Cross-platform deployment automation arrangement system and method based on xinchuang environment

Abstract

The application relates to the field of automation arrangement, in particular to a cross-platform deployment automation arrangement system and method based on a Xinhua environment, which comprises a recognition module for recognizing the CPU architecture and the operating system of each server; a server evaluation module comprising an information collection unit, a first evaluation unit and a second evaluation unit; the information collection unit is used for acquiring the working information of each server in real time; the first evaluation unit is used for constructing a single performance score set according to the working information; and the second evaluation unit is used for calculating a comprehensive score according to the single performance score set. Compared with common simple weighted average or static threshold method and other deployment decision methods, the core advantage of the application lies in that the mutual dependence relationship among server resources is accurately simulated by introducing a dynamic coupling rule, so that the decision pitfalls such as "high-performance CPU with low memory" caused by the independence of the assumed indexes in the traditional method are solved.

Description

Technical Field

[0001] This invention relates to the field of automated orchestration, specifically to a cross-platform automated orchestration system and method based on a domestically developed information technology environment. Background Technology

[0002] In the field of cross-platform automated deployment under the domestic IT innovation environment, existing common deployment orchestration systems typically employ decision-making methods based on static resource thresholds or simple weighted averages for server task allocation. However, these methods have inherent flaws: they generally assume that various performance indicators of servers (such as CPU, memory, network, etc.) are independent of each other, ignoring their coupling effects under real loads, thus leading to significant deviations between allocation decisions and actual operating results. For example, the simple weighted average method may incorrectly schedule computationally intensive database tasks to a server with a high CPU benchmark score but severely insufficient memory capacity, causing a sharp performance drop after deployment due to memory bottlenecks. Furthermore, the static threshold method lacks contextual awareness of the overall resource status of the cluster and cannot dynamically adjust decision-making strategies, making it particularly rigid and inefficient in the highly heterogeneous and resource-volatile domestic IT innovation environment. These problems seriously affect the reliability, performance, and hardware resource utilization of distributed application deployments. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a cross-platform deployment automated orchestration system and method based on the domestic IT innovation environment, which solves the technical problems mentioned in the background.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A cross-platform automated orchestration system based on the domestic IT innovation environment includes:

[0006] The identification module is used to identify the CPU architecture and operating system of each server;

[0007] The server evaluation module includes an information collection unit, a first evaluation unit, and a second evaluation unit;

[0008] The information acquisition unit is used to obtain the working information of each server in real time;

[0009] The first evaluation unit is used to construct a set of individual performance scores based on work information;

[0010] The second evaluation unit is used to calculate the overall score based on the individual performance score set. ;

[0011] The task matching module is used to match tasks based on a comprehensive score. Match a suitable task to each server.

[0012] Furthermore, the recognition module specifically includes the following steps:

[0013] S11. Establish a list of known servers;

[0014] S12. Use the fact-gathering function built into the automation tool to perform basic identification of each server;

[0015] S13. Read and parse specific identifier files in the target operating system;

[0016] S14. Use the package manager command to query detailed information about the core packages that uniquely identify the operating system in order to identify the type and model of the CPU and operating system of each server.

[0017] Furthermore, the work information includes benchmark performance values ​​for each task on each server's CPU architecture, available physical memory capacity of each server, estimated memory capacity required for stable operation of each task, and network latency and network hops between each pair of servers.

[0018] Furthermore, in the first evaluation unit, the individual performance score set includes performance scores. Memory compatibility score Software compatibility score and network proximity score .

[0019] Furthermore, performance rating The calculation formula is: ;

[0020] In the formula, Indicates task On the server Benchmark performance values ​​on the CPU architecture; Indicates task The highest benchmark performance values ​​across all server CPU architectures;

[0021] Memory compatibility rating The calculation formula is: ;

[0022] In the formula, Indicates server Available physical memory capacity; Indicates task Estimated memory capacity required for stable operation; Represents a memory safety constant;

[0023] Software compatibility rating The calculation formula is: ;

[0024] Network Proximity Score The calculation formula is: ;

[0025] In the formula, Indicates server and Network latency between; Indicates server and The number of network hops between them.

[0026] Furthermore, the second evaluation unit specifically includes the following steps:

[0027] S21. Construct a weight vector set based on the individual performance score set. , ;

[0028] S22. Calculate the coupling coefficient set based on the individual performance score set. The coupling coefficient set includes the first coupling coefficient. Second coupling coefficient ;

[0029] S23. According to the scoring sequence Calculate the comprehensive score based on the coupling coefficient set. The calculation formula is as follows: ;

[0030] In the formula, the comprehensive score This indicates the degree to which the server is matched to execute each task.

[0031] Furthermore, step S21 specifically includes the following steps:

[0032] S211. Construct a scoring sequence based on the individual performance score set. ,in, , , and These represent performance scores. Memory compatibility score Software compatibility score and network proximity score ;

[0033] S212, According to the scoring sequence Construct a judgment matrix , ;

[0034] in, express Compared to The importance of;

[0035] S213, According to the judgment matrix Calculate the first weight vector Second weight vector Third weight vector and the fourth weight vector To construct a weight vector set The calculation formula is as follows: ;

[0036] In the formula, This represents the i-th weight vector.

[0037] Furthermore, step S22 specifically includes the following steps:

[0038] S221, Define the first penalty intensity coefficient ;

[0039] S222, Based on the penalty severity coefficient Calculate the first coupling coefficient The calculation formula is as follows: ;

[0040] S223, Define the second penalty intensity coefficient ;

[0041] S224, Based on the second penalty intensity coefficient Calculate the second coupling coefficient The calculation formula is as follows: ;

[0042] S225, According to the first coupling coefficient Second coupling coefficient Construct a set of coupling coefficients.

[0043] The cross-platform deployment and automated orchestration method based on the domestic IT innovation environment includes the following steps:

[0044] S1. Identify the CPU architecture and operating system of each server;

[0045] S2. Obtain real-time working information for each server;

[0046] S3. Construct a set of individual performance scores based on work information;

[0047] S4. Calculate the overall score based on the individual performance score set. ;

[0048] S5. Based on the overall score Match a suitable task to each server.

[0049] Compared with existing technologies, this invention provides a cross-platform deployment automated orchestration system and method based on the domestic IT innovation environment, which has the following beneficial effects:

[0050] 1. Compared with common deployment decision-making methods such as simple weighted average or static threshold method, the core advantage of this invention is that by introducing dynamic coupling rules, it accurately simulates the interdependence between server resources, thereby solving the decision-making traps such as "high-performance CPU with low memory" caused by the assumption of independent indicators in traditional methods.

[0051] 2. This invention has context-aware capabilities and can adaptively adjust decision-making tendencies based on real-time resource conditions, achieving a leap from rigid rules to intelligent judgment. It is particularly suitable for heterogeneous information technology innovation environments, and ultimately, while ensuring the stability of applications after deployment, it significantly improves the utilization rate of hardware resources and the accuracy of automated orchestration. Attached Figure Description

[0052] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0053] Figure 1 This is a schematic diagram of the cross-platform deployment automated orchestration system and method based on the domestic IT innovation environment of the present invention. Detailed Implementation

[0054] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. This will allow for a full understanding of how the present application uses technical means to solve technical problems and achieve technical effects, and to facilitate its implementation.

[0055] Those skilled in the art will understand that all or part of the steps in the methods of the following embodiments can be implemented by a program instructing related hardware. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0056] Cross-platform orchestration systems are typically used to implement complex distributed application deployments. For example, a typical workflow might be: "First, deploy the database on server A (Phytium + Kylin), then deploy the application service on server B (Kunpeng + UOS), and finally deploy a load balancer on server C (x86 + CentOS), automatically configuring the addresses of A and B onto C." In this process, because each server has different characteristics, tasks need to be allocated according to the specific conditions of each server to optimize the overall performance, stability, and cost of the distributed system. Therefore, please refer to [link to relevant documentation]. Figure 1 As shown, the first embodiment of the present invention proposes a cross-platform deployment automated orchestration system based on the information technology innovation environment, including: an identification module, a server evaluation module, and a task matching module;

[0057] The identification module is used to identify the CPU architecture and operating system of each server. Specifically, since the CPU architecture determines the machine language, different CPU architectures (such as x86, ARM, and MIPS) use completely different instruction sets. Software must be "translated" into the corresponding instruction set. Any software (including operating systems, middleware, and applications) ultimately needs to be compiled into machine code that a specific CPU architecture can understand. A software package compiled for x86 architecture (such as a .rpm or .deb file) absolutely cannot run directly on an ARM or MIPS architecture CPU. Forcing an attempt will result in a "file format error" or "execution format error". To ensure that the distributed software packages are executable at the hardware level, the identification module includes the following steps:

[0058] S11. Establish a list of known servers; specifically, compile an official list of all the required domestically developed CPU architectures and operating system versions, such as: Phytium (ARM), Loongson (MIPS), Tongxin UOS (desktop / server version, and its main version numbers), Kylin OS (V10 SP1 / SP2, etc.).

[0059] S12. Use the fact-gathering function built into the automation tool to perform basic identification of each server. Specifically, use the built-in mechanism of the automation tool (such as Ansible) to collect basic facts about the target node. This will obtain general information such as "aarch64" architecture and "Debian series" for quick and preliminary classification, categorizing the node into several major categories (such as ARM-Debian category, MIPS-RedHat category), which will narrow down the scope for subsequent fine identification. It should be noted that the fact-gathering function is a common existing technology and will not be elaborated here.

[0060] S13. Read and parse the specific identification file in the target operating system. Specifically, you can search for manufacturer-specific keywords in the file content, such as "UnionTech", "UOS", "Kylin", "NeoKylin", "Founder", etc. This is a key step in distinguishing different domestic operating systems and can effectively solve problems such as "they are all Debian-based but have different kernels".

[0061] S14. Use package manager commands to query detailed information of core software packages that can uniquely identify the operating system in order to identify the type and model of the CPU and operating system of each server; specifically, the above query method is a common existing technology and will not be elaborated here.

[0062] The server evaluation module includes an information collection unit, a first evaluation unit, and a second evaluation unit;

[0063] The information acquisition unit is used to acquire real-time operational information for each server. Specifically, this operational information includes the benchmark performance value of each task on each server's CPU architecture, the available physical memory capacity of each server, the estimated memory capacity required for stable operation of each task, and the network latency and hop count between any two servers. Specifically, the benchmark performance value can be obtained using benchmark testing tools such as sysbench or unixbench, or automated scripts. The available physical memory capacity can be obtained by pre-installing an agent program (such as Prometheus NodeExporter) on the server, which collects and reports the data. The estimated memory capacity can be obtained using performance testing tools (such as sysbench, jmeter, etc.) or system monitoring tools (such as top, Prometheus+grafana, etc.). Network latency and hop count can be obtained by deploying a central control machine or using the ping and traceroute commands built into the operating system. It should be noted that the above detection methods are common existing technologies and will not be elaborated upon here.

[0064] The first evaluation unit is used to construct a set of individual performance scores based on work information; specifically, in the first evaluation unit, the set of individual performance scores includes performance scores. Memory compatibility score Software compatibility score and network proximity score Specifically, performance rating The calculation formula is: ;

[0065] In the formula, Indicates task On the server Benchmark performance values ​​on the CPU architecture; Indicates task The highest benchmark performance values ​​across all server CPU architectures;

[0066] Memory compatibility rating The calculation formula is: ;

[0067] In the formula, Indicates server Available physical memory capacity; Indicates task Estimated memory capacity required for stable operation; Represents a memory safety constant; in this invention, It is 1.3;

[0068] Software compatibility rating The calculation formula is: ;

[0069] Network Proximity Score The calculation formula is: ;

[0070] In the formula, Indicates server and Network latency between; Indicates server and The number of network hops between them.

[0071] The second evaluation unit is used to calculate the overall score based on the individual performance score set. Specifically, common comprehensive scoring methods only calculate the individual impact of each parameter in the single performance score set on the comprehensive score, without considering the coupling effects between different parameters. For example, efficient CPU operation depends on sufficient memory. If memory is severely insufficient, the system will frequently use the swap partition, leading to a large amount of I / O waiting. At this time, even if the CPU's performance score is high, its actual performance will drop sharply. Therefore, insufficient memory compatibility score will non-linearly amplify the performance score loss. Software compatibility score is a prerequisite. If the software compatibility score is 0 (incompatible), then all other scores become meaningless because the task cannot run at all. Common calculation methods only use weights to reduce its impact, which is not rigorous enough. The degree of network impact is strongly correlated with the task type. For application services that need to frequently access remote databases, the impact of network latency is a multiplicative effect, while common calculation methods use an additive effect, which is not rigorous enough. Therefore, the second evaluation unit specifically includes the following steps:

[0072] S21. Construct a weight vector set based on the individual performance score set. , Specifically, step S21 includes the following steps:

[0073] S211. Construct a scoring sequence based on the individual performance score set. ,in, , , and These represent performance scores. Memory compatibility score Software compatibility score and network proximity score ;

[0074] S212, According to the scoring sequence Construct a judgment matrix , ;

[0075] in, express Compared to The importance; specifically, The value of is determined according to Table 1.

[0076] Table 1 Importance scale meaning 1 Both ratings are equally important. 3 Between the two ratings, one is slightly more important than the other. 5 Between the two ratings, one is clearly more important than the other. 7 Between the two ratings, one is significantly more important than the other. 9 The two ratings are extremely important. 2、4、6、8 The intermediate value of the above adjacent judgment

[0077] It should be noted that the importance scale between the scores was obtained through calibration by experts in the field.

[0078] S213, According to the judgment matrix Calculate the first weight vector Second weight vector Third weight vector and the fourth weight vector To construct a weight vector set The calculation formula is as follows: ;

[0079] In the formula, This represents the i-th weight vector.

[0080] S22. Calculate the coupling coefficient set based on the individual performance score set. The coupling coefficient set includes the first coupling coefficient. Second coupling coefficient Specifically, step S22 includes the following steps:

[0081] S221, Define the first penalty intensity coefficient In this invention, The value is 0.5;

[0082] S222, Based on the penalty severity coefficient Calculate the first coupling coefficient The calculation formula is as follows: ;

[0083] S223, Define the second penalty intensity coefficient In this invention, The value is 0.15;

[0084] S224, Based on the second penalty intensity coefficient Calculate the second coupling coefficient The calculation formula is as follows: ;

[0085] S225, According to the first coupling coefficient Second coupling coefficient Construct a set of coupling coefficients.

[0086] S23. According to the scoring sequence Calculate the comprehensive score based on the coupling coefficient set. The calculation formula is as follows: ;

[0087] In the formula, the comprehensive score This indicates the degree to which the server is matched to execute each task.

[0088] The task matching module is used to match tasks based on a comprehensive score. Match a suitable task to each server.

[0089] Compared with common deployment decision-making methods such as simple weighted average or static threshold method, the core advantage of this invention is that by introducing dynamic coupling rules, it accurately simulates the interdependence between server resources, thereby solving the decision-making traps such as "high-performance CPU with low memory" caused by the assumption of independent indicators in traditional methods.

[0090] This invention possesses context-aware capabilities and can adaptively adjust decision-making tendencies based on real-time resource conditions, achieving a leap from rigid rules to intelligent judgment. It is particularly suitable for heterogeneous information technology innovation environments, and ultimately, while ensuring the stability of applications after deployment, it significantly improves the utilization rate of hardware resources and the accuracy of automated orchestration.

[0091] The purpose of the second embodiment of the present invention is to propose a cross-platform deployment automated orchestration method based on the information technology innovation environment, including the following steps:

[0092] S1. Identify the CPU architecture and operating system of each server;

[0093] S2. Obtain real-time working information for each server;

[0094] S3. Construct a set of individual performance scores based on work information;

[0095] S4. Calculate the overall score based on the individual performance score set. ;

[0096] S5. Based on the overall score Match a suitable task to each server.

[0097] It should be noted that the above formulas are all dimensionless calculations, and the formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0098] The above embodiments provide a detailed description of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A cross-platform automated orchestration system based on a domestically developed information technology environment, characterized in that: include: The identification module is used to identify the CPU architecture and operating system of each server; The server evaluation module includes an information collection unit, a first evaluation unit, and a second evaluation unit; The information acquisition unit is used to obtain the working information of each server in real time; The first evaluation unit is used to construct a set of individual performance scores based on work information; The second evaluation unit is used to calculate the overall score based on the individual performance score set. ; The task matching module is used to match tasks based on a comprehensive score. Match a suitable task to each server.

2. The cross-platform deployment automated orchestration system based on the domestic IT innovation environment according to claim 1, characterized in that, The recognition module specifically includes the following steps: S11. Establish a list of known servers; S12. Use the fact-gathering function built into the automation tool to perform basic identification of each server; S13. Read and parse specific identifier files in the target operating system; S14. Use the package manager command to query detailed information about the core packages that uniquely identify the operating system in order to identify the type and model of the CPU and operating system of each server.

3. The cross-platform deployment automated orchestration system based on the domestic IT innovation environment according to claim 1, characterized in that, The job information includes benchmark performance values ​​for each task on each server's CPU architecture, available physical memory capacity for each server, estimated memory capacity required for stable operation of each task, and network latency and network hops between each pair of servers.

4. The cross-platform deployment automated orchestration system based on the domestic IT innovation environment according to claim 1, characterized in that, In the first evaluation unit, the individual performance score set includes performance scores. Memory compatibility score Software compatibility score and network proximity score .

5. The cross-platform deployment automated orchestration system based on the domestic IT innovation environment according to claim 4, characterized in that, Performance rating The calculation formula is: ; In the formula, Indicates task On the server Benchmark performance values ​​on the CPU architecture; Indicates task The highest benchmark performance values ​​across all server CPU architectures; Memory compatibility rating The calculation formula is: ; In the formula, Indicates server Available physical memory capacity; Indicates task Estimated memory capacity required for stable operation; Represents a memory safety constant; Software compatibility rating The calculation formula is: ； Network Proximity Score The calculation formula is: ; In the formula, Indicates server and Network latency between; Indicates server and The number of network hops between them.

6. The cross-platform deployment automated orchestration system based on the domestic IT innovation environment according to claim 4, characterized in that, The second evaluation unit specifically includes the following steps: S21. Construct a weight vector set based on the individual performance score set. , ; S22. Calculate the coupling coefficient set based on the individual performance score set. The coupling coefficient set includes the first coupling coefficient. Second coupling coefficient ; S23. According to the scoring sequence Calculate the comprehensive score based on the coupling coefficient set. The calculation formula is as follows: ； In the formula, the comprehensive score This indicates the degree to which the server is matched to execute each task.

7. The cross-platform deployment automated orchestration system based on the domestic IT innovation environment according to claim 6, characterized in that, Step S21 specifically includes the following steps: S211. Construct a scoring sequence based on the individual performance score set. ,in, , , and These represent performance scores. Memory compatibility score Software compatibility score and network proximity score ; S212, According to the scoring sequence Construct a judgment matrix , ; in, express Compared to The importance of; S213, Based on the judgment matrix Calculate the first weight vector Second weight vector Third weight vector and the fourth weight vector To construct a weight vector set The calculation formula is as follows: ; In the formula, This represents the i-th weight vector.

8. The cross-platform deployment automated orchestration system based on the domestic IT innovation environment according to claim 6, characterized in that, Step S22 specifically includes the following steps: S221, Define the first penalty intensity coefficient ; S222, Based on the penalty severity coefficient Calculate the first coupling coefficient The calculation formula is as follows: ; S223, Define the second penalty intensity coefficient ; S224, Based on the second penalty intensity coefficient Calculate the second coupling coefficient The calculation formula is as follows: ; S225, According to the first coupling coefficient Second coupling coefficient Construct a set of coupling coefficients.

9. A method for deploying an automated orchestration system across platforms based on a domestically developed information technology environment as described in any one of claims 1-8, characterized in that, Includes the following steps: S1. Identify the CPU architecture and operating system of each server; S2. Obtain real-time working information for each server; S3. Construct a set of individual performance scores based on work information; S4. Calculate the overall score based on the individual performance score set. ; S5. Based on the overall score Match a suitable task to each server.

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