Workload Characterization for Data Warehouse Resource Sizing
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Solution Overview
Problem
Estimating and configuring appropriate hardware for data warehouse systems is challenging due to varying workloads, with conventional methods being inaccurate and based on simplistic rules, inadequate mathematical algorithms, or ignoring platform differences, leading to oversizing or undersizing issues that result in poor performance or excessive costs.
Innovation Solution
A method and system for characterizing workloads by collecting accounting data, determining query concurrency, processor utilization, and data access information to generate a workload profile, providing a more accurate foundation for resource estimation and sizing tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional workload estimation methods are used, then hardware configuration can be determined quickly, but the accuracy of resource estimation deteriorates
Solution Approach 1:
The patent applies preliminary action by collecting and analyzing workload accounting data before final hardware configuration decisions are made. The system gathers historical query execution data, resource consumption patterns, and workload characteristics in advance to build comprehensive workload profiles that inform accurate resource estimation and sizing recommendations.
Solution Approach 2:
The patent implements feedback mechanisms by continuously monitoring actual workload performance and resource utilization, then using this information to refine and update workload profiles. The system compares estimated resource requirements against actual performance metrics and service level agreements, adjusting future resource recommendations based on observed deviations and trends.
2Reliability
If hardware is oversized to ensure adequate capacity, then service level agreement compliance improves, but cost increases
Solution Approach 1:
The patent applies partial action by recommending hardware configurations that provide sufficient capacity to meet service level agreements without excessive over-provisioning. The system analyzes workload profiles to determine the minimum necessary resources required to maintain agreed-upon performance levels, avoiding both undersizing and excessive oversizing.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting resource recommendations based on varying workload characteristics, time-of-day patterns, and seasonal trends. The system modifies hardware sizing parameters according to actual observed workload intensity and complexity, allowing flexible optimization between reliability and resource efficiency.
3Loss of energy
If hardware is undersized to reduce cost, then resource expenditure decreases, but performance deteriorates
Solution Approach 1:
The patent applies partial action by identifying the minimum necessary hardware capacity required to maintain acceptable performance levels for each workload profile. The system calculates threshold values for resource allocation that prevent performance degradation while avoiding unnecessary excess capacity, optimizing the balance between cost and productivity.
4Measurement precision
If detailed workload analysis is performed to improve estimation accuracy, then resource allocation precision improves, but analysis time increases
Solution Approach 1:
The patent extracts and separates critical workload characteristics from the overall analysis process, focusing on the most influential factors such as query concurrency, data access patterns, and resource utilization metrics. By identifying and prioritizing the key parameters that have the greatest impact on resource requirements, the system achieves accurate workload characterization without requiring exhaustive analysis of every possible workload attribute.
Data Source
AI summary
One or more embodiments characterize workloads in a data warehouse system. A set of accounting data associated with a data warehouse system comprising at least one database is collected. A set of query concurrency information associated with the database is determined determining based on the set of accounting data. The set of query concurrency information identifies a plurality of queries executed on the database simultaneously and a duration of this execution. A set of processor utilization distribution information associated with the plurality of queries is determined based on the set of accounting data. A set of data access information indicating a quantity of data accessed by each query in the plurality of queries is determined. A workload profile associated with the database is generated based on the set of query concurrency information, the set of processor utilization distribution information, and the set of data access information.