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Multi-server Optimal Bandwidth Monitoring for QoS based Multimedia Delivery

Multi-server Optimal Bandwidth Monitoring for QoS based Multimedia Delivery. Anup Basu, Irene Cheng and Yinzhe Yu Department of Computing Science U. of Alberta. Request reception. Application Layer. - connection handling -request processing. Adaptation Layer. Monitor. Prepare. Transmit.

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Multi-server Optimal Bandwidth Monitoring for QoS based Multimedia Delivery

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  1. Multi-server Optimal Bandwidth Monitoring for QoS based Multimedia Delivery Anup Basu, Irene Cheng and Yinzhe Yu Department of Computing Science U. of Alberta

  2. Request reception Application Layer -connection handling -request processing Adaptation Layer Monitor Prepare Transmit -monitor/poll bandwidth -determine object size transform requested object to target size deliver object To client Lower Layer feedback from network Object delivery across network Architecture

  3. Assumptions and Notations • We need to make a one-time transmission of a multimedia object (servers to client). • User specified a time limitT on client. It’s expected that the transmission will finish within T by confidence level . • The first fraction t of T will be used for bandwidth testing. • Bandwidth testing is performed by using time slices of equal length . Each time slice has bandwidth sample • , bandwidth population • , bandwidth samples

  4. Our Problem is: • First, given N, n, , , give an estimation of , so that . • Second, determine optimal value of n, in order to maximize • . Notations • , is actual bandwidth we try to estimate. • , is the average of bandwidth samples. • , is the variance of bandwidth samples.

  5. Statistical Background-Sampling and Estimate • Assume the parent population conforms to the normal distribution: , is unknown • is the mean of samples, then conforms to Student’s t-distribution (t-distribution). • If sampling without replacement from a finite population, we should have a finite population correction factor:

  6. t-distribution • As , the t-distribution is identical as normal distribution. • Robust: t-distribution works well, even if the parent population is not exactly normally distributed.

  7. Safe Bandwidth Estimation

  8. Safe Bandwidth Estimation • Safe bandwidth estimation: • t-distribution table: values (v = n - 1)

  9. Expected Object Size • Expect Object Size: • Important property of V(n): Statistically (if we view random variable and s as constant), V(n) has a single maximum value. (Proof omitted) • Intuition of the property: When n is too large, too much time is used for bandwidth testing, leaving little time for real object transmission; when n is too small, value is too large, leading to great margin of under-estimation of bandwidth.

  10. Multi-server Environment • From the perspective of the client, there are several server available to delivery the same content. • Client can request a strip of the object from each server. The size of the strips will be proportioned to relative bandwidth of all the servers.

  11. Multi-server Environment • Suppose we have K channels available, then • We have • is the expected object strip size on ith channel. • The total object size . • Theorem: The total object size has the same property as in single server environment. Statistically, it has a single maximum.

  12. Multi-server Algorithm • The multi-server algorithm: • obtain samples on each channel; • ; • ; • ; • while (V(n)>V(n-1)) { • ; • obtain sample on each channel; • Calculate ; • } • return ; 

  13. Using V(n) Using Y1 Channel #1 Y1>Y2? Y2 Channel #2 t O Refinement of the algorithm • Actually, this simple extension of the algorithm is not always optimal. • When K increases, • It’s possible that . At this time, we’d better drop channel #2.

  14. Step 1 Channel #1 Channel #2 Channel #3 Channel #K Step 2 Pick the largest Sum the largest two Sum the largest three Sum all K values together Step 3 Pick the largest Refinement of the algorithm k: the number of channels for real transmission

  15. Refined multi-server algorithm obtain samples on each channel; Calculate for ; ; ; while(TRUE) { ; obtain sample on each channel; Calculate for ; if (V(n)<V(n-1)) break; } return on each channel that constitutes V(n);

  16. Simulation Results • Average bandwidth: 100kbps and 10kbps. • Parameters: alpha=0.95, 100 total slices. Two channels. • Standard deviations is {0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6} of the bandwidth average. • Results are average of all combination of the standard deviation parameter. 4.5%

  17. 30%, SD: Ch#1=60.0, Ch#2=6.0 15%, SD: Ch#1=60.0, Ch#2 vary from 0.25 to 6.0. Simulation Results • Two channels -- 100kbps and 10kbps. • Standard deviations is {0.025, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6} of the bandwidth average.

  18. Summary • Introduce a statistical model with confidence level to multi-server bandwidth monitoring • Dynamically determine the number of sampling • Drop the unreliable channels

  19. The End Questions and Comments?

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