Resource processing method and device for heterogeneous target range and nonvolatile storage medium

Abstract

The invention discloses a resource processing method and device for a heterogeneous target range and a nonvolatile storage medium. The method comprises the following steps: acquiring respective processor architecture information of a plurality of target range systems; based on the processor architecture information, virtual machines corresponding to the multiple target range systems are determined; respectively deploying application programs corresponding to the plurality of target range systems in the virtual machine, and respectively packaging the application programs in containers corresponding to the plurality of target range systems; and carrying out resource processing in the containers corresponding to the plurality of target range systems. The technical problem that target range systems of different technical architectures are incompatible is solved.

Description

Technical Field

[0001] This invention relates to the field of network security testing technology, and more specifically, to a resource processing method, apparatus, and non-volatile storage medium for heterogeneous test ranges. Background Technology

[0002] In today's rapidly developing information technology field, with the widespread application of systems with diverse technical architectures across various industries, traditional management methods face unprecedented challenges in the coexistence of systems with different architectures such as C86, X86, and ARM. While these systems each exhibit unique advantages in embedded devices, servers, desktop operating systems, and specific high-performance computing areas, the inherent differences in their hardware and software architectures lead to serious compatibility issues, significantly increasing the complexity of management and maintenance. Especially in heterogeneous test environments (i.e., testing or operating environments containing multiple technical architectures), effectively integrating and managing these heterogeneous systems has become a critical problem that urgently needs to be solved.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a resource processing method, apparatus, and non-volatile storage medium for heterogeneous test ranges, to at least solve the technical problem of incompatibility between test range systems with different technical architectures.

[0005] According to one aspect of the present invention, a resource processing method for heterogeneous target ranges is provided, comprising: acquiring processor architecture information of each of a plurality of target range systems; determining virtual machines corresponding to each of the plurality of target range systems based on the processor architecture information; deploying applications corresponding to each of the plurality of target range systems in the virtual machines, and encapsulating them in containers corresponding to each of the plurality of target range systems; and performing resource processing in the containers corresponding to each of the plurality of target range systems.

[0006] Optionally, multiple target adapters are determined in a preset adapter library; based on the multiple target adapters, multiple adaptation layers are constructed, wherein the adaptation layers are used to transmit data between the containers corresponding to the multiple target range systems.

[0007] Optionally, multiple target adapters are determined in a preset adapter library, including: extracting feature vectors of the containers corresponding to the multiple target range systems respectively; calculating the similarity between the containers corresponding to the multiple target range systems based on the feature vectors; and determining multiple target adapters in the preset adapter library based on the similarity.

[0008] Optionally, middleware technology is used to determine message queues between containers corresponding to the multiple target range systems, wherein the message queues are used for asynchronous communication between the containers corresponding to the multiple target range systems.

[0009] Optionally, resource processing is performed in the containers corresponding to each of the multiple target range systems, including: extracting feature vectors of the containers corresponding to each of the multiple target range systems; calculating the weights of the containers corresponding to each of the multiple target range systems based on the feature vectors; allocating resources to the containers corresponding to each of the multiple target range systems based on the weights to obtain the running resources corresponding to each of the multiple target range systems; and processing the running resources in the containers corresponding to each of the multiple target range systems.

[0010] Optionally, the running status of the containers corresponding to each of the multiple target range systems can be detected in real time, including running memory usage, memory consumption, and network traffic; based on the running status, the anomaly detection results of the containers corresponding to each of the multiple target range systems can be determined; based on the anomaly detection results, a prompt signal can be generated and sent to the user terminal.

[0011] According to another aspect of the present invention, a resource processing apparatus for a heterogeneous target range is also provided, comprising: an acquisition module for acquiring processor architecture information of each of a plurality of target range systems; a determination module for determining virtual machines corresponding to each of the plurality of target range systems based on the processor architecture information; an encapsulation module for deploying applications corresponding to each of the plurality of target range systems in the virtual machines and encapsulating them in containers corresponding to each of the plurality of target range systems; and a processing module for performing resource processing in the containers corresponding to each of the plurality of target range systems.

