Risk assessment and early warning method for task scheduling

Abstract

The invention belongs to the technical field of risk assessment and early warning, and particularly relates to a risk assessment and early warning method for task scheduling, and the method comprises the following steps: S1, collecting task scheduling data; s2, time interval and overlap analysis; s3, monitoring system resources in real time; s4, integrating a risk assessment model; and S5, carrying out risk early warning and automatic intervention. According to the invention, starting from intelligent analysis of task scheduling time intervals, task space-time overlap detection and system resource monitoring are innovatively combined, a comprehensive risk assessment model is established, early warning and automatic intervention are realized, and a task scheduling risk prevention and control solution with low cost and high real-time performance is provided for a complex system.

Description

Technical Field

[0001] This invention relates to the field of risk assessment and early warning technology, and in particular to a method for risk assessment and early warning of task scheduling. Background Technology

[0002] In modern enterprise distributed systems, data processing platforms, and microservice architectures, task scheduling frameworks (such as Quartz, Cron, XXL-JOB, and Airflow) are widely used for periodically executing batch processing, data synchronization, API calls, and log cleanup. Within these task scheduling frameworks, existing monitoring technologies mostly only monitor and alert on the execution status of individual tasks (such as success, failure, execution time, and exception logs). As business scales and the number of scheduled tasks continues to increase, traditional solutions, without intervention, reveal the following shortcomings: (1) Overlapping task execution time and resource conflict: Multiple high-frequency or long-duration tasks may start in the same time window, causing task execution congestion. When the task execution time exceeds the expectation or an abnormality occurs, it overlaps with subsequent planned tasks, causing problems such as thread pool exhaustion and database connection pool exhaustion. (2) Complex inter-task dependencies: Most existing monitoring technologies only monitor and alert on the execution status of a single task (such as success, failure, time consumption, and abnormal logs), lack comprehensive analysis of the time correlation between multiple tasks, cannot identify the risk of task groups being triggered in the same time period, and are difficult to predict the resulting system resource competition or scheduling conflicts. (3) Lack of multi-dimensional risk perception: Traditional monitoring often uses the success of task execution as a single indicator, which makes it difficult to identify potential risk situations in a timely manner. It lacks real-time correlation analysis of server resources such as CPU, memory, disk I / O, and network, and cannot provide early warnings. (4) High and passive operation and maintenance costs: When the system experiences resource bottlenecks due to concentrated task triggering, operation and maintenance personnel often only intervene after an alarm is triggered, resulting in delayed processing and high costs. In summary, while existing technologies can monitor the execution status of individual tasks and provide simple alerts, they lack a holistic analysis of the temporal relationships between multiple tasks, the risks of overlapping execution, and their impact on system resource consumption. This fails to meet the needs of forward-looking risk management in large-scale and complex scheduling environments. Therefore, we propose a risk assessment and early warning method for task scheduling. Summary of the Invention

[0003] The present invention proposes a risk assessment and early warning method for task scheduling, which solves the problems existing in the prior art.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A method for risk assessment and early warning of task scheduling includes the following steps: S1: Task scheduling data collection: Periodically pull task metadata from the scheduling system. The task metadata includes task ID, task name, planned execution time, scheduling cycle and priority. The task metadata also includes historical actual start time, end time and execution time distribution. Then write the above task metadata into the "task scheduling log database" to store task logs and model data, which is convenient for subsequent statistics and modeling. S2: Time interval and overlap analysis, the specific steps of which are as follows: S201: Calculate the average execution time μ and standard deviation σ for each task, and estimate the time interval for the next execution [plan start, plan start + μ + kσ]; S202: Use the sliding window algorithm to scan the time intervals of all tasks within the next T minutes. If the intervals of two tasks overlap, it is recorded as a "potential overlap event". S203: Supports dynamic weight allocation, with higher priority tasks having higher weights than lower priority tasks; S3: Real-time monitoring of system resources: Use Agent or Prometheus monitoring components to collect CPU utilization, memory usage, disk I / O and network bandwidth, and then build a resource utilization baseline model; S4: Comprehensive Risk Assessment Model: Input the task overlap degree D, system resource status S and historical failure rate F into the risk assessment model. Its calculation formula is R = α·D + β·S + γ·F, where α, β and γ are adjustable weights. S5: Risk warning and automatic intervention, the specific process of which is as follows: S501: When the risk level reaches medium or high, push alerts via message queue, email, SMS, or Webhook. S502: It can execute automated intervention strategies, adjust the next execution time of low-priority tasks to avoid conflicts, and trigger the cloud platform's automatic scaling strategy and suspend non-critical tasks to ensure core task resources.

