using edge-to-edge feedback control to make assured service more assured in diffserv networks...
TRANSCRIPT
Using Edge-To-Edge Feedback Control to Make Assured Service
More Assured in DiffServ Networks
K.R.R.Kumar, A.L.Ananda, Lillykutty Jacob
Centre for Internet Research
School of Computing
National University of Singapore
Outline Introduction
– Need for QoS– Solutions
TCP over DiffServ– Issues
CATC– Key Observations– Design Considerations– Topology– Edge-to-Edge Feedback Architecture– Marking Algorithm
Simulation Details Results and Analysis Deployment Inferences and Future work
Introduction
Need for QoS– An exponential growth in traffic resulted in
deterioration of QoS.– Over provisioning of networks could be a
solution.– A better solution: An intelligent network service
with better resource allocation and management methods,
Solutions
Integrated Service– Per flow based QoS.– Not scalable.
Differentiated services– QoS for aggregated flows– Scalable– The philosophy: simpler at the core (AQM),
complex at the edges.
DiffServ
Classifier
Meter
MarkerShaper/Dropper
Packets
Logical View of a Packet Classifier and Traffic Conditioner
Drop
Forward
DiffServ cont’d..
Per-Hop behaviours– Expedited forwarding: Deterministic QoS– Assured forwarding: Statistical QoS
Classifier Traffic Conditioner
– Token Bucket (TB), Time Sliding Window (TSW) Meter Marker Shaper/Dropper
TCP over DiffServ
Recent measurements have shown TCP flows being in majority (95% approx. of byte share).
TCP flows are much more sensitive to transient congestion.
Unruly flows like UDP kills TCP traffic Bandwidth assurance affected by size of target rate. Biased against
– Longer RTTs– Smaller window sizes
Congestion Aware Traffic Conditioner (CATC)
Key Observations– Markers ,one of the major building blocks of a
traffic conditioner helps in resource allocation.– Proper understanding of transient congestion in
the network helps.– Edge routers have a better understanding of the
domain traffic.– An early indication of congestion in a network
helps to prioritize the packets in advance.– Existing feedback mechanisms are end-to-end.
Eg: ECN
CATC cont’d..
Design Considerations– Markers should
Be least sensitive to marker or TCP parameters.
Be transparent to end hosts. Maintain optimum marking. Minimize synchronizations. Be fair to different target sizes. Be congestion aware.
Topology
Edge-to-Edge Feedback architecture Two edge routers
– Control sender (CS) and control receiver (CR) Upstream:
– At CS: CS sends control packets (CP) at regular interval of time,
control packet interval (cpi). CPs are given highest priority.
– At Core: Core routers maintain the status of drops of the best effort
packets. Information maintained as a status flag to a max. of cpi time. CP’s congestion notification (CN) bit set or reset based on
status flag.– At CR:
Responds to the incoming CP with a CN bit set by setting the congestion echo (CE) bit of the outgoing acknowledgement.
Feedback arch. Cont’d
Downstream– At CS:
Maintains a parameter, congestion factor (cf). Cf is set to 1 or 0 based on status of the CE
bit in acknowledgement received.
Marking algorithm
For each packet arrival
If avg_rate cir
then
mp=mp+(1- avg_rate/cir)*(1+cf*(cir/cir_max));
mark the packet using :
cp 11 w.p. mp (marked packets)
cp 00 w.p. (1-mp) (unmarked packets)
Marking Algo. Cont’d..
else if avg_rate > cir
then
mp=mp+ (1- avg_rate/cir)*(1-cf*(cir/cir_max));
mark the packet using :
cp 11 w.p. mp (marked packets)
cp 00 w.p. (1-mp) (unmarked packets)
Marking Algo. Cont’d..
where,
avg_rate = the rate estimate on each packet arrival
mp = marking probability ( 1)
cir = committed information rate (target rate)
cf = congestion factor
cir_max = maximum committed information rate
also,
cp denotes ‘codepoint’ and w.p. denotes ‘with probability’.
Algo cont’d..
Marking probability computation based on:– cir– avg_rate– cf– cir_max among all cirs.
Algo. Cont’d..
The effect on mp:– i)Flow component (1- avg_rate/cir) constantly
compares the average rate observed with the target rate to keep the rate closer to the target.
– ii)Network component cf*(cir/cir_max) provides a dynamic indication of congestion level status in the network. The marking probability increment is done in proportion to the target rate by multiplying cf with a weight factor cir/cir_max to mitigate the impact of the target rates.
