Korea Uni Presentation
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Transcript of Korea Uni Presentation
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Achieving QoS Efficiently in the Internet in the Presence of
Bursty Self-Similar TrafficBy
Chandana Watagodakumbura PhD
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Presentation Outline
• Transfer of real-time traffic over the Internet – Significance – Challenges faced
• Intuitive understanding of the problem
• Possible solution directions– Exploiting statistical multiplexing gains
• Understanding statistical multiplexing gains
• Working Through a Solution: Priority Queue with Lower Real-Time Utilisation (PQ-LRTU) framework– Statistical Guarantees for Real-Time Traffic in the Internet
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Real-Time Traffic Over the Internet - Challenges
• What is the significance of transmitting real-time traffic over the Internet?
– More economical approach to real-time, voice/video data transmission
• What are the challenges involved compared to the use of the Internet for traditional best-effort data transmission?
– Self-Similar or bursty nature of Internet traffic causes performance degradation
– Providing quality of service (QoS) guarantees to end-users
– The need to use resources efficiently thereby minimizing cost
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Real-Time Traffic Over the Internet - Challenges Contd.
• How can we get an intuitive idea of the challenges involved?
• What does it really mean by bursty traffic?
• How do you measure burstiness?
– Hurst parameter (H)
• What causes internet traffic busty?
• How do you quantify relatively the adverse performance effects of bursty traffic?
– Figure 1 shows (next slide) adverse effects of self-similarity, or burstiness, on average delay of a data flow
– The results were obtained simulating aggregation of a number of traffic flows of different levels of self-similarity (or different Hurst parameter, H, values)
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Real-Time Traffic Over the Internet - Challenges Contd.
Figure1: Relative variation of mean queue delay with Hurst parameter H and number of micro flows
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Possible Solution Directions:Statistical Multiplexing Gains
• Why does delay decrease when the number of data flows aggregated are increased?
– Allows statistical multiplexing gains
• To represent possible statistical multiplexing gains in an analytical model, we use binomial distribution
• The mean queue delay is dependant on the number of sources that is active (represented by random variable X) out of a maximum of Nmax sources, at a given point in time
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Possible Solutions Directions:Statistical Multiplexing Gains Contd.
Table 1:Probability of At Least More Than The Specified Number of Sources Are Active Using Binomial Distribution
Probability
Number of Maximum Sources (Nmax)
1 2 4 8 12
P[X >= Nmax/2] 0.5 0.75 0.6875 0.6367 0.6128
P[X >= 4Nmax/5] 0.5 0.25 0.0625 0.035 0.01930
0.2
0.4
0.6
0.8
1
1.2
0 2 4 6 8 10 12 14
number of micro f low s
prob
abilit
y
50% or more active 80% or more active66.7% or more active 33.3% or more active20% or more active
Figure 2: Probabilities of time at least the specified fraction of sources is active, using the binomial distribution
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Possible Solution Directions:Statistical Multiplexing Gains Contd.• What are the similarities between figures 1 and 2 obtained
through simulations and analytically respectively?
– The queue delay variations with the number of aggregations (Figure 1) shares similarities with the curves obtained using the binomial distribution with different levels of active sources (Figure 2)
• For example, H=0.80 AND H=0.70 curves are very much similar in their variations with 50% or more active and 80% or more active curves respectively
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Possible Solution Directions:Statistical Multiplexing Gains Contd.
• What inferences can be made comparing figures 1 and 2?
– They give some insights into why mean queue delay reduces when the number of self-similar aggregations increases:
• Reduction of the probability of time a specified number of sources are active
– Further the level of active sources required to have a significant impact on the queue delay determines the shape of the curve
• The level of active sources that makes a significant impact on delay is dependent on the Hurst parameter of the traffic stream
– The higher the Hurst parameter (H), the lower the level of active sources required to have a significant effect on queue delay
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Working Through a Solution: Priority Queue with Lower Real-Time Utilisation
• How do we organize the possible solution into an implementation model?– Data packets are served at each node in a queue – Mean delay of a general queuing discipline is given by
SWD W Swhere is the average wait time in the buffer and is the average service time
– Let the instantaneous queue size, the number of packets waiting to be served at the time of the arrival of a packet be denoted by i and, the probability that the queue-size is equal to i is pi
– Then for constant service time x and maximum queue-size of N for the period of time, the mean delay is given by
(1)
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Working Through a Solution : Priority Queue with Lower Real-Time Utilisation Contd.
xixpDN
ii
0
N
iiipxD
0
)1(
– Now consider mean delay calculated from a slowly decreasing (or nearly constant) pi function at fairly large N values, in the order of hundreds or more (as in a heavy tail distribution)
• gets extremely large as N gets largerD
(2)
(3)
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Working Through a Solution : Priority Queue with Lower Real-Time Utilisation Contd.
