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End-to-End Network Performance Estimation Using Signal ComplexitySlides
 

End-to-End Network Performance Estimation Using Signal ComplexitySlides

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This paper proposes to analyze end-to-end network performance as a signal. Traditionally, network performance is measured by specially designed active probes, which can be singular packets, packet ...

This paper proposes to analyze end-to-end network performance as a signal. Traditionally, network performance is measured by specially designed active probes, which can be singular packets, packet pairs, or longer packet trains, where packet pairs and trains are the default methods for useful performance metrics like available bandwidth, bottleneck capacity, jitter, etc. Probing results are notoriously noisy. This paper shows that if probing data are treated as a signal and processed as such, precision can be improved. Real network experiments and analysis are conducted specifically for available bandwidth, but the fundamental approach can be applied to any performance metric.

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    End-to-End Network Performance Estimation Using Signal ComplexitySlides End-to-End Network Performance Estimation Using Signal ComplexitySlides Presentation Transcript

    • . . What is the Signal? • BLACK BOX: end to end (e2e) network, parts of network, data centers, network/data services, applications, etc. ◦ in this paper specifically the bandwidth (AB) e2e network, even more specifically available INPUT: active probing, basically trains of packets thrown at e2e paths • OUTPUT: the same packet trains on the other side • OBJECTIVE: to measure/understand/model the black box 02 • 02 myself+1 "Modeling Network Performance of End Hosts" IEICE Trans. vol.E95-D, no.7 (2012) M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 2 /19 2/19
    • . . What is the Problem? • it is really really hard 06 11 tools and methods, all come up with mismatched results [07] many design tricks, little math [08] [13] • multiple • • too much specificity, no unified/universal tool 06 M.Jain+1 "End-to-End Available Bandwidth: Dynamics..." IEEE/ACM Trans., vol.11 (2003) 11 C.Dovrolis+2 "What do packet dispersion techniques measure?" INFOCOM, vol.2 (2001) M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 3 /19 3/19
    • . . The Problem: Specifics (AB) available bandwidth (AB) • this paper talks specifically about ◦ free capacity on some end-to-end path ◦ between 6 and 30 hops in real networks realtime • it is a technology! • we cannot stress the network too much ◦ example: if you want to find out how long some guy can hold his/her breath under water, it is wrong to drown the person to find out ◦ this paper will call that the Brutal Method M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 4 /19 4/19
    • . . The Problem: Classification 1 • obvious • diagonal grouping IGI 07 and PathChirp 08 are the two comparison targets ◦ NOTE: so far all three are in the same cell Low Precision IGI, PathChirp Short trains This Method Long Trains High Precision iPerf PathLoad 07 N.Hu+1 "Evaluation and Characterization of AB ... Techniques" IEEE JSAC, vol.21, no.6 (2003) 08 V.Ribeiro+4 "pathChirp: Efficient Available Bandwidth Estimation for Paths" PAM Workshop (2003) M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 5 /19 5/19
    • . . The Problem: Classification 2 distinction • this is where the proposed method finds its • the basic idea: simple trains, complex analysis ◦ this paper uses Permutation Entropy (PE) 09 Simple Design Short trains This Method Long Trains Intricate Design IGI, PathChirp iPerf PathLoad 09 C.Bandt+1 "Permutation Entropy: A Natural Complexity Measure for Time Series" Physical Review Letters, Vol.88(17) (2002) M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 6 /19 6/19
