Adaptive Combiner for MapReduce on cloud computing
| Autor: | Kuo-Chih Chu, Wei-Tsong Lee, Yu-Sheng Ho, Tzu-Chi Huang |
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| Rok vydání: | 2014 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science media_common.quotation_subject Distributed computing Data_MISCELLANEOUS Aggregate (data warehouse) Process (computing) Cloud computing Parallel computing Data_GENERAL Transfer (computing) Programming paradigm business Function (engineering) Software media_common |
| Zdroj: | Cluster Computing. 17:1231-1252 |
| ISSN: | 1573-7543 1386-7857 |
| DOI: | 10.1007/s10586-014-0362-3 |
| Popis: | MapReduce is a programming model to process a massive amount of data on cloud computing. MapReduce processes data in two phases and needs to transfer intermediate data among computers between phases. MapReduce allows programmers to aggregate intermediate data with a function named combiner before transferring it. By leaving programmers the choice of using a combiner, MapReduce has a risk of performance degradation because aggregating intermediate data benefits some applications but harms others. Now, MapReduce can work with our proposal named the Adaptive Combiner for MapReduce (ACMR) to automatically, smartly, and trainer for getting a better performance without any interference of programmers. In experiments on seven applications, MapReduce can utilize ACMR to get the performance comparable to the system that is optimal for an application. |
| Databáze: | OpenAIRE |
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