MapReduce is a key technique among several new concepts in cloud computing. When running a MapReduce job, programmers cannot obtain information about how the performance of their application will be in their own test environment. Several parameters affect the performance of MapReduce, programmers must spend a substantial amount of time identifying the most suitable parameters or studying execution details in MapReduce. Execution details in MapReduce are examined in depth and described using Stochastic Petri Net (SPN) to then develop the SPN-MapReduce (SPN-MR) model. To analyze the performance of SPN-MR, mean delay time formulas are defined for each timed transition. SPN-MR is the first proposed which can estimate the execution time of MapReduce jobs with a known input data size in hundreds of milliseconds and reduce the time spent by programmers in tuning performance. The experimental results of SPN-MR are compared with two benchmarks of actual tests and the average error range is found to be within 5 % under the size of 10 GB input data. Therefore, SPN-MR can enable MapReduce programmers to evaluate performance effectively and help programmers understand execution details in MapReduce.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering