1. A Simple QoS Packet Scheduler for Network Routers
Su Wen
Presenting the paper in SIGCOM 2001
“SRR: An O(1) Time Complexity Packet Scheduler for Flows in Multi-Service Packet Networks”
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2. Quality of Service Current Internet Service QoS Best effort
Packet scheduling: FIFO
QoS
Demanded by new network services
multimedia, audio/video
Meets requirement in bandwidth, delay, jitter
Current Internet IP architecture is best-effort. it’s possible packet will be duplicated in the network or lost. The packet may arrive at the receiver out of order. Generally, to get better than best effort service, the end system must implement mechanisms to provide the service to applications. TCP is such an example. It’s a protocol running on end systems in the network to provide reliable data delivery to end-system applications.
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3. QoS Approaches Integrated Services Differentiated Services
Provide service guarantees to individual flows in the network
GPS - Generalized Processor Sharing
Weighted Fair Queuing
Differentiated Services
Provide some service guarantees to different classes of traffic in the network
Example: hierarchical link-sharing
audio, video, telnet, ftp, mail
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4. Bandwidth Sharing GPS - generalized processor sharing server r flows
ideal model: each flow gets its fair share of BW
Si(t1, t2) / Sj(t1, t2) = wi/wj
ri/r = wi/jwj . r
GPS Scheduler
server
flows
wi - weight of flow i
Si (t1, t2) - amount of traffic served for flow I during time t1 to t2
ri - service rate of flow i
r - service rate of the server
GPS is idealized and cannot be implemented because is assumes that
1. Server transmits more than one flow simultaneously
2. Traffic is infinitely divisible
The GPS server is work conserving and operates at fixed rate r
Time-stamp based: calculate the time the packet should be served, order the packet in the queue. Really, calculate the finish time, packet with smallest finish time is served next.
The time stamp is calculated for each pkt arrival and inserted in the queue according to the time stamp
Round-robin: precalculate the order of service, serve the flow queue for the time proportional to the weight
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5. Bandwidth Sharing Implementations
Weighted Fair Sharing
An approximation/implementation of GPS
Each flow has separate queues
Two approaches:
time-stamp based: e.g. virtual clock
round robin
GPS is idealized and cannot be implemented because is assumes that
1. Server transmits more than one flow simultaneously
2. Traffic is infinitely divisible
The GPS server is work conserving and operates at fixed rate r
Time-stamp based: calculate the time the packet should be served, order the packet in the queue. Really, calculate the finish time, packet with smallest finish time is served next.
The time stamp is calculated for each pkt arrival and inserted in the queue according to the time stamp
Round-robin: precalculate the order of service, serve the flow queue for the time proportional to the weight
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6. Implementation Characteristics
Time Stamp based:
Fairly good fairness and delay bound
Time complexity: O(logN) - O(N)
Round Robin based: DRR, CORR
Time complexity: O(1)
Short term unfairness
bursty output
WFFQ, Start-time FQ, FFQ, SPFQ, time-shift
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7. SRR - Smoothed Round Robin
Good short term and long term fairness
emulate GPS
work conserving
forwards packets as long as there are active flows
O(1) time complexity
one lookup to decide what to send
Better scalability
Simplicity
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8. Data Structures Weight Matrix (WM) WSS - Weight Spread Sequence
number of rows = N - number of flows
number of columns = k = log2wmax + 1
# of binary digits needed to represent the max normalized weight
WM size depends on # of flows and BW granularity
WSS - Weight Spread Sequence
WSS of order k (Sk) is a sequence of integers from 1 to k
recursively defined
S1 = 1
Sk = Sk-1, k, Sk-1
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9. Weight Matrix Four flow f1 - f4, each with different BW requirement:
r1 = 64kbps, r2 = 128kbps, r3 = 320kbps, r4 = 192kbps
w1 = 1 -> {0 0 1}
w2 = 2 -> {0 1 0}
w3 = 5 -> {1 0 1}
w4 = 3 -> {0 1 1}
col1 col2 col3
row
row
row
row
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10. Weight Spread Sequence
Rule:
S1 = 1
Sk = Sk-1, k, Sk-1
To find WSS of order 3 - S3
S2, 3, S2
S1, 2, S1, 3, S1, 2, S1
1, 2, 1, 3, 1, 2, 1
N - number of flows
wi - normalized weight of flow i
k - log2wmax + 1
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11. Scheduling Algorithm Generate a Service Queue:
for each value i in the WSS
if jth row of column i of WM has value 1
add flow j to the queue
Serve the flow indicated in the service queue in a round robin fashion
Serve_flow(): deficitj += Lmax
while (Lj <= deficitj) {
send pktj
deficitj -= Lj }
deficiti - bytes need to be served of flow i
Li - length of packet of flow i
Lmax - length of maximum packet size
f1: {0 0 1}
f2: {0 1 0}
f3: {1 0 1}
f4: {0 1 1}
S3:
1, 2, 1, 3, 1, 2, 1
Flow Service Queue:
f3,
f2, f4, f3
f1, f3, f4,
f3,
f2, f4, f3
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12. Fairness Long Term Short Term Si(0, t) / Sj(0, t) = wi/wj
Si(0, t)/wi - Sj(0, t)/wj = 0
Short Term
Si(0, t)/wi - Sj(0, t)/wj < (k+2)Lmax/2*min(wi,wj)
Si(t1, t2) - amount of traffic served for flow i during time t1 to t2
wi - normalized weight of flow i
k - log2wmax + 1, the order of WSS
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13. Delay Df - maximum delay for flow f
Df < 2*Lmax/wf + 2*(N-1)*Lmax/ i wi
Inverse proportional to the weight of the flow
Proportional to total number of flows
Not strictly rate proportional, but better than DRR
simulation result shows so
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14. Scalability Space requirement Time complexity N x k
At 1kbps granularity, k=16 can accommodate rates up to 64Mbps
At 1Mbps granularity, k=16 can accommodate rates up to 64Gbps
Time complexity
O(1) to choose packets for transmission
O(k) to add or delete a flow
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15. SRR Problems add_flow() and delete_flow() operations
SRR scheduler is called during busy period
packets are back-logged
add_flow() is called when new flow arrives
delete_flow() is called when queue of the flow is empty
If a flow is not back logged, add_flow() and may be called for every packet
quite expensive: O(k)
Solution:
delay the deletion of a flow
not strictly O(1) any more
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16. Conclusion SRR is simple Scalable Good performance
even I can understand!
Scalable
time and space
Good performance
fairness
delay
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17. Questions?
How did he come up with the algorithm?
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