Transcript Hadoop System simulation with Mumak Fei Dong, Tianyu Feng, Hong Zhang

Hadoop System simulation with
Mumak
Fei Dong, Tianyu Feng, Hong Zhang
Dec 8, 2010
Agenda
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Objective
Comparison between MRPerf and Mumak
Modifications to Mumak
Results and discussion
Conclusion
Objective
• Large scale distributed system has enormous
amount of parameters.
• Running time of a user program depends nonlinearly on these parameters.
• Predict the running time under various
settings to help user choose the “optimal”
setting.
• We start by varying the most basic parameter:
cluster size.
MRPerf and Mumak
• MRPerf
– Build upon a network simulator
– Calculate the task running time and network delay
from physical parameters
– Implemented the Hadoop system in TCL
– Flexible in simulation
MRPerf and Mumak
Running
Time
Reduce slots per node
Map slots per node
4 nodes double rack data center (Chunk Size = 64M) By MRPerf
MRPerf and Mumak
4 nodes (Chunk Size = 64M) By Mumak
MRPerf and Mumak
• Mumak
– Inherit the JobTracker class from Hadoop and only
defines the simulation interface
– Use trace file to build the cluster topology / job
story, then feed it into simulator
– Can only reproduce previous finished experiment
– Designed to verify/debug Hadoop system design
– Only simulate the Map/Reduce tasks, no sort
phase and shuffle phase
MRPerf and Mumak
• The approach taken by MRPerf is better
– Take in parameters to estimate running time
– Can make predictions
• MRPerf is simulating their implementation of Hadoop
• The design of Mumak is better
– Inherit source code from Hadoop
– Easy to understand and to extend
• We decide to take the good parts of MRPerf and then
implement them in the framework of Mumak
– Modify the Rumen log to change the parameters
– Modify Mumak source code to add network simulator
Implementation
• Simulate a different cluster size
– Hack the rumen log, change data replication
factor/ locality
– Modify the topology, add in / delete nodes, for
example, from 2 slave nodes to 6 slave nodes.
– The job tracker will assign the tasks to different
nodes.
Implementation
• Simulate network delay
– We defined a simple network
simulator interface
– Modified the source code of
Mumak to add in the network
delay
– Actual the network delay can be
ignored
Results and Discussion
2500000
4 nodes
2000000
time (ms)
1500000
log
estimate
1000000
estimate_local
500000
0
job_0003
job_0004
job_0005
job_0006
job_0007
job_0008
job
job_0009
job_0010
job_0011 Reference
Results and Discussion
2500000
6 nodes
2000000
time (ms)
1500000
log
estimate
1000000
estimate_local
500000
0
job_0003
job_0004
job_0005
job_0006
job_0007
job_0008
job
job_0009
job_0010
job_0011 Reference
Results and Discussion
Results and Discussion
• Limitations and future work
– Sort phase time not included
– Only used single rack topology
– Prediction is not always consistent for the same
job with the same configuration
Conclusion
• Our objective is to predict the running time
with different parameters
• We take the methods of MRPerf and
implemented it on Mumak
• To have more flexible and accurate prediction,
more modification to Mumak is needed
– Independent from trace file
– Solve the unstable problem
Questions?