Job Scheduling via Resource Scoring to Minimize Idle Time
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Solution Overview
Problem
Existing job scheduling systems struggle to minimize idle time and maximize job throughput across a set of computational resources, leading to inefficiencies in data centers and cluster manager systems.
Innovation Solution
A job scheduling system that determines a score for each computational resource by calculating residuals between resource requirements and available resources, and then selects the resource with the highest score to schedule jobs, thereby optimizing resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a job scheduler assigns each received job to the computational resource with the least amount of resources that still meet or exceed the job's resource requirements, then idle time of computational resources is minimized, but the complexity of the scheduling algorithm increases
Solution Approach 1:
The system pre-calculates and maintains scores for each computational resource based on their current resource availability and characteristics. These scores are updated in advance before job arrival, allowing the scheduler to make rapid decisions without complex real-time calculations when jobs are received.
Solution Approach 2:
The patent replaces complex mechanical scheduling algorithms with a scoring-based system that uses a language model to evaluate computational resources. The language model generates scores that reflect resource suitability, substituting traditional algorithmic approaches with a more flexible, adaptive scoring mechanism.
2Productivity
If a job scheduler assigns a received job to the first known computational resource with sufficient resources, then the scheduling process is simple and fast, but resource utilization efficiency deteriorates
Solution Approach 1:
The system continuously monitors computational resource status and incorporates this feedback into the scoring mechanism. The language model receives real-time information about resource availability and adjusts scores accordingly, creating a closed-loop system that adapts to changing conditions and optimizes resource allocation dynamically.
Solution Approach 2:
The patent changes the parameter used for resource selection from simple availability checks to a composite score that incorporates multiple factors including resource capacity, current utilization, and predicted future availability. This parameter transformation enables more nuanced decision-making that balances throughput and efficiency.
Data Source
AI summary
Methods, systems, and apparatuses, including computer programs encoded on computer storage media, for scheduling jobs across a plurality of computational resources. Scheduling jobs (e.g., compute jobs) on a plurality of computational resources (e.g., a cluster that includes physical machines, virtual machines or both) can include assigning jobs to computational resources using respective scores for the computational resources that take into account several attributes, including central processing unit (CPU) requirements, memory requirements, and availability. That is, by generating a score that more accurately reflects the likelihood that a given computational resource is the optimal computational resource to place a given job, the resulting job schedule significantly minimizes idle time of the set of computational resources and enhances the throughput of completed jobs.