Intelligent gray scale and multi-version adaptive deployment system and method based on Kubernetes Operator

Abstract

The invention discloses an intelligent gray scale and multi-version adaptive deployment system and method based on Kubernetes, and the method comprises the steps: through an Operator which operates in Kubernetes and integrates a plurality of intelligent modules, the Operator can collect and fuse a business performance index and a technical performance index, and generates a comprehensive "version health score"; according to the real-time state of the health score, the rhythm and the step length of flow release are adaptively adjusted; meanwhile, through integration with a service grid, staged and context-based accurate flow routing is realized; when serious problems occur, safe rollback can be automatically executed, and cooling locking can be applied. According to the method, the service target and the release process are deeply bound, so that the intelligence of release decision, the self-adaptive adjustment of the release process and the fine control of the risk are realized, and the security, efficiency and success rate of software release are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of cloud-native technology, and in particular to an intelligent canary and multi-version adaptive deployment system and method based on KubernetesOperator. Background Technology

[0002] In today's cloud-native era, developing with a microservices architecture and deploying through container orchestration systems such as Kubernetes has become the mainstream model for enterprises to achieve rapid iteration and business agility. To ensure service stability and continuity during frequent software updates, the industry has developed various deployment strategies, such as rolling updates and blue-green deployments.

[0003] However, existing deployment technologies and automation tools still face several deep-seated challenges in practice: Traditional deployment systems primarily rely on technical monitoring metrics, such as CPU utilization, memory consumption, and HTTP error rates, to determine the "health" of a new version. However, this creates a common pain point: a technically sound version (error-free, low latency) can experience a significant decline in core business metrics like user conversion rates and order volume due to UI redesigns, changes in business logic, or other reasons. Current technologies lack a mechanism to automatically correlate business performance with release decisions.

[0004] Furthermore, whether executed manually or through simple automated scripts, the release process's pacing (e.g., the steps and intervals from 10% traffic to 50% and then to 100%) is typically fixed in advance. This rigid strategy cannot be dynamically adjusted based on the real-time performance of the new version. When the new version performs stably, it cannot automatically accelerate to improve efficiency; when slight performance fluctuations occur, it cannot automatically "slow down" or "pause" for observation, often leading to increased risks or missed opportunities.

[0005] Conventional canary releases typically allocate a certain percentage of traffic randomly to the new version. This "one-size-fits-all" approach cannot achieve precise control over the impact of risks. For example, it is not easy to release a new version to internal employees first, then to users in specific cities, and finally to a wider range of users. This makes refined, step-by-step risk management complex and difficult to automate. Therefore, there is an urgent need for an intelligent canary and multi-version adaptive deployment system and method based on KubernetesOperator. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by proposing an intelligent canary and multi-version adaptive deployment system and method based on KubernetesOperator.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A method for intelligent canary deployment and multi-version adaptive deployment based on KubernetesOperator includes the following steps: S1. Obtain the user-defined deployment strategy, which includes at least one application version to be released, the definition of business performance indicators, and the release schedule strategy; S2. Periodically collect the technical performance metrics of the application version to be released and the business performance metrics collected according to the definition through an Operator running in a Kubernetes cluster; S3. After weighting and normalizing the collected technical performance indicators and business performance indicators, perform fusion analysis to generate a comprehensive version health score. S4. Based on the version health score and the release rhythm strategy, adaptively adjust the traffic allocation weight for the application version to be released.

[0008] Furthermore, before step S3, the method further includes: preprocessing the indicators collected in step S2 through an indicator anomaly detection module, identifying and marking statistically significant anomalies, and using the processed results to generate the version health score.

[0009] Furthermore, the adaptive adjustment step S4 further includes: determining whether the version health score is continuously stable and higher than the health threshold within a preset time window; if so, increasing the adjustment step size of the next traffic allocation weight; otherwise, decreasing the adjustment step size, extending the observation time, or pausing traffic allocation to prevent decision oscillations caused by instantaneous fluctuations in the indicator.

