Power-Aware Scheduling for Data Center Job Execution
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Solution Overview
Problem
Conventional data center schedulers fail to accurately inform users about job execution start and completion times due to assumptions of constant resource availability, which is not true in data centers powered by renewable energy with unpredictable power variability, leading to inefficiencies and inability to suspend or migrate jobs in response to changing resource conditions.
Innovation Solution
A distributed system scheduler that utilizes a new Quality of Service (QoS) model enabling users to determine job completion times by dynamically scaling compute nodes, suspending, and migrating jobs within the data center, incorporating reinforcement learning and hand-written heuristics to manage power variability and resource availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional schedulers assume constant resource availability, then scheduling simplicity is maintained, but job execution time accuracy deteriorates due to unpredictable power variability
Solution Approach 1:
The scheduler transitions from static assumptions of constant resource availability to dynamic adaptation by continuously monitoring actual power availability and adjusting job scheduling decisions in real-time, allowing the system to respond to unpredictable power variability while maintaining operational simplicity
Solution Approach 2:
The system implements feedback mechanisms where the scheduler receives information about actual power availability and job execution progress, uses this information to evaluate whether service level objectives are being met, and adjusts scheduling decisions accordingly to improve execution time accuracy
2Productivity
If jobs are suspended or migrated in response to changing resource conditions, then resource utilization efficiency is improved, but system complexity increases due to additional control mechanisms
Solution Approach 1:
The scheduler performs preliminary actions by proactively suspending or migrating jobs before power availability deteriorates further, based on predictions and current trends, thereby preventing service level objective violations and improving overall resource utilization without requiring complex real-time intervention mechanisms
Solution Approach 2:
The system enables self-service scheduling where the scheduler autonomously makes decisions about job suspension and migration based on monitored power availability and service level objectives, reducing the need for external control mechanisms and simplifying the overall system architecture
3Reliability
If service level objectives are strictly enforced, then customer value is improved, but operational flexibility deteriorates due to inability to adapt to power variability
Solution Approach 1:
The system dynamically balances service level objective enforcement with operational flexibility by continuously monitoring both compliance metrics and power availability conditions, adjusting scheduling decisions to maintain reliability while adapting to changing resource conditions through flexible job management
Data Source
AI summary
A method includes, by a scheduling controller, receiving from a user a request for an application to be executed by a computing system associated with a data center, wherein the application includes a plurality of tasks, and wherein the request includes an estimated execution time corresponding to an estimated amount of real-world time that the tasks will be actively running on the computing system to fully execute the application. The method includes receiving from the user a service level objective indicating a target percentage of a total amount of real-world time that the tasks will be actively running on the computing system and generating, in response to determining that the job can be completed according to the service level objective and the estimated execution time, a notification indicating acceptance of the job.