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12. Experimental Evaluation 18-749: Fault-Tolerant Distributed Systems

12. Experimental Evaluation 18-749: Fault-Tolerant Distributed Systems. Tudor Dumitra ş & Prof. Priya Narasimhan Carnegie Mellon University Recommended readings and these lecture slides are available on CMU’s BlackBoard. What Are We Going To Do Today?. Overview of experimental techniques

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12. Experimental Evaluation 18-749: Fault-Tolerant Distributed Systems

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  1. 12. Experimental Evaluation18-749: Fault-Tolerant Distributed Systems Tudor Dumitraş &Prof. Priya Narasimhan Carnegie Mellon University Recommended readings and these lecture slides are available on CMU’s BlackBoard

  2. What Are We Going To Do Today? • Overview of experimental techniques • Case study: “Fault-Tolerant Middleware and the Magical 1%” • Experimental requirements for the project

  3. Overview of Experimental Techniques • Basics • Probability distributions, density functions • Outlier detection: 3σ test • Visual representation of data • Boxplots • 3D, contour plots • Multivariate plots • Do’s and don’ts of experimental science

  4. Experimental Research “God has chosen that which is the most simple in hypotheses and the most rich in phenomena [...] But when a rule is extremely complex, that which conforms to it passes for random.” Gottfried Wilhelm Leibniz, Discours de Métaphysique, 1686

  5. Statistical Distributions • If a metric is measured repeatedly, then we can determine its probability distribution function (PDF) • PDF(x) is the probability that the metrictakes the value x • Matlab function ksdensity • Common statistics • Mean = sum of values / #measurements (mean) • Median = half the measured values are below this point (median) • Mode = measurement that appears most often in the dataset • Standard deviation (σ) = how widely spread the data points are (std) where Xi is a measurement and X is the mean

  6. Statistical Tools • Percentiles • “The Nth percentile” is a value X such that N% of the measured samples are less than X • The median is the 50th percentile • Matlab function prctile • Outlier detection: 3σ test • Any value that is more than 3 standard deviations away from the mean is an outlier • For example, for latency: • In Matlab: outliers(a) = a (a > mean(a) + 3*std(a))

  7. Basic Plots 0% Data upsets 50% Data upsets Client-perceived throughput[bytes/s] Latency [in µs] • Line plot (plot) • Y-axis is a function of X-axis values • Can use error bars to show standard deviation • Can also do an area plot to emphasize overhead or difference between similar metrics • Scatter plot (plot, scatter) • Determine a relationship between two variables • Reveal clustering of data • Bar graphs (bar, bar3) • Compare discrete values • Pie charts (pie, pie3) • Breakdown of a metric into its constituent components Nodes reached Rounds

  8. Boxplots • A “box and whisker” plot describes a probability distribution • The box represents the size of the inter-quartile range(the difference between the 25th and 75th percentiles of the dataset) • The whiskers indicate the maximum and minimum values • The median is also shown • Matlab function boxplot • In 1970, US Congress instituted a random selection process for the military draft • All 366 possible birth dates were placed in a rotating drum and selected one by one • The order in which the dates were drawn defined the priority for drafting The boxplots show that men born later in the year were more likely to be drafted From http://lib.stat.cmu.edu/DASL/Stories/DraftLottery.html

  9. Impact of Two Variables 62 65 94 67 97 72 70 110 p 80 pupset • 3D plots • Z axis is a function of X and Y values • Surface plots: mesh, surf • Scatter plots: plot3, scatter3 • Volume: display convex hull usingconvhulln and trisurf • Contour plots • Represents a function of 2 variables(the X and Y axes) • Suggests the values of the function through color and annotations • Displays the isolines (variable combinations that yield the same value) of the function

  10. Impact of Many Variables • Multi-variate plot

  11. Do … • Make Results Comparable • Use same hardware for all the experiments • Use same versions of your software • Avoid interference from other programs or make sure you always get the same interference • Vary one parameter at a time • Make Results Reproducible • Record and report all the parameters of your experimental setup • Archive and publish raw data • Be Rigorous • Minimize the impact of your monitoring infrastructure • Report number of runs • Report mean values and standard deviations • Examine statistical distributions (modes, long tails, etc.)

