Observable application full life cycle management method and platform

Abstract

The invention discloses an observable application full life cycle management method and platform, and relates to the technical field of application management, and the method comprises the steps: collecting a development data set of a target application; the method comprises the following steps: receiving a plurality of service requests in a preset monitoring window, carrying out shunting processing on the plurality of service requests to obtain a plurality of service request shunting sequences, and carrying out resource demand analysis on the plurality of service request shunting sequences to obtain a plurality of resource demand analysis result sets; aligning the plurality of service request shunting sequences based on a plurality of request time nodes to obtain an alignment result; and based on the plurality of resource demand analysis result sets and the alignment result, in combination with the development data set, performing state monitoring of the target application, and sending a state monitoring result to a visualization module for display. The technical problem that it is difficult for enterprises to quickly respond to market demand changes in the prior art is solved, and the technical effect of improving the efficiency, quality and maintainability of application development is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of application management, and in particular, to an observable application full life cycle management method and platform. Background Art

[0002] With the continuous deepening of enterprise informatization construction, the information needs faced by enterprises are becoming increasingly diversified, and business requirements are constantly changing. With the improvement of enterprise business complexity, traditional software development models have problems such as long development cycles, high costs, and poor maintainability, making it difficult to meet the changing market demands of enterprises quickly. Summary of the Invention

[0003] This application provides an observable application full life cycle management method and platform for solving the technical problem that the existing technology is difficult to meet the rapid response of enterprises to market demand changes.

[0004] In view of the above problems, this application provides an observable application full life cycle management method and platform.

[0005] In the first aspect of this application, an observable application full life cycle management method is provided. The method includes:

[0006] Collect a set of development data of a target application; receive multiple business requests within a preset monitoring window, and perform a shunt process on the multiple business requests to obtain multiple business request shunt sequences, where the multiple business requests include multiple request time nodes; perform resource requirement analysis on the multiple business request shunt sequences to obtain multiple resource requirement analysis result sets; align the multiple business request shunt sequences based on the multiple request time nodes to obtain an alignment result; based on the multiple resource requirement analysis result sets and the alignment result, combine the development data set to perform status monitoring on the target application, and send the status monitoring result to a visualization module for display.

[0007] In the second aspect of this application, an observable application full life cycle management platform is provided. The platform includes:

[0008] Develop a data set collection module that collects the development data set of the target application; a shunt processing module that receives multiple service requests within a preset monitoring window and performs shunt processing on the multiple service requests to obtain multiple service request shunt sequences, where the multiple service requests include multiple request time nodes; a resource requirement analysis module that performs resource requirement analysis on the multiple service request shunt sequences to obtain multiple resource requirement analysis result sets; a service request shunt sequence alignment module that aligns the multiple service request shunt sequences based on the multiple request time nodes to obtain an alignment result; and a status monitoring module that performs status monitoring of the target application based on the multiple resource requirement analysis result sets and the alignment result, in combination with the development data set, and sends the status monitoring result to the visualization module for display.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] This application collects the development data set of the target application; receives multiple service requests within a preset monitoring window and performs shunt processing on the multiple service requests to obtain multiple service request shunt sequences, where the multiple service requests include multiple request time nodes; performs resource requirement analysis on the multiple service request shunt sequences to obtain multiple resource requirement analysis result sets; aligns the multiple service request shunt sequences based on the multiple request time nodes to obtain an alignment result; performs status monitoring of the target application based on the multiple resource requirement analysis result sets and the alignment result, in combination with the development data set, and sends the status monitoring result to the visualization module for display. This invention solves the technical problem that the prior art is difficult to meet the enterprise's rapid response to market demand changes. By monitoring the status and performance of the application in real time and dynamically, and optimizing resource allocation, it achieves the technical effects of improving the efficiency, quality, and maintainability of application development. Description of the Drawings

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0012] Figure 1 It is a schematic flowchart of an observable application full life cycle management method provided by an embodiment of this application;

[0013] Figure 2Schematic diagram of an observable application full - life - cycle management platform provided by an embodiment of the present application.

