Data Center Job Scheduling with Reinforcement Learning
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Solution Overview
Problem
Conventional data center job scheduling systems fail to consider thermal conditions and energy efficiency when allocating resources, leading to increased temperatures, longer cooling times, and higher energy consumption.
Innovation Solution
The implementation of reinforcement learning techniques using a machine learning model to schedule jobs in a data center, taking into account thermal conditions, energy constraints, and other factors to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional job scheduling is used that only considers topology information, then job allocation is simple and fast, but temperature increases and energy consumption increases
Solution Approach 1:
The patent changes the scheduling parameters from simple topology-based selection to multi-parameter optimization including temperature, power consumption, and topology. The reinforcement learning model dynamically adjusts job placement decisions based on real-time thermal and energy conditions, resolving the contradiction between simple fast scheduling and temperature control.
Solution Approach 2:
The system implements feedback mechanisms where temperature and power consumption data from previous job executions are fed back into the reinforcement learning model. This feedback loop allows the scheduler to learn from past thermal conditions and make informed decisions that prevent temperature accumulation, addressing the contradiction between scheduling speed and temperature management.
2Device complexity
If conventional job scheduling is used that only considers topology information, then scheduling complexity is low, but energy consumption increases
Solution Approach 1:
The patent transforms the scheduling approach by incorporating energy consumption as a key parameter alongside topology. The reinforcement learning model evaluates multiple parameters including power consumption predictions, enabling the system to optimize energy usage while managing the increased complexity through intelligent algorithms.
Solution Approach 2:
The patent replaces traditional rule-based mechanical scheduling logic with a reinforcement learning-based intelligent system. This substitution allows the scheduler to dynamically optimize energy consumption by learning from historical data and real-time conditions, resolving the contradiction between low complexity and energy efficiency.
3Ease of operation
If jobs are placed without considering thermal conditions, then job placement is straightforward, but cooling time increases
Solution Approach 1:
The patent modifies the job placement process by adding thermal conditions as a critical parameter. The reinforcement learning model predicts temperature impacts and selects placement locations that minimize cooling requirements, thereby reducing cooling time while maintaining ease of operation through automated decision-making.
Solution Approach 2:
The system performs preliminary thermal analysis before job placement by using the reinforcement learning model to predict temperature outcomes. This preliminary action allows the scheduler to pre-select optimal locations that will require minimal cooling time, resolving the contradiction between straightforward placement and cooling time efficiency.
4Use of energy by stationary object
If reinforcement learning is used to optimize job scheduling based on thermal and energy conditions, then energy consumption is reduced, but computational complexity increases
Solution Approach 1:
The patent replaces complex rule-based scheduling logic with a reinforcement learning model that learns optimal strategies through training. While the model itself is computationally intensive during training, once trained, it provides efficient real-time scheduling decisions that reduce energy consumption, resolving the contradiction between energy efficiency and system complexity.
Solution Approach 2:
The reinforcement learning model performs preliminary learning and optimization during a training phase before deployment. This preliminary action allows the complex optimization to be done once during training, after which the model provides relatively simple real-time scheduling decisions that reduce energy consumption without requiring complex runtime computations.
Data Source
AI summary
A method includes receiving, using a processing device, a first condition associated with an operation at a data center, where the operation at the data center pertains to a first location at the data center, the first location corresponding to a first parameter value. The method further includes providing the first condition as an input to a machine learning model. The method also includes performing one or more reinforcement learning techniques using the machine learning model to cause the machine learning model to output an indication of a final location associated with the operation, where the final location corresponds to a final parameter value that is closer to a target than the first parameter value corresponding to the first location at the data center.