Thermal Vector Workload Allocation for SSD Throttling
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Solution Overview
Problem
Increasing performance demands in data centers and cloud systems lead to thermal increases, reducing device performance and causing delays in data throughput due to thermal throttling in devices like SSDs, which are not efficiently managed by existing cooling methods.
Innovation Solution
A system that analyzes thermal parameters and workload intensity for each device, using a thermal vector engine to allocate workloads strategically, keeping devices within efficient temperature ranges to prevent throttling and optimize performance, by assigning lighter workloads to devices near their thermal limits and heavier workloads to those further from their limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If performance demands are increased in data centers and cloud systems, then system performance and processing capacity are improved, but thermal increases occur which reduce device performance and cause delays in data throughput
Solution Approach 1:
The patent applies preliminary action by predicting future thermal states of devices before actual thermal throttling occurs. The system uses machine learning models to forecast temperature increases based on current and historical thermal parameters, allowing workload redistribution to be performed proactively rather than reactively. This prevents thermal throttling before it impacts performance.
Solution Approach 2:
The patent implements dynamics by creating a dynamic workload redistribution mechanism that continuously adapts to changing thermal conditions. The system monitors real-time thermal parameters and dynamically adjusts workload allocation across devices, transitioning workloads between devices based on predicted thermal states. This dynamic approach allows the system to maintain optimal performance while responding to thermal changes.
2Reliability
If thermal increases occur in devices, then device performance is reduced due to thermal throttling, but data throughput experiences delays
Solution Approach 1:
The patent introduces an intermediary thermal management system that acts as a mediator between workloads and storage devices. This intermediary layer predicts thermal states and redistributes workloads across multiple devices, preventing any single device from entering thermal throttling. The intermediary absorbs thermal management complexity, allowing devices to operate reliably without direct performance degradation.
Solution Approach 2:
The patent applies parameter changes by modifying workload allocation parameters based on predicted thermal states. The system changes workload distribution parameters dynamically, redirecting I/O operations away from devices approaching thermal limits to devices with lower predicted temperatures. This parameter adjustment prevents thermal throttling and maintains data throughput.
3Temperature
If existing cooling methods are used, then thermal management is provided, but they are not efficient in preventing thermal throttling in devices like SSDs
Solution Approach 1:
The patent applies preliminary action by predicting future thermal states of devices before actual thermal throttling occurs. The system uses machine learning models to forecast temperature increases based on current and historical thermal parameters, allowing workload redistribution to be performed proactively rather than reactively. This prevents thermal throttling before it impacts performance.
Solution Approach 2:
The patent implements feedback by continuously monitoring thermal parameters and using this information to adjust workload allocation. The system collects thermal data from devices, processes it through machine learning models to predict future thermal states, and feeds this information back into the workload distribution decisions. This closed-loop feedback mechanism enables efficient thermal management that prevents throttling.
Data Source
AI summary
Particular embodiments described herein provide for an electronic device that can be configured to receive a plurality of thermal parameters for a device, identify one or more of the plurality of thermal parameters that affect a thermal response of the device, and create a thermal vector for the device using the one or more of the plurality of thermal parameters that affect the thermal response of the device, where the thermal vector can be used to predict a new thermal response of the device. In an example, the thermal vector includes weighted thermal parameters.