1. 1 Components of a Scalable Distributed Relational Information Service Dong Lu June 14, 2005

2. 2 Outline Bird’s Eye View –What is RGIS? –Architecture –What components are studied in the thesis? Size-Based Scheduling With Inaccurate Info –Fairness and efficiency as function of correlation –Other applications: beyond RGIS DualPats: Characterizing and Predicting TCP Throughput on the Wide Area Network –Why TCP throughput prediction? –Flow size / TCP throughput correlation –Issues with simple benchmarking –DualPats algorithm and dynamic rate adjustment Thesis Contributions

3. 3 RGIS Grid computing –Providing dependable, reliable, consistent, pervasive and unlimited computing resources RGIS: Relational Grid Information Service –Represents globally distributed resources, including the network –Relational Model allows complex compositional queries –Relational Model is well studied; large user population –RGIS servers distributed among multiple organizations and sites

4. 4 Query and Update Example A query example –Find a set of 16 Linux machines on the same LAN, each has memory over 1GB, they have a total memory of at least 32 GB, and each has a link capacity >100Mb An update example –Host A has added 1GB memory, and will be available from 1:00 PM to 6:00 PM central time

5. 5 RGIS Architecture Oracle 9i Back End Windows,Linux,Parallel Server,etc Oracle 9i Front End transactional inserts and updates using stored procedures, queries using select statements (uses database’s access control) Update Manager Web Interface Content Delivery Network Interface For loose consistency Query Manager and Rewriter Users Schema, type hierarchy, indices, PL/SQL stored procedures for each object Applications RDBMS site-to-site Updates encrypted using asymmetric cryptography on network. Only those with appropriate keys have access Authenticated Direct Interface SOAP Interface Scoping Rewrite Canned Queries Canned Approximate Queries Nondeterminism Rewrite Time Bounding (And Iteration Of Query)

6. 6 RGIS Web Interface

7. 7 RGIS Architecture Oracle 9i Back End Windows,Linux,Parallel Server,etc Oracle 9i Front End transactional inserts and updates using stored procedures, queries using select statements (uses database’s access control) Update Manager Web Interface Content Delivery Network Interface For loose consistency Query Manager and Rewriter Users Schema, type hierarchy, indices, PL/SQL stored procedures for each object Applications RDBMS site-to-site Updates encrypted using asymmetric cryptography on network. Only those with appropriate keys have access Authenticated Direct Interface SOAP Interface Scoping Rewrite Canned Queries Canned Approximate Queries Nondeterminism Rewrite Time Bounding (And Iteration Of Query)

8. 8 Query Components GridG: the first synthetic grid generator –Topology [Sigmetrics Performance Evaluation Review, Vol 30, No. 4, 2003] –Annotation [SC’03-1] Query rewriting techniques to trade off query time and the result set size –Nondeterministic query [SC’03-2] –Scoped and approximate queries [GRID’03]

9. 9 Update and CDN Components Size-Based Scheduling with inaccurate info to minimize mean update time –Fairness and efficiency as function of correlation [MASCOTS’04-1] –P2P scheduling [LCR’04], one in submission –Web server scheduling, in submission –Other applications [MASCOTS’04-2] Characterizing and predicting TCP throughput on the WAN to determine update transfer time –[ICDCS’05]

10. 10 Update and CDN Components Modeling and taming parallel TCP on the WAN to transfer updates faster –[IPDPS’05] Fat-tree based end-system multicast to disseminate update scalably –[WCW’04], one in submission

11. 11 Outline Bird’s Eye View –What is RGIS? –Architecture –What components are studied in the thesis? Size-Based Scheduling With Inaccurate Info –Fairness and efficiency as function of correlation –Other applications: beyond RGIS DualPats: Characterizing and Predicting TCP Throughput on the Wide Area Network –Why TCP throughput prediction? –Flow size / TCP throughput correlation –Issues with simple benchmarking –DualPats algorithm and dynamic rate adjustment Thesis Contributions

12. 12 Scheduling Section Outline Review of Size-Based Scheduling Motivation Simulation Setup Simulation Results New Applications

13. 13 The scheduling problem Scheduler Database Updates come from CDN Which update to run next? Scheduling: a general problem Goal: minimize the mean response time; be fair 10K8K6K3K Response time: the time from job arrival to its completion

14. 14 Review of Non-size-based scheduling FCFS, PS, etc. FCFS: First Come First Serve –Intuitive –Easiest to implement PS: Processor Sharing –Fair: all jobs accept equal resources –Also easy to implement Problem: Unaware of job size information, which results in high mean response time

