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| Content Provider | ACM Digital Library |
|---|---|
| Author | Gianniti, Eugenio Barbierato, Enrico Rizzi, Alessandro Maria Ardagna, Danilo Gribaudo, Marco |
| Abstract | Big Data applications allow to successfully analyze large amounts of data not necessarily structured, though at the same time they present new challenges. For example, predicting the performance of frameworks such as Hadoop and Spark can be a costly task, hence the necessity to provide models that can be a valuable support for designers and developers. Big Data systems are becoming a central force in society and the use of models can also enable the development of intelligent systems providing Quality of Service (QoS) guarantees to their users through runtime system reconfiguration. This paper provides a new contribution in studying a novel modeling approach based on fluid Petri nets to predict MapReduce and Spark applications execution time which is suitable for runtime performance prediction. Models have been validated by an extensive experimental campaign performed at CINECA, the Italian supercomputing center, and on the Microsoft Azure HDInsight data platform. Results have shown that the achieved accuracy is around 9.5% for Map Reduce and about 10% for Spark of the actual measurements on average. |
| Starting Page | 23 |
| Ending Page | 36 |
| Page Count | 14 |
| File Format | |
| ISSN | 01635999 |
| DOI | 10.1145/3092819.3092824 |
| Journal | ACM SIGMETRICS Performance Evaluation Review (PERV) |
| Volume Number | 44 |
| Issue Number | 4 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-01-10 |
| Publisher Place | New York |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Hadoop Mapreduce Fluid petri nets Spark |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Networks and Communications Hardware and Architecture Software |
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