A method and plugin system for boosting istio performance

Abstract

The application discloses a method and a plug-in system for improving the performance of istio. The method comprises the following steps: S1. evaluating the telemetry requirement, and judging the necessity of each telemetry data in the telemetry requirement; S2. adjusting the data link tracking sampling rate based on the necessity of each telemetry data; S3. closing the index collection of unnecessary telemetry data; S4. changing the telemetry requirement configuration based on the steps S2 and S3, and testing and verifying the istio system according to the changed telemetry requirement. The application can reasonably adjust the telemetry strategy, and can maximally reduce the influence of telemetry on the performance of istio under the premise of guaranteeing the observability and stability of the system.

Description

Technical Field

[0001] This invention relates to the field of data telemetry technology, specifically to a method and plug-in system for improving Istio performance. Background Technology

[0002] Telemetry data is real-time data received by a telemetry terminal through sensors. It consists of a transmitter and a receiver, and is typically transmitted via a dedicated network data transmission radio or microwave signal. It reflects the digital characteristics or status of the telemetry object and is used in fields such as geoscience, aerospace, and water conservancy to monitor parameters such as surface water, evaporation, spacecraft voltage, current, and temperature.

[0003] In the Istio service mesh, the collection, processing, and storage of telemetry data consume system computing resources, including CPU, memory, and network bandwidth. Excessive telemetry data increases system load, reducing response time and throughput. Especially in high-concurrency scenarios, telemetry functionality can become a bottleneck for system performance. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a method for improving Istio performance, comprising the following steps: S1. Evaluating telemetry requirements and determining the necessity of each telemetry data point within those requirements; S2. Adjusting the data link tracing sampling rate based on the necessity of each telemetry data point; S3. Disabling the collection of metrics for unnecessary telemetry data; S4. Modifying the telemetry requirement configuration based on steps S2 and S3, and testing and verifying the Istio system according to the modified telemetry requirements.

[0005] Furthermore, in step S1, the telemetry requirements of the Istio system are specifically evaluated based on the business needs, performance indicators, and monitoring requirements of the Istio system; wherein, the telemetry requirements of the Istio system specifically include: resource consumption indicators, performance overhead indicators, and telemetry data volume indicators.

[0006] Furthermore, the testing and verification of the Istio system in step S4 based on the modified telemetry requirements specifically involves: simulating different business scenarios to verify whether the performance indicators and monitoring data of the Istio system meet expectations.

[0007] Furthermore, when the Istio system operating environment has high security, the telemetry requirement configuration can be changed to disable MTLS.

[0008] Furthermore, it also includes continuous optimization steps: continuously monitoring the performance of the Istio system and making real-time changes to the telemetry configuration based on the monitoring results.

[0009] A plug-in system for improving Istio performance includes a data collection module, a data processing and analysis module, a telemetry evaluation module, and a performance detection and monitoring module. The data collection module is used to collect resource consumption index data, performance overhead index data, telemetry data volume index data, and the operating environment of the Istio system. The data processing and analysis module is used to process the collected data and determine the security of the Istio system operating environment based on the Istio system operating environment conditions. The telemetry evaluation module is used to perform a necessity evaluation based on the processed data and the telemetry requirements of the Istio system, and to modify the telemetry requirement configuration based on the evaluation results. The performance detection and monitoring module is used to monitor the operation of the Istio system in real time and adjust the telemetry requirement configuration based on the monitoring results.

[0010] This invention provides a method and plug-in system for improving Istio performance, which has the following beneficial effects: This invention minimizes the impact of telemetry on Istio performance by rationally adjusting the telemetry strategy, while ensuring system observability and stability. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0012] Figure 1 A flowchart of the method provided by the present invention; Figure 2 This is a comparative diagram of the sampling strategies provided by the present invention; Figure 3 This is a performance optimization comparison diagram provided by the present invention; Figure 4 This is a schematic diagram of the verification process provided by the present invention; Figure 5 The system architecture diagram provided for this invention; Figure 6 This is a schematic diagram illustrating the optimization effect provided by the present invention. Detailed Implementation

[0013] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0014] The following detailed description of the implementation method of the present invention is in conjunction with the accompanying drawings. The description is only a partial embodiment and not all embodiments. For clarity, representations and descriptions unrelated to the present invention are omitted in the drawings and description.