[0014] Explanation of reference numerals: Development data set collection module 11, shunt processing module 12, resource requirement analysis module 13, business request shunt sequence alignment module 14, status monitoring module 15. Detailed implementation manners

[0015] By providing an observable application full - life - cycle management method and platform, the present application aims to solve the technical problem that the prior art is difficult to meet the rapid response of enterprises to market demand changes. By monitoring the status and performance of applications in real - time and dynamically, and optimizing resource allocation, the technical effects of improving the efficiency, quality, and maintainability of application development are achieved.

[0016] Next, the technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0017] It should be noted that any variations of the terms "include" and "have" are intended to cover non - exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0018] Embodiment 1

[0019] As Figure 1 shown, the present application provides an observable application full - life - cycle management method, and the method includes:

[0020] Step S100: Collect the development data set of the target application.

[0021] Furthermore, step S100 in the method provided by the embodiment of the application further includes:

[0022] The development data set includes server configuration data, K transmission links, and load - balancing requirements.

[0023] In the embodiment of the present application, when collecting the development data set of the target application, server configuration data is collected and displayed through server monitoring tools such as Zabbix and Prometheus. K transmission links are determined by means of network performance monitoring tools such as Wireshark and NetFlow. Load - balancing requirements are collected by using a load balancer.

[0024] Through the above process, server configuration data, K transmission links, and load balancing requirements are obtained. These data are integrated to obtain a development data set for the target application.

[0025] Step S200: Receive multiple service requests within a preset monitoring window, and perform traffic splitting on the multiple service requests to obtain multiple service request traffic splitting sequences, where the multiple service requests include multiple request time nodes.

[0026] In the embodiment of the present application, multiple service requests within a preset monitoring window are received. According to the type, priority, source, or other attributes of the service requests, the requests are divided into different categories or groups. According to the classification of the requests and the resource status of the system, appropriate processing resources, including computing resources, storage resources, or network resources, etc., are allocated to each request. During the traffic splitting process, the request time nodes of each service request are recorded. After classification and resource allocation, each request is placed into one or more processing queues or processing paths to form multiple service request traffic splitting sequences. Among them, the multiple service requests include multiple request time nodes.

[0027] Step S300: Perform resource requirement analysis on the multiple service request traffic splitting sequences to obtain multiple resource requirement analysis result sets.

[0028] In the embodiment of the present application, after obtaining multiple service request traffic splitting sequences, machine learning algorithms, such as neural networks, decision trees, etc., are combined to analyze the resource requirements. These algorithms predict the specific resource requirements of each request sequence for resources such as CPU, memory, disk space, network bandwidth, etc., based on historical data and the characteristics of the current requests.

[0029] When performing resource requirement analysis, the collected request data is cleaned and formatted to meet the input requirements of the machine learning model. The machine learning model is trained using historical data so that it can accurately predict resource requirements. The new request data is input into the model to obtain the prediction results of resource requirements, and the prediction results are evaluated using evaluation metrics. Through this process, the training of the model is completed. The multiple service request traffic splitting sequences are input into the machine learning model to obtain multiple resource requirement analysis result sets. These result sets contain information such as the identification of each request sequence, the types of resources required, the specific demand for each type of resource, and the predicted changes in resource requirements.

[0030] Step S400: Align the multiple service request traffic splitting sequences based on multiple request time nodes to obtain an alignment result.

[0031] In the embodiments of the present application, first, a time reference is determined. This reference can be a certain fixed time point or a certain dynamic time point. The time nodes of the requests are extracted from each business request diversion sequence. The request time nodes in each business request diversion sequence are sorted to ensure that they are arranged in chronological order. If a fixed time reference is adopted, the time nodes of each sequence are directly adjusted to this reference time. If a dynamic time reference is adopted, the alignment method is determined according to the actual time nodes in the sequence. For example, the first request time node of each sequence is used as the reference time of this sequence, and then the other time nodes in this sequence are all converted into relative times starting from this reference time.

