Optimal Bandwidth Monitoring. Y.Yu, I.Cheng and A.Basu Department of Computing Science U. of Alberta

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1 Optimal Bandwidth Monitoring Y.Yu, I.Cheng and A.Basu Department of Computing Science U. of Alberta

2 Outline Introduction The problem and objectives The Bandwidth Estimation Algorithm Simulation Results Experiments on Real Networks Conclusion Y.Yu, I.Cheng and A.Basu, UofA 2

3 The Quest of QoS What is Quality of Service (QoS)? Quality of Service is the set of quantitative and qualitative characteristics of a distributed multimedia system necessary to achieve the required functionality of an application QoS Parameters: Lower Layer: Bandwidth, end-to-end delay Higher Layer: Image/video resolution, compression scheme, subjective image/sound quality Y.Yu, I.Cheng and A.Basu, UofA 3

4 Resource Reservation Solution #1: Resource reservation Protocol (RSVP) Service class beyond best-effort Flow specification Admission control Packet Scheduling Problem Require replacement of existing routers Introduce significantly heavier computation burden on routers Y.Yu, I.Cheng and A.Basu, UofA 4

5 Content Adaptation Solution #2: Network-aware Application Do not rely on underlying network to provide guaranteed quality Actively Monitors network performance and adapt multimedia accordingly Provide trade-off between QoS parameters for certain application. E.g. Distant Education, virtual museum. Y.Yu, I.Cheng and A.Basu, UofA 5

6 1. QoS Parameters 3. Fetch Content 2. Trade-off Criteria Y.Yu, I.Cheng and A.Basu, UofA 6

7 Architecture Application Layer Request reception -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 Y.Yu, I.Cheng and A.Basu, UofA 7

8 The Bandwidth Monitoring Problem Obviously, accurate bandwidth estimation is of critical importance Previous solution: Maintain session between server and client Periodically send testing packets to client to obtain bandwidth samples Using some kind of average of sample bandwidth as bandwidth estimation Drawbacks: Bandwidth sample may not be most updated Waste bandwidth on monitoring when no actual transmission is requested Y.Yu, I.Cheng and A.Basu, UofA 8

9 Objectives To design a new algorithm of bandwidth estimation for network-aware applications Assumptions: One client and one or more server(s) need to perform a oneoff transmission of a multimedia object QoS parameter: end user specified time limit First fraction of the time limit is used for bandwidth testing Target: Guarantees the QoS parameter to a certain confidence level Maximize the multimedia object transmitted Y.Yu, I.Cheng and A.Basu, UofA 9

10 The Source of Bandwidth Variance TCP Congestion Control Design goal: detect and sustain the available bandwidth on the network without creating congestion Typical Bandwidth pattern (TCP Vegas) Y.Yu, I.Cheng and A.Basu, UofA 10

11 TCP Evolution TCP is an ever-evolving protocol, with new mechanisms continuously proposed and implemented TCP Tahoe (BSD Network Release 1.0) TCP Reno (BSD Network Release 2.0) TCP Vegas Slow Start Yes Yes Yes Additive Increase/ Multiplicative Decrease Yes Yes Yes Fast Retransmit Yes Yes Yes Fast Recovery No Yes Yes Congestion Detection/ Avoidance No No Yes Y.Yu, I.Cheng and A.Basu, UofA 11

12 Related Research Bandwidth Estimation in TCP layer: Jacobson, base on size of congestion window, RTT. Mathis, Padhye, base on factors like packet loss rate, packet size, number of timeouts, and average duration of retransmission timeout. Pros and Cons: Accessible to micro-factors of TCP, yielding accurate estimation. Need frequent revision and re-evaluation as new mechanisms are added to TCP version. Not available to application programmers for the purpose of developing network-aware applications. Y.Yu, I.Cheng and A.Basu, UofA 12