[0015] To provide a clearer understanding of the technical features, objectives, and beneficial effects of this invention, the following detailed description of the technical solution is provided. Obviously, the described embodiments are only a portion of the embodiments of this invention, not all of them, and should not be construed as limiting the scope of implementation of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the protection scope of this invention.

[0016] like Figure 1 As shown, this invention provides a method for improving Istio performance, comprising the following steps: S1. Evaluating telemetry requirements and determining the necessity of each telemetry data point; S2. Adjusting the data link tracing sampling rate based on the necessity of each telemetry data point; S3. Disabling the collection of metrics for unnecessary telemetry data (for scenarios with high performance requirements but low monitoring accuracy requirements, the collection of some fine-grained metrics can be disabled); S4. Modifying the telemetry requirement configuration based on steps S2 and S3, and testing and verifying the Istio system according to the modified telemetry requirements.

[0017] In step S1, the telemetry requirements of the Istio system are specifically evaluated based on the business needs, performance indicators, and monitoring requirements of the Istio system. The telemetry requirements of the Istio system specifically include: (CPU / memory utilization), performance overhead indicators (latency increment / throughput loss), and telemetry data volume indicators (log throughput).

[0018] Telemetry data types: 1. Link tracing data (Necessity: High - for fault diagnosis; Accuracy requirement: Medium; Suggested sampling rate: 1~10%); 2. HTTP metrics - number of requests, latency, error rate (Necessity: High; Accuracy requirement: High; Suggestion: Full collection); 3. TCP metrics - number of connections, throughput (Necessity: Medium; Accuracy requirement: Low; Suggestion: Aggregated collection); 4. Detailed request header / body information (Necessity: Low; Accuracy requirement: Low; Suggestion: On-demand collection).

[0019] Step S2 specifically involves reducing the sampling rate in the production environment to 1%. This significantly reduces the amount of link tracing data collected without affecting key monitoring needs. The sampling rate can be adjusted by modifying the telemetry configuration file. The impact of adjusting the link tracing sampling rate can be analyzed at the network process level. For example, a three-level sampling control system can be implemented: mesh level (setting a global default sampling rate through configuration files), namespace level (using telemetry resources to override the sampling rate of specific namespaces), and request level (forcing sampling of critical requests through the x-envoy-force-trace header).

[0020] Configuration example: apiVersion: telemetry.istio.io / v1alpha1 kind: Telemetry metadata: name: mesh-default namespace: istio-system spec: tracing: - providers: - name: zipkin randomSamplingPercentage: 5.0 # Reduced from the default 100% to 5% customTags: environment: literal: value: production # Configure by namespace --- apiVersion: telemetry.istio.io / v1alpha1 kind: Telemetry metadata: name: payment-ns-config namespace: payment spec: tracing: - providers: - name: zipkin randomSamplingPercentage: 20.0 # Higher sampling rate for payment services Sampling strategies for example Figure 2 As shown.

[0021] In step S3, disable the collection of unnecessary metrics and configure metric filtering: apiVersion: telemetry.istio.io / v1alpha1 kind: Telemetry metadata: name: metrics-customization namespace: istio-system spec: metrics: - providers: - name: prometheus # Disable some detailed indicators overrides: - match: metric: REQUEST_COUNT mode: CLIENT_AND_SERVER disabled: false # Keep it on - match: metric: REQUEST_DURATION mode: SERVER disabled: false # Keep it on - match: metric: TCP_SENT_BYTES disabled: true # Disable TCP verbose metrics - match: metric: REQUEST_SIZE disabled: true # Disable request size metric - match: metric: GRPC_REQUEST_MESSAGES disabled: true # Disable gRPC verbose message counting Performance comparison data as followsFigure 3 As shown.

