Wait Chain Coverage Analysis for Rare Cloud Performance Issues
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Solution Overview
Problem
Identifying and fixing rare computing performance issues in cloud computing environments is difficult and time-consuming using conventional techniques, which can lead to breaches of uptime and performance service-level agreements (SLAs) due to the complexity of tracing these issues in systems with tens to hundreds of thousands of servers.
Innovation Solution
An automated wait chain-based analysis of trace data is performed to identify traces with relatively larger durations of unknown wait events, using critical path analysis and signature coverage to differentiate between known and rare performance issues, allowing for focused analysis and optimization of system maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional techniques are used to identify performance issues in large-scale cloud computing environments, then common performance bottlenecks can be identified, but rare performance issues become exceedingly difficult and time-consuming to identify
Solution Approach 1:
The patent segments the analysis process into distinct phases: trace data collection, wait chain extraction, signature matching, and coverage calculation. By dividing the complex analysis into manageable segments, the system can efficiently process large volumes of trace data and identify rare performance issues without excessive time consumption
Solution Approach 2:
The patent creates wait chain representations that copy and abstract the essential timing relationships from complex trace data. These wait chain copies serve as simplified models that can be quickly analyzed against signature patterns, enabling rapid identification of rare performance issues without analyzing the full complexity of the original trace data
2Reliability
If comprehensive trace analysis is performed on all traces to identify rare issues, then rare performance bottlenecks can be detected, but the complexity and resource requirements increase significantly
Solution Approach 1:
The patent extracts only the essential timing information and wait chain relationships from comprehensive trace data, separating the critical elements needed for SLA compliance analysis from the overwhelming volume of detailed trace information. This extraction approach maintains reliability for detecting rare issues while reducing system complexity
Solution Approach 2:
The patent transforms trace data into wait chain representations with specific parameters (wait durations, event sequences, timing relationships). By changing the parameter representation from raw trace events to structured wait chain attributes, the system can efficiently analyze SLA compliance without managing the full complexity of original trace data
3Measurement precision
If detailed analysis of all wait operations is performed, then unknown wait states can be identified, but the computational resources and time required increase substantially
Solution Approach 1:
The patent introduces wait chain signatures as an intermediary layer between raw trace data and analysis results. These signatures act as pre-defined patterns that mediate the matching process, enabling efficient identification of unknown wait states through pattern recognition rather than exhaustive analysis, thus maintaining measurement precision while improving productivity
Solution Approach 2:
The patent performs preliminary actions by pre-defining wait chain signatures and preparing analysis frameworks before actual trace analysis begins. This preliminary preparation includes creating signature libraries and establishing matching criteria, which enables rapid analysis of unknown wait states without requiring computational resources during the actual analysis phase
Data Source
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AI summary
Identifying a trace based on wait chain coverage analysis comprises identifying a scenario covered by a plurality of traces, as well as a plurality of scenario phases. Each phase has corresponding phase beginning and ending, and is covered by corresponding trace(s) of the plurality of traces. For each prior instance of each phase, a corresponding wait chain comprising a series of wait operations is identified. After identifying signatures configured to match a subset of wait operations in wait chain(s), at least a signature coverage is calculated over the identified corresponding wait chains. The signature coverage characterizes portions(s) of these wait chains that are matched by the signature(s). Based on calculating the signature coverage, one or more of the plurality of traces are identified as having unknown wait states, based on the identified trace(s) corresponding to a larger amount of uncovered wait chain portions than others of the plurality of traces.