– The above is demonstrated in the following equation
110
N
Mic
M
ii ipipxD
That is,
12
))(1(
0
MNNMpipxD c
M
ii
where pc is a constant probability value and M and N are very large. The summation term from i = M+1 to i = N has a greater impact on mean delay as M and N get larger
(4)
(5)
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Working Through a Solution : Priority Queue with Lower Real-Time Utilisation Contd.
– Further (4) can be written as
where is the delay component composed of exponential decay portion of queue-size distribution
whereas is the component composed of the power-law decay portion
1 pe DDxD
(6)
Figure 3: Variation Dp with Maximum Queue
Size N for an Arbitrarily Small pc Value of
0.01 and M=100
eD
pD
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Working Through a Solution : Priority Queue with Lower Real-Time Utilisation Contd.
– As seen from (5) and Figure 3, when the queue-size becomes larger the resulting mean queue delay increases exponentially
• What causes the instantaneous queue size to grow?– Hurst parameter (H)
• H depends on the traffic stream or the source
– Utilization of aggregated traffic • It depends on the number of flows aggregated and the arrival rate of
Each flow
• How controllable are H and real-time utilization level (RTF)? – H is a traffic flow attribute whereas the real time utilization level
is an attribute that the network operator has control of
• Therefore, it is possible to control the queue-size, in effect mean queue delay, even with streams of very high H with an appropriately small utilization level
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Working Through a Solution : Priority Queue with Lower Real-Time Utilisation Contd.
• We have already seen the effect of H on delay– In the same way as mean delay increased with H, it also increases
with RTF; values for 70% and 80% RTF were very much higher than the value for 60% RTF
0
50
100
150
200
0 2 4 6 8 10 12 14
Number of micro f low s
Mea
n qu
eue
dela
y in
ms
H=0.80-RTF 60 H=0.82-RTF 60 H=0.80-RTF 80
H=0.82-RTF 80 Exponential H=0.82-RTF 70
H=0.80-RTF 70
Figure 5. Variation of Mean Queue Delay with RTF, H and Number of
Micro Flows
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Working Through a Solution : Priority Queue with Lower Real-Time Utilisation Contd.
• What we have seen is that instantaneous queue size gets larger as the level of utilization or H get larger
– In fact, it has a dramatic increase beyond a certain H and utilization level
– For example, in Table 2, for the same H value of 0.82, 60% utilization produces a mean delay of 26ms whereas it was 180ms for 80% utilization
Table 2: Variation of Queue Size and Mean Delay with Different Traffic
Conditions
Traffic Conditions
RTF 80% RTF 80% RTF 60% Measured Parameter
H = 0.82 H = 0.80 H = 0.82 Exponential
Maximum queue-size 1810 1151 644 15
Maximum mean delay 180 78 26 3.6
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Working Through a Solution : Priority Queue with Lower Real-Time Utilisation Contd.
• When real-time traffic utilization level is maintained at a lower value how can we achieve a higher overall queue efficiency?
– Use a class-based priority queuing discipline with real-time traffic treated with the highest priority at a relatively lower utilization relative to the whole queue
• The end users can be provided with statistical guarantees for their real-time data
– Non-real-time data are treated with lower priorities with no performance guarantees
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BENEFITS OF PRIORITY QUEUEING WITH LOWER REAL-TIME UTILISATION THRESHOLD ON OPTICAL BURST SWITCHING
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Optical Burst Switching (OBS)• What does it mean by optical burst switching?
– Vast bandwidth offered by optical wavelength-division multiplexing (WDM) technology can be merged with IP networks for cost effective data transmission
– Optical Burst Switching (OBS) is a prime candidate for switching in the optical domain using currently available technology
– OBS combines the positive features of both wavelength routing (WR) and optical packet switching (OPS)
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OBS with PQ-LRTU
• Is there any benefit of using PQ-LRTU with OBS? If Yes, what are they?
