Akamas Docs
3.1.2
3.1.2
  • How to use this documentation
  • Getting started with Akamas
    • Introduction to Akamas
    • 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 3.1
  • Installing Akamas
    • Akamas Architecture
    • 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
    • Install the Akamas CLI
      • Setup the Akamas CLI
      • Verify the Akamas CLI
      • Initialize Akamas CLI
      • Change CLI configuration
    • Verify the Akamas Server
    • Install the Akamas license
    • Manage anonymous data collection
    • Install an Akamas Workstation
    • Troubleshoot install issues
    • Manage the Akamas Server
      • Akamas logs
      • Audit logs
      • Install upgrades and patches
      • Monitor the Akamas Server
      • Backup & Recover of the Akamas Server
  • Using Akamas
    • General optimization process and methodology
    • Preparing optimization studies
      • Modeling systems
      • Modeling components
        • Creating custom optimization packs
        • Managing optimization packs
      • Creating telemetry instances
      • Creating automation workflows
        • Creating workflows for offline studies
        • Performing load testing to support optimization activities
        • Creating workflows for live optimizations
      • Creating optimization studies
        • Defining optimization goal & constraints
        • Defining windowing policies
        • Defining KPIs
        • Defining parameters & metrics
        • Defining workloads
        • Defining optimization steps
        • Setting safety policies
    • Running optimization studies
      • Before running optimization studies
      • Analyzing results of offline optimization studies
        • Optimization Insights
      • Analyzing results of live optimization studies
      • Before applying optimization results
    • Guidelines for choosing optimization parameters
      • Guidelines for JVM (OpenJ9)
      • Guidelines for JVM layer (OpenJDK)
      • Guidelines for Oracle Database
      • Guidelines for PostgreSQL
    • Guidelines for defining optimization studies
      • Optimizing Linux
      • Optimizing Java OpenJDK
      • Optimizing OpenJ9
      • Optimizing Web Applications
      • Optimizing Kubernetes
      • Optimizing Spark
      • Optimizing Oracle Database
      • Optimizing MongoDB
      • Optimizing MySQL Database
      • Optimizing PostgreSQL
  • Integrating Akamas
    • Integrating Telemetry Providers
      • CSV provider
        • Install CSV provider
        • Create CSV provider instances
      • Dynatrace provider
        • Install Dynatrace provider
        • Create Dynatrace provider instances
      • Prometheus provider
        • Install Prometheus provider
        • Create Prometheus provider instances
        • CloudWatch Exporter
        • OracleDB Exporter
      • Spark History Server provider
        • Install Spark History Server provider
        • Create Spark History Server provider instances
      • NeoLoadWeb provider
        • Setup NeoLoadWeb telemetry provider
        • Create NeoLoadWeb provider instances
      • LoadRunner Professional provider
        • Install LoadRunner Professional provider
        • Create LoadRunner Professional provider instances
      • LoadRunner Enterprise provider
        • Install LoadRunner Enterprise provider
        • Create LoadRunner Enterprise provider instances
      • AWS provider
        • Install AWS provider
        • Create AWS provider instances
    • Integrating Configuration Management
    • Integrating Value Stream Delivery
    • Integrating Load Testing
      • Integrating NeoLoad
      • Integrating Load Runner Professional
      • Integrating LoadRunner Enterprise
  • Akamas Reference
    • Glossary
      • System
      • Component
      • Metric
      • Parameter
      • Component Type
      • Workflow
      • Telemetry Provider
      • Telemetry Instance
      • Optimization Pack
      • Goals & Constraints
      • KPI
      • Optimization Study
      • Offline Optimization Study
      • Live Optimization Study
      • Workspace
    • 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
    • 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
      • OpenJ9 optimization pack
        • IBM J9 VM 6
        • IBM J9 VM 8
        • Eclipse Open J9 11
      • NodeJS optimization pack
        • NodeJS
      • 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
  • Knowledge Base
    • 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 Java OpenJDK 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 a sample application running on AWS
    • Optimizing a Spark application
    • 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 K8s deployment
    • Optimizing a live full-stack deployment (K8s + JVM)
  • Akamas Free Trial
Powered by GitBook
On this page
  • Optimizer
  • Optimizer options for offline studies
  • Optimizer options for live studies
  • Failures
  • Inizialitations
  • Examples

Was this helpful?