[0010] Furthermore, it also includes: when the version health score is lower than a preset emergency rollback threshold, automatically switching all traffic back to a known stable version and applying a temporary cooling-off lock to the application's deployment strategy to suspend the automated release process.

[0011] Furthermore, before executing the deployment method, the method further includes: using a deployment strategy simulation module to simulate and extrapolate the deployment strategy using historical indicator data, and generating a potential risk assessment report.

[0012] A smart canary and multi-version adaptive deployment system based on KubernetesOperator, used to execute the aforementioned smart canary and multi-version adaptive deployment method based on KubernetesOperator, includes a processor, memory, and a Kubernetes Operator running thereon. The Operator is characterized in that it integrates the following cooperative modules as an organic whole: The policy parsing module is used to obtain and parse user-defined deployment policies; The multi-dimensional monitoring module is used to collect technical performance indicators and business performance indicators; The indicator anomaly detection module is used to preprocess the collected raw indicators to identify statistically significant anomalies and filter noise. The fusion decision engine is used to receive the cleanliness indicators processed by the anomaly detection module, generate a version health score, and generate traffic adjustment or rollback instructions accordingly. The fusion decision engine is also configured to generate auditable decision logs for each decision. The flow control module is used to execute the flow adjustment command; The security rollback and locking module is used to perform traffic switching and apply a cooling lock to the deployment policy when a rollback command is received.

[0013] Furthermore, when generating version health scores, the fusion decision engine is configured to apply user-defined weighting factors to differentiate the importance of different business performance indicators and technical performance indicators, thereby enabling business-priority decisions.

[0014] Furthermore, the fusion decision engine further includes an adaptive speed regulator module, which is configured to dynamically adjust the step size and interval of traffic allocation based on the stable state of the version health score within a preset time window.

[0015] Furthermore, the traffic control module is configured to interact with the service mesh and apply different context routing rules step by step according to the ordered release phases defined in the deployment strategy.

[0016] Furthermore, the system also includes a deployment strategy simulation module, which is configured to perform offline simulation and risk assessment of the deployment strategy before deployment execution, and provide the assessment results to the user. Compared with the prior art, the advantages of the present invention are: By integrating the decision engine, business performance indicators that directly reflect commercial value are weighted and normalized with traditional technical performance indicators to generate a comprehensive "version health score." This ensures that release decisions are no longer based solely on technical performance, but are strongly correlated with business success, guaranteeing that the released version is not only technically robust, but also creates commercial value.

[0017] The adaptive speed controller module dynamically adjusts the step size and pace of traffic increases based on the real-time status and stability trend of the version health score. It automatically accelerates when the version is performing stably and automatically decelerates or pauses when signs of instability appear, overcoming the rigidity of traditional release strategies and making the release process smooth and efficient.

[0018] Through deep integration with the service mesh, contextual routing based on multiple dimensions such as user identity, geographic location, and request characteristics is supported. Users can define phased, incremental release strategies to precisely isolate change risks within the intended audience for step-by-step verification, greatly reducing the possibility of a new version impacting all users.

[0019] Furthermore, this invention not only has the ability to quickly roll back after an event, but also realizes pre-event "sandbox" simulation through the deployment strategy simulation module to proactively prevent risks. At the same time, by utilizing the safe rollback and locking mechanism, it fundamentally eliminates system jitter and invalid retries caused by momentary problems, thereby enhancing the stability and unattended operation capability of the entire automation system. Attached Figure Description

[0020] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0021] Figure 1 This is a schematic diagram of the overall architecture of an intelligent grayscale and multi-version adaptive deployment system provided in an embodiment of the present invention; Figure 2 This is an overall flowchart of an intelligent grayscale and multi-version adaptive deployment method provided by an embodiment of the present invention; Figure 3 This is a schematic diagram of module interaction during a successful adaptive release process in an embodiment of the present invention; Figure 4 This is a schematic diagram of the process for triggering the security rollback and locking mechanism in an embodiment of the present invention. Detailed Implementation

[0022] 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.

