Queuing Network Model for Resource Bottleneck Identification
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Solution Overview
Problem
Conventional capacity planning and performance tuning tools are labor-intensive, require manual input, and fail to identify primary resource bottlenecks effectively, leading to inefficient performance improvements and zero-sum gains, as they rely on queuing models and simulations rather than real-time system snapshots.
Innovation Solution
A method and system that automatically detect primary resource bottlenecks in a computer system without user-defined parameters by generating queuing network models from snapshots, analyzing thread call stacks and lock graphs, and prioritizing tuning actions based on resource consumption relations, allowing for lightweight, non-intrusive performance tuning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional capacity planning techniques are used to predict performance and identify bottlenecks, then performance prediction capability is improved, but the process becomes extremely labor-intensive and time-consuming due to extensive manual data collection, modeling, and curve fitting
Solution Approach 1:
The system automatically collects performance data, builds queuing network models, and identifies bottlenecks without requiring manual user input for data collection or model parameter specification. The performance monitoring agent autonomously gathers metrics from system components and the model builder automatically constructs predictive models from this data.
Solution Approach 2:
The system performs preliminary data collection and model building in advance, maintaining updated queuing network models that can quickly predict performance impacts of proposed changes without requiring extensive on-demand analysis. Historical performance data is collected and stored for future predictive queries.
2Productivity
If conventional performance tuning tools focus on hot calling contexts, then code optimization is improved, but the tools fail to identify actual system bottlenecks because they ignore resource saturation and system-wide interactions
Solution Approach 1:
The queuing network model serves as an intermediary that connects code-level performance data with system-level resource constraints. The model translates hot spot identification into meaningful bottleneck analysis by incorporating resource saturation states and system-wide interactions, allowing the system to distinguish between code optimization opportunities and resource capacity limitations.
Solution Approach 2:
The system adds a system-level dimension to traditional code-level performance analysis by incorporating resource saturation metrics and queuing theory. This transforms the analysis from solely examining calling contexts to a multi-dimensional view that includes resource availability, thread waiting queues, and system-wide performance interactions.
3Measurement precision
If conventional tools rely on queuing models and simulations for performance analysis, then theoretical performance prediction is improved, but the tools cannot provide lightweight, real-time performance problem identification from system snapshots
Solution Approach 1:
The system applies partial queuing model analysis focused specifically on identifying current bottlenecks from snapshots, rather than performing complete simulations for every analysis. The model builder constructs simplified models from snapshot data that provide sufficient accuracy for bottleneck identification without requiring exhaustive simulation of all system scenarios.
Data Source
AI summary
A non-transitory, computer readable, storage medium storing a program of instructions executable by a machine to perform a method of gauging a benefit of a tuning action, the method includes acquiring a set of time series data sampled from an environment of an application, using a processor.