[0032] After time alignment, the requests in each business request diversion sequence will have a unified time reference, and the alignment result is obtained.

[0033] Step S500: Based on the multiple resource requirement analysis result sets and the alignment result, combined with the development data set, perform status monitoring on the target application, and send the status monitoring result to the visualization module for display.

[0034] Furthermore, the method provided by the embodiments of the application further includes:

[0035] Randomly extract the first alignment interval from multiple alignment intervals in the alignment result, where the first alignment interval includes multiple first aligned business requests; match the multiple first aligned business requests with the multiple resource requirement analysis result sets to generate multiple first aligned resource requirement analysis results; perform application resource analysis based on the development data set to obtain deployable resource data; perform status monitoring according to the deployable resource data and the multiple first aligned resource requirement analysis results to obtain a first status monitoring result; send the first status monitoring result to the visualization module for display.

[0036] In the embodiments of the present application, a random alignment interval is extracted from the alignment result as the first alignment interval. The first alignment interval contains multiple business requests aligned within the same time window, which are called first aligned business requests. The multiple first aligned business requests within the first alignment interval are matched with the resource requirement analysis result set to find the corresponding resource requirement prediction data, and multiple first aligned resource requirement analysis results are generated.

[0037] According to the development data set, information such as the architecture, components, and resource allocation strategy of the target application is obtained. Based on the development data set, the deployable resource data in the current application is analyzed, including CPU, memory, network bandwidth, etc., to obtain the deployable resource data.

[0038] Compare the first aligned resource requirement analysis results with the allocable resource data to evaluate whether the current resources can meet the requirements of the first aligned service request. Combining the development data set, further evaluate the performance status of the target application within the first aligned interval, such as response time, throughput, etc. Let technical experts set reasonable thresholds or warning conditions, and once insufficient resources, performance degradation, or abnormal behavior are detected, trigger the anomaly detection mechanism. Based on the above analysis, generate the status monitoring results for the first aligned interval, including resource usage, performance status, anomaly information, etc. Organize the first status monitoring results into a format suitable for visualization, such as time series data, chart data, etc. Use visualization tools or custom interfaces to display the first status monitoring results. The display content includes resource usage charts, performance metric curves, anomaly warnings, etc.

[0039] Furthermore, the method provided by the application embodiment further includes:

[0040] Construct a resource analysis network layer; use the resource analysis network layer to analyze the server configuration data, K transmission links, and load balancing requirements to obtain allocable resource data.

[0041] In the embodiment of the present application, when constructing the resource analysis network layer, first clarify the main functions of the resource analysis network layer, including collecting, integrating, analyzing, and predicting resource data related to servers, transmission links, and load balancing. Based on the requirements analysis, select or develop a suitable network layer technology stack, including data collection tools, data analysis algorithms, databases, or data warehouses, etc. Design the architecture of the resource analysis network layer, including data collection points, data processing centers, data storage, and retrieval mechanisms, etc. Complete the construction of the resource analysis network layer through this process.

[0042] Use the resource analysis network layer to collect server configuration information, including server model, number of CPU cores, memory size, storage capacity, etc. Based on the collected configuration information, evaluate the performance of each server, including computing power, storage capacity, and expansion ability, etc. According to the evaluation results and load balancing requirements, calculate the currently available server resources and determine the number or configuration of allocable servers. Use the resource analysis network layer to collect information about K transmission links, including bandwidth, latency, packet loss rate, etc. Based on the collected link information, evaluate the quality of each transmission link and determine which links are more suitable for carrying high-priority or critical services. Allocate transmission link resources according to business requirements, link quality, and load balancing requirements. Use the resource analysis network layer to collect the load information of the servers, including CPU usage rate, memory occupancy rate, network traffic, etc. Based on the collected load information, analyze the distribution of the load and determine which servers or links are currently heavily or lightly loaded.

[0043] Integrate the analysis results of server configuration data, transmission links, and load balancing requirements to form complete deployable resource data.

