| Type: | Package |
| Title: | Cloud Utilization Plots |
| Version: | 0.1.12 |
| Author: | Christian Panse <cp@fgcz.ethz.ch>, Ermir Qeli <ermir.qeli@gmail.com> |
| Maintainer: | Christian Panse <cp@fgcz.ethz.ch> |
| Depends: | R (≥ 2.11.0), methods |
| Description: | Provides means of plots for comparing utilization data of compute systems. |
| License: | GPL-2 |
| Collate: | cloudUtilPlot.R |
| URL: | https://cran.r-project.org/package=cloudUtil |
| NeedsCompilation: | no |
| Packaged: | 2016-06-09 11:45:35 UTC; cp |
| Repository: | CRAN |
| Date/Publication: | 2016-06-09 15:19:22 |
Cloud Utilization Plot
Description
On the utilization graphic each horizontal line indicates the start and the end of one job. Color groups different runs.
Usage
cloudUtilPlot(group, begin, end, id,
colormap,
normalize,
plotConcurrent,
plotConcurrentMax,
main)
Arguments
begin |
a time series of the starting events; the time format is unix time stamp (seconds since Jan 01 1970). |
end |
a time series of the corresponding ending events. |
id |
identifyer of each event. |
group |
group of each event. |
main |
an overall title for the plot. |
colormap |
color vector for the data. default is the rainbow colormap having as much colors as we have groups. |
normalize |
normalizes the time in a way that the groups are relative to each other; default is set to TRUE. |
plotConcurrent |
plot concurrent running tasks as solid lines; default is set to TRUE. |
plotConcurrentMax |
plot maximum of concurrent running tasks; default is set to FALSE. |
Details
The cloudUtilPlot function reqires the four arguments begin,
end, group, and id. All other arguments are optional.
Author(s)
Christian Panse
References
Tyanko Aleksiev, Simon Barkow, Peter Kunszt, Sergio Maffioletti, Riccardo Murri, Christian Panse (2013), VM-MAD: a cloud/cluster software for service-oriented academic environments, https://arxiv.org/abs/1302.2529.
Examples
data(cloudms2)
#green
col.amazon<-rgb(0.1,0.8,0.1,alpha=0.2)
col.amazon2<-rgb(0.1,0.8,0.1,alpha=0.2)
#blue
col.fgcz<-rgb(0.1,0.1,0.8,alpha=0.2)
col.fgcz2<-rgb(0.1,0.1,0.5,alpha=0.2)
#red
col.uzh<-rgb(0.8,0.1,0.1,alpha=0.2)
col.uzh2<-rgb(0.5,0.1,0.1,alpha=0.2)
cm<-c(col.amazon, col.amazon2, col.fgcz, col.fgcz2, col.uzh, col.uzh2)
cloudUtilPlot(begin=cloudms2$BEGIN_PROCESS,
end=cloudms2$END_PROCESS,
id=cloudms2$id,
group=cloudms2$CLOUD,
colormap=cm,
normalize=FALSE,
plotConcurrent=TRUE);
cloudUtilPlot(begin=cloudms2$BEGIN_PROCESS,
end=cloudms2$END_PROCESS,
id=cloudms2$id,
group=cloudms2$CLOUD,
colormap=cm,
normalize=TRUE,
plotConcurrent=TRUE)
Benchmark data set for cloudUtil
Description
The data set provides the measurements of benchmark using three compute infrastructers namely uzh.ch Schroedinger, Amazon EC2 (region us-east.), and the FGCZ internal compute server. The number crunshing was done by doing tandem mass spectra peptide sequence assignments of a large scale proteomics Drosophila experiment similar to the data processing published here [E. Brunner, et. al., NBT, 2007] having approx. 1800 (LC)-MS/MS runs, 8474960 fragment ion spectra(tandem MS or MS2), identidying 498000 redundant and 72281 distinct peptides, and 9124 proteins. The data volume is approx. 0.3TB splitted into ~1800 jobs.
Format
A data frame with 10969 rows and 15 variables