15. 15 Review of size-based scheduling SRPT, FSP, etc. Use the job size (processing time, service time) information for scheduling –Optimal in mean response time –Fair? –Easy to implement? We use Job Size to refer to the Processing Time (Service Time) of the job

16. 16 Shortest Remaining Processing Time (SRPT) Always serve the job with minimum remaining processing time first, Preemptive scheduling Yields minimum mean response time [Schrage, Operations Research, 1968] Surprisingly, it is fair for heavy-tail job size distribution [Bansal and Harchol-Balter, Sigmetrics ‘01] Easy to implement? –With accurate a priori job size information, YES –Otherwise, NO

17. 17 Fair Sojourn Protocol (FSP) Combined SRPT with PS, preemptive scheduling Mean response time is close to that of SRPT; and more fair than SRPT and PS [Friedman, et al, Sigmetrics ‘03] Easy to implement? –With accurate a priori job size information, YES –Otherwise, NO

18. 18 Scheduling Section Outline Review of Size-Based Scheduling Motivation Simulation Setup Simulation Results New Applications

19. 19 Motivation Size-based scheduling requires accurate knowledge of job sizes In practice, a priori job size information is not always available All the previous work assumes perfect knowledge of job sizes a priori How does performance depend on quality of job size information?

20. 20 Correlation We study the performance of Size-based schedulers as a function of the correlation coefficient (Pearson’s R) between actual job sizes and estimated job sizes.

21. 21 Scheduling Section Outline Review of Size-Based Scheduling Motivation Simulation Setup Simulation Results New Applications

22. 22 Trace generator Trace Generator Correlation (Pearson’s R) Distribution ADistribution B X Y 1 100 5 300. Correlated random pairs of X and Y X has distribution A Y has distribution B X and Y are correlated to R

23. 23 Trace generator algorithm Algorithm: “Normal-To-Anything” –First developed by Cario and Nelson, on INFORMS Journal on Computing 10, 1 (1998). –We simplified the algorithm and first introduced it into the simulation studies of computer systems

24. 24 Scatter plot of example traces R=0.13 R=0.78 Y X Y X

25. 25 Performance metrics Mean response time: Sojourn time, Turn- around time Slowdown: the ratio of response time to its size. Fairness metric

26. 26 Simulator –Supports M/G/1 and G/G/n/m queuing model Simulator validation –Little’s law –Repeat the simulations in the FSP paper [Friedman, et al, Sigmetrics ‘03] –Compare with available theoretical results [Bansal and Harchol-Balter, Sigmetrics ‘01]

27. 27 Scheduling Policies PS: Processor sharing Size-based scheduling policies –SRPT: Ideal SRPT scheduler –SRPT-E: SRPT scheduler using estimated job size –FSP: Ideal Fair Sojourn Protocol –FSP-E: FSP scheduler using estimated job size Each simulation is repeated 20 times and we present the average

28. 28 Scheduling Section Outline Review of Size-Based Scheduling Motivation Simulation Setup Simulation Results New Applications

29. 29 Mean response time as function of R

30. 30 Slowdown (R=0.0224)

31. 31 Slowdown (R=0.239)

32. 32 Slowdown (R=0.4022)

33. 33 Slowdown (R=0.5366)

34. 34 Slowdown (R=0.7322)

35. 35 Slowdown (R=0.9779)

36. 36 Simulation Results: Conclusions Performance heavily depends on correlation –SRPT-E and FSP-E can outperform PS given an effective job size estimator Crossover point of performance metrics is a function of correlation –Also of job size distributions (See TR NWU-CS-04- 33)

37. 37 Scheduling Section Outline Review of Size-Based Scheduling Motivation Simulation Setup Simulation Results New Applications

38. 38 New Applications: Web server scheduling (TR NWU-CS-04-33) Is file size a good estimator of a job’s service time (processing time)? Not Really (R  0.14) Service time (wall clock time) File Size

39. 39 New Applications: Web server scheduling Domain-based estimator: much more accurate prediction of the service time at low overhead

40. 40 New Applications: P2P server side scheduling (LCR ’04) “Server side” of current file sharing P2P applications superficially similar to web server –Both send back files upon requests. However, P2P application can’t even know the file size accurately a priori –Partial downloads Our ongoing work shows that SRPT-E performs well using our time-series based job size estimators.