[0012] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium including a stored program, wherein, when the program is running, the device where the non-volatile storage medium is located is controlled to execute any of the above-described resource processing methods for heterogeneous test ranges.

[0013] According to another aspect of the present invention, a computer device is also provided, the computer device including a processor, the processor being configured to run a program, wherein the program, when running, executes any of the above-described resource processing methods for heterogeneous target ranges.

[0014] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements any one of the above-described resource processing methods for heterogeneous target ranges.

[0015] In this embodiment of the invention, a resource processing method for heterogeneous test ranges is employed. This method involves acquiring the processor architecture information of multiple target test range systems; determining the virtual machines corresponding to each of the multiple target test range systems based on the processor architecture information; deploying the applications corresponding to each of the multiple target test range systems within the virtual machines, and encapsulating them in containers corresponding to each of the multiple target test range systems; and performing resource processing within the containers corresponding to each of the multiple target test range systems. This achieves the goal of unified management and efficient resource scheduling of heterogeneous test range systems, thereby improving resource utilization and system operating efficiency, and ultimately solving the technical problem of incompatibility between test range systems with different technical architectures. Attached Figure Description

[0016] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0017] Figure 1 A hardware block diagram of a computer terminal for implementing a resource processing method for heterogeneous test ranges is shown.

[0018] Figure 2 This is a schematic flowchart of a resource processing method for heterogeneous test ranges provided in an embodiment of the present invention;

[0019] Figure 3 This is a schematic diagram of a feature matching process provided by an optional embodiment of the present invention;

[0020] Figure 4 This is a flowchart of a unified management system for heterogeneous scenarios provided by an optional embodiment of the present invention;

[0021] Figure 5 This is a structural block diagram of a resource processing device for heterogeneous test ranges provided according to an embodiment of the present invention. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] According to an embodiment of the present invention, a resource processing method for heterogeneous test ranges is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0025] The method embodiment provided in Embodiment 1 of this application can be executed on a mobile terminal, computer terminal, or similar computing device. Figure 1 A hardware block diagram of a computer terminal for implementing a resource processing method for heterogeneous test ranges is shown. Figure 1 As shown, the computer terminal 10 may include one or more processors (shown as 102a, 102b, ..., 102n in the figure) (the processor may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of a BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the aforementioned electronic device. For example, computer terminal 10 may also include... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0026] It should be noted that the aforementioned one or more processors and / or other data processing circuits are generally referred to herein as "data processing circuits". These data processing circuits may be implemented wholly or partially as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuits may be a single, independent processing module, or may be wholly or partially integrated into any other element in the computer terminal 10. As involved in the embodiments of this application, the data processing circuits serve as processor control (e.g., selection of a variable resistor termination path connected to an interface).

[0027] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the resource processing method for heterogeneous test ranges in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, thereby implementing the above-mentioned application program for resource processing in heterogeneous test ranges. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor, and these remote memories can be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0028] The display may be, for example, a touchscreen liquid crystal display (LCD) that allows the user to interact with the user interface of the computer terminal 10.

[0029] Figure 2 This is a flowchart illustrating a resource processing method for heterogeneous test ranges provided by an embodiment of the present invention, as shown below. Figure 2 As shown, the method includes the following steps:

[0030] Step S201: Obtain the processor architecture information of each of the multiple target range systems.

[0031] In this step, different processor architectures, such as C86, x86, and ARM, have their specific instruction sets, data paths, and performance characteristics. These differences directly affect system compatibility, resource utilization efficiency, and management methods. Therefore, before implementing unified management, it is essential to accurately identify the processor architecture used by each test range system in order to formulate appropriate virtualization strategies and adaptation layer designs.

[0032] Step S202: Based on the processor architecture information, determine the virtual machines corresponding to each of the multiple target range systems.