[0044] Furthermore, the method provided by the application embodiment further includes:

[0045] Extract features from the multiple service requests to obtain multiple service request feature sets; perform shunt processing on the multiple service requests according to the multiple service request feature sets, and perform serialization processing on the shunt results in the order from front to back in time to obtain the multiple service request shunt sequences.

[0046] In the embodiment of the present application, first collect all service request data to be processed, and these data include attributes such as the type, source, size, timestamp, and priority of the request. According to service requirements and analysis objectives, select features that have an important impact on shunt processing. For example, the priority, type, source, etc. of the request. For each service request, extract the selected features from its original data to obtain multiple service request feature sets.

[0047] According to service requirements, formulate shunt strategies. These strategies are based on the priority, type, source, or other features of the request. For example, high-priority requests are assigned to servers with better performance, while low-priority requests are assigned to servers with lower loads. Use the formulated shunt strategies to perform shunt processing on multiple service requests. According to the features of each request, assign it to the corresponding processing queue or resource pool. Sort the shunt results in the order from front to back according to the timestamp of the service requests. Combine the serialized data into a sequence in time order, that is, the service request shunt sequence. This sequence contains all service requests to be processed and their shunt results and sorting information.

[0048] Furthermore, the method provided by the application embodiment also includes:

[0049] Randomly extract multiple first service request feature sets from the multiple service request feature sets to construct multiple decision nodes of the shunt decision branch; use the multiple decision nodes to perform shunt processing on the multiple service requests to obtain shunt results.

[0050] In the embodiments of the present application, multiple first service request feature sets are randomly selected from the multiple extracted service request feature sets. These extracted feature sets will serve as the basis for constructing the traffic diversion decision branches. Each extracted first service request feature set will represent a decision node. The decision node can divert service requests according to selected features such as request type, priority, source, etc. Set decision conditions for each decision node. These conditions are based on the value range of the features, whether a certain condition is met, etc. For example, if the request type belongs to "high priority", it will be diverted to a specific processing queue. Connect multiple decision nodes according to the logical relationship and decision conditions to form a decision tree or a decision graph.

[0051] Input the service requests to be processed into the traffic diversion decision branch. Starting from the root node of the decision tree or the starting point of the decision graph, traverse the decision tree or the decision graph according to the feature values of the service requests. At each decision node, judge the service requests according to the set decision conditions. If the condition of a certain node is met, process the service request according to the diversion rule of that node; if not, continue to traverse other nodes. During the traversal process, record the diversion results of each service request. These results include the assigned processing queue, resource pool, or other identification information. After all service requests have traversed the decision tree or the decision graph, output the diversion results of each service request.

[0052] Furthermore, step S400 in the method provided by the application embodiments further includes:

[0053] Extract the service requests at the first position in the multiple service request diversion sequences to obtain multiple first service requests; based on the multiple first request time nodes corresponding to the multiple first service requests, align the multiple service request diversion sequences according to a preset alignment time interval to obtain the alignment result.

[0054] In the embodiments of the present application, when extracting multiple first service requests, for each service request diversion sequence, locate the starting position of the sequence. Extract the service request at the first position from the starting position of each sequence, and use these requests as the first service requests of their respective sequences.

[0055] Select a suitable time point or time interval as the alignment benchmark. This benchmark can be a certain fixed time point or a dynamic time interval. Based on the alignment benchmark, set a time interval as the alignment reference range. This interval is large enough to accommodate the differences of all first request time nodes.

[0056] For each business request diversion sequence, calculate the offset between the first request time node of the sequence and the alignment reference. According to the calculated offset, adjust each business request diversion sequence on the time axis accordingly, so that the first request time node of the sequence is aligned with the alignment reference. If the start time of the sequence is earlier than the alignment reference, move the sequence forward; if it is later than the alignment reference, move the sequence backward. If, during the alignment process, the starting part of a sequence exceeds the preset alignment time interval, select to discard the exceeded part; if there are gaps in the alignment time interval of the sequence, fill them by methods such as inserting empty requests or repeating requests. After the above steps, all business request diversion sequences will be adjusted to the same time interval, and the first request time node is aligned with the alignment reference. At this time, the alignment result is generated.

