Android cloud computing power cluster management method and system based on Docker container

Abstract

The invention provides an Android cloud computing power cluster management method and system based on a Docker container, and the method comprises the following steps: firstly, installing a Docker environment on a target server, configuring a container environment supporting the operation of an Android virtual machine, deploying a management program on a main server, and building cross-server SSH connection; secondly, constructing or importing a Docker mirror image of the Android virtual machine, and distributing the Docker mirror image to a target server; the batch creation, destruction and resource limitation of the Android virtual machine container are realized through the Docker container management module; the centralized monitoring module collects and displays the operation states of the target server and the container in real time; the client interaction module realizes remote control, a user accesses the Android virtual machine by connecting a container port through an ADB interface, and configures a QoS priority to guarantee communication flow; abnormal early warning and log analysis functions are provided; according to the method, the Android virtual machine cluster is efficiently deployed and managed in a centralized manner, the resource utilization efficiency is improved, the management process is simplified, and the monitoring and early warning capabilities are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing technology, and in particular to an Android cloud computing cluster management method and system based on a Docker container. Background Art

[0002] With the widespread application of Docker container technology, its advantages in software deployment, resource isolation and efficient management have become increasingly prominent, especially in virtualization and resource-intensive scenarios. Clustered deployment of Android virtual machines is in great demand in areas such as mobile application testing, big data analysis and cloud services, but the traditional Android virtual machine deployment method has the following problems:

[0003] High resource usage: The operation of traditional Android virtual machine clusters consumes a lot of resources and has low operating efficiency.

[0004] Complex management: The installation, parameter adjustment, monitoring, and troubleshooting steps require a high technical threshold. Operation requires a series of components, resulting in high management costs, high learning costs, and many concepts and terms.

[0005] Inconvenient monitoring: The lack of centralized status display and abnormal warning mechanism increases the burden of operation and maintenance.

[0006] Therefore, it is necessary to design an Android cloud computing cluster management method based on Docker containers to solve the above problems. Summary of the invention

[0007] The present invention provides an Android cloud computing cluster management method and system based on Docker container, which are used to solve the technical problems mentioned in the background technology.

[0008] To solve the above problems, the present invention provides the following technical solution: an Android cloud computing cluster management method based on a Docker container, comprising the following steps:

[0009] S100, install the Docker environment on the target server, configure the container environment that supports the operation of the Android virtual machine, deploy the management program on the main server, record the IP address, user name and password of the target server through the configuration file, and establish an SSH remote connection across servers;

[0010] S200, builds or imports the Docker image of the Android virtual machine and distributes it to the target server;

[0011] S300, performing batch creation, destruction and resource restriction operations of Android virtual machine containers through the Docker container management module, the operations specifically include:

[0012] Specify resource limits such as CPU and memory of the container through parameterized configuration;

[0013] Use the port binding mechanism to map the ADB access port of the virtual machine in the container to the external port of the target server;

[0014] S400, collects and displays the operating status information of the target server and container in real time through the centralized monitoring module;

[0015] S500, remotely controlling the Android virtual machine in the container through the client interaction module, wherein the control includes:

[0016] The user connects to the container port of the specified target server through the ADB interface to access the Android virtual machine in the container;

[0017] Configure QoS priority to ensure network bandwidth for ADB and scrcpy communication traffic;

[0018] S600 provides abnormal warning mechanism and log analysis functions, including:

[0019] Configure alert rules for abnormalities such as high CPU usage and insufficient memory, and send abnormal notifications to administrators via email, SMS or Webhook.

[0020] Furthermore, in step S100, the configuration includes:

[0021] Enable KVM hardware acceleration and mount the host's kvm device in privileged mode;

[0022] Configure the container runtime to support GPU hardware acceleration and accelerate graphics rendering by mounting the GPU device file;

[0023] Build a lightweight Docker image and separate the basic dependencies of the Android virtual machine from the simulator running files through layered construction technology to reduce the image size.

[0024] Furthermore, in step S200, the distribution includes:

[0025] Deploy a Docker image repository on the main server to centrally store and distribute Android virtual machine images;

[0026] Synchronize mirror data through the global file system to reduce the time of cross-server mirror allocation;

[0027] Image layering optimization technology is used to compress the image volume to reduce bandwidth usage during image transmission.

