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An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems
Title: | An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems |
Authors: | Jamshidi Dermani, P Casale, G |
Item Type: | Dataset |
Abstract: | The archive contains 10 comma separated datasets representing performance measurements (throughput and latency) for 3 different stream benchmark applications. These have been experimentally collected on 5 different cloud cluster over the course of 3 months (24/7). Each row in the datasets represents a different configuration setting for the application and the last two columns represent the average performance of the application measured over the course of 10 minutes under that specific configuration setting. The datasets contains a full factorial and exhaustive measurements for all possible settings limited to a predetermined interval for each variable. Each dataset is named in the following format: "benchmark_application-dimensions-cluster_name". For example, "wc-6d-c1" refers to WordCount benchmark application with 6 dimensions (i.e., we varied 6 configuration parameters) and the application was deployed on c1 cluster (OpenNebula, see Appendix). This resulted in a dataset of size 2880, i.e., it has taken 2880*10m=480h=20days for collecting the data The archive contains 10 comma separated datasets representing performance measurements (throughput and latency) for 3 different stream benchmark applications. These have been experimentally collected on 5 different cloud cluster over the course of 3 months (24/7). Each row in the datasets represents a different configuration setting for the application and the last two columns represent the average performance of the application measured over the course of 10 minutes under that specific configuration setting. The datasets contains a full factorial and exhaustive measurements for all possible settings limited to a predetermined interval for each variable. Each dataset is named in the following format: "benchmark_application-dimensions-cluster_name". For example, "wc-6d-c1" refers to WordCount benchmark application with 6 dimensions (i.e., we varied 6 configuration parameters) and the application was deployed on c1 cluster (OpenNebula, see Appendix). This resulted in a dataset of size 2880, i.e., it has taken 2880*10m=480h=20days for collecting the data |
Issue Date: | 22-Jun-2016 |
URI: | http://hdl.handle.net/10044/1/40575 |
DOI: | dx.doi.org/10.5281/zenodo.56238 |
Sponsor/Funder: | Commission of the European Communities |
Funder's Grant Number: | 644869 |
Keywords: | Stream Processing System DevOps Big Data |
Access Data Notes: | https://zenodo.org/record/56238 https://zenodo.org/record/56238 |
Appears in Collections: | Faculty of Engineering - Research Data |