Simulation Details
NS (2.1b7a) simulator on Red Hat 7.0 Modified Nortel’s DiffServ module for
our architecture implementation. Core routers use RIO like mechanism FTP bulk data transfer for TCP traffic
Simulation Parameters
TCP segment size 536 bytesRTT 100 mssimulation time 210 sTSW window length 1 sControl packet interval 1 msControl packet size 41 bytesLink bandwidth 10 Mbps
Marked UnmarkedMin_th(packets) 250 150Max_th(packets) 500 300Max_dp 0.02 0.1
Simulation details cont’d..
Experiments conducted:– Assured services (AS) for aggregates.
AS in under- and well- subscribed cases. AS in the oversubscribed case.
– Protection from BE UDP flows– Effect of UDP flows with assured (target)
rates.
R&A: under- and well- subscribedExpt #
Rt 1 Rt 2 Ra1 Ra2 BE TCP flow Link goodput (Mbps) (Mbps)
1 1 1 2.54 2.58 3.76 8.882 1 2 2.54 2.58 3.76 8.883 1 3 2.41 2.93 3.46 8.84 2 3 2.36 2.89 3.58 8.835 3 3 2.8 2.8 3.21 8.816 3 4 2.73 3.49 2.59 8.81
8.835
Target Rates(Mbps) Achieved Rates (Mbps)
Average link bandwidth (Mbps)
R&A:over-subscribed
Expt #Rt 1 Rt 2 Ra1 Ra2 BE TCP flow Link goodput
(Mbps) (Mbps)1 2 6 1.83 4.85 2.06 8.742 3 5 2.5 4.04 2.05 8.593 3 6 2.4 4.6 1.53 8.534 1 8 1.2 6 1.28 8.485 4 6 3.17 4.5 0.11 7.786 2 8 1.55 6.16 0.72 8.43
8.425
Target Rates(Mbps) Achieved Rates (Mbps)
Average link bandwidth (Mbps)
R&A: Goodput vs Time Graph (2/6 Mbps target rate.)
-1
0
1
2
3
4
5
6
0 50 100 150 200 250
t ime (s)
good
put (
Mbp
s)
-2
0
2
4
6
8
10
0 50 100 150 200 250
time (s)
good
put (
Mbp
s)
Analysis
CATC Able to achieve the target rates for the
under- and well- subscribed cases. Maintain the achieved rate close to its
target rate. Total link utilization remains more or
less constant throughout.
R&A:AS in presence of BE UDP and TCP
Expt #Rt 1 Rt 2 Ra1 Ra2 BE TCP flow BE UDP Link goodput
(Mbps) (Mbps) (Mbps)1 2 6 1.52 4.18 0.46 3.54 6.162 3 5 2.08 3.41 0.44 2.52 5.933 3 6 2 4.42 0.13 2.12 6.554 1 8 0.66 6.34 0.01 1.87 7.015 4 6 2.65 4.6 0 1.5 7.256 2 8 1.21 6 0 1.6 7.21
6.685
Target Rates(Mbps) Achieved Rates (Mbps)
Average link bandwidth (Mbps)
R&A:AS in presence of AS UDP and BE TCP
Expt #Rt 1 Rt 2 Ra1 Ra2 BE TCP flow AS UDP Link goodput
(Mbps) (Mbps) (Mbps)1 1 1 1.7 1.77 2.61 2.99 6.082 2 2 1.92 1.88 2.27 2.99 6.073 3 3 2.37 2.47 1.18 2.99 6.024 4 4 2.92 2.98 0.13 2.98 6.035 5 5 3.12 2.83 0.1 2.97 6.05
6.05
Target Rates(Mbps) Achieved Rates (Mbps)
Average link bandwidth (Mbps)
Analysis
CATC– Achieves goodput close to the target rates.– Succeeds in taking the share of BE TCP and
UDP flows in the worst case scenario.– The average link utilization pretty good.– The AS UDP flow gets its assured rate.
Deployment
MPLS over DiffServ. Marker anywhere (lack of sensitivity to
marker parameters).
Inferences and Future work
The architecture is transparent to TCP sources and hence doesn’t require any modifications at the end hosts.
The edge-to-edge feedback control loop helps the marker to take proactive measures in maintaining the assured service effectively, especially during periods of congestion.
A single feedback control is used for an aggregated flow. Hence this architecture is scalable to any number of flows between the two edge gateways.
The architecture is adaptive to changes in load and network conditions.
The marking algorithm takes care of any bursts in the flows.
Future work
Extend present architecture to take care of drops in priority queues.
A new algorithm to incorporate this.
Q&A
Thank You!