Skip to main content

An automatic experiment framework for microservice.

Project description

Introduction

AEFM is an Automatic Experiment Framework for Microservice, it provides a basic configuration that helps you to profiling basic information of microservices, and also the programmable interface to extend its usage.

AEFM treat every experiment as a lifecycle, through the lifecycle, it will trigger different events. Users can customize different event handlers to achieve different objectives.

The basic events of a lifecycle are as follows:

  • Start Experiment
  • Init Environment
  • Start Single Test Case
  • Start Data Collection
  • [Customized Events to Handle Test Case Ends]
  • End Experiment

Components

AEFM relies on different components to perform efficiently and correctly.

Manager

Manager is the highest level component that used to manage whole experiment. It provides events handling, globally data accessing and component registration. You can customize your experiment workflow, register new events, or replace default components with the help of manager object. You can also extend the class and create your customized manager.

Deployer

Deployer is used to manage kubernetes resources such as pod, deployments, etc. We manage theses resources based on YAML files, which can help users to detect which part is incorrect more efficient by directly look at YAML files.

Workload Generator

Workload generator is used to provide pressure to applications. By default, we use wrk as workload generator, which can generate HTTP requests. You can also use other workload generating tools, as they provides the required API.

Data Collector

Data collector will collect data from different data sources, and save them as files. By default, we use Jaeger to collect traces data, Prometheus to collect hardware data and rely on output of wrk to collect throughput data. You can use other data sources by writing your own collectors, as they follow the collector interface, they can be set as component and let manager to involve it.

Interference Generator

Interference generator generates CPU, memory capacity/bandwidth and network bandwidth interferences by default. User can also customized their own interference generator to generates other types of intererence.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

AEFM-0.0.2.tar.gz (25.9 MB view details)

Uploaded Source

Built Distribution

AEFM-0.0.2-py3-none-any.whl (26.1 MB view details)

Uploaded Python 3

File details

Details for the file AEFM-0.0.2.tar.gz.

File metadata

  • Download URL: AEFM-0.0.2.tar.gz
  • Upload date:
  • Size: 25.9 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for AEFM-0.0.2.tar.gz
Algorithm Hash digest
SHA256 cff791c9b32fb1917f524f8d54f968f927443e29a97d10402cf7fad5614e369f
MD5 4614d6f0178ad61fda647cadc852922c
BLAKE2b-256 2b89d00cfa52b2e697f5679a63fa38709adbb05d14411f5c2982badc7c004f63

See more details on using hashes here.

File details

Details for the file AEFM-0.0.2-py3-none-any.whl.

File metadata

  • Download URL: AEFM-0.0.2-py3-none-any.whl
  • Upload date:
  • Size: 26.1 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.11.4

File hashes

Hashes for AEFM-0.0.2-py3-none-any.whl
Algorithm Hash digest
SHA256 00379c48d3fb35be041a5d079330f5065b60368f759aaea345b9e900e850a155
MD5 9bdcde83fb7c852eaf7ea92b2861f9b9
BLAKE2b-256 39c34cf95d8821cd20d5b01c70dce47440f9ae4eb601485c7a20a3618ce270db

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page