Scalable Continuous Media Streaming Systems in .NET framework

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Scalable Continuous Media Streaming Systems
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to negligible levels (order of 10 1 percentage bit-rate variation averaged over 1 second interval) without any impact on other parts of the system.
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18.7.2 Experimental Results
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We conducted extensive benchmarking experiments to collect three performance results, namely start-up latency, aggregate bit-rate of all channels, and peak aggregate reception bit-rate to compare with the theoretical calculations. In all experiments, we use the system parameters of L = 4,401, C = 2.84, m = 2 and b = 1.42. The media stream is a MPEG-1 encoded system stream multiplexing one video stream with one audio stream. We conducted benchmarks for a total of 7 GCB system con gurations, with the number of media segments N ranging from 50 to 1,000. For each con guration, we obtain the performance data by averaging data collected from 20 benchmark runs. The results are summarized in Table 18.2. We rst consider start-up latency that is measured from within the client software. The results show that the experimental results agree closely with the theoretical calculations. The minor differences are likely due to network delay and software processing delay. Next, we measured the aggregate network bit-rate of all channels using a hardware protocol analyzer connected to the Ethernet switch s mirroring port, which forwards all packets passing through the switch. The measured results exhibit a consistent 5% increase in bandwidth usage compared to the theoretical calculations. This increase is due to the header overheads in the application-layer protocol (8 bytes), UDP (8 bytes), IP (24 bytes), and Ethernet (18 bytes). With a UDP datagram payload of 1,400 bytes, the combined header overhead is equal to (8 + 8 + 24 + 18)/1458 = 4%, which closely matches the measured results. Finally, we measure the aggregate reception bandwidth usage in the client access link, again using a hardware protocol analyzer. Unlike the aggregate network bit-rate, the reception bitrate is not constant and does vary depending on which media segments are being received. Nevertheless, we are more interested in the peak bandwidth usage and thus we measure the maximum bandwidth usage averaged over a 10-second window. The results show similar header overhead-induced bit-rate increases ( 5%) for con gurations with N up to 200. For larger values of N, the differences widen further up to 9.51%. Our study of the log data shows that two factors lead to the bit-rate increase.
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Table 18.2 Comparison of theoretical and experimental results (with m = 2) Con g N /NG * Latency Aggregate Bit-rate of all channels Peak aggregate reception bit-rate
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Theory Measured Theory 176.10 110.05 88.04 44.05 18.00 11.20 9.00 5.8208 6.8302 7.2779 8.6611 10.6277 11.5828 12.1096
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Measured Difference (%) Theory Measured Difference (%) 6.11 7.17 7.64 9.10 11.16 12.16 12.71 +4.97 +4.97 +4.98 +5.07 +5.00 +4.98 +4.96 2.84 2.84 2.84 2.84 2.84 2.84 2.84 2.98 2.98 2.98 3.00 3.05 3.11 3.09 +4.93 +4.93 +4.93 +5.63 +7.39 +9.51 +8.80
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50/19 176.04 80/21 110.03 100/22 88.02 200/28 44.01 500/33 17.60 800/37 11.00 1000/38 8.80
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Note.*N and NG are the number of video segments and number of channels respectively.
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Open-Loop Algorithms
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First, larger values of N result in more frequent channel switching, and as discussed earlier in Section 18.7.1, there is some delay from the time the client leaves a multicast group to the time the network switch stops forwarding the multicast data. This results in some duplicated data being transmitted to the client, only to be discarded by the client s operating system. The second reason is due to the speci c network switch we used in the experiment. Our results show that there seems to be bugs in the switch s hardware, resulting in some random multicast data transmitted to the client after the switch has pruned the multicast tree. This speci c problem is easy to miss because the random multicast data will be discarded by the client s operating system (as the client has left the multicast group already) and thus will not cause any data transmission or application error. We expect this problem to be resolved in future revisions of the switch hardware.
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18.8 Summary
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In this chapter we have reviewed some open-loop multicast streaming algorithms in the context of a taxonomy, which classi es the algorithms according to the media segmentation and transmission bandwidth schemes adopted. To further illustrate the design and trade-offs in developing an open-loop algorithm, we described in detail as well as analyzed the performance of the Consonant Broadcasting algorithm. We also addressed some practical issues in the implementation and deployment of the Consonant Broadcasting algorithm, which are likely to be applicable to other open-loop algorithms as well. Unlike the closed-loop algorithms, the resources consumed by open-loop algorithms are xed irrespective of the system load, i.e., number of concurrent users. The upside is that openloop algorithms will be very cost-effective in serving popular media streams (e.g., popular movies) of a large user population. The downside, however, is that for unpopular media streams the resources requirement could exceed those of closed-loop algorithms, which are more ef cient when the system load is lighter. In the next chapter, we illustrate a hybrid approach to multicast streaming, combining elements of both open-loop and closed-loop algorithms in the same architecture.
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