[0005] Preferably, in step S1, the collection frequency of periodically pulling task metadata from the scheduling system can be adaptively adjusted according to the load of the scheduling system. The default collection frequency is 30 seconds to 1 minute to ensure a balance between data real-time performance and system overhead.

[0006] Preferably, in step S202, an "overlap" index is calculated for any combination of n tasks, where the "overlap" index D = Σ (overlap duration × task weight).

[0007] Preferably, in step S3, the specific process of constructing the resource utilization baseline model is as follows: the normal fluctuation range is calculated using a moving average or ARIMA model, and if the current utilization rate deviates from the baseline by more than a threshold, it is marked as "resource stress state".

[0008] Preferably, in step S4, the risk assessment model supports online learning and can dynamically adjust weights based on actual feedback from historical alarms. The risk assessment model sets multiple risk thresholds: low risk: R < R1; medium risk: R1 ≤ R < R2; high risk: R ≥ R2.

[0009] Preferably, in step S502, all intervention operations are recorded in the log for subsequent auditing and model optimization.

[0010] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention can identify high-risk periods in the future and issue early warnings through a predictive model before task execution conflicts actually occur. The system can provide early warnings several minutes or even hours before the planned execution, allowing maintenance personnel to take measures such as resource scheduling or task order adjustment in advance to avoid system downtime, delays or performance degradation caused by intensive task execution, thus realizing the transformation from post-processing to pre-event prevention. 2. Based on the analysis of task spatiotemporal overlap, this invention integrates the system's real-time resource utilization rate and historical task failure rate to form a multi-dimensional risk assessment model. Through a comprehensive algorithm with adjustable weights, it can significantly reduce the false alarm and false negative rates of single-dimensional monitoring and ensure high accuracy of risk prediction in various business scenarios. 3. The risk assessment model of this invention has an online learning and adaptive parameter tuning mechanism, which can dynamically adjust the weights and thresholds based on the actual feedback of historical alarms. As the task execution mode, quantity or system load changes, the model can be automatically updated without manual intervention, and can maintain the prediction accuracy and stability that matches the actual business environment in the long term. 4. This invention does not rely on a specific task scheduling framework or dedicated hardware. It can be deployed with only common log output interfaces and standardized monitoring data sources. It can be seamlessly integrated with mainstream scheduling systems and is suitable for public cloud, private cloud and hybrid cloud environments.

[0011] In summary, this invention starts with intelligent analysis of task scheduling time intervals, innovatively combines task spatiotemporal overlap detection with system resource monitoring, establishes a comprehensive risk assessment model, and achieves early warning and automatic intervention, providing a low-cost, high-real-time task scheduling risk prevention and control solution for complex systems. Attached Figure Description

[0012] Figure 1 This is a flowchart of a risk assessment and early warning method for task scheduling proposed in this invention. Detailed Implementation

[0013] 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.

