qos ii - adaptive virtual queue - fair queueing for multiple link
DESCRIPTION
QoS II - Adaptive Virtual Queue - Fair Queueing for Multiple Link. 12 th Mar., 2002 Eun-Chan Park CSL, SoEECS, SNU. S. Kunniyur, R.Srikant “ Analysis and Design of an Adaptive Virtual Queue (AVQ) Algorithm for Active Queue Management ,” SIGCOMM 2001. Contents. Background - PowerPoint PPT PresentationTRANSCRIPT
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QoS II- Adaptive Virtual Queue - Fair Queueing for Multiple Link
12th Mar., 2002
Eun-Chan Park
CSL, SoEECS, SNU
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S. Kunniyur, R.Srikant
“Analysis and Design of an Adaptive Virtual
Queue (AVQ) Algorithm
for Active Queue Management,”
SIGCOMM 2001
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Contents Background
Congestion control Active Queue Management
Related works RED, PI, REM
Adaptive Virtual Queue (AVQ) AVQ algorithm Stability analysis Simulation results
Conclusion
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Congestion Control Congestion control schemes
End-to-end control
- TCP-Tahoe, TCP-Reno, TCP-Vegas Router supported control
- AQM: RED, REM, PI control, AVQ
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TCP congestion control algorithm Window-based transmission control
sender limits its transmission rate by controlling window size
slow-start, congestion avoidance, fast-recovery Feedback
Implicit: Timeout, Duplicate ACKs Explicit Congestion Notification (ECN)
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Active Queue Management Drop-tail queue has several problems
Reduce rates only after overflow loss Results in significant packet loss Packet drop could result in a global sync.
Active Queue Management Resolves the problem of drop-tail Drops or marks packets at the buffer of router
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Random Early Detection (RED) S. Floyd and V. Jacobson, “random early detection gateways for
congestion avoidance,” IEEE/ACM trans. On networking, vol. 1, pp. 397-413, 1993.
Detect congestion using average queue size Intelligently drop/mark packet before buffer overflow
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RED (cont.) Advantages
Prevent global synchronization Reduce packet loss Minimize biases against bursty traffic Simple, low-overhead
Disadvantages Difficulty of appropriate parameter setting Sensitive queueing delay and throughput to the traffic load
and to parameters Argument in the case of small buffer size
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Random Exponential Marking (REM) S. Athuraliya et al., “REM: Active Queue
Management,” IEEE Network Magazine May/June. 2001
“Match Rate, Clear Buffer” Match aggregated input rate to network capacity Stabilize queue around a small target
Sum prices Prices: Differentiated from the calculation of dropping or
marking prob. End-to-End marking prob. exponentially increases with the
sum of prices
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REM (Cont.)
Link price
Updated periodically Depends on mismatches of rate and queue length
Marking Prob.
])}[][()][({][]1[ * nCnxbnbnpnp lllllll
ll np ][
1
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Proportional-Integral (PI) Controller C.Hollot et al., “On designing improved controllers for AQM
routers supporting TCP flows,” IEEE INFOCOM, 2001 Uses instantaneous queue length, while RED uses EWMA. Proportional to queue length mismatch and to its accumulation
(time integral)
Equivalent to the price of REM
is
refref
iebTkebakp
qkqbqkqakpkp
][][)(][
)][()]1[(][]1[
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AVQ Algorithm C : link capacity, : virtual capacity VQ : virtual queue VQ=B (buffer size) On a packet arrival, it enqueues VQ, if no rooms
available in VQ, marks it updates on each packet arrival
If input rate is below than desired rate,
VC increases and less aggressive marking Otherwise, VC decreases and more aggressive marking
C~
CC ~
C~
)(~ CC
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AVQ Algorithm (cont.) At a packet arrival Update VQ size: If VQ+b > B, then mark packet Else, VQVQ+b Update VC
)0,~
max( TCVQVQ
)0,),~
max(min(~
bCTCCC
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Properties of AVQ Rate-based marking
Provides early feedback Achieves input rate to the desired utilization
Regulates utilization instead of queue length as RED,PI,REM
Robust to short flows Two design parameters (alpha, gamma)
determine robustness and stability
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TCP/AVQ model for analysis
Similar to stochastic fluid-based TCP dynamics [14] Ignores slow-start and time-out of TCP Characterize AIMD
Linearize TCP/AQM model
))(~
),(()()(1
2dtCdtxpdtxtx
dx
jjiii
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Stability Analysis of AVQ (1/3) Main ideas
Take Laplace Transform to the linearized TCP/AVQ model
Obtain the characteristic equation Find the condition where all roots of char. Eq. is on
the LPF.