    • . . PathChirp 08 exponentially shrinking trains will self-load (temporarily) • idea: • the upper plot is the expected trend Departure gap • this is Arrival gap Departure gap Things that Don't Work (1) 8 Breaking point 1 2 3 4 7 5 6 1 2 3 4 5 Exponential curve 6 7 8 Time/packet sequence 08 V.Ribeiro+4 "pathChirp: Efficient Available Bandwidth Estimation for Paths" PAM Workshop (2003) M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 7 /19 7/19
    • . . Things that Don't Work (2) PathChirp in real life • this is Mixed normalized values pathchirp (diff) pathchirp (departure) pathchirp (arrival) back-to-back (arrival) 1 0.6 0.2 -0.2 0 20 40 60 80 Probe packet/time sequence 01 myself "e2eprobe: source code of probing methods" https://github.com/maratishe/e2eprobe (2013) M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 100 8 /19 8/19
    • . . Other Things that Don't Work groping by making small adjustment to packet space, like in IGI 07 different combinations of packet size in the same probe basic packet pair (just two packets) cannot measure AB, either 11 • any kind of • • • ... good comparative study at 07 • ... source code for IGI, PathChirp and the proposal at 01 07 N.Hu+1 "Evaluation and Characterization of AB ... Techniques" IEEE JSAC, vol.21, no.6 (2003) 11 C.Dovrolis+2 "What do packet dispersion techniques measure?" INFOCOM, vol.2 (2001) 07 N.Hu+1 "Evaluation and Characterization of AB ... Techniques" IEEE JSAC, vol.21, no.6 (2003) 01 myself "e2eprobe: source code of probing methods" https://github.com/maratishe/e2eprobe (2013) M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 9 /19 9/19
    • . . The Solution: Observations . The most frequent pattern is ... . ... that packets experience network . extreme states of being affected by the • see the PathChirp in action a couple of slides ago • the pattern is found for most train designs, single packets, packet pairs, etc. . The Solution therefore is... . ...to analyze data as a combination of basically) . M.Zhanikeev -- maratishe@gmail.com -- binary states (zeros and ones, E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 10 /19 10/19
    • . . The Solution: PE (algorithm) { } • arrival packet • gaps x(i), i = 1, 2, .. embed into m-dimensional space: Xi = [x(i), x(i + d), ..., x(i + (m − 1)d)] ◦ for each i, there are multiple is ◦ warping is allowed to maintain same size in all vectors ◦ d is delay, the paper uses d = 1, so only neighbors -- supported by practice 02 Xi turned into the vector [0, 1, ..., m − 1] where value is the other, i.e. 0th, 1th in Xi . m! permutations for each vector, each becomes a symbol signal is then simply m-d space using in total i symbols for each permutation result: normalized entropy of the set of symbols ◦ in this paper the best m (smallest entropy) is found using 10 • each • • • 02 myself+1 "Modeling Network Performance of End Hosts" IEICE Trans. vol.E95-D, no.7 (2012) 09 C.Bandt+1 "Permutation Entropy: A Natural Complexity Measure for Time Series" Physical Review Letters, Vol.88(17) (2002) 10 A.Brandmaier "Permutation Distribution Clustering and Structural Equation Model Trees" PhD Diss. (2012) M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 11 /19 11/19
    • . . The Solution: Actual Methods • static method (BC: bottleneck capacity) (r: tuning parameter = 0.8) AB = BC − rBC(1 − PE). • (1) adaptive method: reference frame via calibration (max/min AB and PE values) AB = ABmax − M.Zhanikeev -- maratishe@gmail.com -- (ABmax − ABmin )(PE − PEmin ) . (PEmax − PEmin ) E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- (2) 12 /19 12/19
    • . . Experiments: Probing Network another location in the city another lab lab P1 Probing point 1 Traffic dump M.Zhanikeev -- maratishe@gmail.com -- P2 Probing point 2 Probing target P3 Probing point 3 E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 13 /19 13/19
    • . . • using software in 01 (IGI, PathChirp, PE) noisy, so catch when diagonal line • IGI and PathChirp are is crossed (simple heuristic) • PE methods (0.8 and adaptive) are from the previous slide -no probe design Receiving rate (Mbps) Experiments: Methods igi 100 pathchirp Detection point 90 80 70 60 50 40 40 50 60 70 80 90 Sending rate (Mbps) 01 myself "e2eprobe: source code of probing methods" https://github.com/maratishe/e2eprobe (2013) M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 100 14 /19 14/19
