Stage-aware performance modeling for cluster sizing
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Solution Overview
Problem
Users face challenges in determining the optimal cluster size for cost-effective performance in data processing jobs, requiring in-depth understanding of application characteristics and existing methods lack efficient configuration based on job stages.
Innovation Solution
A method and apparatus that receive job and cluster information, identify stage performance models, predict stage performance times, and combine them to determine a predicted job performance time, allowing for efficient configuration of the computer cluster.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional cluster configuration methods are used, then cluster setup is simple, but performance prediction accuracy is poor and resource allocation is inefficient
Solution Approach 1:
The patent segments the data processing job into multiple stages and creates separate performance models for each stage type. This allows accurate prediction of each stage's performance characteristics while maintaining manageable model complexity. The overall job performance is then derived by combining these stage-level predictions according to the job's stage sequence.
2Productivity
If cluster size is increased to improve performance, then processing speed increases, but cost increases
Solution Approach 1:
The system performs preliminary performance prediction before actually executing the data processing job by using trained stage performance models to forecast how long each stage will take on different cluster configurations. This allows users to select the optimal cluster size that achieves required performance targets without unnecessarily increasing cluster size and cost.
3Ease of operation
If stage-specific performance modeling is implemented, then resource allocation optimization is enabled, but model identification complexity increases
Solution Approach 1:
The patent creates simplified performance models that copy the essential characteristics of each job stage without requiring complete replication of the actual stage complexity. These models capture the key performance factors for each stage type, enabling resource allocation optimization while keeping model identification and management tractable.
Data Source
AI summary
A method, apparatus, and computer program product for configuring a computer cluster. Job information identifying a data processing job to be performed is received by a processor unit. The data processing job to be performed comprises a plurality of stages. Cluster information identifying a candidate computer cluster is also received by the processor unit. The processor unit identifies stage performance models for modeled stages that are similar to the plurality of stages. The processor unit predicts predicted stage performance times for performing the plurality of stages on the candidate computer cluster using the stage performance models and combines the predicted stage performance times for the plurality of stages to determine a predicted job performance time. The predicted job performance time may be used to configure the computer cluster.