Simulation Results and Discussions
4.2 Simulation Results
In this section, we show the performance of the PRNN-based predictive Q-DBA method.
All the simulation results of the proposed PRNN-based predictive Q-DBA Method are comparing to that in DBAM [16] and Q-DBA method [9]. The traffic arrival rates are set as follows:
Voice service: 4.48Mbps x 32 (iid)
Video service: 0.55Mbps x 32 (iid) ~ 15.75Mbps x 32 (iid) Data service: 0.28Mbps x 32 (iid) ~ 7.27Mbps x 32 (iid)
For DBAM [16], the ONUs send report message according to their queues’ occupancies and the waiting times between last and present timeslots. A maximum window of total bandwidth requirement is pre-assigned according to service level agreement (SLA) in OLT.
When the total bandwidth requirement of the ONUi is less than the pre-assigned maximum window, the allocated total bandwidth equals its requirement. When the total bandwidth requirement of the ONUi is larger than the pre-assigned maximum window, the allocated total bandwidth equals the pre-assigned maximum windows. Besides, the rule of allocating bandwidth to voice and video packets is similar as that in allocating the total bandwidth for ONUi. At last, the residual bandwidth is allocated to data packets.
For Q-DBA, The ONUi send six kinds of information of queues to the OLT. Upon receiving the report messages from each ONU, the OLT allocates the available bandwidth according to the priorities of report information in a proportional method. The steps in bandwidth allocation are set as follows:
(i) Bandwidth allocation to voice packets.
(ii) Bandwidth allocation to video packets with the 2nd and 3rd priority.
(iii) Bandwidth allocation to data packets with the 4th priority.
(iv) Bandwidth allocation to video packets with the 5th priority.
(v) Bandwidth allocation to data packets with the 6th priority.
(vi) Residual bandwidth allocation.
Since the voice dropping probability is zero whether in predictive Q-DBA, Q-DBA or DBAM, we omit to show the simulation result, this result is due to the QoS requirement for voice packets.
Figure 4.1: Average voice delay time versus the system load in EPON
Figure 4.1 shows the average voice delay time versus the system load in EPON. It can be found that all the voice delays of the three schemes are within the requirement. However, the voice delay in DBAM and Q-DBA increases with the increasing of the system load, but in
Average Voice Packet Delay (s) Average Voice Packet Delay (s)
predictive Q-DBA, the delay time is almost the same when the system load is below 0.8. It is because the predictive Q-DBA makes a prediction of the new arrival voice packets which arrive between two consecutive reporting times for each ONU, thus the allocated bandwidth could meet the actual traffic condition of each ONU. When the system load is larger than 0.8, it can be seen that the delay time increases apparently because of the greatly increasing of the burst packets. It also can be found that the delay time in predictive Q-DBA has improved by about 26% and 21% on the average over Q-DBA and DBAM, respectively.
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Figure 4.2: Average video delay time versus the system load in EPON
Figure 4.2 shows the average video delay time versus the system load in EPON. It can be found that, when the system load is below 0.8, the average video delay in Q-DBA and predictive Q-DBA is far from the video delay requirement because the system has enough bandwidth to allocate, so the ONUs could get more bandwidth to transmit the video packets
Average Video Packet Delay (s)
which are not reported. But in DBAM, the average video delay increases almost smoothly with the increasing of system load because of the maximum window in DBAM, and the prediction in ONUs. Since the PRNN-based predictor is known to be good at the prediction of burst traffic, the predictive Q-DBA improves the delay time by about 29% over Q-DBA (90%
better than DBAM) when the system load is below 0.8. When the system load is larger than 0.8, the packets are dropped because the capacity of fiber link is limited, so the average video delay is close to the video delay requirement. At last, it can be easily found that the video delay requirement is still satisfied and is good when the system load is below 0.8.
0.0 0.1 0.2 0.3 0.4 0.5
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
System Load
Packet Delay
DBAM Q-DBA
Predictive Q-DBA
Data Delay Bound
Figure 4.3: Average data delay time versus the system load in EPON
Figure 4.3 illustrates the average data delay time versus the system load in EPON. In DBAM, it can be seen that the average data delay increases with the increasing of system load, because the maximum window does not meet the real requirement of data packets, and the
Average Data Packet Delay (s)
burst arrival cannot be served instantly. When the system load is below 0.8, the average data delay in Q-DBA and predictive Q-DBA increases almost smoothly with the increasing of system load. This is the same phenomenon as in video delay. Since both the Q-DBA and predictive Q-DBA have the prediction of the new arrival data packets, the delay time is small when the system load is below 0.8. Furthermore, due to the Q-DBA and predictive Q-DBA considers the condition of waiting bound, the data packets in Q-DBA and predictive Q-DBA does not violate the delay bound as early as that in DBAM does. For the better prediction performance of the PRNN-based predictor, the predictive Q-DBA improves the delay time about 34% than Q-DBA and 43% than DBAM. When the system load is larger than 0.8, the delay time increases apparently because of the greatly increasing of the burst packets. The reason is similar to the average video delay. As same as video delay, it also can be found that the data delay requirement is still satisfied and is good when the system load is below 0.8.