Export as PDF
  1. Akamas Reference
  2. Construct templates
  3. Study template
  4. Steps

Optimize step

Last updated 1 year ago

Was this helpful?

An optimize step generates optimized configurations according to the defined optimization strategy. During this step, Akamas AI is used to generate such optimized configurations.

The optimize step has the following structure:

Field
Type
Value restrictions
Is required
Default value
Description

type

string

optimize

yes

The type of the step, in this case, optimize

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

numberOfExperiments

integer

numberOfExperiments > 0 and

numberOfExperiments >= numberOfInitExperiments

yes

The number of experiments to execute - see below

numberOfTrials

integer

numberOfTrials > 0

no

1

The number of trials to execute for each experiment

numberOfInitExperiments

integer

numberOfInitExperiments < numberOfExperiments

no

10

The number of initialization experiment to execute - see below.

maxFailedExperiments

integer

maxFailedExperiments > 1

no

30

The number of experiment failures (as either workflow errors or constraint violations) to accept before the step is marked as failed

optimizer

string

AKAMAS SOBOL RANDOM

no

AKAMAS

The type of optimizer to use to generate the configuration of the experiments - see below

optimizerOptions

object

see below

no

Some options for the AKAMAS optimizer - see below

doNotRenderParameters

string

no

renderParameters

string

no

Optimizer

The optimizer field allows selecting the desired optimizer:

  • AKAMAS identifies the standard AI optimizer used by Akamas

  • SOBOL identifies an optimizer that generates configurations using

  • RANDOM identifies an optimization that generates configurations using random numbers

Notice that SOBOL and RANDOM optimizers do not perform initialization experiments, hence the field numberOfInitExperiments is ignored.

Optimizer options for offline studies

For offline optimization studies, the optimizerOptions field can be used to specify whether beta-warping optimization (a more sophisticated optimization that requires longer time) should be used for how many experiments (as a percentage):

# half the experiments
# should be done
# with beta warping
experimentsWithBeta: "50%"

where experimentsWithBeta can be:

  • A percentage between 0 and 100%

  • A number less than or equal to numberOfExperiments

Optimizer options for live studies

For live optimization studies, the optimizerOptions field can be used to specify several important parameters governing the live optimization, which can be defined at the study level and also overridden at the step level (only for steps of type optimize):

optimizerOptions:
  onlineMode: RECOMMEND                    # [RECOMMEND|FULLY_AUTONOMOUS]
  safetyMode: GLOBAL                       # [GLOBAL|LOCAL]
  workloadOptimizedForStrategy: MAXIMIN    # [MAXIMIN|MEDIAN|LAST|MOST_VIOLATED]
  safetyFactor: 0.55                       # 0 <= safetyFactor <= 1
  explorationFactor: 0.05                  # 0 <= explorationFactor <= 1 or FULL_EXPLORATION
Online Mode
Safety Mode
Workload strategy

RECOMMEND

GLOBAL

MAXIMIN

FULLY_AUTONOMOUS

LOCAL

LAST

Online Mode

The onlineMode field specifies how the Akamas optimizer should operate:

  • RECOMMEND: configurations are recommended to the user by Akamas and are only applied after having been approved (and possibly modified) by the user;

  • FULLY AUTONOMOUS MODE: configurations are immediately applied by Akamas.

Safety Mode

The safetyMode field describes how the Akamas optimizer should evaluate the goal constraints on a candidate configuration for that configuration to be considered valid:

  • GLOBAL: the constraints must be satisfied by the configuration under all observed workloads in the configuration history - this is the value taken in case onlineMode is set to RECOMMEND;

  • LOCAL: the constraints are evaluated only under the workload selected according to the workload strategy - this should be used with onlineMode set to FULLY_AUTONOMOUS.

Notice that when setting the safetyMode to LOCAL, the recommended configuration is only expected to be good for the specific workload selected under the defined workload strategy, but it might violate constraints under another workload.