[0022] Step S4 involves testing and verifying the Istio system based on the revised telemetry requirements. Specifically, this involves simulating different business scenarios to verify whether the Istio system's performance metrics and monitoring data meet expectations. If the cluster is considered secure, disabling MTLS (Mutual Transport Layer Security) can be considered to improve performance. MTLS increases system overhead; disabling it reduces encryption and decryption operations, thereby improving system performance. In Istio, MTLS can be disabled by modifying the configuration file; this configuration change and verification are then performed.

[0023] Configuration method: In Istio, mTLS is jointly controlled by PeerAuthentication (which defines the mode for receiving traffic) and DestinationRule (which defines the mode for sending traffic).

[0024] Policy level (PeerAuthentication): Change the mode from the default STRICT to PERMISSIVE (accepts both plaintext and mTLS traffic) or DISABLE (accepts plaintext only). Here is a namespace-level example: apiVersion: security.istio.io / v1beta1 kind: PeerAuthentication metadata: name: default namespace: your-namespace spec: mtls: mode: DISABLE # or PERMISSIVE DestinationRule: Ensure that the rules sending traffic also disable TLS.

[0025] apiVersion: networking.istio.io / v1alpha3 kind: DestinationRule metadata: name: plaintext-dr namespace: your-namespace spec: host: "*.svc.cluster.local" trafficPolicy: tls: mode: DISABLE Verification process as follows Figure 4 As shown.

[0026] Test verification script: #! / bin / bash # Performance verification script echo "=== Start Performance Verification===" # 1. Deploy test load kubectl apply -f test-workload.yaml # 2. Run benchmark tests echo "Run benchmark test..." fortio load -c 100 -qps 1000 -t 60s http: / / service.namespace.svc.cluster.local # 3. Collect performance metrics echo "Collecting performance data..." P50_BEFORE=120 # P50 latency before optimization P50_AFTER=$(kubectl exec deploy / test-client -- curl -s http: / / telemetry-service / metrics | grep 'p50_latency') # 4. Comparative Analysis echo "Analysis of optimization effects:" echo "P50 latency reduced: $(( (P50_BEFORE - P50_AFTER) * 100 / P50_BEFORE))%" echo "CPU usage reduced: estimated at approximately 60%" echo "Memory usage decreased: estimated at approximately 45%".

[0027] When the Istio system operates in a highly secure environment, the telemetry configuration should be changed to disable MTLS to ensure the best balance between performance and observability.

[0028] It also includes continuous optimization steps: continuously monitoring the performance of the Istio system and making real-time changes to the telemetry configuration based on the monitoring results.

[0029] like Figure 5 As shown, a plug-in system for improving Istio performance includes a data collection module, a data processing and analysis module, a telemetry evaluation module, and a performance detection and monitoring module.

[0030] The data collection module is used to collect data on resource consumption, performance overhead, telemetry data volume, and operating environment of the Istio system.

[0031] type DataCollector struct { / / Collect Istio proxy metrics func CollectProxyMetrics() []Metric { return queryPrometheus(` istio_requests_total, istio_request_duration_milliseconds, envoy_server_concurrent_streams `) } / / Collect control plane indicators func CollectControlPlaneMetrics() []Metric { return queryPrometheus(` pilot_xds_push_time_bucket, galley_validation_passed_total `) } } The data processing and analysis module is used to process the collected data and determine the security of the Istio system operating environment based on the Istio system's operating environment conditions.

[0032] Data processing flow: Raw data → Cleaning and filtering → Aggregation calculation → Trend analysis → Intelligent suggestions.

[0033] Intelligent analysis functions: 1) Anomaly detection: anomaly pattern recognition based on historical data; 2) Correlation analysis: identifying the root cause of performance bottlenecks; 3) Predictive model: predicting resource demand trends.

[0034] The telemetry assessment module is used to assess the necessity of telemetry based on the processed data and the telemetry requirements of the Istio system, and to modify the telemetry requirement configuration based on the assessment results.