[0033] In this step, based on the collected processor architecture information, the most suitable virtualization technology is selected to create virtual machines. Different virtualization technologies have varying degrees of compatibility with processor architectures. A Virtual Machine Monitor (VMM, also known as a Hypervisor) abstracts physical resources from different architectures into unified virtual resources, enabling unified management and scheduling of systems with multiple architectures. The core of this technology lies in resource isolation and sharing, allowing multiple virtual machines to run in parallel on the same physical server without interfering with each other. VMware ESXi and Microsoft Hyper-V are widely used hypervisors that support multiple processor architectures such as x86 and ARM, and provide highly flexible resource configuration capabilities. Based on the characteristics and requirements of each system, the hardware resources of the virtual machines are configured, including CPU, memory, hard disk, and network resources. This ensures that the virtual machine configuration meets the operational requirements of the target system, while also considering efficient resource utilization and overall system stability.

[0034] Step S203: In the virtual machine, deploy the applications corresponding to each of the multiple target range systems and encapsulate them in the containers corresponding to each of the multiple target range systems.

[0035] In this step, after the virtual machines are ready, the next step is to deploy the target range system's applications on each virtual machine. Since heterogeneous ranges may contain various types of applications, such as web servers, database services, and security proxies, these applications need to be deployed to the virtual machines best suited for their operation, based on specific requirements and compatibility considerations. To further improve resource utilization efficiency and application isolation, container technology is used to encapsulate each application. Containers are a lightweight, portable encapsulation method used to package applications and all their required dependencies, including library files, environment variables, and configuration files, ensuring that the application can run consistently in any environment.

[0036] Container technologies (such as Docker) can encapsulate operating systems with different architectures into independent container instances, enabling cross-platform application deployment and management. Compared to traditional virtual machines, containers are more lightweight, start up faster, and consume fewer resources. Container technology achieves efficient resource utilization and rapid deployment capabilities by sharing the host machine's operating system kernel. Whether in development, testing, or production environments, containers can ensure application consistency and reduce problems caused by environmental differences. Containers can run on any platform that supports Docker, whether it's a local machine, a cloud server, or an edge device. Containers are ideal for building microservice architectures, where each service can be deployed, scaled, and updated independently, improving system flexibility and maintainability.

[0037] Step S204: Resource processing is performed in the containers corresponding to each of the multiple target range systems.

[0038] In this step, container technology is used to encapsulate applications on each system, independently allocating and optimizing their CPU, memory, and other resources. Containerization ensures application environment consistency, allows dynamic adjustment of resource quotas to adapt to load changes, and avoids conflicts between applications through resource isolation, improving system stability and resource utilization efficiency. With the help of container orchestration tools, such as Kubernetes, fine-grained resource management and automated scheduling can be achieved, ensuring the high-performance operation of critical applications while facilitating monitoring of resource consumption and timely performance optimization and fault recovery. This strategy demonstrates excellent application flexibility and system controllability in heterogeneous environments, providing efficient resource management and operational support for testbed environments.

[0039] Through the above steps, the goal of unified management and efficient resource scheduling of heterogeneous test range systems is achieved, thereby realizing the technical effects of improving resource utilization and system operating efficiency, and thus solving the technical problem of incompatibility between test range systems with different technical architectures.

[0040] As an optional embodiment, multiple target adapters are determined in a preset adapter library; based on the multiple target adapters, multiple adaptation layers are constructed, wherein the adaptation layers are used to transmit data between the containers corresponding to the multiple target range systems.

[0041] Optionally, the adaptation layer is used to convert interfaces and protocols between systems with different architectures, ensuring seamless transmission of data and control commands. The adaptation layer typically includes various adapters and converters to handle compatibility issues between different systems. For example, in enterprise integration, the ESB (Enterprise Service Bus) is a typical adaptation layer, which can be configured and programmed to achieve data exchange and business process integration between different systems. This includes converting one communication protocol to another, such as from HTTP to SOAP; converting one data format to another, such as from JSON to XML; and adapting the API interface of one system to the API interface of another system, achieving interface standardization and unification.

[0042] Building multiple adaptation layers first requires selecting adapters from the adapter library that match the target system architecture. These adapters can handle the interfaces, protocols, and data formats under a specific architecture, ensuring seamless data conversion and transmission. Next, the selected target adapters are used to build the adaptation layer. This layer sits between containerized applications, operating above the service bus, and is responsible for data encoding, decoding, and format adjustment, ensuring that data interaction between different containers follows a unified standard, even if these containers are running on virtual machines based on different architectures. Through dynamic loading and configuration of adapters, the adaptation layer can intelligently identify and adapt to data transmission requirements, enabling efficient communication between containers across architectures while ensuring data consistency and security. It supports unified management and operation in heterogeneous testbed scenarios, improving the overall interoperability and flexibility of the system.