41. 41 Scheduling Section Summary Performance of size-based scheduling policies depends on correlation between size estimates and actual sizes –Fairness, mean response time, etc. Estimator must preserve ordering of job sizes for high performance –Performance degrades as correlation degrades Effective new estimators for Web and P2P

42. 42 Outline Bird’s Eye View –What is RGIS? –Architecture –What components are studied in the thesis? Size-Based Scheduling With Inaccurate Info –Fairness and efficiency as function of correlation –Other applications: beyond RGIS DualPats: Characterizing and Predicting TCP Throughput on the Wide Area Network –Why TCP throughput prediction? –Flow size / TCP throughput correlation –Issues with simple benchmarking –DualPats algorithm and dynamic rate adjustment Thesis Contributions

43. 43 DualPats Overview Algorithm for predicting the TCP throughput as function of flow size Minimal active probing Dynamic probe rate adjustment Explaining flow size / throughput correlation Explaining why simple active probing fails Large scale empirical study

44. 44 DualPats Section Outline Why TCP Throughput Prediction? Particulars of Study Flow Size / TCP Throughput Correlation Issues with Simple Benchmarking DualPats Algorithm Stability and Dynamic Rate Adjustment

45. 45 Goal A library call BW = PredictTransfer(src,dst,numbytes); Expected Time = numbytes/BW; Ideally, we want a confidence interval: (BWLow,BWHigh) = PredictTransfer(src,dst,numbytes,p);

46. 46 Available Bandwidth Maximum rate a path can offer a flow without slowing other flows –pathchar, cprobe, nettimer, delphi, IGI, pathchirp, pathload … –mainly for traffic engineering Available bandwidth can differ significantly from TCP throughput Not real time, takes at least tens of seconds to run

47. 47 Simple TCP Benchmarking Benchmark paths with a single small probe –BW = ProbeSize/Time –Widely used Network Weather Service (NWS) and others (Remos benchmarking collector) Not accurate for large transfers on the current high speed Internet –Numerous papers show this and attempt to fix it

48. 48 Fixing Simple TCP Benchmarking Logs [Sundharshan]: correlate real transfer measurements with benchmarking measurements Recent transfers needed Similar size transfers needed Measurements at application chosen times CDF-matching [Swany]: correlate CDF of real transfer measurements with CDF of benchmarking measurements Recent transfers still needed Measurements at application chosen times

49. 49 Analysis of TCP Extensive research on TCP throughput modeling in networking community Really intended to build better TCPs Difficult to use models online because of hard to measure parameters Future loss rate and RTT

50. 50 DualPats Section Outline Why TCP Throughput Prediction? Particulars of Study Flow Size / TCP Throughput Correlation Issues with Simple Benchmarking DualPats Algorithm Stability and Dynamic Rate Adjustment

51. 51 Our Study PlanetLab and additional machines –Located all over the world Measurements of throughput –Wide open socket buffers (1-3 MB) –Simple client/server –scp –GridFTP Four separate sets of measurements

52. 52 Four sets of measurements Distribution set: for analysis of TCP throughput stability and distributions Correlation set: for studying correlation between throughput and flow size, initial testing of algorithm Verification Set: test our benchmarking mechanism Online Evaluation Set: test our online algorithm

53. 53 Distribution Set For analysis of TCP throughput stability and distributions 60 randomly chosen paths among PlanetLab machines 1.6 million transfers (client/server) –100 KB, 200 KB, 400 KB, … 10 MB flows –3000 consecutive transfers per path+flow size

54. 54 Correlation Set For studying correlation between throughput and flow size, initial testing of algorithm 60 randomly chosen paths among PlanetLab machines 2.4 million transfers, 270 thousand runs, client/server –100 KB, 200 KB, 400 KB, … 10 MB flows –Run = sweep flow size for path

55. 55 Verification Set Test algorithm 30 randomly chosen paths among PlanetLab machines and others 4800 transfers, 300 runs, scp and GridFTP –5 KB to 1 GB flows –Run = sweep flow size for path

56. 56 Online Evaluation Set Test online algorithm 50 randomly chosen paths among PlanetLab machines and others 14000 transfers, scp and GridFTP –40 MB or 160 MB file, randomly chosen –10 days

57. 57 DualPats Section Outline Why TCP Throughput Prediction? Particulars of Study Flow Size / TCP Throughput Correlation Issues with Simple Benchmarking DualPats Algorithm Stability and Dynamic Rate Adjustment

58. 58 Strong Correlation Between Throughput and Flow Size Correlation and Verification Sets

