2013 IEEE 5th International Conference on Cloud Computing Technology and Science (2013)
Bristol, United Kingdom United Kingdom
Dec. 2, 2013 to Dec. 5, 2013
MapReduce is a widely used programming model for large scale data processing. In order to estimate the performance of MapReduce job and analyze the bottleneck of MapReduce job, a practical performance model for MapReduce is needed. Many works have been done on modeling the performance of MapReduce jobs. However, existing performance models ignore some important factors, such as I/O congestions and task failures over cluster, which may significantly change the execution costs of MapReduce job. This paper, aiming at predicting the execution time of a MapReduce job, presents an enhanced performance model that takes the resource contention and task failures into consideration. In addition, the experimental results show that the model is more accurate than those without considering the contention and failure factors.
Mathematical model, Equations, Throughput, Analytical models, Exponential distribution, Fitting, Writing
X. Cui, X. Lin, C. Hu, R. Zhang and C. Wang, "Modeling the Performance of MapReduce under Resource Contentions and Task Failures," 2013 IEEE 5th International Conference on Cloud Computing Technology and Science(CLOUDCOM), Bristol, United Kingdom United Kingdom, 2013, pp. 158-163.