[0043] As an optional embodiment, multiple target adapters are determined in a preset adapter library, including: extracting feature vectors of the containers corresponding to each of the multiple target range systems; calculating the similarity between the containers corresponding to each of the multiple target range systems based on the feature vectors; and determining multiple target adapters in the preset adapter library based on the similarity.

[0044] Optionally, firstly, feature vectors are extracted from the containers corresponding to each target range system. These feature vectors may include key attributes such as the container's hardware requirements, software environment, data format, and communication protocol, used to describe the container's basic characteristics and communication needs. Next, a hybrid feature similarity algorithm is used to calculate the similarity between different containers. This calculation combines the container's hardware and software features with discrete features, dynamically adjusting the weights of the similarity calculation through an empirical optimization coefficient α, thereby obtaining a comprehensive similarity score. The mathematical formula for the hybrid feature similarity algorithm is as follows:

[0045]

[0046] Among them, A i B i For the standardized eigenvector components, F A F B Let be a discrete feature set, and α be an empirical optimization coefficient. Based on the calculated similarity, the most suitable adapter can be selected from a pre-defined adapter library. The adapter library contains various adapters that can handle different data formats and communication protocols. Higher similarity means that the two containers are closer in their communication needs, so the system will prioritize the adapter that can meet this proximity requirement, and then build the adaptation layer.

[0047] Figure 3 This is a schematic diagram of a feature matching process provided by an optional embodiment of the present invention. For example... Figure 3As shown, firstly, feature information, including hardware requirements and software environment, is collected from the containers of the target system. These features are then encoded into vectors. Next, a hybrid feature similarity algorithm is used to calculate the similarity between containers. This algorithm considers both continuous and discrete features and achieves accurate matching through adjustable optimization coefficients. Based on the similarity results, decision tree classification technology determines the container category, guiding the selection and loading of adapters, thus enabling seamless data exchange and efficient system collaboration.

[0048] As an optional embodiment, middleware technology is used to determine message queues between containers corresponding to multiple target range systems, wherein the message queues are used for asynchronous communication between containers corresponding to multiple target range systems.

[0049] Optionally, message queue-based middleware technology can be used to achieve loosely coupled communication between systems with different architectures. Through a service bus, systems can communicate asynchronously, improving system scalability and reliability. Common service bus products include Apache Kafka and RabbitMQ. Systems communicate through message queues instead of directly calling each other's interfaces, reducing system coupling. Message queues can buffer large numbers of messages; when system load increases, processing capacity can be increased by adding consumers. Message queues guarantee reliable message delivery; even if a system is temporarily unavailable, messages will not be lost and processing will continue once the system recovers.

[0050] Specifically, the system first identifies and determines the communication requirements between containers in multiple test range systems. Through middleware technology, these requirements are mapped to appropriate message queues to support inter-container communication. Message queues not only buffer and store messages, ensuring no message loss even when the receiver is unavailable, but also categorize messages according to priority and type, enhancing the flexibility and reliability of communication.

[0051] As an optional embodiment, resource processing is performed in the containers corresponding to each of the multiple target range systems, including: extracting feature vectors of the containers corresponding to each of the multiple target range systems; calculating the weights of the containers corresponding to each of the multiple target range systems based on the feature vectors; allocating resources to the containers corresponding to each of the multiple target range systems based on the weights to obtain the running resources corresponding to each of the multiple target range systems; and processing the running resources in the containers corresponding to each of the multiple target range systems.