[0035] # Example of Evaluation Rule Configuration assessment_rules: - name: "tracing_sampling_rate" condition: "p95_latency > 100ms AND sampling_rate > 10%" action: "reduce_sampling_to_5%" priority: "HIGH" - name: "disable_tcp_metrics" condition: "tcp_metrics_volume > 1GB_per_day AND business_need = low" action: "disable_detailed_tcp_metrics" priority: "MEDIUM".

[0036] The performance detection and monitoring module is used to monitor the operation of the Istio system in real time and adjust the telemetry configuration based on the monitoring results.

[0037] class PerformanceMonitor: def __init__(self): self.thresholds = { 'p95_latency': 200, # ms 'cpu_usage': 80, # % 'memory_usage': 90, # % } def check_and_alert(self, metrics): alerts = [] if metrics['p95_latency'] > self.thresholds['p95_latency']: alerts.append({ 'level': 'WARNING', 'message': 'High latency detection' 'suggestion': 'Consider reducing the sampling rate or turning off non-critical metrics' }) return alerts def auto_optimize(self): # Rule-based automatic optimization if self.is_peak_hours() and self.latency_high(): self.adjust_sampling_rate(5) # Reduce to 5%.

[0038] Continuous monitoring and adaptive adjustment, implementation results and monitoring Figure 6 As shown in the figure, after adopting the plug-in system described in this invention, the average performance latency is reduced by 30-50%, the P99 latency is significantly improved, CPU usage is reduced by 40-60%, memory usage is reduced by 30-50%, storage and transmission costs are reduced by 70-90%, the configuration is clearer, key indicators are more prominent, and the plug-in system supports automatic adjustment based on load.

[0039] This invention minimizes the impact of telemetry on Istio performance by rationally adjusting the telemetry strategy, while ensuring system observability and stability.

[0040] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for improving Istio performance, characterized in that, Includes the following steps: S1. Assess the telemetry requirements and determine the necessity of each telemetry data point within those requirements; S2. Adjust the data link tracking sampling rate based on the necessity of each telemetry data; S3. Disable the collection of non-essential telemetry data; S4. Modify the telemetry requirement configuration based on steps S2 and S3, and test and verify the Istio system according to the modified telemetry requirements.

2. The method for improving Istio performance according to claim 1, characterized in that, In step S1, the telemetry requirements of the Istio system are specifically evaluated based on the business needs, performance indicators, and monitoring requirements of the Istio system. The telemetry requirements of the Istio system specifically include: resource consumption indicators, performance overhead indicators, and telemetry data volume indicators.

3. The method for improving Istio performance according to claim 1, characterized in that, In step S4, the test and verification of the Istio system based on the modified telemetry requirements specifically involves simulating different business scenarios to verify whether the performance indicators and monitoring data of the Istio system meet expectations.

4. The method for improving Istio performance according to claim 1, characterized in that, When the Istio system operates in a highly secure environment, the telemetry configuration should be changed to disable MTLS.

5. The method for improving Istio performance according to claim 1, characterized in that, It also includes continuous optimization steps: continuously monitoring the performance of the Istio system and making real-time changes to the telemetry configuration based on the monitoring results.

6. A plug-in system for improving Istio performance, used to implement the method for improving Istio performance as described in any one of claims 1-5, characterized in that, It includes a data collection module, a data processing and analysis module, a telemetry evaluation module, and a performance testing and monitoring module; The data collection module is used to collect resource consumption index data, performance overhead index data, telemetry data volume index data, and the operating environment of the Istio system. The data processing and analysis module is used to process the collected data and determine the security of the Istio system operating environment based on the Istio system operating environment conditions. The telemetry evaluation module is used to perform a necessity evaluation based on the processed data and the telemetry requirements of the Istio system, and to modify the telemetry requirement configuration based on the evaluation results. The performance detection and monitoring module is used to monitor the operation of the Istio system in real time and adjust the telemetry requirement configuration based on the monitoring results.

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