Name
Abstract | ||
|---|---|---|
MapReduce framework is widely used to parallelize batch jobs since it exploits a high degree of multi-tasking to process them. However, it has been observed that when the number of servers increases, the map phase can take much longer than expected. This paper analytically shows that the stochastic behavior of the servers has a negative effect on the completion time of a MapReduce job, and continu... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TCC.2016.2552516 | IEEE Transactions on Cloud Computing |
Keywords | Field | DocType |
Servers,Stochastic processes,Computational modeling,Delays,Optimal scheduling,Cloud computing,Synchronization | Mean sojourn time,Synchronization,Computer science,Scheduling (computing),Server,Performance metric,Parallel computing,Stochastic process,Real-time computing,Batch processing,sync,Distributed computing | Journal |
Volume | Issue | ISSN |
6 | 4 | 2168-7161 |
Citations | PageRank | References |
3 | 0.41 | 18 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Farshid Farhat | 1 | 41 | 6.90 |
| Tootaghaj, D.Z. | 2 | 28 | 5.38 |
| Yuxiong He | 3 | 666 | 40.52 |
| Anand Sivasubramaniam | 4 | 4485 | 291.86 |
| Mahmut T. Kandemir | 5 | 7371 | 568.54 |
| Chita R. Das | 6 | 1467 | 80.03 |