[0052] Optionally, firstly, the system extracts feature vectors reflecting the characteristics and needs of each container, including key information such as hardware resource usage, software environment configuration, and network requirements. Then, using a dynamic weight allocation model, the container's features are transformed into quantified weights. This process considers the actual needs and priorities of the containers at a specific moment, ensuring more intelligent and fair resource allocation. The mathematical formula for the dynamic weight allocation model is as follows:

[0053]

[0054] Among them, I(f) i The information entropy is defined as , and k = 0.5 is a smoothing factor. Based on the calculated weights, the system dynamically adjusts its resource allocation strategy, allocating the most suitable resources for each container's operation, including CPU, memory, storage, and network bandwidth. This results in customized runtime resources that meet the container's operational needs while fully utilizing system resources and avoiding waste. Finally, each container is initialized and configured according to its allocated runtime resources to ensure optimal operation. The system also supports real-time monitoring and dynamic adjustment of container resource usage, improving the overall operational efficiency and resource utilization of the heterogeneous testbed. This series of operations comprehensively utilizes data science and automation technologies, demonstrating the key role of the technological innovations presented in the disclosure document in improving the management efficiency of heterogeneous systems.

[0055] As an optional embodiment, the running status of the containers corresponding to each of the multiple target range systems is detected in real time, wherein the running status includes running memory usage, memory consumption, and network traffic; based on the running status, the anomaly detection results of the containers corresponding to each of the multiple target range systems are determined; based on the anomaly detection results, a prompt signal is generated and sent to the user terminal.

[0056] Optionally, a unified management platform can monitor the operational status of systems with different architectures in real time, including key metrics such as CPU utilization, memory usage, and network traffic. Centralized monitoring not only promptly identifies performance bottlenecks and potential problems in the system but also generates detailed reports and charts to assist operations and maintenance personnel in analysis and decision-making. For example, Prometheus and Grafana are commonly used monitoring tool combinations that provide powerful monitoring and visualization capabilities. Real-time collection and display of key system performance indicators ensures stable system operation. When system anomalies occur, alarm notifications are automatically sent to remind operations and maintenance personnel to handle them promptly. Historical monitoring data is saved for convenient trend analysis and troubleshooting.

[0057] Specifically, by continuously monitoring several key metrics of containers, such as memory usage, resource consumption, and network traffic, the system can monitor the container's operational status in real time. These metrics reflect the container's resource requirements and utilization, as well as its interaction with the external environment. Based on the collected operational status data, the system employs anomaly detection algorithms to analyze the performance trends of each container and identify abnormal patterns, such as sudden spikes in resource usage or abnormal fluctuations in network traffic. These patterns may indicate application failures within the container or external attacks. Once an anomaly is detected, the system immediately generates corresponding alert signals and sends these signals to the operations personnel's user terminals, such as mobile phones and computers, via secure and rapid communication methods, including email, SMS, or in-app notifications, ensuring that anomalies are detected and addressed promptly.

[0058] Furthermore, automated scripts and tools can be used to automate the configuration, updates, and fault recovery of systems with different architectures, thereby reducing operational costs and human error. Automated operations and maintenance tools such as Ansible, Puppet, and Chef can help operations personnel write automated scripts to achieve Infrastructure as Code (IaC), improving operational efficiency and consistency. Automated scripts replace manual work.

[0059] As an optional embodiment, a unified management system for heterogeneous scenarios is also provided. Figure 4 This is a flowchart of a unified management system for heterogeneous scenarios provided by an optional embodiment of the present invention, such as... Figure 4 As shown, firstly, the high-performance x86 server serves as the master node. With its powerful computing power and high availability, it plays the central role of the unified management platform, responsible for overall scheduling and data processing, ensuring that the system can cope with large-scale concurrent tasks.

[0060] At the storage level, high-speed SSDs are used to store critical data such as virtual machine and container images. Combined with distributed storage solutions like Ceph, this not only improves data read / write speeds and reduces I / O latency but also enhances data reliability and system scalability. On the network side, by building a high-speed local area network and utilizing SDN technology, supplemented by VLAN and QoS policies, efficient communication between heterogeneous systems and high bandwidth, low latency guarantees for critical services are achieved.

[0061] The virtualization layer utilizes mature technologies such as VMware and KVM, along with the Docker container engine, to abstract resources, allowing diverse operating systems and applications to run in parallel on the same platform while ensuring environment isolation and improving security. The middleware layer leverages software such as Apache Kafka and RabbitMQ to build an adaptation layer and service bus, acting as a communication bridge between heterogeneous systems and ensuring cross-architecture service exchange and data consistency.