[0028] Furthermore, in step S300, it also includes:

[0029] When creating a container, you can use parameterized configuration to specify resource limits such as CPU, memory, and disk I / O for the container.

[0030] Record the container's operating status, port binding relationship, and resource usage, and store the information in a centralized management database;

[0031] When the container is destroyed, the port binding relationship is automatically released and the records in the management database are updated synchronously.

[0032] Furthermore, in step S400, it includes:

[0033] Use Prometheus to regularly collect the operating status data of the target server and its containers, including CPU, memory, disk I / O, network traffic, etc.

[0034] Build a visual monitoring page in Grafana to display the resource usage of each server and container in real time;

[0035] Configure custom alarm rules for abnormal indicators, such as CPU usage exceeding 80%, insufficient memory, or abnormal network traffic.

[0036] Furthermore, it also includes:

[0037] Integrate log aggregation tools to centrally collect and store container operation logs of target servers;

[0038] Analyze the container operation status based on abnormal log data and locate the faulty container or problematic server.

[0039] The monitoring page will simultaneously display the time, type and impact scope of each abnormal record, and provide detailed analysis results.

[0040] Furthermore, the network optimization steps are also included:

[0041] Configure Docker's bridge network mode for the container to provide network isolation between containers;

[0042] Use a load balancer to distribute user requests to avoid single point overload;

[0043] Configure QoS priority guarantee for ADB and scrcpy communication traffic, and allocate their network bandwidth first.

[0044] Compared with the prior art, the present invention has at least the following beneficial effects:

[0045] Based on Docker container technology, the present invention proposes an Android cluster management method and system, which efficiently deploys and centrally manages Android virtual machine clusters, which not only improves resource utilization efficiency, but also simplifies the management process and provides comprehensive monitoring, abnormal warning and log analysis functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 It is a flowchart of the steps of an Android cloud computing cluster management method based on a Docker container of the present invention;

[0048] Figure 2 It is a structural schematic diagram of an Android cloud computing power cluster management system based on Docker container in the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention. It is understood that the drawings are only provided for reference and explanation, and are not used to limit the present invention.

[0050] The present invention discloses an Android cloud computing cluster management method and system based on Docker containers, which is used to efficiently deploy and centrally manage Android virtual machine clusters, solving the problems of resource waste, complex management and insufficient monitoring in traditional deployment methods. The main technical solutions are as follows:

[0051] See also Figure 1 , an Android cloud computing cluster management method based on Docker container, including:

[0052] Step S100: Install the Docker environment and configure a container environment that supports the operation of the Android virtual machine

[0053] 101. Install Docker on the target server:

[0054] Install the Docker engine on the target server and ensure that its version meets the hardware and software requirements for running the Android virtual machine; the Docker container is a standard, lightweight runtime environment for running applications and all their dependencies.

[0055] Configure the Docker runtime environment and enable necessary kernel modules (such as KVM, libvirt, etc.); KVM is an open source virtualization technology and libvirt is an open source API.

[0056] 102. KVM hardware acceleration:

[0057] Enable KVM hardware acceleration and mount the host's KVM device in --privileged mode; --privileged is an option in Docker;

[0058] Pass the host's device files (such as / dev / kvm) to the container to support hardware acceleration of the Android emulator.

[0059] 103. GPU acceleration configuration:

[0060] When configuring a container runtime that supports GPU hardware acceleration, graphics rendering acceleration is achieved by mounting the host machine's GPU device file (such as / dev / dri).

[0061] Use NVIDIA Docker tools such as nvidia-container-runtime to further optimize GPU performance.

[0062] 104. Remote connection configuration:

[0063] Deploy the management program on the main server and record the IP, username and password of the target server through the configuration file;

[0064] Effect: Use the SSH protocol to establish a cross-machine connection between the primary server and the target server to ensure unified management.

[0065] Step S200: Build or import the Android virtual machine Docker image and distribute it to the target server

[0066] 201. Lightweight image construction:

[0067] Build a lightweight Docker image and separate the basic dependencies of the Android virtual machine from the simulator running files through layered construction technology to reduce the image size.