  12. Don’t … • Forget to label the axes of your figures • Use different axis limits when comparing results • Plot mean values without looking at the error margin

  13. FT Middleware and the Magical 1% • Unpredictability of FT middleware • Unpredictability limited to 1% of remote invocations T. Dumitraş and P. Narasimhan. Fault-Tolerant Middleware and the Magical 1%. In ACM/IFIP/USENIX Conference on Middleware, Grenoble, France, Nov.-Dec. 2005.http://www.ece.cmu.edu/~tdumitra/public_documents/dumitras05magical.pdf

  14. Predictability in FT Middleware Systems ? • Faults are inherently unpredictable • What about the fault-free case? Srv Replicated Server Replicated Client C Srv Cli R Group Communication C C R Client R Server Host OS CORBA CORBA Replicator Replicator Networking Host OS Host OS Networking

  15. System Configuration for Predictability • Can we configure an FT CORBA system for predictable latency? • Software configuration • Operating system: RedHat Linux w/ TimeSys 3.1 kernel • Group Communication: Spread v. 1.3.1 • Replication: MEAD v. 1.1 • ORB: TAO Real Time ORB v. 1.4 • Micro-benchmark: 10,000 remote invocations per client • Hardware configuration • 25 hosts on the Emulab test bed • Pentium III at 850 MHz • 100 Mb/s LAN

  16. Experimental Methodology • Parameters varied: • Replication style: active, warm passive • Replication degree: 1, 2, 3 replicas • Number of clients: 1, 4, 7, 10, 13, 16, 19, 22 clients • Request arrival rates: 0, 0.5, 2, 8, 32 ms client pause • Sizes of reply messages: 16, 256, 4096, 65536 bytes • Tested all 960 combinations, collected 9.1 Gb of data • Trace available at: www.ece.cmu.edu/~tdumitra/MEAD_trace • Statistical analysis of end-to-end latency: • Means, medians, standard deviations • Maximum and minimum values • 1st, 5th, 95th, 99th percentiles • Numbers and sizes of the outliers

  17. Example of Unpredictability • Maximum latency can be several orders of magnitude larger than the average • Distribution is skewed to the right and has a long tail • Long tail occurs on only one side because the latency cannot be arbitrarily low • MEAD latency is lower-bounded by CORBA and group communication latency

  18. Systematic Unpredictability • Average values increase linearly with the number of clients • Maximum values are unpredictable

  19. Counting the Outliers • An outlier is a measurement that fails the 3σ test • In most cases, less than 1% of the measured latencies are outliers • Outliers originate in various modules of the system: • The ORB • The group communication • The application

  20. The “Magical” 1%

  21. The “Magical” 1% The “haircut” effect of removing 1% of the highest remote latencies

  22. Observable Trends 7 6 10 10 6 5 10 10 Maximum latency [ms] 99% latency [ms] 5 4 10 10 4 3 10 10 65536 65536 2000 2000 4096 4096 1500 1500 256 256 1000 1000 16 16 500 500 0 0 Request size [bytes] Request size [bytes] Request rate [req/s] Request rate [req/s] • The 99th percentile helps us identify trends in the data • E.g., latency increases with request rate and size

  23. Interpretation • Predictable maximum latencies are hard to achieve • Tried to achieve predictability by selecting a good FT CORBA configuration • Even in the fault-free case, end-to-end latencies have skewed distributions for almost all 960 parameter combinations • Maximums are several orders of magnitude higher than averages • Unpredictability cannot be isolated to a single component • Magical 1%: achieving predictability through statistical approaches • We remove 1% of the highest measured latencies • Remaining samples have more deterministic properties • 99th percentile helps us identify trends in the data • This allows us to extract tunable, predictable behavior out of fairly complex, dependable systems

  24. Experimental Evaluation of 18-749 Projects • Requirements for experimental evaluation • List of client invocations • Probes • Graphs • Tips • Digging deeper

  25. Requirements for Experimental Evaluation • Things to hand in: • List of client invocations – the server methods you’re going to exercise • Raw data from the 7 probes in your application • Graphs of end-to-end latency • Interpretation of the results • Constraints • All clients must run on separate machines • Each client must issue at least 10,000 requests • All requests must receive a reply (two-way invocations) • The middle tier must have 2 replicas (e.g., primary & backup) • Try all 48 combinations of the following: • Number of clients: 1, 4, 7, 10 • Size of reply message: original, 256, 512, 1024 bytes • Inter-request time: 0 (no pause), 20, 40 ms • Administrative • Each team must designate a chief experimenter

  26. List of Client Invocations METHOD ONE_WAY DB_ACCESS SZ_REQUEST SZ_REPLY createObj() No Yes 16 4 getInfo() No Yes 4 256 deleteObj() No Yes 4 4 Name of remote invocation Size of the forward message before marshaling (the combined sizes of all the in and inout parameters) Is it a one-way (no reply)? Size of the return message before marshaling (the combined sizes of all the out and inout parameters) Does it require a DB access(all 3 tiers are involved)?