[0023] Example 1: This invention discloses an intelligent canary and multi-version adaptive deployment system based on Kubernetes Operator. The system includes a specially designed Operator running in a Kubernetes cluster. This Operator, as a highly integrated control plane, achieves intelligent, adaptive, and closed-loop management of application deployments through the collaborative work of multiple internally integrated functional modules.

[0024] like Figure 1 As shown, the Operator of the system integrates the following cooperating modules: The strategy parsing module serves as the system's configuration entry point. It continuously monitors custom resources (CRDs) created or updated by users in the Kubernetes cluster, primarily DeploymentStrategy CRs. Users use these CRs to declaratively define all deployment intents, including: the identifier of the new application version to be deployed, business performance metrics to be monitored (e.g., user conversion rates exposed via API endpoints) and their health thresholds, technical performance metrics to be monitored (e.g., P95 response latency queried via Prometheus) and their health thresholds, deployment pacing strategies (e.g., conservative or aggressive), and audience rules for phased deployments. The strategy parsing module is responsible for parsing these CRs and transforming them into internal data structures for use by other modules.

[0025] The multi-dimensional monitoring module is the system's data acquisition unit. Based on the monitoring metric definitions provided by the strategy parsing module, it periodically pulls data from different data sources. For example, it obtains technical performance metrics such as CPU utilization, memory consumption, request error rate, and response latency by calling the Kubernetes Metrics API or connecting to the Prometheus service. At the same time, it also obtains business performance metrics such as user conversion rate and average order value from the business monitoring system or specific API endpoints via HTTP requests or other predefined methods.

[0026] The indicator anomaly detection module acts as a data preprocessor, located between the multi-dimensional monitoring module and the fusion decision engine, responsible for improving the signal-to-noise ratio of the decision input. It receives the raw indicator stream from the multi-dimensional monitoring module and performs real-time analysis using statistical methods (such as the 3-sigma principle and moving average trend analysis). Its main function is to identify and mark significant outliers in the indicator data, while filtering out random fluctuations or "noise" within the normal range, thus delivering clean and reliable data to the decision engine.

[0027] The fusion decision engine is the central hub of the system, responsible for core intelligent decision-making. It receives clean metric data preprocessed by the metric anomaly detection module. First, it weights and normalizes business and technical metrics from different sources and with different scales based on the weighting factors defined by the user in the deployment strategy. Then, it merges these into a single, quantitative, comprehensive "Version Health Score." Internally, the engine also includes an adaptive speed controller module. This module dynamically generates traffic adjustment instructions (e.g., increasing the step size to accelerate releases, decreasing the step size to slow down the pace, or pausing releases) based on the current value of the Version Health Score and its stable state within a preset time window. When the Version Health Score falls below a preset emergency rollback threshold, the engine immediately generates a rollback instruction. Furthermore, each time the engine makes a decision, it generates a structured log containing the decision basis (key metrics and values), the decision result, and a timestamp for auditing purposes.

[0028] The traffic control module is one of the system's execution units, responsible for precisely manipulating traffic allocation. It receives traffic adjustment instructions from the fusion decision engine and interacts with the control plane of the service mesh (such as Istio or Linkerd) deployed in the cluster to dynamically create or update routing rule resources (such as Istio's VirtualService). This allows it to implement complex traffic segmentation based on request context (such as HTTP headers and user geographic regions) and progressively expand the traffic exposure surface according to the ordered release phases defined in the instructions.

[0029] The security rollback and locking module is the system's security unit. When it receives an emergency rollback command from the fusion decision engine, it immediately performs two operations: First, it routes 100% of traffic back to the known stable version via the traffic control module; second, it applies a temporary "cooldown lock" to the current application's DeploymentStrategy CR. During the lock period, the Operator will suspend all automated deployment operations for the application, thus providing a stable time window for development or operations personnel to intervene and troubleshoot the problem, effectively preventing a cycle of repeated deployment failures before the problem is resolved.