[0062] Develop or select existing middleware software (such as Apache Kafka, RabbitMQ) to build an adaptation layer and service bus. Middleware acts as a bridge between different systems and applications, providing functions such as message passing, data transformation, and protocol adaptation. Through middleware, seamless integration between heterogeneous systems can be achieved, improving system interoperability and maintainability.

[0063] Establish a high-speed local area network (LAN) to ensure efficient communication between systems with different architectures. Utilize high-performance switches and routers, combined with SDN (Software-Defined Networking) technology, to achieve flexible network management and optimization. Through VLAN (Virtual Local Area Network) and QoS (Quality of Service) policies, guarantee the bandwidth requirements and low latency requirements of critical services.

[0064] The unified management platform integrates monitoring, automated operation and maintenance, and security management functions. Its built-in AI and machine learning technologies further enhance the ability of intelligent decision-making and predictive maintenance, providing operation and maintenance personnel with a comprehensive and centralized control interface, greatly simplifying daily operation and maintenance work, and improving the overall security and stability of the system.

[0065] Figure 4 The system showcased employs a layered architecture, consisting of infrastructure, virtualization, middleware, and application layers from bottom to top. These layers are tightly connected through standardized interfaces, ensuring independent development and maintenance of each layer while maintaining overall system consistency. This architecture not only promotes modularity, facilitating technology iteration and functional expansion, but also reduces system complexity and maintenance costs, enhancing overall reliability and scalability, enabling it to efficiently adapt to complex and ever-changing enterprise application scenarios. Each functional module is developed and tested independently, facilitating subsequent upgrades and expansions. Modular design not only improves development efficiency but also reduces system complexity and maintenance costs. Through clearly defined interfaces and specifications, each module can evolve independently while maintaining overall system consistency. Furthermore, modular design facilitates the introduction of new technologies and components to adapt to evolving business needs and technological trends.

[0066] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0067] Through the above description of the embodiments, those skilled in the art can clearly understand that the resource processing method for heterogeneous test ranges according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0068] According to embodiments of the present invention, an apparatus for implementing the above-described resource processing method for heterogeneous test ranges is also provided. Figure 5 This is a structural block diagram of a resource processing device for heterogeneous target ranges provided according to an embodiment of the present invention, such as... Figure 5 As shown, the device includes: an acquisition module 51, a determination module 52, an encapsulation module 53, and a processing module 54. The device will be described below.

[0069] The acquisition module 51 is used to acquire the processor architecture information of each of the multiple target range systems.

[0070] The determination module 52, connected to the acquisition module 51, is used to determine the virtual machines corresponding to each of the multiple target range systems based on processor architecture information.

[0071] The encapsulation module 53, connected to the determination module 52, is used to deploy the applications corresponding to each of the multiple target range systems in the virtual machine, and encapsulate them in the containers corresponding to each of the multiple target range systems.

[0072] The processing module 54, connected to the encapsulation module 53, is used to process resources in the containers corresponding to the multiple target range systems.

[0073] It should be noted that the acquisition module 51, determination module 52, encapsulation module 53, and processing module 54 mentioned above correspond to steps S201 to S204 in the embodiments. Multiple modules implement the same instances and application scenarios as their corresponding steps, but are not limited to the content disclosed in the above embodiments. It should also be noted that the above modules, as part of the device, can run in the computer terminal 10 provided in the embodiments.

[0074] Embodiments of the present invention may provide a computer device. Optionally, in this embodiment, the computer device may be located in at least one of a plurality of network devices in a computer network. The computer device includes a memory and a processor.

[0075] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the resource processing method and apparatus for heterogeneous test ranges in this embodiment of the invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby realizing the aforementioned resource processing method for heterogeneous test ranges. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to a computer terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0076] The processor can access information and applications stored in memory via a transmission device to perform the following steps: obtain processor architecture information for each of the multiple target range systems; determine the virtual machines corresponding to each of the multiple target range systems based on the processor architecture information; deploy the applications corresponding to each of the multiple target range systems in the virtual machines and encapsulate them in containers corresponding to each of the multiple target range systems; and perform resource processing in the containers corresponding to each of the multiple target range systems.

