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Add step-by-step guide for scaling execution into README file.

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Signed-off-by: Morales Quispe, Marcela <marcela.morales.quispe@intel.com>
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Morales Quispe, Marcela
2019-09-26 23:49:01 -05:00
committed by Obed N Munoz
parent 6170fe3d3b
commit 908a9f72f5
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@@ -25,3 +25,102 @@ function in that file has the ability to also `curl` or `socat` the JSON results
by environment variables (see the file source for details). This method has been used to store results in
Elasticsearch and InfluxDB databases for instance, but should be adaptable to use with any REST API that accepts
JSON input.
## Scaling execution
This section describes a complete step-by-step scaling execution upto results reporting by using `scaling/k8s_scale.sh` tool which launches a series of workloads and take memory metric measurements after each launch.
**Requirements**
* A Kubernetes cluster up and running (tested on v1.15.3).
* `bc` and `jq` packages.
* Docker (only for report generation).
The steps to execute a run of the scaling framework are listed below, which need to be executed on the master node of a Kubernetes cluster to avoid network issues:
1. Clone `cloud-native-setup` repository into a preferred directory and change directory upto `cloud-native-setup/metrics`:
```sh
$ git clone https://github.com/clearlinux/cloud-native-setup.git
$ cd cloud-native-setup/metrics
```
2. Launch the execution by:
```sh
$ ./scaling/k8s_scale.sh
INFO: Initialising
command: bc: yes
command: jq: yes
INFO: Checking k8s accessible
INFO: 1 k8s nodes in 'Ready' state found
starting kubectl proxy
Starting to serve on 127.0.0.1:8090
daemonset.apps/stats created
Waiting for daemon set "stats" rollout to finish: 0 of 1 updated pods are available...
daemon set "stats" successfully rolled out
INFO: Running test
INFO: And grab some stats
INFO: idle [98.49] free [29031100] launch [0] node [clr-30f01b5149ba4ab8b05a7ee03b6812a5] inodes_free [31103039]
INFO: Testing replicas 1 of 20
INFO: Content of runtime_command=:/@RUNTIMECLASS@/d
...
```
The above execution might take about 4min because it launch upto 20 pods by default and takes measurements for CPU utilization, memory utilization and pod boot time, finally it will generate a `k8s-scaling.json` result file at `result` directory.
**Note**: by default the scaling framework makes call to the Kubernetes API directly so, if facing conectivity issues verify that `kubelet` service's proxies and `no_proxy` environment variable are properly setup.
**Note**: by default the scaling framework uses default values for all its required variables, which can be checked through `scaling/k8s_scale.sh -h` and updated when launching the execution, i.e.:
```
$ ./scaling/k8s_scale.sh -h
Usage: ./scaling/k8s_scale.sh [-h] [options]
Description:
Launch a series of workloads and take memory metric measurements after
each launch.
Options:
-h, Help page.
Environment variables:
Name (default)
Description
TEST_NAME (k8s scaling)
Can be set to over-ride the default JSON results filename
NUM_PODS (20)
Number of pods to launch
STEP (1)
Number of pods to launch per cycle
wait_time (30)
Seconds to wait for pods to become ready
delete_wait_time (600)
Seconds to wait for all pods to be deleted
settle_time (5)
Seconds to wait after pods ready before taking measurements
use_api (yes)
specify yes or no to use the API to launch pods
grace (30)
specify the grace period in seconds for workload pod termination
$ use_api=no ./scaling/k8s_scale.sh
```
The steps to generate the result report are listed below:
1. Having the `results/k8s-scaling.json` result file, create a subdirectory in the `results` directory with a preferred name and copy the `k8s-scaling.json` file into it, so the file distribution looks like:
```sh
$ tree result
results/
└── scaling
└── k8s-scaling.json
```
2. Launch the report generation by:
```sh
./report/makereport.sh
```
**Note**: the first time you launch the report generation it will build a docker container to generate the reports and this process can take several minutes. Subsequent runs will be much faster.
The above execution will generate a `report/output` directory with the final reports, such as:
```sh
$ tree report/output/
report/output/
├── dut-1.png
├── metrics_report.pdf
├── scaling-1.png
├── scaling-2.png
├── scaling-3.png
└── scaling-4.png
```
More details about result reporting can be reviewed at [`report`](./report) directory.