[0030] Example 2: Reference Figure 2 Based on Example 1, and in conjunction with the above-described system modules, a complete workflow of the method of the present invention is further disclosed: A method for intelligent canary deployment and multi-version adaptive deployment based on KubernetesOperator includes the following steps: As an optional step, before the application is actually deployed, users can invoke the system's deployment strategy simulation module. Users provide a draft of the DeploymentStrategy CR, and the module will use historical telemetry data to perform an offline simulation of the strategy. For example, it will analyze whether last week's business traffic would cause a simulated rollback due to overly sensitive latency thresholds if this strategy were adopted. Finally, the module will output a risk assessment report to help users optimize and validate the rationality of the deployment strategy.

[0031] S1: The user creates a DeploymentStrategy CR, defining the target of this release as webapp:v2. The strategy specifies: monitoring P95 latency (threshold <200ms) and user order conversion rate (threshold must not be lower than 98% of the baseline version); adopting a "conservative" release pacing; the release process is divided into two phases: the first phase opens 100% traffic to internal test users (identified by HTTP Header user-group: internal), and the second phase opens it to 10% of public network users. The user submits the CR to the Kubernetes cluster using the kubectl apply command.

[0032] S2: After the deployment strategy defined in step S1 is successfully parsed by the Operator's strategy parsing module, the data collection and preprocessing process begins. The system's multi-dimensional monitoring module actively polls the various data sources defined in the deployment strategy. For example, it queries the Prometheus service to obtain the P95 response latency of webapp:v2 version, and simultaneously calls the business analysis API via HTTP to obtain the real-time user order conversion rate. The collected raw metric stream is transmitted in real time to the metric anomaly detection module. This module acts as an intelligent data "purifier," filtering out instantaneous data fluctuations that can be considered normal noise, such as an isolated latency spike; however, for statistically significant, persistent negative trends, such as a continuous decline in conversion rate over multiple collection periods, it marks them as genuine anomalies and passes this clean data with an anomaly label to the next stage.

[0033] S3: The pre-processed clean indicator data enters the system's fusion decision engine. At this stage, the fusion decision engine first standardizes indicators with varying dimensions and properties. For example, it maps latency in milliseconds and conversion rates in percentages to a unified, dimensionless scoring range of 0 to 1. Subsequently, the engine performs a weighted sum of the scores for each indicator based on the "conservative" weights defined in the deployment strategy—in this scenario, the importance of the business indicator "order conversion rate" may be three times that of the technical indicator "P95 latency." In this way, the engine ultimately calculates a "version health score" that comprehensively and accurately reflects the overall performance of the new version. In the initial stage of this embodiment, due to the excellent performance of version v2, its health score is stably calculated to be 0.95. The engine's built-in adaptive speed regulator component further analyzes this score, confirming that it remains above the health threshold of 0.9 within a preset 5-minute evaluation window, thus providing a reliable decision-making basis for entering the next release stage.

[0034] like Figure 3 As shown, S4: Based on the decision result of S3, the system's adaptive speed controller module determines that the first stage is successful and generates an instruction to enter the second stage and open traffic to 5% of public network users. This instruction triggers the following ordered execution flow: First, the instruction is sent to the predictive resource scaling module. Based on the upcoming 5% public network traffic and the historical resource consumption model of the webapp service, this module predicts that version v2 will require at least two replicas to cope smoothly. Therefore, it will preemptively adjust the minimum number of replicas of the v2 Deployment or its HPA to two.

[0035] After confirming that resources were ready, the instructions were finally executed by the traffic control module. This module precisely directed 5% of public network traffic to version v2 by updating the service mesh's routing rules, thus completing this release step.

[0036] In an example scenario: like Figure 4 As shown, the present invention also includes the ability to automatically handle release failure scenarios. For example, if a hidden bug appears in version v2 when public network traffic reaches 10%, the order conversion rate will plummet to 80% of the baseline version.

[0037] The multi-dimensional monitoring module immediately detected this change; The "version health score" calculated by the fusion decision engine plummeted to 0.6, below the emergency rollback threshold of 0.7; The engine immediately issued a rollback command. The security rollback and locking module took over the process, switching 100% of public network traffic back to the v1 stable version via the traffic control module, and imposing a 1-hour cooling-off lock on the DeploymentStrategy CR. Simultaneously, the system sent a notification to the operations team's alert channel via Webhook, stating "Automatic rollback triggered due to a severe drop in business metrics [conversion rate]," along with a snapshot of the decision log.