[0077] Optionally, the processor may also execute program code that performs the following steps: determining multiple target adapters in a preset adapter library; and constructing multiple adaptation layers based on the multiple target adapters, wherein the adaptation layers are used to transmit data between the containers corresponding to the multiple target range systems.

[0078] Optionally, the processor may also execute program code that performs the following steps: determining multiple target adapters in a preset adapter library, including: extracting feature vectors of the containers corresponding to the multiple target range systems respectively; calculating the similarity between the containers corresponding to the multiple target range systems based on the feature vectors; and determining multiple target adapters in the preset adapter library based on the similarity.

[0079] Optionally, the processor may also execute program code that performs the following steps: using middleware technology, determining message queues between containers corresponding to each of the multiple target range systems, wherein the message queues are used for asynchronous communication between containers corresponding to each of the multiple target range systems.

[0080] Optionally, the processor may also execute program code that performs the following steps: resource processing in containers corresponding to each of the multiple target range systems, including: extracting feature vectors of containers corresponding to each of the multiple target range systems; calculating weights of containers corresponding to each of the multiple target range systems based on the feature vectors; allocating resources to containers corresponding to each of the multiple target range systems based on the weights to obtain runtime resources corresponding to each of the multiple target range systems; and processing the runtime resources in containers corresponding to each of the multiple target range systems.

[0081] Optionally, the processor may also execute program code that performs the following steps: real-time detection of the running status of the containers corresponding to each of the multiple target range systems, wherein the running status includes running memory usage, memory consumption, and network traffic; based on the running status, determining the anomaly detection results of the containers corresponding to each of the multiple target range systems; and based on the anomaly detection results, generating a prompt signal and sending it to the user terminal.

[0082] This invention provides a resource processing method for heterogeneous test ranges. By acquiring the processor architecture information of multiple target test range systems; determining the corresponding virtual machines for each target test range system based on the processor architecture information; deploying the corresponding applications for each target test range system within the virtual machines, and encapsulating them in their respective containers; and performing resource processing within these containers, the method achieves unified management and efficient resource scheduling of heterogeneous test range systems. This improves resource utilization and system operating efficiency, thereby resolving the technical problem of incompatibility between test range systems with different technical architectures.

[0083] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing the hardware related to the terminal device. The program can be stored in a non-volatile storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc.

[0084] Embodiments of the present invention also provide a non-volatile storage medium. Optionally, in this embodiment, the aforementioned non-volatile storage medium can be used to store the program code executed by the resource processing method for heterogeneous test ranges provided in the above embodiments.

[0085] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals.

[0086] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: obtaining processor architecture information for each of the multiple target range systems; determining virtual machines corresponding to each of the multiple target range systems based on the processor architecture information; deploying applications corresponding to each of the multiple target range systems in the virtual machines, and encapsulating them in containers corresponding to each of the multiple target range systems; and performing resource processing in the containers corresponding to each of the multiple target range systems.

[0087] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining multiple target adapters in a preset adapter library; constructing multiple adaptation layers based on the multiple target adapters, wherein the adaptation layers are used for data transmission between the containers corresponding to the multiple target range systems.

[0088] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining multiple target adapters in a preset adapter library, including: extracting feature vectors of the containers corresponding to the multiple target range systems respectively; calculating the similarity between the containers corresponding to the multiple target range systems respectively based on the feature vectors; and determining multiple target adapters in the preset adapter library based on the similarity.

[0089] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: determining message queues between containers corresponding to multiple target range systems through middleware technology, wherein the message queues are used for asynchronous communication between containers corresponding to multiple target range systems.

[0090] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: resource processing in containers corresponding to each of the multiple target range systems, including: extracting feature vectors of containers corresponding to each of the multiple target range systems; calculating weights of containers corresponding to each of the multiple target range systems based on the feature vectors; allocating resources to containers corresponding to each of the multiple target range systems based on the weights to obtain runtime resources corresponding to each of the multiple target range systems; and processing the runtime resources in containers corresponding to each of the multiple target range systems.

