Akamas Docs
3.5
3.5
  • Home
  • Getting started
    • Introduction
    • Free Trial
    • Licensing
    • Deployment
      • Cloud Hosting
    • Security
    • Maintenance & Support (M&S) Services
      • Customer Support Services
      • Support levels for Customer Support Services
      • Support levels for software versions
      • Support levels with Akamas
  • Installing
    • Architecture
    • Docker compose installation
      • Prerequisites
        • Hardware Requirements
        • Software Requirements
        • Network requirements
      • Install Akamas dependencies
      • Install the Akamas Server
        • Online installation mode
          • Online installation behind a Proxy server
        • Offline installation mode
        • Changing UI Ports
        • Setup HTTPS configuration
      • Troubleshoot Docker installation issues
    • Kubernetes installation
      • Prerequisites
        • Cluster Requirements
        • Software Requirements
      • Install Akamas
        • Online Installation
        • Offline Installation - Private registry
      • Installing on OpenShift
      • Accessing Akamas
      • Useful commands
    • Install the CLI
      • Setup the CLI
      • Initialize the CLI
      • Change CLI configuration
      • Use a proxy server
    • Verify the installation
    • Installing the toolbox
    • Install the license
    • Manage anonymous data collection
  • Managing Akamas
    • Akamas logs
    • Audit logs
    • Upgrade Akamas
      • Docker compose
      • Kubernetes
    • Monitor Akamas status
    • Backup & Recover of the Akamas Server
    • Users management
      • Accessing Keycloak admin console
      • Configure an external identity provider
        • Azure Active Directory
        • Google
      • Limit users sessions
        • Local users
        • Identity provider users
    • Collecting support information
  • Using
    • System
    • Telemetry
    • Workflow
    • Study
      • Offline Study
      • Live Study
        • Analyzing results of live optimization studies
      • Windowing
      • Parameters and constraints
  • Optimization Guides
    • Optimize application costs and resource efficiency
      • Kubernetes microservices
        • Optimize cost of a Kubernetes deployment subject to Horizontal Pod Autoscaler
        • Optimize cost of a Kubernetes microservice while preserving SLOs in production
        • Optimize cost of a Java microservice on Kubernetes while preserving SLOs in production
      • Application runtime
        • Optimizing a sample Java OpenJDK application
        • Optimizing cost of a Node.js application with performance tests
        • Optimizing cost of a Golang application with performance tests
        • Optimizing cost of a .NET application with performance tests
      • Applications running on cloud instances
        • Optimizing a sample application running on AWS
      • Spark applications
        • Optimizing a Spark application
    • Optimize application performance and reliability
      • Kubernetes microservices
        • Optimizing cost of a Kubernetes microservice while preserving SLOs in production
        • Optimizing cost of a Java microservice on Kubernetes while preserving SLOs in production
      • Applications running on cloud instances
      • Spark applications
  • Integrating
    • Integrating Telemetry Providers
      • CSV provider
        • Install CSV provider
        • Create CSV telemetry instances
      • Dynatrace provider
        • Install Dynatrace provider
        • Create Dynatrace telemetry instances
          • Import Key Requests
      • Prometheus provider
        • Install Prometheus provider
        • Create Prometheus telemetry instances
        • CloudWatch Exporter
        • OracleDB Exporter
      • Spark History Server provider
        • Install Spark History Server provider
        • Create Spark History Server telemetry instances
      • NeoLoadWeb provider
        • Install NeoLoadWeb telemetry provider
        • Create NeoLoadWeb telemetry instances
      • LoadRunner Professional provider
        • Install LoadRunner Professional provider
        • Create LoadRunner Professional telemetry instances
      • LoadRunner Enterprise provider
        • Install LoadRunner Enterprise provider
        • Create LoadRunner Enterprise telemetry instances
      • AWS provider
        • Install AWS provider
        • Create AWS telemetry instances
    • Integrating Configuration Management
    • Integrating with pipelines
    • Integrating Load Testing
      • Integrating NeoLoad
      • Integrating LoadRunner Professional
      • Integrating LoadRunner Enterprise
  • Reference
    • Glossary
      • System
      • Component
      • Metric
      • Parameter
      • Component Type
      • Workflow
      • Telemetry Provider
      • Telemetry Instance
      • Optimization Pack
      • Goals & Constraints
      • KPI
      • Optimization Study
      • Workspace
      • Safety Policies
    • Construct templates
      • System template
      • Component template
      • Parameter template
      • Metric template
      • Component Types template
      • Telemetry Provider template
      • Telemetry Instance template
      • Workflows template
      • Study template
        • Goal & Constraints
        • Windowing policy
          • Trim windowing
          • Stability windowing
        • Parameter selection
        • Metric selection
        • Workload selection
        • KPIs
        • Steps
          • Baseline step
          • Bootstrap step
          • Preset step
          • Optimize step
        • Parameter rendering
        • Optimizer Options
    • Workflow Operators
      • General operator arguments
      • Executor Operator
      • FileConfigurator Operator
      • LinuxConfigurator Operator
      • WindowsExecutor Operator
      • WindowsFileConfigurator Operator
      • Sleep Operator
      • OracleExecutor Operator
      • OracleConfigurator Operator
      • SparkSSHSubmit Operator
      • SparkSubmit Operator
      • SparkLivy Operator
      • NeoLoadWeb Operator
      • LoadRunner Operator
      • LoadRunnerEnteprise Operator
    • Telemetry metric mapping
      • Dynatrace metrics mapping
      • Prometheus metrics mapping
      • NeoLoadWeb metrics mapping
      • Spark History Server metrics mapping
      • LoadRunner metrics mapping
    • Optimization Packs
      • Linux optimization pack
        • Amazon Linux
        • Amazon Linux 2
        • Amazon Linux 2022
        • CentOS 7
        • CentOS 8
        • RHEL 7
        • RHEL 8
        • Ubuntu 16.04
        • Ubuntu 18.04
        • Ubuntu 20.04
      • DotNet optimization pack
        • DotNet Core 3.1
      • Java OpenJDK optimization pack
        • Java OpenJDK 8
        • Java OpenJDK 11
        • Java OpenJDK 17
      • OpenJ9 optimization pack
        • IBM J9 VM 6
        • IBM J9 VM 8
        • Eclipse Open J9 11
      • Node JS optimization pack
        • Node JS 18
      • GO optimization pack
        • GO 1
      • Web Application optimization pack
        • Web Application
      • Docker optimization pack
        • Container
      • Kubernetes optimization pack
        • Kubernetes Pod
        • Kubernetes Container
        • Kubernetes Workload
        • Kubernetes Namespace
        • Kubernetes Cluster
      • WebSphere optimization pack
        • WebSphere 8.5
        • WebSphere Liberty ND
      • AWS optimization pack
        • EC2
        • Lambda
      • PostgreSQL optimization pack
        • PostgreSQL 11
        • PostgreSQL 12
      • Cassandra optimization pack
        • Cassandra
      • MySQL Database optimization pack
        • MySQL 8.0
      • Oracle Database optimization pack
        • Oracle Database 12c
        • Oracle Database 18c
        • Oracle Database 19c
        • RDS Oracle Database 11g
        • RDS Oracle Database 12c
      • MongoDB optimization pack
        • MongoDB 4
        • MongoDB 5
      • Elasticsearch optimization pack
        • Elasticsearch 6
      • Spark optimization pack
        • Spark Application 2.2.0
        • Spark Application 2.3.0
        • Spark Application 2.4.0
    • Command Line commands
      • Administration commands
      • User and Workspace management commands
      • Authentication commands
      • Resource management commands
      • Optimizer options commands
    • Release Notes
  • Knowledge Base
    • Creating custom optimization packs
    • Setting up a Konakart environment for testing Akamas
    • Modeling a sample Java-based e-commerce application (Konakart)
    • Optimizing a web application
    • Optimizing a sample Java OpenJ9 application
    • Optimizing a sample Linux system
    • Optimizing a MongoDB server instance
    • Optimizing a Kubernetes application
    • Leveraging Ansible to automate AWS instance management
    • Guidelines for optimizing AWS EC2 instances
    • Optimizing an Oracle Database server instance
    • Optimizing an Oracle Database for an e-commerce service
    • Guidelines for optimizing Oracle RDS
    • Optimizing a MySQL server database running Sysbench
    • Optimizing a MySQL server database running OLTPBench
    • Optimizing a live full-stack deployment (K8s + JVM)
    • Setup Instana integration
Powered by GitBook
On this page