[0038] Those skilled in the art will understand that the systems and methods of the embodiments of the present invention can be implemented using computer-executable instructions. These instructions can be stored in a computer-readable storage medium (such as ROM, RAM, disk, optical disk, etc.). During execution, one or more processors (such as CPU, GPU) read and execute these instructions to perform the functions and steps of the various modules and methods described in the present invention. This system can run on a single server node or be deployed as a distributed system in a Kubernetes cluster environment consisting of multiple computing nodes.

[0039] In another example scenario: After the release process in Example 2 progressed to the point where public network traffic reached 20%, a downstream service on which the new version v2 depended experienced network jitter, causing the P95 response latency of version v2 to fluctuate frequently between 180ms and 220ms, briefly touching the 200ms health threshold multiple times. The specific implementation process is as follows: The multi-dimensional monitoring module captures the fluctuation data of latency, and the indicator anomaly detection module determines that the fluctuation is not an isolated noise point, but a continuous and unstable state, and transmits this situation to the decision engine. The "version health score" calculated by the fusion decision engine thus hovered between 0.88 and 0.92, consistently below the stable acceleration threshold of 0.95, but above the emergency rollback threshold of 0.7. At this point, the adaptive speed regulator module within the engine determined that the version health was poor but had not yet reached a dangerous level, and therefore generated a "pause release" instruction. After receiving the instruction, the flow control module maintains the current 20% flow rate and does not increase it. Simultaneously, the system enters a "release pause" state, in which the adaptive speed controller continuously evaluates the health score at shorter intervals (e.g., every 30 seconds). After a 15-minute pause, the downstream service network stabilized, and the latency of version v2 returned to normal levels. The version health score subsequently rebounded and remained stable at 0.96 for 5 consecutive minutes. The adaptive speed controller determined that the system had recovered its health, so it lifted the pause and cautiously increased the traffic to 22% in a step size smaller than the initial value (e.g., 2%) to continue the release process.

[0040] In this example, the invention demonstrates a high degree of intelligence and robustness by employing a fine-grained adjustment capability of "slowing down and pausing" rather than simply "rolling over" when faced with non-fatal but unstable performance issues.

[0041] In another example scenario: A large application plans to release a new version v3 with a major UI overhaul. To minimize risk, the user has defined a three-phase DeploymentStrategy CR, with the following implementation process: Phase 1: For internal employees and specific test users, traffic is 100% open to requests that meet any of the following conditions: HTTP header user-group: internal or user-id is in the predefined "core experience officer" list; The system initially applies this rule, granting traffic only to internal employees and a small number of core test users. During this phase, the focus is on monitoring the new UI's business performance metrics, such as "new feature click-through rate" and "average page dwell time." After 24 hours of observation, if the version health score stabilizes and meets the target, the system automatically proceeds to the next phase.

[0042] Phase Two: For a portion of users in specific regions, in addition to the previous phase, an additional 10% of user traffic from the "Shanghai" region (determined by the regional identifier of the request source IP or business gateway) will be redirected to version v3. The traffic control module updates the service mesh routing rules to achieve traffic segmentation for specific regions. This stage focuses on monitoring technical performance indicators and verifying the latency and error rate of the new version under specific regional network environments. Once the version health score consistently meets the standards, the system automatically advances to the next stage.

[0043] Phase 3: Entering the final percentage-based rollout phase, starting with 5% and gradually opening up traffic to all public network users; The system applies the final weighted routing rules, and the adaptive speed regulator module dynamically adjusts the rate of traffic increase based on the real-time calculated global version health score until 100% full deployment is completed.

[0044] In this scenario, we can understand how the present invention, through deep integration with the service mesh, supports highly flexible and multi-dimensional context routing, and realizes a smooth, step-by-step risk control strategy from "user identity" to "geographic location" and then to "global percentage," which is difficult to achieve using traditional deployment tools.

