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$i^{2}MapReduce:$ incremental iterative MapReduce
| Content Provider | ACM Digital Library |
|---|---|
| Author | Zhang, Yanfeng Chen, Shimin |
| Abstract | Cloud intelligence applications often perform iterative computations (e.g., PageRank) on constantly changing data sets (e.g., Web graph). While previous studies extend MapReduce for efficient iterative computations, it is too expensive to perform an entirely new large-scale MapReduce iterative job to timely accommodate new changes to the underlying data sets. In this paper, we propose $i^{2}MapReduce$ to support incremental iterative computation. We observe that in many cases, the changes impact only a very small fraction of the data sets, and the newly iteratively converged state is quite close to the previously converged state. $i^{2}MapReduce$ exploits this observation to save re-computation by starting from the previously converged state, and by performing incremental updates on the changing data. Our preliminary result is quite promising. $i^{2}MapReduce$ sees significant performance improvement over re-computing iterative jobs in MapReduce. |
| Starting Page | 1 |
| Ending Page | 4 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450321082 |
| DOI | 10.1145/2501928.2501930 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-08-26 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Iterative computation Incremental processing Mapreduce |
| Content Type | Text |
| Resource Type | Article |
