Dynamic Sampling Rate Adjustment for Statistical Data Collection
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Solution Overview
Problem
In distributed computing systems, the default sampling rate for remote procedure calls (RPCs) is often too low for small services, resulting in incomplete statistics, and too high for large services, leading to infrastructure overload, necessitating a dynamic adjustment of sampling rates to balance data collection and resource usage.
Innovation Solution
A method and apparatus for dynamically adjusting the sampling rate for statistical data collection, using a control server to determine a target sampling rate based on minimum and maximum quotas, comparing it to the current rate, and adjusting accordingly, applicable to any sampling-based data gathering where rate variation is significant.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high sampling rate is used for statistical data collection, then measurement precision is improved, but loss of energy increases due to infrastructure overload
Solution Approach 1:
The patent implements dynamic sampling rate adjustment where the sampling rate is not fixed but adapts continuously based on service characteristics and system state. The system determines a target sampling rate for each service by comparing minimum and maximum quotas, then dynamically adjusts the actual sampling rate accordingly, transforming a static parameter into a dynamic one that responds to changing conditions.
Solution Approach 2:
The core invention involves changing the sampling rate parameter based on service needs. The system calculates a target sampling rate using the formula: target sampling rate = minimum quota + (maximum quota - minimum quota) × service weight, where service weight is derived from service characteristics. This parameter change allows the system to optimize between data collection quality and resource consumption.
2Loss of energy
If a low sampling rate is used for statistical data collection, then loss of energy is reduced, but measurement precision deteriorates resulting in incomplete statistics
Solution Approach 1:
The system changes the sampling rate parameter dynamically based on service characteristics. By calculating a target sampling rate that respects both minimum and maximum quotas, the system ensures that even services with lower priorities receive sufficient sampling to maintain statistical validity, while preventing any single service from consuming excessive resources.
Solution Approach 2:
The patent applies different sampling rates to different services based on their individual characteristics and priorities. Each service receives a customized sampling rate determined by its service weight, which is calculated based on factors such as service importance, traffic patterns, and resource constraints. This local quality approach ensures that each service gets the appropriate level of monitoring without uniformly impacting all services.
3Device complexity
If a fixed sampling rate is used for all services, then device complexity is reduced, but adaptability deteriorates as it cannot accommodate varying service needs
Solution Approach 1:
The system implements local quality by assigning different sampling rates to different services based on their specific needs. Each service is evaluated individually using its service weight, which considers factors like importance, traffic volume, and resource requirements. This allows the system to optimize data collection for each service locally rather than applying a one-size-fits-all approach.
Solution Approach 2:
The system enables self-service by automatically determining appropriate sampling rates for each service without requiring manual configuration. The automated calculation of target sampling rates based on minimum and maximum quotas, combined with service weight evaluation, allows the system to adapt to varying service needs autonomously, reducing operational complexity while maintaining high adaptability.
Data Source
AI summary
Methods and apparatuses for dynamically adjusting sampling rates for statistical data collection are taught. Records associated with a first task communicating with a second task that are sampled at a first sampling rate are received. At least the first task is associated with a first processing job. A target sampling rate for the first processing job is determined using the records where the target sampling rate is based on a minimum sampling quota and a maximum sampling quota. The target sampling rate is compared to the first sampling rate, and a determination of whether to adjust the first sampling rate to the target sampling rate is based on a result of the comparison. In this manner, tracing of processing jobs, such as those including a plurality of tasks, can be adjusted based on the needs and size of the job and network limitations.