[0045] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent canary deployment and multi-version adaptive deployment based on KubernetesOperator, characterized in that, Includes the following steps: S1. Obtain the user-defined deployment strategy, which includes at least one application version to be released, the definition of business performance indicators, and the release schedule strategy; S2. Through an Operator running in a Kubernetes cluster, periodically collect technical performance metrics of the application version to be released and business performance metrics collected according to the definition. S3. After weighting and normalizing the collected technical performance indicators and business performance indicators, perform fusion analysis to generate a comprehensive version health score. S4. Based on the version health score and the release rhythm strategy, adaptively adjust the traffic allocation weight for the application version to be released.

2. The intelligent canary deployment and multi-version adaptive deployment method based on KubernetesOperator according to claim 1, characterized in that, Before step S3, the method further includes: preprocessing the indicators collected in step S2 through the indicator anomaly detection module, identifying and marking statistically significant anomalies, and using the processed results to generate the version health score.

3. The intelligent canary deployment and multi-version adaptive deployment method based on KubernetesOperator according to claim 1, characterized in that, S4 further includes: Determine whether the health score of the version remains stable and above the health threshold within a preset time window. If so, increase the adjustment step size of the traffic allocation weight in the next iteration. Conversely, the adjustment step size should be reduced, the observation time extended, or the flow allocation suspended to prevent decision oscillations caused by instantaneous fluctuations in indicators.

4. The intelligent canary deployment and multi-version adaptive deployment method based on KubernetesOperator according to claim 1, characterized in that, The method further includes: when the version health score is lower than a preset emergency rollback threshold, automatically switching all traffic back to a known stable version and applying a temporary cooling-off lock to the application's deployment strategy to suspend the automated release process.

5. The intelligent canary deployment and multi-version adaptive deployment method based on KubernetesOperator according to claim 1, characterized in that, Before executing the deployment method, the method further includes: performing a simulation, wherein the deployment strategy is analyzed using historical indicator data, and a potential risk assessment report is generated.

6. A smart canary and multi-version adaptive deployment system based on Kubernetes Operator, used to execute the smart canary and multi-version adaptive deployment method based on Kubernetes Operator as described in any one of claims 1-5, comprising a processor, a memory, and a Kubernetes Operator running thereon, characterized in that, The Operator, as an organic whole, integrates the following cooperative modules: The policy parsing module is used to obtain and parse user-defined deployment policies; The multi-dimensional monitoring module is used to collect technical performance indicators and business performance indicators; The indicator anomaly detection module is used to preprocess the collected raw indicators to identify statistically significant anomalies and filter noise. The fusion decision engine is used to receive the cleanliness indicators processed by the anomaly detection module, generate a version health score, and generate traffic adjustment or rollback instructions accordingly. The fusion decision engine is also configured to generate auditable decision logs for each decision. The flow control module is used to execute flow adjustment commands; The security rollback and locking module is used to perform traffic switching and apply a cooling lock to the deployment policy when a rollback command is received.

7. The intelligent canary deployment and multi-version adaptive deployment system based on KubernetesOperator according to claim 6, characterized in that, When generating version health scores, the fusion decision engine is configured to apply user-defined weighting factors to differentiate the importance of different business performance indicators and technical performance indicators, thereby enabling business-priority decisions.

8. The intelligent canary deployment and multi-version adaptive deployment system based on KubernetesOperator according to claim 6, characterized in that, The fusion decision engine further includes an adaptive speed regulator module, which is configured to dynamically adjust the step size and interval of traffic allocation based on the stable state of the version health score within a preset time window.

9. The intelligent canary and multi-version adaptive deployment system based on KubernetesOperator according to claim 6, characterized in that, The traffic control module is configured to interact with the service mesh and apply different context routing rules step by step according to the ordered release phases defined in the deployment strategy.

10. The intelligent canary and multi-version adaptive deployment system based on KubernetesOperator according to claim 6, characterized in that, The system also includes a deployment strategy simulation module, which is configured to perform offline simulation and risk assessment of the deployment strategy before deployment execution, and provide the assessment results to the user.

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