  27. Application Modifications • Use only two-way invocations • The client must receive a reply from the server for each invocation • Suggestion: have at least 2 different invocations in your benchmark • Tunable size of replies • Add a variable-sized parameter that is returned by the server (e.g., sequence<octet>) • Try the following reply sizes: original, 256 bytes, 512 bytes and 1024 bytes • Inter-request time • Insert a pause in-between requests • Try the following pauses: 0 (no pause), 20, 40 ms • CAUTION: • sleep(0) inserts a non-zero pause • On most Linux kernels, you cannot pause for less than 10 ms • For more information: http://www.atl.lmco.com/projects/QoS/RTOS_html/periodic.html

  28. Experiments Make Your Life Meaningful 

  29. Stages of an Invocation Client Server Database Application out in out in in out in out Replication out in out in Middleware reply reply request request Network

  30. Data Probes (1 of 7) Client Server Database P1 File Name DATA749_app_out_cli_${STY}_2srv_${C}cli_${IRT}us_${BYT}req_${HOST}_team${N}.txt Data Time (in µs) when each request is issued Example 67605 69070 69877 72807 ... Application out in out in in out in out Replication out in out in Middleware reply reply request request Network Legend ${STY} Replication style (ACTIVE or WARM_PASSIVE) ${C} Number of clients ${IRT} Inter-request time (in µs) ${BYT} Reply size (in bytes) ${HOST} Hostname ${N} Your team number

  31. Data Probes (2 of 7) File Name DATA749_app_in_cli_${STY}_2srv_${C}cli_${IRT}us_${BYT}req_${HOST}_team${N}.txt Data Time (in µs) when each reply is received Example 67605 69070 69877 72807 ... Client Server Database P1 P2 Application out in out in in out in out Replication out in out in Middleware reply reply request request Network Legend ${STY} Replication style (ACTIVE or WARM_PASSIVE) ${C} Number of clients ${IRT} Inter-request time (in µs) ${BYT} Reply size (in bytes) ${HOST} Hostname ${N} Your team number

  32. Data Probes (3 of 7) File Name DATA749_app_msg_cli_${STY}_2srv_${C}cli_${IRT}us_${BYT}req_${HOST}_team${N}.txt Data Name of each invocation Example createObj() createObj() getInfo() deleteObj() ... Client Server Database P3 P1 P2 Application out in out in in out in out Replication out in out in Middleware reply reply request request Network Legend ${STY} Replication style (ACTIVE or WARM_PASSIVE) ${C} Number of clients ${IRT} Inter-request time (in µs) ${BYT} Reply size (in bytes) ${HOST} Hostname ${N} Your team number

  33. Data Probes (example) Client Server Database P3 P1 P2 Application out in out in in out in out Replication out in out in Middleware reply reply request request Network Example: probe1.record (new Long(gettimeofday())); remoteFactory.createObj (); probe2.record (new Long(gettimeofday())); probe3.record (new String(“createObj()”));

  34. Data Probes (4 of 7) File Name DATA749_app_in_srv_${STY}_2srv_${C}cli_${IRT}us_${BYT}req_${HOST}_team${N}.txt Data Time (in µs) when each request is received Example 67605 69070 69877 72807 ... Client Server Database P3 P1 P2 Application P4 out in out in in out in out Replication out in out in Middleware reply reply request request Network Legend ${STY} Replication style (ACTIVE or WARM_PASSIVE) ${C} Number of clients ${IRT} Inter-request time (in µs) ${BYT} Reply size (in bytes) ${HOST} Hostname ${N} Your team number

  35. Data Probes (5 of 7) File Name DATA749_app_out_srv_${STY}_2srv_${C}cli_${IRT}us_${BYT}req_${HOST}_team${N}.txt Data Time (in µs) when each reply is completed Example 67605 69070 69877 72807 ... Client Server Database P3 P1 P2 Application P5 P4 out in out in in out in out Replication out in out in Middleware reply reply request request Network Legend ${STY} Replication style (ACTIVE or WARM_PASSIVE) ${C} Number of clients ${IRT} Inter-request time (in µs) ${BYT} Reply size (in bytes) ${HOST} Hostname ${N} Your team number