13 Our Contribution A bandwidth estimation algorithm working on Application Layer. Based on statistical model, shielded from microfactors of TCP. Easily integrated to network-aware applications by application programmers. Y.Yu, I.Cheng and A.Basu, UofA 13

14 Outline Introduction The problem and objectives The Bandwidth Estimation Algorithm Simulation Results Experiments on Real Networks Conclusion Y.Yu, I.Cheng and A.Basu, UofA 14

15 Assumptions and Notations One server and one client need to make a one-time transmission of a multimedia object (server to client). User specified a time limit T on client. It s expected that the transmission will finish within T by confidence level α. The first fraction t of T will be used for bandwidth testing. Bandwidth testing is performed by using time slices of equal length ts. Each time slice has bandwidth sample x i = Ci / ts N = T / t s, bandwidth population X, 1 X 2,..., X N n t /, bandwidth samples x x,..., x = t, s 1 2 n Y.Yu, I.Cheng and A.Basu, UofA 15

16 Notations µ s x 2 N = i = 1 N n = i = 1 n Our Problem is: n X x, is the average of bandwidth samples. ( x x) i= 1 = n 1 i i i 2, is the variance of bandwidth samples., is actual bandwidth we try to estimate. x 2 s µ First, given N, n,,, give an estimation of, so that P( µ est < µ ) = α. Second, determine optimal value of n, in order to maximize µ est ( N n) ts. [Exercise: FIX ONE PROBLEM IN THE PROB. Expression] Y.Yu, I.Cheng and A.Basu, UofA 16

17 Student-t Distribution d = s n µ x N n N 1 student-t distribution. is a continuous random variable following Y.Yu, I.Cheng and A.Basu, UofA 17

18 Safe Bandwidth Estimation Probability distribution µ = Safe bandwidth estimation x + d s n N N n 1 µ est = x d α (, n) s n N N n 1 d,n t-distribution table ( ( α ) values) Alpha=0.75 Alpha=0.90 Alpha=0.95 n= n= n= n= n= n= Y.Yu, I.Cheng and A.Basu, UofA 18

19 Expected Object Size Expect Object Size Important property of V(n): Statistically (if we view random variable x and s as constant), V(n) has a single maximum value. (Proof omitted) Intuition of the property: V ( n) = µ est ( n) ( N n) ts When n is too large, too much time is used for bandwidth testing, leaving little time for real object transmission; d,n when n is too small, ( α ) value is too large, leading to great margin of under-estimation of bandwidth. Y.Yu, I.Cheng and A.Basu, UofA 19

20 Single Server Optimal Bandwidth Testing The Algorithm: obtain samples ; Calculate V(1) and V(2); n 2 ; while (V(n)>V(n-1)) { ; obtain sample ; Calculate V(n); } return n n +1 µ est ; x, x x 1 2, x n+1 3 Y.Yu, I.Cheng and A.Basu, UofA 20

21 Outline Introduction The problem and objectives The Bandwidth Estimation Algorithm Simulation Results Experiments on Real Networks Conclusion Y.Yu, I.Cheng and A.Basu, UofA 21

22 Goal of Simulation Experiments Verify the effectiveness of the algorithm on: How well the confidence level on time limit is met? Does it find the optimal amount of bandwidth testing? Use the real object transmission time as a gauge to measure the merit of algorithms Comparing our algorithm with a straightforward method of taking fixed number of bandwidth samples. Y.Yu, I.Cheng and A.Basu, UofA 22

23 Simulator Start Simulation Result Set parameter Parameters: µ,σ, which represent the bandwidth characteristics of the channel. N, total number of time slices α, the confidence level on time limit. Y.Yu, I.Cheng and A.Basu, UofA 23

