We implemented MapReduce cluster benchmark TeraSort by derivative free optimization (DFO) method having runtime function object. In this, every iteration of DFO method uses new values for Hadoop parameter configuration. These parameters are specified within the framework, we used Chef server and client tool which assists in this cluster configuration to ensure proper implementation of TeraSort application.

1. Using Derivation-Free Optimization
in the Hadoop Cluster
with Terasort
Renato dos Santos Alves & Sarosh Farjam
Projeto de Experimentos ~ 03.7.2014

2. Sequence
• Abstract
• Introduction
• Workload Analysis of Search Engines
• Benchmarking Methodology and Decisions
• Scaleable Data Generation Tool
• Case Studies
• Conclusions

3. Introduction
• Implementation of the MapReduce cluster Benckmark
TeraSort by DFO method
• Every interacting DFO method presents new values ​​for
parameter configuration of Hadoop.
• For these parameters, specified within the framework we
need to use a tool that assists in this cluster configuration to
ensure proper implementation of TeraSort application.
• Chef server and Chef client

4. TeraSort Benchmark
Terasort includes 3 MapReduce
applications:
● Teragen: generates the data.
● Terasort: samples the input data
and uses them with MapReduce to
sort the data.
● Teravalidate: validates the output is
sorted

5. DFO Method
• Derivative free optimization is a subject
of mathematical optimization.
• It refers to problems for which derivative information is
unavailable or
• methods that do not use derivatives.
• The derivative of a function of a real variable measures the
sensitivity to change of a quantity (dependent variable) which is
determined by another quantity (independent variable). E.g. the
derivative of the position of a moving object with respect to time is
the object's velocity.

6. Algorithm BOBYQA
• BOBYQA (Bound Optimization BY Quadratic Approximation) is
a numerical optimization algorithm by Michael J. D. Powell.
• Name of Powell's Fortran 77 implementation of the algorithm.
• BOBYQA solves bound constrained optimization problems without
using derivatives of the objective function, which makes it
a derivative-free algorithm.
• The algorithm solves the problem using a trust region method that
forms quadratic models by interpolation. One new point is
computed on each iteration, usually by solving a trust region sub
problem, subject to the bound constraints.

7. Algorithm COBYLA
• Constrained optimization by linear approximation
(COBYLA) is a numerical optimization method
for constrained problems where the derivative of the
objective function is not known,
• invented by Michael J. D. Powell.
• Powell invented COBYLA while working for Westland
Helicopters.
• COBYLA proceeds by iteratively approximating the
actual objective function with linear programs.

8. Hadoop Environment
• A physical cluster with 29 nodes was used,
• A master Hadoop server (responsible for
implementing the JobTracker and NameNode services)
• 28 Hadoop Slaves (dedicated to the implementation of
TaskTracker and DataNode services).
• 2 Gigabit Ethernet to perform the connectivity
between the 29 nodes

9. Hadoop Environment
• A front-end access to the cluster server, that
server is configured as a Chef Server also used
to organize the executions of DFO TeraSort
application is then characterized the
synchronization functions of the DFO plays
and updating parameter settings Hadoop
based on each iteration of DFO TeraSort
method.

10. Experiment Execution
• Nemesis a server that is not part of the cluster is used as a front end for the
implementation TeraSort application, running the DFO method and updating
settings Hadoop based on their output.
• The synchronization of executions TeraSort updates and Hadoop with the output
of DFO method is performed by dfo_hadoop_terasort application executed on the
front-end server.
• The implementation of dfo_hadoop_terasort application is supplied with a file that
contains the initial values ​​of the configuration parameters of Hadoop, restrictions
so that these values ​​do not reach unwanted data out value for the objective
function, tolerance value for the restrictions and maximum amount of interactions.
With the processing of the input file and the interaction with the Hadoop cluster is
discovered which parameter values ​​cause a greater impact for faster execution of
TeraSort application, taking as output a file with the best configuration parameters
of that.

11. Experiment Execution
• As the cluster was composed of 28 servidoers slaves and each
server with two processors, for a total of 56 slots available
processing was decided to maintain 10% of this total, available for
tasks due to failures in implementation were spaced more than
once. Therefore, we used about 100 Gigabyte generated by Hadoop
Teragen.
• To confront the optimization of the execution time of Jobs, was
executed two DFO BOBYCA And COBYLA method, aiming to identify
which method best suits the application TeraSort forcenida by
Hadoop ....
• Two runs with both algorithms and 50 iterations to identify at what
time the executions were carried out can converge to a better
runtime.

12. Switching COBYLA, BOBYQA
• /* algoritmo COBYLA,Constrained Optimization
BY Linear Approximations */
• opt = nlopt_create(NLOPT_LN_COBYLA, N);
• //opt = nlopt_create(NLOPT_LN_BOBYQA, N);
• nlopt_set_lower_bounds(opt, lb);
• nlopt_set_upper_bounds(opt, ub);
• nlopt_set_max_objective(opt, objetivo,
NULL);

13. Experiment Execution

14. Commands
• First - Generate Tera sort
• *Teragen will generate approximately 100 GB
100 000 179 688 bytes
• $ hadoop jar $HADOOP_HOME/hadoop-*examples*.jar
teragen <number of 100-byte rows> <output dir>
• $ hadoop jar hadoop-examples-1.0.4.jar teragen
1000000000 terasort-input

15. Commands
• Second
• [hadoop@nemesis otimizacao]$ nohup sudo
time ./dfo_hadoop_terasort < entrada >
log_execucao_terasort &

16. Results
• We used of DFO method with BOBYQA and
COBYLA algorithms
• Presented the main difference in variation of
execution time of each iteration Jobs with
dfo_hadoop_terasort application,
• it is characterized mainly, how they treat
approximations of the points for the object
function, the quadratic or linear form
respectively.