59. 59 An example of Strong Correlation

60. 60 Why Does The Correlation Exist? Slow start and user effects [Zhang] Non-negligible startup overheads –Control messages in scp and GridFTP Residual slow start effect –SACK results in slow convergence to equilibrium

61. 61 DualPats Section Outline Why TCP Throughput Prediction? Particulars of Study Flow Size / TCP Throughput Correlation Issues with Simple Benchmarking DualPats Algorithm Stability and Dynamic Rate Adjustment

62. 62 Why Simple Benchmarking Fails Probes are too small Need more than one probe to capture correlation   

63. 63 DualPats Section Outline Why TCP Throughput Prediction? Particulars of Study Flow Size / TCP Throughput Correlation Issues with Simple Benchmarking DualPats Algorithm Stability and Dynamic Rate Adjustment

64. 64 Our Approach Two consecutive probes, both larger than the noise region

65. 65 Our Approach Two consecutive probes are integrated into a single probe –400KB, 800 KB in single 800 KB probe 0T1T2 Probe one Probe two

66. 66 Our Approach Flow size Transfer Time Solve For A and B Predict Throughput For Some Other Transfer

67. 67 Model Fit is Excellent Correlation Set Low and Normally Distributed Relative Errors At All Flow Sizes

68. 68 DualPats Section Outline Why TCP Throughput Prediction? Particulars of Study Flow Size / TCP Throughput Correlation Issues with Simple Benchmarking DualPats Algorithm Stability and Dynamic Rate Adjustment

69. 69 Stability How long does the TCP throughput function remain stable? –How frequently should we probe the path? What’s the distribution of throughput around the function (i.e., the error)?

70. 70 Throughput is Stable For Long Periods Correlation Set Increasing Max/Min Throughput in Interval

71. 71 Throughput For a Given Flow Size Is Normally Distributed In An Interval Distribution Set

72. 72 Online DualPats Algorithm Fetch probe sequence for destination –Start probing process if no data exists Project probe sequence ahead –20 point moving average over values with current sampling interval Apply model using projected data Return result –confidence interval computed using normality assumptions

73. 73 Dynamic Sampling Rate Adjust sampling interval to correspond to the path’s stable intervals Limit rate (20 to 1200 seconds) Additive increase / Additive decrease of interval based on difference between last two probes – increase interval – > 15% => decrease interval

74. 74 Evaluation 0 1 0.4-0.4 Mean relative error Mean abs(relative error) Relative error P[mean error < X] Slight conservative bias About 90 % of predictions have < 20% error Online Evaluation Set

75. 75 Section Summary Algorithm for predicting the TCP throughput as function of flow size Minimal active probing Dynamic probe rate adjustment Explaining flow size / throughput correlation Explaining why simple active probing fails Large scale empirical study

76. 76 Outline Bird’s Eye View –What is RGIS? –Architecture –What components are studied in the thesis? Size-Based Scheduling With Inaccurate Info –Fairness and efficiency as function of correlation –Other applications: beyond RGIS DualPats: Characterizing and Predicting TCP Throughput on the Wide Area Network –Why TCP throughput prediction? –Flow size / TCP throughput correlation –Issues with simple benchmarking –DualPats algorithm and dynamic rate adjustment Thesis Contributions

77. 77 Thesis Contributions It is feasible to build a scalable distributed Relational Grid Information Service RGIS architecture Query rewriting –Trade off query time with the size of result set GridG –First synthetic grid generator –Relationship between power-laws

78. 78 Thesis Contributions Size-based scheduling with imperfect info DualPats: monitoring and predicting TCP throughput TameParallelTCP: modeling and taming parallel TCP FatNemo: fat-tree based end-system multicast

79. 79 Future work Integration of research components with RGIS system Highly dynamic grid information: how to incorporate data from services such as RPS, NWS, DualPats, Remos Passive monitoring of TCP throughput Understanding size-based scheduling in the presence of backfilling

80. 80 Acknowledgements Collaborators –P2P scheduling: Yi Qiao, Fabian Bustamante –FatNemo: Stefan Birrer, Fabian Bustamante RGIS implementation –Andrew Weinrich, Jack Lange, Andrew Simpson

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84. 84 Current State Prototyped RGIS system –Schema, stored SQL –Query manager/rewriter –Soap interface, web interface –Publish/subscribe based CDN To be done –Integration of update scheduling, TCP throughput monitoring, end-system multicast based CDN

85. 85 Finding Sufficiently Large Probe Size Default values: 400 KB / 800 KB Upper bound Additive increase until prediction error are less than threshold, all with same sign.