Figure 4.4 illustrates the average video dropping probability versus the system load in EPON. It can be found that the average video dropping probability of Q-DBA and predictive Q-DBA equals to zero when the system load is below 0.8. It is because in both Q-DBA and predictive Q-DBA, the priority of video packets with the problem of delay requirement will be raised. The video packets with higher priority will be served prior to the video packets with original priority. When the system load is larger than 0.8, the average video dropping probability exceeds the video dropping probability requirement because the capacity of fiber link is limited. It can also be found that the dropping probability in DBAM cannot be guaranteed. It is because the maximum window cannot totally support the burst arrival, therefore the video dropping probability is violated.
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Figure 4.4: Average video dropping probability versus the system load in EPON
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Figure 4.5: Average data packet blocking probability versus the system load in EPON Average Video Packet Dropping Probability Average Data Packet Blocking Probability
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Figure 4.6: Average data starvation ratio versus the system load in EPON
Figure 4.5 illustrates the average blocking probability of data packets versus the system load in EPON. It can be found that the average blocking probability of data packets in DBAM, Q-DBA and predictive Q-DBA equals to zero when the system load is below 0.7. It is because the system has enough capacity to support the system load. When the system load is larger than 0.7, the blocking probability increases greatly. It is because the system cannot afford the burst arrival data packets. Due to no limitation of allocated bandwidth in Q-DBA, predictive Q-DBA and the consideration of waiting bound for data packets, the blocking probability in Q-DBA and predictive Q-DBA does not increase as greatly as that in DBAM.
Figure 4.6 illustrates the average starvation ratio of data packets versus the system load in EPON. The starvation ratio of data packets is defined to express the percentage of data packets whose delay time exceed the delay bound among the total transmitted data packets. It can be seen that the starvation ratio of data packets in DBAM, Q-DBA and predictive Q-DBA
Average Data Packet Starvation Ratio
is zero when the system load is less than 0.7. That is, the starvation does not occur in all of them. It is because the system has enough bandwidth to support the system. When the system load is larger than 0.8, the starvation ratio goes high. It is because the data packets have lower priority than video packets. When the arrival video and data packets increase rapidly simultaneously, the video packets will be served earlier than data packets. Thus, the starvation ratio of data packets will have a conspicuous increase. However, due to the priority of data packets will be raised when considering the waiting bound in Q-DBA and predictive Q-DBA, and the data packets with a higher priority are more easily transmitted than before. Therefore, the starvation in Q-DBA and predictive Q-DBA is less than that in DBAM.
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Figure 4.7: System utilization versus the system load in EPON
System Utilization
0.17 0.26 0.36 0.45 0.53 0.61 0.70 0.80 0.9
Figure 4.7 shows the system utilization versus the system load in EPON. It can be found that the system utilization in predictive Q-DBA is better than that in DBAM, Q-DBA by an amount of 4%, 2% on the average. Firstly, because the bandwidth in Q-DBA and predictive Q-DBA is allocated step by step to different classes rather than set a maximum window to each class in advance, so the system utilization in Q-DBA and predictive Q-DBA is better than that in DBAM. Secondly, with the increasing of system load, the available residual bandwidth decreases, rather than allocates the residual bandwidth to all classes of packets proportionally in Q-DBA, the predictive Q-DBA allocates more residual bandwidth to the high priority traffic than that in Q-DBA. That is, the packets with higher priority in predictive Q-DBA will get more bandwidth to transmit. Therefore, the sytem utilization in predictive Q-DBA is superior to that in Q-DBA method.
Chapter 5 2 Conclusion
In this thesis, we propose a predictive Q-DBA method which introduced a PRNN-based predictor. Owing to the characteristics of accurate and fast convergence, the PRNN-based predictor is known to be good for the prediction of the burst traffic and the ability to make the decision fleetly. Three classes of packets, real-time voice, real-time video, and non-real-time data are taking into consideration. Voice packets are strictly delay sensitive, and video packets are delay sensitive. The dropping probability of voice and video packets are also concerned.
The predictive Q-DBA method sets six different priorities in order to meet the QoS requirement. They are voice packets, video packets with delay and dropping problem, data packets with starvation problem, video and data packets. The OLT combines all these six reported information and the prediction of the unreported packets which arrive between two consecutive report times, therefore the OLT could allocate more bandwidth to the high priority traffic. Thus, the total performance will improve in a certain degree, especially when the system load is high.
The performance of the proposed predictive Q-DBA is compared to DBAM [16] and Q-DBA [9]. The simulation results show that the performance of predictive Q-DBA is better than DBAM and Q-DBA in many respects. The predictive Q-DBA improves the average
voice delay time by an amount of 26% and 21% over Q-DBA and DBAM, respectively. It also improves the average video delay time by an amount of 29% and 90% over Q-DBA and DBAM, respectively. As for the data service, the predictive Q-DBA improves the average data delay time about 34% than Q-DBA and 43% than DBAM. The system utilization in predictive Q-DBA is better than that in DBAM, Q-DBA by an amount of 4% and 2% on the average. As for the video dropping probability, data blocking probability, and data starvation ratio, the predictive Q-DBA is better than DBAM and almost the same with Q-DBA.
In this thesis, the predictive Q-DBA can support most of the system load without violating the QoS requirements. If the proposed predictive Q-DBA method could be adopted to manage the resource in EPON, the customers will get more benefits.
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Vita
Hsing-Yi Wu was born in Taipei, Taiwan. He received B.E. degree in department of electrical engineering from National Taiwan Institute of Technology, Taiwan, in 1994, and the M.S.
degree in the degree program of electrical engineering and computer science in National Chiao Tung University, Taiwan, in 2007. His research interests include resource management and optical network.