24 Simulation Process Bandwidth testing A normal distributed random number is generated as the bandwidth of the time slice. Our algorithm is used to determine whether bandwidth testing should continue and the expect object size V(n). Real Transmission For each time slice a random number is generated again and used to compute the byte count received in this slice. The byte count is subtracted from the remaining object size. The transmission finishes when remaining size drop below zero. Results Transmitted object size. Whether it finish within time limit. Real transmission time. Y.Yu, I.Cheng and A.Basu, UofA 24

25 Continuous decrease method Serrated V(n) curve Although Statistically, V(n) has one maximum, it is actually serrated, with multiple local maximums. We use three continuous drop in V(n) as the termination condition. Results is not very good. Y.Yu, I.Cheng and A.Basu, UofA 25

26 Moving Average Method Hotel 1 Hotel 2 Results much better We keep the average of past four V(n) for each time slice. The simulator terminates the bandwidth testing when there are three continuous drop in this moving average of V(n). Results is much better. done Y.Yu, I.Cheng and A.Basu, UofA 26

27 Real Transmission Time, Fix Sampling Large sample numbers suffers because too much time used for BW testing. Small sample numbers suffer because of large Under-estimation Margin. Experiment parameters: alpha=0.95, 500 total time slices. Bandwidth average 100kbps, standard deviation is {5, 10, 15, 20, 25, 30, 35, 40, 45, 55, 60}. Y.Yu, I.Cheng and A.Basu, UofA 27

28 Our Algorithm vs. Fix Sampling, 500 slices 20% Room information 5% 4% 7% Y.Yu, I.Cheng and A.Basu, UofA 28

29 Real Transmission Time, Fix Sampling 2 Experiment parameters: alpha=0.95, 100 total time slices. Bandwidth average 100kbps, standard deviation is {5, 10, 15, 20, 25, 30, 35, 40, 45, 55, 60}. Y.Yu, I.Cheng and A.Basu, UofA 29

30 Our Algorithm vs. Fix Sampling, 100 slices 40% 25% Y.Yu, I.Cheng and A.Basu, UofA 30

31 Time Limit Confidence Level Results Number of Overtime Run (out of 200) Time Limit Confidene Level Result (100 slices) 7% Number of Fix Samples Dynamic Sampling Fix Sampling Number of rtime Run (out of 200) Time Limit ConfidenceLevel Result(500slice) 5.5% Number of Fix Samples Dynamic Sampling Fix Sampling Each bar is average of all the bandwidth standard deviation. Alpha has been 0.95 for all the experiments. The number of runs exceeding time limit is around 5.5% and 7%. Y.Yu, I.Cheng and A.Basu, UofA 31

32 Outline Introduction The problem and objectives The Bandwidth Estimation Algorithm Simulation Results Experiments on Real Networks Conclusion Y.Yu, I.Cheng and A.Basu, UofA 32

33 Purpose of experiments on real networks Verify assumption We assume the first n samples are representative of the entire bandwidth population Address practical issues E.g.: selecting time slice size Check computation cost Y.Yu, I.Cheng and A.Basu, UofA 33

34 Implementation Statistics Start Simulation Bandwidth Samples Parameter Setting Y.Yu, I.Cheng and A.Basu, UofA 34

35 Transmission procedure Client: creates a TCP connection to server, and sent a TEST command. Server: begin to send a stream of random bytes to client Client runs our algorithm to perform bandwidth testing, until the termination condition is met. Client tears down the TCP connection and makes a new one. Client sends a TRANSMIT command with parameter of target object size, T t µ which equals to ( ) est Server sends the object with size ( T t) µ est Client finishes receiving, records finish time, calculates and displays statistics of the transmission. Y.Yu, I.Cheng and A.Basu, UofA 35

36 Experiment Scenario 1: campus network Campus Network eva133.cs.ualberta.ca ( ) ipiatik.cs.ualberta.ca ( ) Y.Yu, I.Cheng and A.Basu, UofA 36

