Workload Management for Power Cap Preemption
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Solution Overview
Problem
Managing energy consumption in high-performance computing environments, such as grids and clusters, is challenging due to the heterogeneous nature of shared resources, multiple layers of schedulers, and varying resource availability, leading to inefficiencies and increased electricity usage.
Innovation Solution
Implementing power-state preemption principles that migrate jobs to less power-consuming resources, reduce clock speeds, or cancel jobs to optimize power usage, while allowing jobs to continue processing if statistical variance indicates a low risk of power cap violations, and utilizing intelligent policies to control power consumption and reporting mechanisms to monitor and report resource states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If power-state preemption is implemented to migrate jobs to less power-consuming resources, then power consumption is reduced, but job processing time may increase
Solution Approach 1:
The system dynamically adjusts power states of compute resources based on workload conditions. Jobs are migrated to lower power states when power consumption exceeds thresholds, and the system continuously monitors and adapts power state assignments, creating a dynamic balance between power consumption and processing speed.
Solution Approach 2:
The invention changes operational parameters by adjusting power states (e.g., CPU frequency, voltage levels) of compute resources. By modifying these physical parameters, the system can reduce power consumption while managing the impact on job processing through controlled transitions between power states.
2Loss of energy
If jobs are migrated to new compute resources to reduce power consumption, then energy efficiency improves, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring power consumption metrics and job performance. This feedback loop enables the workload manager to make informed decisions about when and where to migrate jobs, balancing energy efficiency goals with system complexity management through data-driven control.
Solution Approach 2:
A workload manager acts as an intermediary between jobs and compute resources, mediating the complexity of power state management. This intermediary layer handles the sophisticated logic of job migration and power state assignment, shielding individual components from the overall system complexity while enabling coordinated energy optimization.
3Use of energy by moving object
If power state reduction actions are performed on jobs, then power consumption decreases, but processing speed is reduced
Solution Approach 1:
The system applies partial power state reduction rather than complete shutdown or maximum performance modes. By using intermediate power states that provide moderate performance at reduced power consumption, the system achieves energy savings without completely sacrificing processing speed, finding a partial solution that balances both requirements.
Data Source
AI summary
Disclosed are systems and methods of performing a power cap processing in a compute environment. The method includes determining of one of committed resources and dedicated resources in a compute environment exceed a threshold value for a job. If a determination is yes that the threshold value is exceeded, then the method includes preempting processing of the job in the compute environment by performing one of migrating the job to a new compute resources and performing a power reduction action associated with the job, such as slowing down a processor associated with a job or cancelling the job. When such a power state reduction action is taken, reservations associated with other jobs may also be adjusted.