    • . . Experiments: One Run • real probing in the wild • prefer to run all the methods roughly at • same time PE and Brute AB are based on back-to-back probe Probing stream 1 Back-to-back probe 2 Pathchirp probe Feedback 3 IGI probe * 5 gaps Pick random psize, probesize Run the probing client To the next run M.Zhanikeev -- maratishe@gmail.com -- Store data on server side the var psize; // random [100, 1000] var probesize; // random [50, 350] // open TCP client to remote IP and port var client = new Client( rip, rport); // run back-to-back probe // … and wait for RX throughput runBackToBackTX( psize, probesize); var thru = client->waitForStatus(); // pathchirp probe var low = 0.5 * thru; var high = 5 * thru; var alpha = pickAlpha( low, high); runPathChirpTX( psize, low, high, alpha); // IGI probe var low = 0.5 * thru; var high = 1.5 * thru; var step = ( high - low) / 5; for ( var rate = low; rate <= high; rate += step) { runIgiTX( psize, probesize, rate); } E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 15 /19 15/19
    • . . Results (1) result: 0.8 adaptive is the best! 30 P1 Error 30 igi igi 50 70 Brute AB 40 pathchirp 30 igi 20 adaptive 0.8 10 90 70 84 AB M.Zhanikeev -- maratishe@gmail.com -- adaptive 60 40 20 P2 20 igi 0.8 0 56 80 98 50 40 igi pathchirp 40 60 80 Brute AB igi 0.8 adaptive 20 pathchirp igi 0 adaptive 0.8 30 50 70 90 AB adaptive 0.8 90 0.8 70 50 adaptive igi 30 pathchirp 10 P3 10 60 Error 50 0.8 0.8 0.8 Error 70 100 Estimated AB pathchirp adaptive 0.8 90 Estimated AB Estimated AB • is better on average, but • performance varies with network distance 30 50 70 90 Brute AB pathchirp 0.8 40 pathchirp igi adaptive igi igi 0.8 adaptive 30 50 70 90 AB E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 16 /19 16/19
    • . . Results (2) : CCF Analysis 0.6 0.4 0.53 CCF 0.2 -0.2 -0.3 -0.6 -0.58 P vs Brute AB M.Zhanikeev -- maratishe@gmail.com -- Brute AB vs Brute PE Brute AB vs Pathchirp AB Brute AB vs IGI AB E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 17 /19 17/19
    • . . Wrapup • PE helps by 1. using dumb probes with simple designs 2. reducing analysis to complexity of binary response • the method can be used for any metric, not just AB ◦ jitter ◦ tomography (sensing of topology) ◦ load or utilization of a system ◦ etc.... • will cover all these in M.Zhanikeev -- maratishe@gmail.com -- future work ... E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 18 /19 18/19
    • . . That’s all, thank you ... M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 19 /19 19/19
    • . . [01] myself (2013) e2eprobe: source code of probing methods https://github.com/maratishe/e2eprobe [02] myself+1 (2012) Modeling Network Performance of End Hosts IEICE Trans. vol.E95-D, no.7 [03] 1+myself+1 (2006) Rate-Based and Gap-Based Available Bandwidth Estimation... Springer LNCS, vol.4238 [04] 1+myself+1 (2006) ABshoot: A Reliable and Efficient Scheme for End-to-End AB... IEEE TENCON [05] 1+M.Zhanikeev (2009) Active Network Measurement: Theory, Methods, and Tools ITU Japan M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 19 /19 19/19
    • . . [06] M.Jain+1 (2003) End-to-End Available Bandwidth: Dynamics... IEEE/ACM Trans., vol.11 [07] N.Hu+1 (2003) Evaluation and Characterization of AB ... Techniques IEEE JSAC, vol.21, no.6 [08] V.Ribeiro+4 (2003) pathChirp: Efficient Available Bandwidth Estimation for Paths PAM Workshop [09] C.Bandt+1 (2002) Permutation Entropy: A Natural Complexity Measure for Time Series Physical Review Letters, Vol.88(17) [10] A.Brandmaier (2012) Permutation Distribution Clustering and Structural Equation Model Trees PhD Diss. M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 19 /19 19/19
    • . . [11] C.Dovrolis+2 (2001) What do packet dispersion techniques measure? INFOCOM, vol.2 [13] J.Strauss+2 (20s03) A Measurement Study of Available Bandwidth Estimation Tools 3rd ACM SIGCOMM M.Zhanikeev -- maratishe@gmail.com -- E2E Network Performance Estimation Using Signal Complexity -- http://tinyurl.com/kyutech131113 --- 19 /19 19/19