  36. Data Probes (6 of 7) File Name DATA749_app_msg_srv_${STY}_2srv_${C}cli_${IRT}us_${BYT}req_${HOST}_team${N}.txt Data Name of each invocation Example createObj() createObj() getInfo() deleteObj() ... Client Server Database P3 P1 P2 P6 Application P5 P4 out in out in in out in out Replication out in out in Middleware reply reply request request Network Legend ${STY} Replication style (ACTIVE or WARM_PASSIVE) ${C} Number of clients ${IRT} Inter-request time (in µs) ${BYT} Reply size (in bytes) ${HOST} Hostname ${N} Your team number

  37. Data Probes (7 of 7) File Name DATA749_app_source_srv_${STY}_2srv_${C}cli_${IRT}us_${BYT}req_${HOST}_team${N}.txt Data Hostname of client sending the invocation Example black black blue magenta ... Client Server Database P3 P1 P2 P6 P7 Application P5 P4 out in out in in out in out Replication out in out in Middleware reply reply request request Network Legend ${STY} Replication style (ACTIVE or WARM_PASSIVE) ${C} Number of clients ${IRT} Inter-request time (in µs) ${BYT} Reply size (in bytes) ${HOST} Hostname ${N} Your team number

  38. Probe Invariant Client Server Database P3 P1 P2 P6 P7 Application P5 P4 out in out in in out in out Replication out in out in Middleware reply reply request request Network Probes at the same side and same level must have the same number of records!

  39. Computing End-To-End Latency Client Server Database P3 P1 P2 P6 P7 Application P5 P4 out in out in in out in out Replication out in out in Middleware reply reply request request Network For request i:

  40. Computing the Components of Latency Client Server Database P3 P1 P2 P6 P7 Application P5 P4 out in out in in out in out Replication out in out in Middleware reply reply request request Network For request i:

  41. Computing the Request Arrival Rate Client Server Database P3 P1 P2 P6 P7 Application P5 P4 out in out in in out in out Replication out in out in Middleware reply reply request request Network For request i:

  42. Computing the Server Throughput Client Server Database P3 P1 P2 P6 P7 Application P5 P4 out in out in in out in out Replication out in out in Middleware reply reply request request Network For request i:

  43. Graphs Required • Line plots of latency for increasing number of clients and different reply sizes (no pause) • Area plots of (mean, max) latency and (mean, 99%) latency, sorted by increasing mean values • Bar graphs of latency component break-down for outliers and normal requests • 3D scatter plots of reply size and request rate impact on max and 99% latency • Latency vs. throughput

  44. Interpretation of Results • Short write-up containing the “lessons learned” from the experiments • What did you learn about your system? • What can you tell (good or bad) about the performance, dependability and robustness of your application? • Were the results surprising? • If you observed some behavior you didn’t expect, how can you explain it? • What further experiments would be needed to verify your hypothesis? • Do your results confirm or infirm the magical 1% theory?

  45. Tips for Experimental Evaluation • Avoid interference • Use separate machines for each client, server replica, NamingService/JNDI, FT manager, database, etc. • Make sure there are no other processes using your CPU or bandwidth • Minimize impact of monitoring • Store data in pre-allocated memory buffer • Flush buffers to the disk at the end • Record timestamps as time from the start of the process • Use 4-byte integers (long) for the timestamps • Automate the experimental process as much as possible • Create scripts for launching the servers and clients, for collecting data, for analyzing it and for creating the graphs • Use Matlab for graphs and data processing • This is installed on the ECE cluster and is available to students • Can also download it from https://www.cmu.edu/myandrew/ • If you need help with plotting your graphs, please send email to us

  46. Digging Deeper • Do the same thing while injecting faults • Other probes • CPU usage (time spend in kernel, user mode) • Memory (total, resident set) • Bandwidth usage • Context switches • Major/minor page faults (page not in physical memory) • Other ways to represent data • Boxplots for end-to-end latency • Impact of varying #clients, size, request rate on #outliers, size of outliers, latency, etc. • Do you see multi-modal distributions (can you explain them)? • Interpretation of results • Are outliers isolated or do they come in bursts? • What is the source of the outliers? • Can you predict anything about the behavior of your system? • What questions can you answer by looking at this data?

  47. Summary of Lecture • What matters to you? • What experiments should you run? • What data should you collect? • How should you present your data? • What should you analyze? • What lessons might you learn about your system? • Email all questions to the course mailing list • The other two TAs and myself (Tudor) are on this list • We’re happy to sit down and work out the details with you and to help you run your experiments • It might sound like a lot of work, but the hard part is behind you – you’ve already built your system • Now, it’s time to understand what you actually built!

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