– In this section, attempts are made to bring out the positive implications of PQ-LRTU framework, implemented in the electrical domain, to the optical domain, more specifically on OBS
– That is, we put forth that better end-to-end performance for real time traffic can be achieved when PQ-LRTU framework is merged with WDM networks using OBS
• Simulated environment and the results are shown in the next two slides
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Suggested Environment
Figure 7: Electrical domain using PQ-LRTU framework and the Optical domain using OBS
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Effects of Burst Assembling / Disassembling Process on Mean Delay
Variation of delay (OBS and Non-OBS) with RTF
0.00
50.00
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300.00
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450.00
500.00
0 2 4 6 8 10 12 14
Number of Aggregations
Dela
y (
ms)
RTF=0.2 OBS RTF=0.2 NON-OBS RTF=0.4 OBS
RTF=0.4 NON-OBS RTF=0.6 OBS RTF=0.6 NON-OBS
Figure 8: Variation of mean delay of OBS and non-OBS flows with RTF
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Effects of Burst Assembling / Disassembling Process on Mean Delay Contd.
• What is the significance of delay variation in the previous diagram?
– The queuing phenomenon, underlying the burst assembling and disassembling process, introduced additional latency compared to their non-OBS counterparts
– This additional latency was more significant at higher RTF levels, when combined with lower aggregation levels
– In addition to the considerable additional latency in OBS traffic, at all levels of aggregations, mean delay was seen as an increasing function aggregation, beyond a certain threshold value of aggregation (See Figure 9 in the next slide)
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Effects of Real-Time Utilization Threshold (RTF) on Burst Assembling/Disassembling Process
Variation of OBS delay with RTF
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1200.00
1400.00
0 2 4 6 8 10 12 14
Number of Aggregations
Dela
y (
ms)
RTF=0.2 RTF=0.4 RTF=0.6 RTF=0.7
Figure 9: Variation of mean queue delay of OBS flows with RTF
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Effects of Real-Time Utilization Threshold (RTF) on Burst Assembling/Disassembling Process Contd.
• What is the significance of delay variation with RTF in OBS?
– As in the case of non-OBS traffic, mean end-to-end delay is a
decreasing function of RTF level
• The reduction being significant for lower levels of aggregation
– There is more apparent increase in delay with the level of aggregation, for lower RTF levels
– On the whole, an RTF level around 0.4, seemingly, results in somewhat uniform ideal range of delay, for all levels of aggregation
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OBS with PQ-LRTU: Important Findings
• What are the benefits PQ-LRTU carries to optical domain, OBS in particular?
– The underlying queuing phenomenon of the burst assembling / disassembling process of OBS introduced a considerable amount of latency, generally, at all levels of aggregation
– Despite this adverse effect of burst forming process, the PQ-LRTU
framework carries its benefits to optical domain, except for one significant difference
• Unlike in non-OBS flows, where delay reduces to the extent the RTF is lowered, in OBS flows, there tend to be an optimal RTF value that results in minimum delay
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OBS with PQ-LRTU: Important Findings Contd.
– It is also important to differentiate that additional latency incurred in OBS is not merely due to assembling / disassembling tasks, but due to their effects on changing the dynamics within the queues themselves
– The delay reduction, with the level of RTF, is significant for lower levels of aggregation, and these reductions are bound to be more significant, when larger burst sizes are used
– PQ-LRTU will accompany its other benefits, such as less starvation of lower priority packets etc. to the optical domain
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OBS with Composite Burst Assembly Used with PQ-LRTU
• How do we overcome any limitation of PQ-LRTU on OBS, if any?
– The purpose of this section is to highlight composite burst assembly as means of overcoming adverse effects of higher burst sizes on queue delay, when aggregate self-similar traffic is present within the electrical domain
– Further, we highlight the benefits of combining a lower real-time class traffic utilization threshold with composite burst assembly for higher performance gains in aggregate, class-based traffic environment
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Comparison of Effects of Composite andNon-Composite Burst Size on Average Delay
(a) RTF = 0.4(b) RTF = 0.6
Figure 14
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Composite OBS with PQ-LRTU: Important Findings
• What are the benefits of using composite OBS with PQ-LRTU?
– When larger burst sizes are used in OBS, with non-composite burst assembly, a counter-productive performance effect is caused in a framework using a lower real-time class utilization threshold
– This negative impact is more significant at lower real time utilization threshold levels (RTF levels), which necessitated a tradeoff between the burst size and the RTF level, in order to maximize effectiveness
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Composite OBS with PQ-LRTU: Important Findings Contd.
– However, the composite burst assembly mechanism removes the negative performance effects on the highest-priority, or real time, traffic class at higher burst sizes.
– As a result, the above framework can be used in conjunction with the composite burst assembly technique, without compromising the prospective performance gains for the real time traffic class
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THANK YOU!
Any Questions?