[0014] Reference Figure 1 A risk assessment and early warning method for task scheduling includes the following steps: S1: Task scheduling data collection: Task metadata is periodically pulled from the scheduling system. The collection frequency of task metadata can be adaptively adjusted according to the load of the scheduling system. The default collection frequency is 30 seconds to 1 minute to ensure a balance between data real-time performance and system overhead. The task metadata includes task ID, task name, planned execution time, scheduling cycle and priority. The task metadata also includes historical actual start time, end time and execution time distribution. Then, the above task metadata is written to the "task scheduling log database" to store task logs and model data, which is convenient for subsequent statistics and modeling. S2: Time interval and overlap analysis, the specific steps of which are as follows: S201: Calculate the average execution time μ and standard deviation σ for each task, and estimate the time interval for the next execution [plan start, plan start + μ + kσ]; S202: The sliding window algorithm is used to scan the time interval of all tasks within the next T minutes. If the intervals of two tasks intersect, it is recorded as a "potential overlap event". The "overlap" index is calculated for any combination of n tasks, where the "overlap" index D = Σ (overlap duration × task weight). S203: Supports dynamic weight allocation, with higher priority tasks having higher weights than lower priority tasks; S3: Real-time monitoring of system resources: Use Agent or Prometheus monitoring components to collect CPU utilization, memory usage, disk I / O and network bandwidth, and then build a resource utilization baseline model. The specific process is as follows: Use a moving average or ARIMA model to calculate the normal fluctuation range. If the current utilization deviates from the baseline by more than a threshold, it is marked as "resource stress". S4: Comprehensive Risk Assessment Model: Input the task overlap degree D, system resource status S, and historical failure rate F into the risk assessment model. The calculation formula is R = α·D + β·S + γ·F, where α, β, and γ are adjustable weights. The risk assessment model supports online learning and can dynamically adjust the weights based on the actual feedback from historical alarms. The risk assessment model sets multiple risk thresholds: Low risk: R < R1; Medium risk: R1 ≤ R < R2; High risk: R ≥ R2. S5: Risk warning and automatic intervention, the specific process of which is as follows: S501: When the risk level reaches medium or high, push alerts via message queue, email, SMS, or Webhook. S502: It can execute automated intervention strategies, adjust the next execution time of low-priority tasks to avoid conflicts, and trigger the cloud platform's automatic scaling strategy and suspend non-critical tasks to ensure core task resources. In addition, all intervention operations are recorded in the log for subsequent auditing and model optimization. Therefore, this embodiment starts with intelligent analysis of task scheduling time intervals, innovatively combines task spatiotemporal overlap detection with system resource monitoring, establishes a comprehensive risk assessment model, realizes early warning and automatic intervention, and provides a low-cost, high-real-time task scheduling risk prevention and control solution for complex systems.

[0015] 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 risk assessment and early warning of task scheduling, characterized in that, Includes the following steps: S1: Task scheduling data collection: Periodically pull task metadata from the scheduling system. The task metadata includes task ID, task name, planned execution time, scheduling cycle and priority. The task metadata also includes historical actual start time, end time and execution time distribution. Then write the above task metadata into the "task scheduling log database" to store task logs and model data. S2: Time interval and overlap analysis, the specific steps of which are as follows: S201: Calculate the average execution time μ and standard deviation σ for each task, and estimate the time interval for the next execution [plan start, plan start + μ + kσ]; S202: Use the sliding window algorithm to scan the time intervals of all tasks within the next T minutes. If the intervals of two tasks overlap, it is recorded as a "potential overlap event". S203: Supports dynamic weight allocation, with higher priority tasks having higher weights than lower priority tasks; S3: Real-time monitoring of system resources: Use Agent or Prometheus monitoring components to collect CPU utilization, memory usage, disk I / O and network bandwidth, and then build a resource utilization baseline model; S4: Comprehensive Risk Assessment Model: Input the task overlap degree D, system resource status S and historical failure rate F into the risk assessment model. Its calculation formula is R = α·D + β·S + γ·F, where α, β and γ are adjustable weights. S5: Risk warning and automatic intervention, the specific process of which is as follows: S501: When the risk level reaches medium or high, push alerts via message queue, email, SMS, or Webhook. S502: It can execute automated intervention strategies, adjust the next execution time of low-priority tasks to avoid conflicts, and trigger the cloud platform's automatic scaling strategy and suspend non-critical tasks to ensure core task resources.

2. The method for risk assessment and early warning of task scheduling according to claim 1, characterized in that, In S1, the collection frequency of periodically pulling task metadata from the scheduling system can be adaptively adjusted according to the load of the scheduling system. The default collection frequency is 30 seconds to 1 minute to ensure a balance between data real-time performance and system overhead.

3. The method for risk assessment and early warning of task scheduling according to claim 1, characterized in that, In step S202, the "overlap" index is calculated for any combination of n tasks, where the "overlap" index D = Σ (overlap duration × task weight).

4. The method for risk assessment and early warning of task scheduling according to claim 1, characterized in that, In S3, the specific process of constructing the resource utilization baseline model is as follows: the normal fluctuation range is calculated using a moving average or ARIMA model. If the current utilization rate deviates from the baseline by more than a threshold, it is marked as "resource stress".

5. The method for risk assessment and early warning of task scheduling according to claim 1, characterized in that, In S4, the risk assessment model supports online learning and can dynamically adjust weights based on actual feedback from historical alarms. The risk assessment model sets multiple risk thresholds.

6. The method for risk assessment and early warning of task scheduling according to claim 1, characterized in that, In S502, all intervention operations are recorded in the log.

Images