17. TeraSort 50 a
Iterações Tempo F
1 1491
2 1501
3 1447
4 1889
5 2076
6 1466
7 1470
8 1319
9 1897
10 1611
11 1440
12 1588
13 1321
14 1897
15 1289
16 1704
17 1294
18 1313
19 1728
20 1971
21 1842

18. 1000
1200
1400
1600
1800
2000
2200
2400
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
timeinseconds
number of iterations
TeraSort 50 A using BOBYQA
execution progress
1289

19. TeraSort 50 c
Iterações Tempo F
1 1587
2 1721
3 1473
4 1669
5 1801
6 1833
7 1486
8 1709
9 1510
10 1962
11 1988
12 1934
13 1898
14 2277
15 1933
16 1516
17 1601
18 1561
19 1639
20 1515
21 1507
22 2205
23 1838
24 2419
25 1744
26 1566
27 1619
28 1890
29 1988
30 1875
31 1620
32 1780
33 1607
34 1536
35 1621
36 1580
37 1626
38 1675
39 2065

20. 1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
3000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38
timeinseconds
number of iterations
TeraSort 50 C using BOBYQA execution progress
1473

21. TeraSort 50 b
Iterações Tempo F
1 1437
2 1343
3 1335
4 1228
5 1213
6 1240
7 1198
8 1203
9 1231
10 1178
11 1174
12 1187
13 1186
14 1204
15 1128
16 1150
17 1190
18 1165
19 1190
20 1208
21 1204
22 1113
23 1171
24 1185
25 1190
26 1170
27 1155
28 1211
29 1159
30 1198
31 1206
32 1144
33 1177
34 1179
35 1232
36 1157
37 1201
38 1150
39 1195
40 1178
41 1237
42 1196
43 1233
44 1356
45 1400
46 1674
47 1424
48 1365
49 1366
50 1320

22. 1000
1100
1200
1300
1400
1500
1600
1700
1800
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
timeinseconds
number of iterations
TeraSort 50 B using COBYLA execution progress
1113

23. TeraSort 50 new
Iterações Tempo F
1 1442
2 1298
3 1285
4 1285
5 1274
6 1329
7 1343
8 1314
9 1289
10 1308
11 1304
12 1322
13 1345
14 1421
15 1369
16 1336
17 1348
18 1335
19 1333
20 1307
21 1367
22 1369
23 1352
24 1362
25 1390
26 1350
27 1324
28 1382
29 1347
30 1339

24. 1000
1100
1200
1300
1400
1500
1600
1700
1800
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
timeinseconds
number of iterations
TeraSort 50 New using COBYLA execution progress
1274

25. 1000
1100
1200
1300
1400
1500
1600
1700
1800
1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930
number of iterations
TeraSort 50 New using COBYLA
execution progress
1000
1100
1200
1300
1400
1500
1600
1700
1800
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49
number of iterations
TeraSort 50 B using COBYLA
execution progress
1000
1200
1400
1600
1800
2000
2200
2400
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
number of iterations
TeraSort 50 A using BOBYQA
execution progress
1000
1500
2000
2500
3000
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37
number of iterations
TeraSort 50 C using BOBYQA
execution progress

26. The use of DFO method with BOBYQA and COBYLA algorithms and presents as main difference the variation of
execution time of each iteration Jobs dfo_hadoop_terasort application, it is mainly how they are treated
approximations of the points for the object function the quadratic or linear form respectively.
1000
1200
1400
1600
1800
2000
2200
2400
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49
timeinseconds
Difference between Algorithms COBYLA and BOBYQA
TeraSort 50 New/COBYLA TeraSort 50 B/COBYLA TeraSort 50 A/BOBYQA TeraSort 50 C/BOBYQA

27. Conclusion
• The convergence of the total time proves to be more
stable in COBYLA and without many fluctuations when
compared to BOBYQA algorithm.
• The Speedup BOBYQA algorithm in the execution of
TeraSort application is 12% on average
• And the results reported by COBYLA algorithm, in the
execution of TeraSort application demonstrates
Speedup on average 21.15% over the initial settings of
Hadoop and a greater optimization than the BOBYQA
algorithm.

28. References
• [1] O'Malley, O. (2008, May). TeraByte Sort on Apache Hadoop.
Retrieved from http://sortbenchmark.org/YahooHadoop.pdf
• [2] Anand, A. (2009, May). Hadoop Sorts a Petabyte in 16:25 Hours
and a Terabyte in 62 Seconds. Retrieved from
https://developer.yahoo.com/blogs/hadoop/hadoop-sorts-
petabyte-16-25-hours-terabyte-62-422.html
• [3] Gray, J. (n.d.). Sort Benchmark Home Page. Retrieved from
http://sortbenchmark.org/
• [4] A Measure ofTransaction Processing Power. (1985) Datamation,
31 (7), 112-118.
• [5] Wikipedia; http://en.wikipedia.org/