[0068] For example, the base image is defined to contain the core files of the Android system, while the application layer dependencies are built as independent incremental layers.

[0069] 202. Image distribution optimization:

[0070] Deploy a Docker image repository on the main server for centralized storage and distribution of images;

[0071] Use a global file system (such as GlusterFS or Ceph) to synchronize image data and reduce the time for cross-server image distribution;

[0072] Use image layering optimization and compression technology to reduce network bandwidth usage during image transmission.

[0073] 203. Image management:

[0074] Image version control: create separate tags for different versions of Android virtual machines (such as emulators with different API levels);

[0075] Image recycling mechanism: Regularly clean up unused images to free up storage space.

[0076] Effect: By optimizing the image building and distribution process, the image loading speed and transmission efficiency are greatly improved.

[0077] Step S300: Batch management of the creation, destruction and resource limits of Android virtual machine containers

[0078] 301. Create containers in batches:

[0079] Use the Docker container management module to specify container resource limits (such as CPU, memory, disk I / O, etc.) through parameterized configuration;

[0080] Use the port binding mechanism to map the ADB access port of the Android virtual machine in the container to the external port of the target server.

[0081] 302. Container running status record:

[0082] When the container is started, the running status of the container (such as port binding relationship, resource usage, etc.) is stored in the centralized management database;

[0083] Update container operation logs regularly and back up key data for subsequent analysis.

[0084] 303. Container destruction and recycling:

[0085] In the container destruction operation, the port binding relationship is automatically released;

[0086] Effect: Synchronously update the management database to ensure the real-time accuracy of resource allocation information.

[0087] Step S400: Real-time monitoring of the running status of the target server and container

[0088] 401. Data collection and storage:

[0089] Use Prometheus to regularly collect the operating status data of the target server and its containers, including CPU, memory, disk I / O, network traffic, etc.; Prometheus is an open source system monitoring and alert toolkit designed for efficient indicator collection and query.

[0090] The collected monitoring data is stored in the time series database to achieve real-time updates.

[0091] 402. Visual monitoring display:

[0092] Build a visual monitoring page in Grafana to display the resource usage of each target server and container in real time; Grafana is an open source analysis and monitoring platform for visualizing time series data from different data sources.

[0093] Supports custom monitoring panels and adjusts monitoring indicators as needed.

[0094] 403. Alarm rule configuration:

[0095] Configure custom alarm rules for high CPU usage (e.g., over 80%), insufficient memory, or abnormal network traffic;

[0096] Send alert notifications to administrators via email, SMS, or Webhook.

[0097] Effect: Through the centralized monitoring module, the system operation status can be mastered in real time, effectively reducing the operation and maintenance pressure.

[0098] Step S500: Remote control and client interaction 501. ADB interface and remote control: The user connects to the container port of the target server through the ADB interface and accesses the Android virtual machine in the container; ADB is Android Debug Bridge, which is a general command line tool that allows developers to communicate with Android devices for operations such as installing applications, debugging codes, and transferring files.

[0101] Supports remote screen display and operation through the scrcpy tool, which is an open source tool that allows users to mirror and control Android devices through a computer screen.

[0102] 502. Communication optimization:

[0103] Configure QoS priority to ensure network bandwidth for ADB and scrcpy communication traffic. QoS (Quality of Service) priority is a technology used in network management to distinguish different types of traffic and ensure that critical applications obtain the required network resources.

[0104] Use a load balancer (such as HAProxy) to distribute user requests and avoid single point overload.

[0105] Effect: By optimizing remote interactive operations, the user experience is significantly improved.

[0106] Step S600: abnormal warning and log analysis

[0107] 601. Abnormal warning mechanism:

[0108] Configure abnormal alarm rules (such as high CPU usage, insufficient memory, etc.) to promptly notify administrators via email or SMS;

[0109] Integrate the Webhook interface to support linkage with the enterprise's internal monitoring system.

[0110] 602. Log collection and analysis:

[0111] Integrate log aggregation tools (such as ELK Stack) to centrally collect the operation logs of target servers and containers;

[0112] Analyze the container operation status based on the exception log and locate the faulty container or problematic server.

[0113] 603. Abnormal record display:

[0114] The time, type and impact scope of each abnormal record are displayed synchronously on the monitoring page;

[0115] Provides detailed analysis results and repair suggestions.

