Data Analytics Thermal Load Mapping for Data Center Applications
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Solution Overview
Problem
Current methods for mitigating thermal issues in data centers do not effectively align resolution actions with business events, application characteristics, and resiliency requirements, leading to potential availability issues and revenue impacts.
Innovation Solution
A computer-implemented method that retrieves data on infrastructure component activities, application-to-infrastructure maps, and business priorities to determine thermal loads and execution priorities, generating a resolution plan that dynamically adjusts application execution based on thermal parameters and business considerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Temperature
If non-critical applications are shut down to reduce heat dissipation, then thermal parameters are maintained under control, but application availability and business continuity are compromised
Solution Approach 1:
The system dynamically changes operational parameters by adjusting the execution priority and thermal load allocation of applications based on real-time thermal conditions and business priority data, rather than simply shutting down applications. This allows for nuanced control that maintains thermal parameters while preserving critical application availability.
Solution Approach 2:
The system implements dynamic resolution plans that adapt in real-time to changing thermal conditions and business priorities. The resolution actions are not static but are continuously adjusted based on monitored thermal parameters and updated business event data, allowing the system to respond flexibly to maintain both thermal control and application availability.
2Temperature
If traditional thermal mitigation approaches are used, then thermal control is achieved, but resolution actions do not align with business events and application characteristics
Solution Approach 1:
The system incorporates feedback loops that continuously monitor both thermal parameters and business event data, then use this combined information to adjust resolution actions. The business priority data and application characteristics feed into the resolution plan generation, ensuring that thermal mitigation actions are aligned with current business needs and application requirements.
Solution Approach 2:
The system performs preliminary analysis by pre-establishing the correlative mapping between execution priority and thermal load, and by having resolution plans ready that are tailored to specific business scenarios. When thermal issues arise, the system can quickly deploy pre-planned resolution actions that are already aligned with the relevant business events and application characteristics.
3Reliability
If critical applications are maintained during thermal events, then business continuity is preserved, but thermal load on infrastructure increases
Solution Approach 1:
The system changes thermal management parameters by dynamically adjusting the thermal load allocation to prioritize critical applications. Instead of uniform thermal control, the system modifies operational parameters to allow critical applications to maintain higher thermal output while non-critical applications reduce their thermal contribution, based on the correlative mapping of execution priority to thermal load.
Solution Approach 2:
The system applies different thermal management strategies to different applications based on their criticality and business priority. Rather than treating all applications uniformly, the system implements local quality control where critical applications receive preferential thermal management while non-critical applications undergo more aggressive thermal control, optimizing both business continuity and thermal load management.
Data Source
AI summary
Mitigating the impact of data center thermal environmental issues on production applications includes retrieving, by a computer, from a centralized repository first data corresponding to I/O and processing activities of an infrastructure component executing one or more applications, second data corresponding to an application-to-infrastructure map, and third data corresponding to a business priority of the one or more applications. Based on the first data and the second data, the one or more applications are mapped to heat generation values of the infrastructure component, and based on the mapping a thermal load of the one or more applications on the infrastructure component is determined using data analytics. Using the third data, the computer identifies an execution priority for the one or more applications, generates a correlative mapping between the execution priority and the thermal load of the one or more applications, and generates a resolution plan based on the correlative mapping.