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Ingress-Egress Flow Granularity

• Fairness with Ingress-Egress flow granularity
• Incorrectly rewards nodes for spreading out
  traffic to many destinations vs. all to hub node
• Wrong flow granularity counts 6 flows and gives
  rate 1/6
• (RIAS-fair all green flows together get ¼ vs ½)

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DVSR

• Nodes construct a proxy of virtual time at the
  ingress-aggregated flow granularity
• Using per-ingress byte counts
• The proxy is a lower bound on virtual time
  temporally aggregated over time and spatially
  aggregated over traffic flows sharing the same
  ingress point (IA flows)

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Distributed Fair Bandwidth Allocation

• Remote Fair Queuing
• Control of upstream rate controllers via use of
  ingress-aggregated virtual time as a congestion
  message received from downstream nodes
• Conceptually an ideal GPS processor
• Delayed and Temporally Aggregated Control
  Information
• Proxy of Virtual Time
• Multinode RIAS Fairness
• Three Steps to approximate RIAS

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Remote Fair Queuing Single Resource Illustration

• Control of upstream rate controllers via
  downstream virtual time progression
• True fair queueing replaced with rate controllers
  multiplexer
• Note no packets queued in mux when D 0

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Example

• Link capacity 1 pkt/sec
• T 10 pkt transmission times
• b 0.8 (fraction of time busy)
• ? gt 0
• Controller set at t for rates in t-T- ?, t- ?

Limiter value 0.8
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Step I Local Fairness

• Label nodes 1, , N and links 1, , N-1
• rij is the traffic demand between nodes i and j
  at a particular time instant
• rin is the Ingress Aggregated traffic from
  ingress node i at link n
• rin ?jgtnrij
• The locally fair allocation on link n is
• Rin max_mini(C,r1n,r2n,,rin,, rnn)

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Footnote on max_min

• What is max_mini( )?
• The textbook definition of (locally) fair
• Would be achieved by fair queueing if fair
  queueing was performed on ingress aggregates
• Can write down the exact computation
  BerGal92,p527
• Maximizing the network use allocated to the
  sessions with the minimum allocation

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Step II Ingress Fairly Sub-allocates Per-link
Bandwidths

• Rijn max_minj(Rin,ri,n1,ri,n2,,ri,j,,ri,N)
• Ingress has bandwidth Rin on link n and divides
  it fairly among flows traversing n
• End-to-End rate is the bottleneck rate
• ri,j minnRijn, ni, i1,,j-1

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Step III Iterate

• There may be further bandwidth available for
  spatial reuse
• Due to multiple congestion points
• Iterate process such that all excess capacity is
  fairly reclaimed
• Set new capacity to all unallocated capacity
• CnCn-?ijRijn
• Go to Step I

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DVSR Protocol

• Scheduling of Station versus Transit Packets
• FIFO queue
• Class A is not taken in consideration
• Feedback Signal Computation
• Feedback Signal Transmission
• Control message is N bytes while there exist N
  stations
• Each station i writes its value at i bytes
• Rate Limit Computation
• Suballocate its per-link fair rates to the flows
  with different egress nodes

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DVSR Protocol

• Scheduling
• FIFO (or SP)
• Computation of feedback signal
• Byte count for each ingress node - lower bound of
  virtual time
• Order such that
• l1 l2 lk

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Analysis of DVSR

• Fairness Bound
• Lemma 1
• A node-backlogged flow in DVSR can be
  under-throttled by at most (1-(1/N))CT
• Lemma 2
• A node-backlogged flow in DVSR can be
  over-throttled by at most (1-(1/N))CT
• Lemma 3
• The service difference during any interval for
  two flows i and j with infinite demand is bounded
  by 2(C-(1/N)C)T under DVSR

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Simulations Results

• Fairness and Spatial Reuse
• Fairness in the Parking Lot
• Performance Isolation for TCP Traffic
• RIAS versus Proportional Fairness for TCP Traffic
• Spatial Reuse in the Parallel Parking Lot
• Convergence Time Comparison

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Fairness in the Parking Lot

• Four constant-rate UDP flows sending at 622 Mbps
• DVSR provides RIAS fair shares
• GigE does not

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Spatial Reuse in the Parallel Parking Lot
CBR UDP flows sending at the link capacity

• DVSR is within ?1 of RIAS fair rates
• GigE favors downstream flows cannot achieve
  spatial reuse
• Darwin achieves only if using multi-choke
  option

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Upstream Parallel Parking Lot(Results in
Unbalanced Traffic Even with Balanced Inputs)

• Darwin oscillation range is 0.25 to 0.75 and
  throughput loss is 14
• Many other scenarios can result in traffic
  imbalances and throughput losses
• DVSR within 0.1 of RIAS

Darwin Behavior
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RIAS vs. Proportional Fairness for TCP Traffic

• Each flow 1 TCP micro flow (ftp/TCP Reno)
• Rate within ?1 of RIAS fair rates for 1 TCP
  micro-flow
• GigE tends to provide proportional fair rates

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Convergence Time in the Parking Lot
DVSR
Gandalf

• CBR UDP flows with rate 0.4 (248.8Mbps)
• Flow(1,5), (2,5), (3,5), (4,5) begin transmission
  at times 0.0, 0.1, 0.2, and 0.3 seconds
  respectively
• Convergence time 0.2 msec for DVSR, 50 msec for
  Darwin
• Richer feedback signal allows faster convergence

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Inter-Node Performance Isolation of TCP/UDP
Traffic

• Flow (1,5) TCP micro-flows
• Others are CBR UDP flows with rate 0.3
• More TCP micro-flows DVSR able to achieve RIAS
  fairness
• Darwin performance unknown (MAC sim incompatible
  with TCP)

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Conclusions

• Link capacity does not be considered in RPR
• Do my_rate and forward_rate in RPR fit the
  bandwidth allocation?
• DVSR approximate RIAS quicker than RPR
• RPR may have better performance if feedback
  mechanism is modified

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Reference

• V. Gambiroza, P. Yuan, B. Balzano, Y. Liu,
  S.Sheafor, Design, Analysis, and Implementation
  of DVSR A Fair High-Performance Protocol for
  Packet Rings, IEEE/ACM Transactions on
  Networking, Feb. 2004
• F. Davik, M.Yilmaz, S. Gjessing, N. Uzun, IEEE
  802.17 Resilient Packet Ring Tutorial, IEEE
  Communicaion Magazine, Mar. 2004
• http//www.ece.rice.edu/networks/RPR/