Adaptive Statistical Modeling for Cloud Workload Optimization
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Solution Overview
Problem
Existing systems management frameworks face challenges in scalability, reaction time to workload variations, and accuracy in petabyte-scale environments, particularly with non-adaptive information models that are resource-intensive and susceptible to missing data points.
Innovation Solution
A method and system for adaptive statistical modeling in a clustered computing environment that obtains performance data, develops and analyzes statistical models to determine computation time and accuracy, and selects the optimal model for workload processing, using time-series trends and abnormality triggers to refine models and minimize resource overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If non-linear statistical models are adapted to data after creation, then measurement precision is improved, but loss of time increases due to significantly higher overhead
Solution Approach 1:
The system dynamically selects between linear and non-linear modeling approaches based on real-time data characteristics and workload conditions. The modeling algorithm adapts its complexity level dynamically, choosing simpler linear models when appropriate and more complex non-linear models only when data patterns justify the additional computational overhead, thereby optimizing the balance between accuracy and response time.
Solution Approach 2:
The system changes the parameter of model complexity based on data characteristics. By analyzing data patterns, the system adjusts the modeling approach parameter - using linear models for straightforward patterns and non-linear models for complex patterns - thus achieving high accuracy when needed while maintaining fast response times for simpler cases.
2Manufacturing precision
If the entire history is maintained as a single model, then manufacturing precision is improved, but productivity decreases due to non-scalability with peta-scale growth
Solution Approach 1:
The system segments the historical data into multiple time-based partitions or chunks, creating separate statistical models for different time periods or data segments. This segmentation allows the system to maintain high modeling accuracy by capturing temporal patterns while improving computational scalability, as each segment can be processed independently and only relevant segments need to be analyzed for current workloads.
Solution Approach 2:
Instead of applying a single global model to all historical data, the system applies different modeling approaches or parameters to different local segments of the data based on their specific characteristics. This local quality approach ensures high accuracy for each segment while maintaining overall system scalability, as computations are localized to relevant data portions rather than processing the entire history uniformly.
3Measurement precision
If non-linear models are used for rapid workload variations, then measurement precision is improved, but device complexity increases leading to higher resource overhead
Solution Approach 1:
The system dynamically adjusts the complexity of statistical models based on the detected workload characteristics. When rapid workload variations are detected, the system activates more complex non-linear models to capture these patterns accurately. When workloads are stable, simpler models are used, reducing resource overhead. This dynamic adaptation allows the system to maintain high measurement precision for rapid variations without permanently increasing device complexity.
Solution Approach 2:
The system changes the modeling parameter (model complexity) based on workload conditions. For rapid workload variations, the parameter is adjusted to use non-linear models with higher precision. For stable workloads, the parameter is adjusted to use simpler linear models, thereby reducing resource overhead while maintaining adequate accuracy across different operational states.
4Measurement precision
If algorithms are adapted to data after creating non-linear models, then measurement precision is improved, but loss of time increases due to significantly higher overhead
Solution Approach 1:
The system performs preliminary analysis of data characteristics before selecting and applying statistical models. By pre-evaluating data patterns, the system can determine whether linear or non-linear models are appropriate without requiring extensive post-creation adaptation. This preliminary action reduces the time and computational overhead associated with adapting algorithms after model creation, while still achieving high measurement precision when non-linear patterns are detected.
Data Source
AI summary
Embodiments of the present invention provide an approach for adapting an information extraction middleware for a clustered computing environment (e.g., a cloud environment) by creating and managing a set of statistical models generated from performance statistics of operating devices within the clustered computing environment. This approach takes into account the required accuracy in modeling, including computation cost of modeling, to pick the best modeling solution at a given point in time. When higher accuracy is desired (e.g., nearing workload saturation), the approach adapts to use an appropriate modeling algorithm. Adapting statistical models to the data characteristics ensures optimal accuracy with minimal computation time and resources for modeling. This approach provides intelligent selective refinement of models using accuracy-based and operating probability-based triggers to optimize the clustered computing environment, i.e., maximize accuracy and minimize computation time.