Latency Degradation Identification in Distributed Systems
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Solution Overview
Problem
Large-scale distributed computing systems face significant challenges in identifying and addressing latency degradations due to the prohibitively expensive computation required to enumerate all possible variable combinations, leading to inefficiencies in resource usage and alert generation.
Innovation Solution
The system divides total latency into components like server, client, and network latency, and uses a slicing strategy to focus on subsets of variables, generating combinations for study classes, comparing latency distributions with benchmark values using statistical tests to determine degradation and generate targeted alerts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible variable combinations are enumerated to identify latency causes, then measurement precision is improved, but use of energy and computing resources become prohibitively expensive
Solution Approach 1:
The patent segments the latency measurement problem by dividing variables into two categories: target variables (suspected causes of latency degradation) and control variables (other system variables). This segmentation allows the system to focus computational resources on analyzing only the target variables while using control variables as covariates in statistical models, thereby reducing the search space from all possible variable combinations to a manageable subset that still provides accurate latency degradation identification.
2Measurement precision
If all possible variable combinations are enumerated to identify latency causes, then measurement precision is improved, but loss of time increases due to computational expense
Solution Approach 1:
The patent applies segmentation by categorizing variables into target variables (potential latency causes) and control variables (other system factors). This division enables the system to perform statistical analysis only on the target variables while adjusting for control variables, significantly reducing the time required compared to enumerating all possible variable combinations while maintaining identification accuracy.
Solution Approach 2:
The patent extracts only the necessary variables (target variables) from the complete set of system variables for latency analysis. By taking out only the variables suspected of causing latency degradation and using control variables merely as covariates in statistical models, the system reduces the analytical scope from all variable combinations to a focused subset, thereby reducing analysis time while preserving measurement precision.
3Use of energy by moving object
If a simplified approach is used to reduce computing resources, then use of energy is improved, but measurement precision deteriorates
Solution Approach 1:
The patent introduces statistical models (such as linear regression, logistic regression, or decision trees) as intermediary mechanisms between the reduced set of target variables and the latency degradation identification. These statistical models process the simplified variable subset and control variables to produce accurate predictions and identifications, thereby maintaining measurement precision while using far fewer computing resources than would be required to analyze all possible variable combinations.
Data Source
AI summary
Methods and apparatus, including computer program products, implementing and using techniques for finding application latency degradation causes in an application on a distributed computing system. Variables associated with the application and the computing system are identified, including variables that are candidates for being responsible for latency degradation. A total latency is divided into latency components, that each corresponds to an aspect of the total application latency. Each latency component is divided into study classes, that each includes a subset of the candidate variables. For each study class, combinations are generated for the subset of the variables; a latency distribution is determined for each variable combination; the determined latency distributions for the combinations are compared with corresponding latency benchmark values for the same combinations to determine whether a degradation in latency distributions has occurred for particular combinations among the one or more combinations, and a result is provided to a user.