Workload Strategy

The workloadOptimizedForStrategy field specifies the workload strategy that drives how Akamas leverages the workload information when looking for the next configuration:

  • MAXIMIN: the optimizer looks for a configuration that maximizes the minimum improvements for all the already observed workloads;

  • MEDIAN: for each workload, a median of all its values is considered - this works well to find a configuration that is good for the median of all the workloads;

  • MOST_VIOLATED: for each workload, the workload of the configuration which results in most violations is considered.

Safety Factor

The safetyFactor field specifies how much the optimizer should stay on the safe side in evaluating a candidate configuration with respect to the goal constraints. A higher safety factor corresponds to a safer configuration, that is a configuration that is less likely to violate goal constraints.

Acceptable values are all the real values ranging between 0 and 1, with (safetyFactor - 0.5) representing the allowed margin for staying within the defined constraint:

  • 0 means "no safety", as with this value the optimizer totally ignores goal constraints violations;

  • 0.5 means "safe, but no margin", as with this value the optimizer only tries configurations that do not violate the goal constraints, by remaining as close as possible to them;

  • 1 means "super safe", as with this value the optimize only tries configurations that are very far from goal constraints.

For live optimization studies, 0.6 is the default value, while for offline optimization studies, the default value is 0.5.

Exploration Factor

The explorationFactor field specifies how much the optimizer explores the (unknown) optimization space when looking for new configurations. For any parameter, this factor measures the delta between already tried values and the value of a new possible configuration. A higher exploration factor corresponds to a broader exploration of never tried before parameter values.

Acceptable values are all the real values ranging between 0 and 1, plus the special string FULL_EXPLORATION:

  • 0 means "no exploration", as with this value the optimizer chooses a value among the previously seen values for each parameter;

  • 1 means "full exploration, except for categories", as with this value the optimizer for a non-categorical parameter any value among all its domain values can be chosen, while only values (categories) that have already been seen in previous configurations are chosen for a categorical parameter;

  • FULL_EXPLORATION means "full exploration, including categories" as with this value the optimizer chooses any value among all its domain values, including categories, even if not already seen in previous configurations.

Failures

An optimize step is fault-tolerant and tries to relaunch experiments when they fail. Nevertheless, the step imposes a limit on the number of failed experiments: if too many experiments fail, then the entire step fails too. By default, at most 30 experiments can fail while Akamas is optimizing systems. An experiment is considered failed when it either failed to run (i.e., there is an error in the workflow) or violated some constraint.

Inizialitations

An optimize step launches some initialization experiments (by default 10) that do not apply the AI optimizer and are used to find good configurations. By default, the step performs 10 initialization experiments.

Initialization experiments take into account bootstrapped experiments, experiments executed in preset steps, and baseline experiments.

Examples

The following fragment refers to an optimization study that runs 50 experiments using the SOBOL optimizer:

name: "my_optimize" # name of the step
type: "optimize" # type of the step (optimize)
optimizer: "SOBOL"
numberOfExperiments: 50 # amount of experiments to execute
numberOfTrials: 2 # amount of trials for each experiment

The following fragment refers to an optimization where 50% of the experiments need to use the beta-warping option, which enables a more sophisticated but longer optimization:

# half the experiments
# should be done
# with beta warping
experimentsWithBeta: "50%"

Parameters not to be rendered. - see

Parameters to be rendered. - see

Please notice that also the safetyFactor option discussed in the context of live optimization studies can be applied to offline optimization studies.

Notice that while available as independent options, the optimizer options onlineMode (described ), workloadOptimizedForStrategy () and the safetyFactor () works in conjunction according to the following schema:

All these optimizer options can be changed at any time, that is while the optimization study is running, to become immediately effective. The page in the reference guide provides these specific update commands.

LAST: for each workload, the last observed workload is considered - this works well to find a configuration that is good for the last workloads - this is often used in conjunction with a LOCAL safety mode (see );

In case the desired explorationFactoris 1 but there are some specific parameters that also need to be explored with respect to all its categories, then PRESET steps (refer to the page) can be used to run an optimization study with these values. For an example of a live optimization study where this approach is adopted see .

Sobol sequences
Optimizer options commands
Preset step
Optimizing a live full-stack deployment (K8s + JVM)
here below
here below
here below
here below
here above
Parameter rending
Parameter rending