Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/CloudCom.2013.28 | CloudCom (1) |
Keywords | Field | DocType |
task failures,resource contention,execution time,existing performance model,resource contentions,task failure,practical performance model,o congestion,enhanced performance model,programming model,execution cost,mapreduce job,parallel programming | Bottleneck,Data processing,Programming paradigm,Computer science,Resource contention,Parallel computing,Real-time computing,Execution time,Performance model,Exponential distribution,Throughput,Distributed computing | Conference |
ISSN | Citations | PageRank |
2330-2194 | 6 | 0.49 |
References | Authors | |
12 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaolong Cui | 1 | 26 | 3.60 |
| Xuelian Lin | 2 | 118 | 9.83 |
| Chunming Hu | 3 | 324 | 35.37 |
| Richong Zhang | 4 | 232 | 39.67 |
| Chengzhang Wang | 5 | 6 | 1.16 |