[0057] In the embodiments of the present application, in summary, the embodiments of the present application at least have the following technical effects:

[0058] The present application collects the development data set of the target application; receives multiple business requests within a preset monitoring window, and performs diversion processing on the multiple business requests to obtain multiple business request diversion sequences, where the multiple business requests include multiple request time nodes; performs resource requirement analysis on the multiple business request diversion sequences to obtain multiple resource requirement analysis result sets; aligns the multiple business request diversion sequences based on the multiple request time nodes to obtain an alignment result; monitors the state of the target application based on the multiple resource requirement analysis result sets and the alignment result, in combination with the development data set, and sends the state monitoring result to the visualization module for display. The present invention solves the technical problem that the prior art is difficult to meet the rapid response of enterprises to market demand changes. By monitoring the state and performance of the application in real time and dynamically, and optimizing resource allocation, the technical effects of improving the efficiency, quality, and maintainability of application development are achieved.

[0059] Embodiment 2

[0060] Based on the same inventive concept as the observable application full life cycle management method in the foregoing embodiment, as Figure 2 shown, the present application provides an observable application full life cycle management platform. The platform in the embodiments of the present application and the method embodiments are based on the same inventive concept. Among them, the platform includes:

[0061] A development data set collection module 11, which collects the development data set of the target application;

[0062] A shunt processing module 12, which receives multiple service requests within a preset monitoring window and performs shunt processing on the multiple service requests to obtain multiple service request shunt sequences. Among them, the multiple service requests include multiple request time nodes;

[0063] A resource requirement analysis module 13, which performs resource requirement analysis on the multiple service request shunt sequences to obtain multiple resource requirement analysis result sets;

[0064] A service request shunt sequence alignment module 14, which aligns the multiple service request shunt sequences based on multiple request time nodes to obtain an alignment result;

[0065] A status monitoring module 15, which performs status monitoring of the target application based on the multiple resource requirement analysis result sets and the alignment result, in combination with the development data set, and sends the status monitoring result to the visualization module for display.

[0066] Further, the platform is also used to implement the following functions:

[0067] The development data set includes server configuration data, K transmission links, and load balancing requirements.

[0068] Further, the platform is also used to implement the following functions:

[0069] Extract features from the multiple service requests to obtain multiple service request feature sets;

[0070] Perform shunt processing on the multiple service requests according to the multiple service request feature sets, and serialize the shunt results in the order from front to back in time to obtain the multiple service request shunt sequences.

[0071] Further, the platform is also used to implement the following functions:

[0072] Randomly extract multiple first service request feature sets from the multiple service request feature sets to construct multiple decision nodes of the shunt decision branch;

[0073] Use the multiple decision nodes to perform shunt processing on the multiple service requests to obtain a shunt result.

[0074] Further, the platform is also used to implement the following functions:

[0075] Extract the service requests ranked first in the multiple service request shunt sequences to obtain multiple first-position service requests;

[0076] Based on the multiple first request time nodes corresponding to the multiple first business requests, align the multiple business request diversion sequences according to a preset alignment time interval to obtain the alignment result.

[0077] Further, the platform is also used to implement the following functions:

[0078] Randomly select a first alignment interval from the multiple alignment intervals in the alignment result, where the first alignment interval includes multiple first aligned business requests;

[0079] Match the multiple first aligned business requests with the multiple resource requirement analysis result sets to generate multiple first aligned resource requirement analysis results;

[0080] Perform application resource analysis based on the development data set to obtain deployable resource data;

[0081] Perform status monitoring based on the deployable resource data and the multiple first aligned resource requirement analysis results to obtain a first status monitoring result;

[0082] Send the first status monitoring result to the visualization module for display.

