1. Measuring performance of Kargo

status

ready

version

1.0

Abstract

This document describes a test plan for quantifying the performance of Kargo as a one of the Kubernetes deployment solutions. Kargo comes with Fuel Containerized Control Plane installer and intended to be fast, scalable and reliable K8S deployment tool. Its code mostly implemented as a set of ansible playbooks with some bash launcher scripts.

1.1. Test Plan

This test plan aims to get overall performance, timing and resource utilization during K8S deployment with Kargo on a different number of nodes, using specified list of performance measurements and tools.

1.1.1. Test Environment

1.1.1.1. Preparation

  1. The following package needs to be installed on the first node in the planned K8S cluster in order to collect performance metrics.

Software to be installed

package name

version

source

dstat

0.7.2

Ubuntu xenial universe repository

1.1.1.2. Environment description

Test results MUST include a description of the environment used. The following items should be included:

  • Hardware configuration of each server. If virtual machines are used then both physical and virtual hardware should be fully documented. An example format is given below:

Description of server hardware

server

name

role

vendor,model

operating_system

CPU

vendor,model

processor_count

core_count

frequency_MHz

RAM

vendor,model

amount_MB

NETWORK

interface_name

vendor,model

bandwidth

STORAGE

dev_name

vendor,model

SSD/HDD

size

  • Configuration of hardware network switches. The configuration file from the switch can be downloaded and attached.

  • Network scheme. The plan should show how all hardware is connected and how the components communicate. All ethernet/fibrechannel and VLAN channels should be included. Each interface of every hardware component should be matched with the corresponding L2 channel and IP address.

1.1.2. Test Case

1.1.2.1. Description

This specific test plan contains only one test case, that needs to be run step by step on the environments differing list of parameters below.

1.1.2.2. Parameters

Parameter name

Value

number of nodes

50, 150, 350

1.1.2.3. List of performance metrics

The table below shows the list of test metrics to be collected. The priority is the relative ranking of the importance of each metric in evaluating the performance of the system.

List of performance metrics

Priority

Value

Measurement Units

Description

1

DEPLOYMENT_TIME

seconds
The elapsed time deploy ready K8S
cluster on a different number of nodes.

2

NET_ALL

bit/s
Total incoming/outgoing network
bandwidth usage as a function of the
number of nodes.

3

CPU

percentage
CPU utilization as a function of the
number of nodes.

3

RAM

MB
Active memory usage as a function of
the number of nodes.

3

DISK_ALL

operations/second
Storage total read/write IO bandwidth
as a function of the number of nodes.

1.1.2.4. Measuring performance values

  1. Start the collection of CPU, memory, network, and storage metrics on the first K8S cluster node. Use the dstat program which can collect all of these metrics in CSV format into a log file.

  2. Launch Kargo which will begin K8S deployment.

  3. Wait until the deployment process get finished.

  4. Stop the dstat program.

  5. Prepare collected data for analysis. dstat provides a large amount of information, which can be pruned by saving only the following:

    • “system”[time]. Save as given.

    • 100-“total cpu usage”[idl]. dstat provides only the idle CPU value. CPU utilization is calculated by subtracting the idle value from 100%.

    • “memory usage”[used]. dstat provides this value in Bytes. This is converted it to Megabytes by dividing by 1024*1024=1048576.

    • “net/total”[send/recv] The total receive and transmit bandwidth on the NIC. dstat provides these values in Bytes per second. They are converted to Bits per second dividing by 8.

    • “io/total”[read]+”io/total”[writ]. The total read and write storage IO bandwidth.

    These values will be graphed and maximum values reported.

    Additional tests will be performed if some anomalous behavior is found. These may require the collection of additional performance metrics.

  6. The result of this part of test will be:

  • to provide the following graphs, one for each number of provisioned nodes:

    1. One dependence on one graph.

      • ALL_NET(TIME) Dependence on time of total network bandwidth usage.

    2. One dependence on one graph.

      • CPU(TIME) Dependence on time of CPU utilization.

    3. One dependence on one graph.

      • RAM(TIME) Dependence on time of active memory usage.

    4. One dependence on one graph.

      • ALL_IO(TIME) Dependence on time of total storage IO bandwidth.

  • to calculate following values and describe they in the table with dependency

to the certain number of nodes:

number of nodes

50

150

350

deployment time

cpu_usage_max

cpu_usage_min

cpu_usage_average

cpu_usage_percentile 90%

ram_usage_max

ram_usage_min

ram_usage_average

ram_usage_percentile 90%

net_all_max

net_all_min

net_all_average

net_all_percentile 90%

dsk_io_all_max

dsk_io_all_min

dsk_io_all_average

dsk_io_all_percentile 90%

1.2. Reports

Test plan execution reports: