Heterogeneous Cloud Thermal Management via Segmentation
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Solution Overview
Problem
Data centers with heterogeneous computing clouds face inefficiencies due to uneven thermal profiles and customized hardware, leading to increased cooling and maintenance costs, as existing solutions are primarily designed for homogeneous environments.
Innovation Solution
A system that includes monitoring components to track climate controls and operating parameters, a controller to regulate heterogeneous computing clouds and climate controls, and spilling computing clouds to manage excess workload without violating contracts, activated based on temperature, resource demand, and wear-out characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If heterogeneous computing clouds with customized hardware are deployed to meet diverse client needs, then adaptability and client satisfaction improve, but thermal variations and cooling costs increase
Solution Approach 1:
The data center is segmented into multiple thermal zones based on the thermal profiles of different heterogeneous computing clouds. Each zone is independently monitored and controlled, allowing optimized cooling strategies for each segment rather than uniform cooling across the entire facility, thereby reducing overall cooling costs while maintaining adaptability to diverse hardware requirements.
Solution Approach 2:
Different cooling strategies and parameters are applied to different regions of the data center according to the specific thermal characteristics and hardware configurations of each computing cloud. This localized approach ensures that each heterogeneous cloud receives appropriate thermal management tailored to its specific needs, reducing energy waste from over-cooling or uniform treatment.
2Loss of energy
If workload is dynamically migrated to optimize thermal performance, then energy efficiency improves, but system complexity and control difficulty increase
Solution Approach 1:
A feedback control system continuously monitors thermal parameters, workload distribution, and energy consumption across heterogeneous computing clouds. The controller uses this real-time feedback to dynamically adjust workload migration decisions and cooling strategies, optimizing energy efficiency while managing system complexity through closed-loop control rather than complex open-loop algorithms.
Solution Approach 2:
The system implements dynamic workload migration that adapts to changing thermal conditions and workload patterns in real-time. Rather than static allocation, the system continuously adjusts workload distribution based on current thermal states, allowing optimization of energy efficiency while using simple dynamic rules rather than complex static configurations.
3Reliability
If dedicated hardware is assigned to individual clients in heterogeneous clouds, then service level agreement compliance improves, but thermal management difficulty and maintenance costs increase
Solution Approach 1:
Dedicated hardware assignments for different clients are organized into distinct thermal zones or computing clouds, each with characteristic thermal profiles. This segmentation allows the system to manage thermal conditions for each client's dedicated hardware independently, maintaining service level agreements while simplifying thermal management through zone-based control rather than individual device management.
Data Source
AI summary
A system to improve operation of a data center with heterogeneous computing clouds may include monitoring components to track data center climate controls and individual heterogeneous computing clouds' operating parameters within the data center. The system may also include a controller that regulates the individual heterogeneous computing clouds and data center climate controls based upon data generated by the monitoring components to improve the operating performance of the individual heterogeneous computing clouds as well as the operating performance of the data center. The system may further include spilling computing clouds to receive excess workload of an individual heterogeneous computing cloud without violating individual heterogeneous computing clouds contracts.