[0083] Further, the platform is also used to implement the following functions:

[0084] Construct a resource analysis network layer;

[0085] Use the resource analysis network layer to analyze the server configuration data, K transmission links, and load balancing requirements to obtain deployable resource data.

[0086] It should be noted that the above sequence of embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the above describes specific embodiments of this specification. The processes depicted in the drawings do not necessarily require the specific order and continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the protection scope of the present application.

[0088] This specification and the drawings are merely exemplary descriptions of the present application and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application is intended to include these changes and modifications therein.

Claims

1. An observable application life cycle management method, characterized in that: The method comprises: Collect the development data set of the target application; Receiving multiple service requests within a preset monitoring window, and performing diversion processing on the multiple service requests to obtain multiple service request diversion sequences, wherein the multiple service requests include multiple request time nodes; Performing resource demand analysis on the multiple service request diversion sequences to obtain multiple resource demand analysis result sets; Aligning the multiple service request diversion sequences based on multiple request time nodes to obtain an alignment result; Based on the multiple resource requirement analysis result sets and the alignment results, the status of the target application is monitored in combination with the development data set, and the status monitoring results are sent to the visualization module for display.

2. The observable application lifecycle management method according to claim 1, characterized in that: The development data set includes server configuration data, K transmission links and load balancing requirements.

3. The observable application lifecycle management method according to claim 1, characterized in that: The method comprises: Extracting features from the multiple service requests to obtain multiple service request feature sets; The multiple service requests are diverted according to the multiple service request feature sets, and the diversion results are serialized in chronological order to obtain the multiple service request diversion sequences.

4. The observable application lifecycle management method according to claim 3, characterized in that: The method comprises: Randomly extracting multiple first service request feature sets from the multiple service request feature sets to construct multiple decision nodes of the diversion decision branch; The multiple decision nodes are used to perform diversion processing on the multiple service requests to obtain a diversion result.

5. The observable application lifecycle management method according to claim 1, characterized in that: The method comprises: Extracting the first service request in the plurality of service request diversion sequences to obtain a plurality of first service requests; Based on a plurality of first request time nodes corresponding to the plurality of first service requests, the plurality of service request diversion sequences are aligned according to a preset alignment time interval to obtain the alignment result.

6. The observable application lifecycle management method according to claim 2, characterized in that: The method comprises: Randomly extracting a first alignment interval of multiple alignment intervals in the alignment result, wherein the first alignment interval includes multiple first alignment service requests; Generate a plurality of first alignment resource requirement analysis results by matching the plurality of first alignment service requests with the plurality of resource requirement analysis result sets; Perform application resource analysis based on the development data set to obtain deployable resource data; Performing status monitoring according to the deployable resource data and the plurality of first aligned resource requirement analysis results to obtain a first status monitoring result; The first status monitoring result is sent to the visualization module for display.

7. The observable application lifecycle management method according to claim 6, characterized in that: The method comprises: Build a resource analysis network layer; The resource analysis network layer is used to analyze the server configuration data, K transmission links and load balancing requirements to obtain deployable resource data.

8. An observable application lifecycle management platform, characterized in that: The platform includes: A development data set acquisition module, wherein the development data set acquisition module acquires a development data set of a target application; A traffic diversion processing module, wherein the traffic diversion processing module receives a plurality of service requests within a preset monitoring window, and performs traffic diversion processing on the plurality of service requests to obtain a plurality of service request diversion sequences, wherein the plurality of service requests include a plurality of request time nodes; A resource demand analysis module, wherein the resource demand analysis module performs resource demand analysis on the plurality of service request diversion sequences to obtain a plurality of resource demand analysis result sets; A service request diversion sequence alignment module, wherein the service request diversion sequence alignment module aligns the multiple service request diversion sequences based on multiple request time nodes to obtain an alignment result; A status monitoring module, wherein the status monitoring module performs status monitoring of the target application based on the multiple resource demand analysis result sets and the alignment result in combination with the development data set, and sends the status monitoring result to the visualization module for display.

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