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Characteristic Eq.
Stability Analysis (2/3)
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What value of K yields? Can it guarantee the unique d?
Make the condition less strict
Find necessary condition
Stability Analysis (3/3)
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Simulation (1/3) Compare performance (loss, utilization, avg. queue
length) of various AQM schemes when traffic load varies
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Simulation (2/3) Compare responsiveness of AQM schemes when
flows are dropped and then established again
t= 0 s, N=140
t=100 s, N=35
t=150 s, N=140
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Simulation (3/3) Investigate the effect of short-flow
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Conclusion AVQ algorithm is proposed
Maintains small queue length with consistent utilization and small loss
Robust to short-flows Stability is analyzed relating to control parameters
and feedback delay A design guideline is provided However,
Simulation result showing the validity of analysis is missed Simulation results are unfair (number of packet drop) Queueing delay is not effectively regulated
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J. M. Blanquer, B. Ozden
“Fair Queueing for Aggregated Multiple Links,”
SIGCOMM 2001
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Contents Introduction & Background
GPS, PGPS (WFQ) MSFQ
Preliminary properties of MSFQ Bound on packet delay of MSFQ Bound on per-flow service of MSFQ
Fairness and MSF2Q Applications Conclusion
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Introduction Fair queueing/scheduling is required due to
Increased variety of traffic diverse requirement for QoS limited network resources
Fair queueing disciplines based on GPS have been studied considerably in case of single server, however, not in case of multiple server system
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Generalized Processor Sharing (GPS) L. Kleinrock “Queueing Systems Vol 2: Computer Applications,”
Wiley, 1976 GPS server serving N flows is characterized by
where, is the amount of traffic for flow i served in With Leaky Bucket algorithm, it guarantees bandwidth share
Also provides an end-to-end bounded delay service
Nii ,2,1,
j
i
j
i
tW
tW
),(
),(
),( tWi ],[ t
rr
jj
ii
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GPS (Cont.) Idealized discipline that it can not be implemented
A server can transmit only one packet at a time, not several packets simultaneously
Traffic can not be divided infinitely As a solution to implementation, several realizable
schemes proposed Packet-by-packet GPS (PGPS), Weighted Fair Queueing
(WFQ) Virtual clock, Self-clocked fair queueing, Start-time fair
queueing
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Packet-by-packet GPS (PGPS)
K. Parekh, “A Generalized Processor Sharing Approach to Flow Control in IntServ Network,” IEEE Tran. On Networking, 1993
Also known as Weighted Fair Queueing (WFQ) A. Demers, et al. “Design and Analysis of a Fair Queueing Algorithm,”
SIGCOMM 1989 Provides guarantees on throughput and worst-case packet delay
Packet delay compared to that of GPS is not grater than the transmission time of one maximum size packet
Bits served for each flow do not fall behind corresponding GPS
by more than one maximum size packet
r
Ldd GPSpPGPSp
max,,
max,, ),0(),0( LtWtW PGPSiGPSi
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GPS & PGPS
Packet Arrivals of flow 1 and flow 2 < Comparison of GPS & PGPS >
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MSFQMulti-server version of WFQ multi-server version of GPS
(MSFQ,N,r) (GPS,1,Nr) Compare how well (MSFQ,N,r) approximates
(GPS,1,Nr) in terms of worst-case packet delay amount of traffic served for each flow
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Preliminary properties of MSFQ: Total service Let the total # of bits serviced in by GPS, MSFQ be
and , respectively, then
Left ineq. implies: When GPS is busy, MSFQ is busy, too. However, the converse is not true.
