Long-Term Supported Versions

    Getting to Know A-Tune


    An operating system (OS) is basic software that connects applications and hardware. It is critical for users to adjust OS and application configurations and make full use of software and hardware capabilities to achieve optimal service performance. However, numerous workload types and varied applications run on the OS, and the requirements on resources are different. Currently, the application environment composed of hardware and software involves more than 7000 configuration objects. As the service complexity and optimization objects increase, the time cost for optimization increases exponentially. As a result, optimization efficiency decreases sharply. Optimization becomes complex and brings great challenges to users.

    Second, as infrastructure software, the OS provides a large number of software and hardware management capabilities. The capability required varies in different scenarios. Therefore, capabilities need to be enabled or disabled depending on scenarios, and a combination of capabilities will maximize the optimal performance of applications.

    In addition, the actual business embraces hundreds and thousands of scenarios, and each scenario involves a wide variety of hardware configurations for computing, network, and storage. The lab cannot list all applications, business scenarios, and hardware combinations.

    To address the preceding challenges, openEuler launches A-Tune.

    A-Tune is an AI-based engine that optimizes system performance. It uses AI technologies to precisely profile business scenarios, discover and infer business characteristics, so as to make intelligent decisions, match with the optimal system parameter configuration combination, and give recommendations, ensuring the optimal business running status.


    The following figure shows the A-Tune core technical architecture, which consists of intelligent decision-making, system profile, and interaction system.

    • Intelligent decision-making layer: consists of the awareness and decision-making subsystems, which implements intelligent awareness of applications and system optimization decision-making, respectively.
    • System profile layer: consists of the labeling and learning subsystems. The labeling subsystem is used to cluster service models, and the learning subsystem is used to learn and classify service models.
    • Interaction system layer: monitors and configures various system resources and executes optimization policies.

    Supported Features and Service Models

    Supported Features

    Table 1 describes the main features supported by A-Tune, feature maturity, and usage suggestions.

    Table 1 Feature maturity



    Usage Suggestion

    Auto optimization of 11 applications in seven workload types



    User-defined workload types and service models



    Automatic parameter optimization



    Supported Service Models

    Based on the workload characteristics of applications, A-Tune classifies services into seven types. For details about the workload characteristics of each type and the applications supported by A-Tune, see Table 2.

    Table 2 Supported workload types and applications



    Workload Characteristic

    Supported Application


    Default type

    The usage of CPU, memory bandwidth, network, and I/O resources is low.



    HTTPS application

    The CPU usage is high.




    • Relational database

      Read: The usage of CPU, memory bandwidth, and network is high.

      Write: The usage of I/O is high.

    • Non-relational database

      The usage of CPU and I/O is high.

    MongoDB, MySQL, PostgreSQL, and MariaDB


    Big data

    The usage of CPU and I/O is high.

    Hadoop and Spark


    Memory-intensive application

    The usage of CPU and memory bandwidth is high.



    Computing- and network-intensive application

    The usage of a single-core CPU is high, and the network usage is high in multi-instance scenarios.



    Computing-intensive application

    The usage of a single-core CPU is high, and the usage of memory bandwidth of some subitems is high.



    Network-intensive application

    The usage of CPU and network is high.



    System in idle state

    The system is in idle state and no applications are running.


    Bug Catching

    Buggy Content

    Bug Description

    Submit As Issue

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    Bug Type
    Specifications and Common Mistakes

    ● Misspellings or punctuation mistakes;

    ● Incorrect links, empty cells, or wrong formats;

    ● Chinese characters in English context;

    ● Minor inconsistencies between the UI and descriptions;

    ● Low writing fluency that does not affect understanding;

    ● Incorrect version numbers, including software package names and version numbers on the UI.


    ● Incorrect or missing key steps;

    ● Missing prerequisites or precautions;

    ● Ambiguous figures, tables, or texts;

    ● Unclear logic, such as missing classifications, items, and steps.


    ● Technical principles, function descriptions, or specifications inconsistent with those of the software;

    ● Incorrect schematic or architecture diagrams;

    ● Incorrect commands or command parameters;

    ● Incorrect code;

    ● Commands inconsistent with the functions;

    ● Wrong screenshots.

    Risk Warnings

    ● Lack of risk warnings for operations that may damage the system or important data.

    Content Compliance

    ● Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions;

    ● Copyright infringement.

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    Bug Catching
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