[0091] Optionally, in this embodiment, the non-volatile storage medium is configured to store program code for performing the following steps: real-time detection of the operating status of the containers corresponding to each of the multiple target range systems, wherein the operating status includes running memory usage, memory occupation, and network traffic; based on the operating status, determining the anomaly detection results of the containers corresponding to each of the multiple target range systems; and based on the anomaly detection results, generating a prompt signal and sending it to the user terminal.

[0092] Embodiments of the present invention also provide a computer program product, including a computer program. Optionally, in this embodiment, when the computer program is executed by a processor, it can: acquire processor architecture information of each of the multiple target range systems; determine virtual machines corresponding to each of the multiple target range systems based on the processor architecture information; deploy applications corresponding to each of the multiple target range systems in the virtual machines, and encapsulate them in containers corresponding to each of the multiple target range systems; and perform resource processing in the containers corresponding to each of the multiple target range systems.

[0093] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0094] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0095] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0096] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0097] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0098] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a non-volatile storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0099] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A resource processing method for heterogeneous target ranges, characterized in that, include: Obtain processor architecture information for each of the multiple target range systems; Based on the processor architecture information, determine the virtual machines corresponding to each of the multiple target range systems; In the virtual machine, the applications corresponding to each of the multiple target range systems are deployed and encapsulated in the containers corresponding to each of the multiple target range systems. Resource processing is performed in the containers corresponding to each of the multiple target range systems.

2. The method according to claim 1, characterized in that, Also includes: Identify multiple target adapters from the pre-defined adapter library; Based on the multiple target adapters, multiple adaptation layers are constructed, wherein the adaptation layers are used to transmit data between the containers corresponding to the multiple target range systems.

3. The method according to claim 2, characterized in that, The step of determining multiple target adapters in a preset adapter library includes: Extract the feature vectors of the containers corresponding to each of the multiple target range systems; Based on the feature vectors, the similarity between the containers corresponding to each of the multiple target range systems is calculated. Based on the similarity, the plurality of target adapters are determined in a preset adapter library.

4. The method according to claim 1, characterized in that, Also includes: Using middleware technology, message queues are determined between the containers corresponding to the multiple target range systems, wherein the message queues are used for asynchronous communication between the containers corresponding to the multiple target range systems.

5. The method according to claim 1, characterized in that, The resource processing in the containers corresponding to the multiple target range systems includes: Extract the feature vectors of the containers corresponding to each of the multiple target range systems; Based on the feature vectors, the weights of the containers corresponding to each of the multiple target range systems are calculated respectively; Based on the weights, resources are allocated to the containers corresponding to each of the multiple target range systems to obtain the operating resources corresponding to each of the multiple target range systems. The operational resources are processed in their respective containers within the multiple target range systems.

6. The method according to claim 1, characterized in that, Also includes: The system monitors the running status of the containers corresponding to each of the multiple target range systems in real time, wherein the running status includes running memory usage, memory consumption, and network traffic. Based on the operating status, determine the anomaly detection results of the containers corresponding to each of the multiple target range systems; Based on the anomaly detection results, a prompt signal is generated and sent to the user terminal.

7. A resource processing device for heterogeneous target ranges, characterized in that, include: The acquisition module is used to acquire processor architecture information for each of the multiple target range systems. The determining module is used to determine the virtual machine corresponding to each of the plurality of target range systems based on the processor architecture information; An encapsulation module is used to deploy the applications corresponding to the multiple target range systems in the virtual machine, and encapsulate them in the containers corresponding to the multiple target range systems. The processing module is used to perform resource processing in the respective containers of the multiple target range systems.

8. A non-volatile storage medium, characterized in that, The non-volatile storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the non-volatile storage medium to perform the resource processing method for heterogeneous test ranges as described in any one of claims 1 to 6.

9. A computer device, characterized in that, include: Memory and processor The memory stores computer programs; The processor is configured to execute a computer program stored in the memory, wherein when the computer program is executed, the processor performs the resource processing method for heterogeneous test ranges as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the resource processing method for heterogeneous test ranges as described in any one of claims 1 to 6.

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