Transaction Response Time Estimation for Hardware Upgrades
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Solution Overview
Problem
Customers face challenges in assessing how new hardware upgrades, particularly those with different processor configurations, impact response times for mission-critical workloads in mainframe systems, necessitating a predictive solution to manage performance and service level agreements effectively.
Innovation Solution
A system comprising data collection, workload analysis, transaction analysis, workload construction, and response time estimation modules that build workload models and transaction models to predict response times before hardware upgrades, using machine learning to analyze CPU utilization and resource consumption, and calculate new response times based on changed characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Power
If new hardware with enhanced processing capacity is upgraded, then processing capacity is improved, but response time prediction accuracy deteriorates due to configuration variables
Solution Approach 1:
The system performs preliminary actions by collecting workload performance data and building predictive models before the hardware upgrade is executed. This allows the system to anticipate response time changes and adjust configurations proactively, rather than reacting after the upgrade has already impacted performance.
Solution Approach 2:
The system employs dynamic predictive models that can adapt to different hardware configurations and workload types. The models are trained on historical data and can dynamically adjust predictions based on the specific characteristics of the upgraded hardware and the workloads being executed, maintaining accuracy across varying conditions.
2Adaptability or versatility
If hardware upgrade is performed, then system capabilities are improved, but workload performance assessment becomes more difficult
Solution Approach 1:
The system introduces predictive modeling as an intermediary between hardware upgrades and workload performance assessment. Instead of directly measuring complex performance changes, the system uses trained models that act as intermediaries to translate hardware configuration changes into predicted response time impacts, simplifying the assessment process.
Solution Approach 2:
The system replaces direct mechanical performance measurement with computational prediction. Rather than physically measuring and analyzing complex workload performance metrics after hardware changes, the system uses software-based predictive models that compute expected performance outcomes based on input parameters, reducing measurement complexity.
3Productivity
If processor configuration is changed in new hardware, then processing efficiency is improved, but response time variability increases
Solution Approach 1:
The system applies parameter changes by adjusting workload placement and resource allocation parameters based on predictive model outputs. When hardware upgrades introduce variability in response times, the system modifies operational parameters such as workload distribution across processors or timing parameters to compensate for the variability and maintain stable performance.
Data Source
AI summary
A method and system for predicting a response time for a workload prior to making a hardware upgrade to a computing system. Data related to operation of the system is collected. Then a workload model of a plurality of workloads and CPU utilization for the plurality of workloads and a transaction model for each transaction within a workload of the plurality of workloads are built. Next the process determines that a characteristic of at least one workload in the plurality of workloads will change due to the hardware upgrade. As a result of the change, a new workload model for the changed workload is built based on the changed characteristic, and the response time for the workload based on the new workload model is calculated.