[0116] Effect: Through comprehensive abnormal warning and log analysis functions, the troubleshooting time is shortened from hours to minutes.

[0117] Through the above method, the beneficial effects of the present invention are:

[0118] High resource utilization efficiency: The use of Docker containerization technology significantly reduces the occupancy rate of CPU, memory and storage resources.

[0119] Simplified management process: Provides centralized management tools to support batch operations, automated deployment, and dynamic resource scheduling.

[0120] Comprehensive monitoring and exception handling: Real-time monitoring based on Prometheus and Grafana, combined with log analysis functions, greatly improves the efficiency of problem location and processing. Prometheus is responsible for efficiently capturing and storing time series data, while Grafana provides a powerful visualization interface, presenting this data in real time through charts and dashboards, helping administrators quickly identify and respond to potential problems and ensure service stability and performance.

[0121] Network performance optimization: QoS priority and load balancing technology ensure communication performance in high-concurrency scenarios.

[0122] See also Figure 2 The present invention also discloses an Android cloud computing cluster management system based on Docker container, including the following modules:

[0123] Hardware deployment module 11: includes a target server and a main server. The target server is used to install and run a container environment that supports Android virtual machines. The main server is used to deploy a management program, record the hardware information of the target server through a configuration file, including the IP address, user name and password, and establish an SSH remote connection across servers.

[0124] Storage and distribution module 12: including a storage device for building or importing an Android virtual machine image, and a distribution hardware component for distributing the built or imported Android virtual machine image to a target server;

[0125] The container management module 13 is used to implement the following functions through parameterized configuration: resource allocation unit: specifying resource limits such as CPU and memory of the container through hardware configuration; port mapping unit: using the port binding mechanism supported by the hardware to map the ADB access port of the virtual machine in the container to the external port of the target server;

[0126] Centralized monitoring module 14: including real-time operation status collection hardware components and status display hardware components, used to collect the operation status information of the target server and container through hardware, including CPU utilization, memory usage, network traffic, etc., and visualize the collected information;

[0127] Remote interaction module 15: includes a dedicated hardware interface for realizing remote control of the Android virtual machine in the container, wherein the control includes: a user connects to the container port of the target server through the hardware-supported ADB interface to access the Android virtual machine in the container, configures the hardware-supported QoS priority, and ensures the network bandwidth of the ADB and scrcpy communication traffic;

[0128] The abnormal warning and log analysis module 16 includes a hardware alarm unit and a log processing unit, which are used to implement the following functions: configure alarm rules for abnormalities such as high CPU usage and insufficient memory, and send abnormal notifications to administrators through the hardware sending interface; parse and analyze the target server and container operation logs through the hardware log collection unit.

[0129] In the specification and claims of the present application, the words "include / comprise" and the words "have / include" and their variations are used to specify the existence of stated features, values, steps or components, but do not exclude the existence or addition of one or more other features, values, steps, components or combinations thereof.

[0130] Some features of the present invention are described in different embodiments for clarity of description, however, these features may also be described in combination in a single embodiment. Conversely, some features of the present invention are described in only a single embodiment for brevity, however, these features may also be described in different embodiments alone or in any suitable combination.

[0131] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for managing an Android cloud computing cluster based on a Docker container, characterized in that: The following steps are involved: S100, install the Docker environment on the target server, configure the container environment that supports the operation of the Android virtual machine, deploy the management program on the main server, record the IP address, user name and password of the target server through the configuration file, and establish an SSH remote connection across servers; S200, builds or imports the Docker image of the Android virtual machine and distributes it to the target server; S300, performing batch creation, destruction and resource restriction operations of Android virtual machine containers through the Docker container management module, the operations specifically include: Specify resource limits for CPU, memory, and disk I / O of a container through parameterized configuration; Use the port binding mechanism to map the ADB access port of the virtual machine in the container to the external port of the target server; S400, collects and displays the operating status information of the target server and container in real time through the centralized monitoring module; S500, remotely controlling the Android virtual machine in the container through the client interaction module, wherein the control includes: The user connects to the container port of the specified target server through the ADB interface to access the Android virtual machine in the container; Configure QoS priority to ensure network bandwidth for ADB and scrcpy communication traffic; S600 provides abnormal warning mechanism and log analysis functions, including: Configure alert rules for high CPU usage, insufficient memory, and insufficient disk, and send exception notifications to administrators via email, SMS, or Webhook.