Right ineq. implies the need for a buffer size of
),0( GPSW),0( MSFQW
],0[
max)1(),0(),0(0 LNWW MSFQGPS
max)1( LN
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Preliminary properties of MSFQ:Waiting time of packet
Upper bound of waiting time for packet k to be scheduled
Pf: Consider the worst case:
i) The previous packets have occupied all N server
just before the arrival of packet k,
ii) all servers finish at the same time
NrLabti
ikkkW )(,
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Bound on packet delay of MSFQ
(1) Consider two extreme cases
For GPS, best case: (2)with assumption
For MSFQ, worst case: (3) (3)-(2) yields (1)
Compare it with the delay in single server
r
L
Nr
LNdd k
GPSkMSFQkmax
,,
)1(
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Lad k
GPSkGPSk ,,
2,12 flowk
r
L
r
Lad p
MSFQkMSFQk max,,
r
Ldd GPSkPGPSk
max,,
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Bound on per-flow service of MSFQ
Maximum difference occurs when flow i becomes idle in GPS a packet of flow i begins transmission in MSFQ
Proof done case by case (follow yourself ^^) For total service: For a single server:
max,, ),0(),0( NLWW MSFQiGPSi
kMSFQkMSFQiGPSkGPSi LLNbWdW max,,,, )1(),0(),0(
max)1(),0(),0(0 LNWW MSFQGPS
max,, ),0(),0( LtWtW PGPSiGPSi
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Fairness of MSFQ The eq. is incompl
ete to guarantee fairness Why?
The eq. does not ensure that the amount of per-flow service does not exceed arbitrary the amount under GPS
i.e., there is no lower limit in the eq. To resolve this problem
Introduce MSF2 Q, which is an extended version of WF2 Q for multi-server system
max,, ),0(),0( NLWW MSFQiGPSi
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MSF2 Q (1/3) Queued packets at t=0:
ten packets of flow 1 one packet of each flow 2~N
GPS Scheduling MSF2 Q Scheduling
Ni
ii
,2,05.0
,1,5.0
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MSF2 Q (2/3) Scheduling discipline
Define # of outstanding packet of flow i at time t
where, outstanding packet is a packet being transmitted of picked for transmission
At time t, when a server is idle and there is a packet to serve, MSF2Q schedules among flows satisfying:
)(2,to
QMSFi
r
trtotWtW i
QMSFiGPSiQMSFi
)()(and),0(),0( 22 ,,,
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MSF2 Q (3/3) Properties of MSF2 Q
MSF2 Q provides the lower bound of difference of per-flow service
Similar to WF2Q
Note that MSF2 Q is not work-conserving Future work: investigate implement of work-conser
ving scheduler
max,,max, ),0(),0( NLWWNL MSFQiGPSii
max,,max, ),0(),0(1 2 LWWLr
rQWFiGPSii
i
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Applications Ethernet link aggregation
Cost-effective and fault tolerant solution for scaling the network capacity
IEEE 802.3ad: Standard for Ethernet link aggregation
Access of storage I/O RAID system with multiple SCIS channels to
improve I/O performance MSF2Q is expected to provide QoS guarantee and
fair sharing of multiple I/O channels
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Conclusion Service guarantee and Fairness and for aggregated lin
ks have been studied Extended version of PGPS for multiple server has been analy
zed in terms of packet delay and per-flow service Proposed a new fair queueing in multiple servers, MSF2Q
Future works Implementation issues Quantitative comparison to the approach of partitioning flows Extension of hierarchal GPS and servers with different rates