37 Experiment results Confidence Goal Average No. of Sample Sample Average (kbps) Sample Standard Error Estimation (kbps) Parameters: Time limit: 10s Time slice: 0.02s Results are average of 100 runs. Controlled under-estimation Actual Bandwidth Avg (kbps) Actual Object Size (KB) Actual Transmission Time (s) % of time used for real transmission! Actual Total Time (s) Total No. of Exception in 100 runs 4 20 Time limit confidence level satisfactorily met. Y.Yu, I.Cheng and A.Basu, UofA 37

38 Comparison with fix sampling Our algorithm Y.Yu, I.Cheng and A.Basu, UofA 38

39 Experiment Scenario 2: Internet other PC ADSL Modem Internet University campus networks 100Mb Ethernet Hub university edge router Client -- home PC Server -- ipiatik.cs.ualberta.ca Experiment Settings: Server: Intel Pentium II 400MHz, 512MB RAM, Redhat Linux 6.1. Client: Intel Celeron 433MHz, 256MB RAM, Window 2000 Professional. 11 hops between client and server. Y.Yu, I.Cheng and A.Basu, UofA 39

40 Experiment results Confidence Goal Average No. of Sample Sample Average (kbps) Sample Standard Error Parameters: Time limit: 10s Time slice: 0.05s Results are average of 100 runs. Estimation (kbps) Actual Bandwidth Avg (kbps) Relatively larger standard error in samples. Actual Object Size (KB) Actual Transmission Time (s) In turn, less time for real transmission. Actual Total Time (s) Total No. of Exception in 100 runs 3 16 Time limit confidence level still within range. Y.Yu, I.Cheng and A.Basu, UofA 40

41 Time slice = 0.02s Time slice = 0.05s Different time slice size Time slice = 0.10s Y.Yu, I.Cheng and A.Basu, UofA 41

42 Different time slice size Time Slice Size (s) Total time slice number Average No. of Sample No. of sample decrease, but sampling time increase! Sample Average (kbps) Sample Standard Error % different in estimation Estimation (kbps) Actual Bandwidth Avg (kbps) Actual Object Size (KB) K K K 3.5% different in object size. Actual Transmission Time (s) Actual Total Time (s) There is a best slice size which maximize object size. However, the difference between using different slice sizes is relatively small. Y.Yu, I.Cheng and A.Basu, UofA 42

43 Experiment Scenario 3: Internet with wireless link other PC AD SL M odem Internet University campus networks 100Mb Ethernet Hub Home PC (Router) university edge router Server -- ipiatik.cs.ualberta.ca C lient laptop Experiment Settings: Server: Intel Pentium II 400MHz, 512MB RAM, Redhat Linux 6.1. Client: Intel Pentium MMX 233MHz laptop, 64MB RAM, Window hops between client and server. Y.Yu, I.Cheng and A.Basu, UofA 43

44 Experiment results Results similar with wired link. Wireless link distance (feet) Total Time Slices Number Average No. of Sample Sample Average (kbps) Results at different distance. Alpha=0.95 in both cases. Lower bandwidth Sample Standard Error Larger variance Estimation (kbps) Actual Bandwidth Avg (kbps) Actual Object Size (KB) Actual Transmission Time (s) 126K K 7.29 Low time limit utilization Actual Total Time (s) Total No. of Exception in 100 runs More exception runs Y.Yu, I.Cheng and A.Basu, UofA 44

45 Experiment results summary The assumption that bandwidth samples represent the entire bandwidth population is reasonable. Slice sizes within a relatively wide range all perform quite well. The algorithm runs smoothly on 233MHz laptop with Java implementation. The computation requirement is not high. The algorithm should be suitable to run on handheld devices. Y.Yu, I.Cheng and A.Basu, UofA 45

46 Outline Introduction The problem and thesis objective The Bandwidth Estimation Algorithm Simulation Results Extension of the Algorithm to Multi-server Scenario Experiments on Real Networks Conclusion Y.Yu, I.Cheng and A.Basu, UofA 46

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