Was this helpful?

Export as PDF

Last updated 11 months ago

Was this helpful?

A preset step performs a single experiment with a specific configuration. The purpose of this step is to help you quickly understand how good is a particular configuration.

A preset step offers two options when selecting the configuration of the experiment to be executed:

  • Use a configuration taken from an experiment of a study (can be the same study)

  • Use a custom configuration

The preset step has the following structure:

Field
Type
Value restrictions
Is required
Default value
Description

where the from field should have the following structure:

with

  • study contains the name or ID of the study from which to take the configuration. In the case this is omitted, the same study of the step is considered for experiments from which taking configurations

  • experiments contains the number of the experiment from which to take the configuration

Examples

Custom configuration

You can provide a custom configuration by setting values:

Configuration from another study

You can select a configuration taken from another study by setting from:

Configuration from the same study

You can select a configuration taken from the same study by setting from but by omitting the study field:

Notice: the from and experiments fields are defined as a list, but can only contain one element.

  1. Reference
  2. Construct templates
  3. Study template
  4. Steps

Preset step

name: "my_preset"   # name of the step
type: "preset"      # type of the step (preset)
values:
  jvm1.maxHeapSize: 1024 # parameter maxHeapSize of jvm1 is set to 1024
  jvm2.maxHeapSize: 2048 # parameter maxHeapSize of jvm2 is set to 2048
name: "my_preset"  # name of the step
type: "preset"     # type of the step (preset)
from:
  - study: "preset_study_1" # name or ID of the study from which to take the configuration
    experiments: [1] # the number of the experiment from which to take the configuration
name: "my_preset"    # name of the step
type: "preset"       # type of the step (preset)
from:
  - experiments: [1] # the step will take the configuration of the experiment #1 of the same study of the step

type

string

preset

yes

The type of the step, in this case, preset

name

string

yes

The name of the step

runOnFailure

boolean

true false

no

false

The execution policy of the step:

  • false prevents the step from running in case the previous step failed

  • true allows the step to run even if the previous step failed

from

array of objects

Each object should have the structure described below

no

The study and the experiment from which to take the configuration of the experiment

The from and experiments fields are defined as an array, but it can only contain one element

This can be set only if values is not set

values

object

The keys should match existing parameters

no

The configuration with which execute the experiment

This can be set only if from is not set

doNotRenderParameters

string

this cannot be used when using a from option since no experiment is actually executed

no

renderParameters

string

this cannot be used when using a from option since no experiment is actually executed

no

study: "study_preset_1"
experiments: [1]

Parameters not to be rendered. - see

Parameters to be rendered. - see

Parameter rending
Parameter rending