2. According to the Android cloud computing cluster management method based on Docker container according to claim 1, it is characterized in that: In step S100, the configuration includes: Enable KVM hardware acceleration and mount the host's kvm device in privileged mode; Configure the container runtime to support GPU hardware acceleration and accelerate graphics rendering by mounting the GPU device file; Build a lightweight Docker image and separate the basic dependencies of the Android virtual machine from the simulator running files through layered construction technology to reduce the image size.

3. The Android cloud computing cluster management method based on Docker container according to claim 2 is characterized in that: In step S200, the distribution includes: Deploy a Docker image repository on the main server to centrally store and distribute Android virtual machine images; Synchronize mirror data through the global file system to reduce the time of cross-server mirror allocation; Image layering optimization technology is used to compress the image volume to reduce bandwidth usage during image transmission.

4. The Android cloud computing cluster management method based on Docker container according to claim 1 is characterized in that: In step S300, it also includes: When creating a container, you can use parameterized configuration to specify the container's CPU, memory, and disk I / O resource limits. Record the container's operating status, port binding relationship, and resource usage, and store the information in a centralized management database; When the container is destroyed, the port binding relationship is automatically released and the records in the management database are updated synchronously.

5. The Android cloud computing cluster management method based on Docker container according to claim 4 is characterized in that: In step S400, it includes: Use Prometheus to regularly collect the operating status data of the target server and its containers, including CPU, memory, disk I / O, and network traffic; Build a visual monitoring page in Grafana to display the resource usage of each server and container in real time; Configure custom alarm rules for abnormal indicators, such as CPU usage exceeding 80%, insufficient memory, or abnormal network traffic.

6. The Android cloud computing cluster management method based on Docker container according to claim 5 is characterized in that: Further including: Integrate log aggregation tools to centrally collect and store container operation logs of target servers; Analyze the container operation status based on abnormal log data and locate the faulty container or problematic server. The monitoring page will simultaneously display the time, type and impact scope of each abnormal record, and provide detailed analysis results.

7. The Android cloud computing cluster management method based on Docker container according to claim 1 is characterized in that: It also includes steps for network optimization: Configure Docker's bridge network mode for the container to provide network isolation between containers; Use a load balancer to distribute user requests to avoid single point overload; Configure QoS priority guarantee for ADB and scrcpy communication traffic, and allocate their network bandwidth first.

8. An Android cloud computing cluster management system based on Docker container, characterized in that: Includes the following modules: Hardware deployment module: includes the target server and the main server. The target server is used to install and run the container environment that supports the Android virtual machine. The master server is used to deploy the management program, record the hardware information of the target server through the configuration file, including the IP address, user name and password, and establish a cross-server SSH remote connection; Storage and distribution module: including storage for building or importing Android virtual machine images, and distribution hardware components for distributing the built or imported Android virtual machine images to target servers; The container management module is used to implement the following functions through parameterized configuration: Resource allocation unit: specifies the resource limits of the container's CPU, memory, and disk I / O through hardware configuration; Port mapping unit: uses the port binding mechanism supported by the hardware to map the ADB access port of the virtual machine in the container to the external port of the target server; Centralized monitoring module: including real-time operation status collection hardware components and status display hardware components, which are used to collect the operation status information of the target server and container through hardware, including CPU utilization, memory usage, network traffic, and visualize the collected information; Remote interaction module: including a dedicated hardware interface for realizing remote control of the Android virtual machine in the container, wherein the control includes: the user connects to the container port of the target server through the hardware-supported ADB interface to access the Android virtual machine in the container, configures the hardware-supported QoS priority, and ensures the network bandwidth of the ADB and scrcpy communication traffic; The abnormal warning and log analysis module includes a hardware alarm unit and a log processing unit, which are used to implement the following functions: configure alarm rules for abnormalities such as high CPU usage, insufficient memory, and insufficient disk, and send abnormal notifications to administrators through the hardware sending interface; parse and analyze the target server and container operation logs through the hardware log collection unit.

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