Task Scheduling Simulation for Cluster Resource Optimization
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Solution Overview
Problem
Current job scheduling techniques in networked computer clusters often result in non-optimal task preemption, leading to inefficient use of compute resources as they prioritize evicting lowest priority tasks, which can leave resources unavailable for other tasks.
Innovation Solution
A system that includes a scheduler, preemption simulator, system state evaluator, and task queue manager to assess and simulate task preemption based on system state scores, prioritizing tasks to optimize resource utilization by preempting running tasks with lower priority when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the scheduler evicts the lowest priority running task, then the task queue is sorted by priority, but compute resources remain unavailable for scheduling other tasks
Solution Approach 1:
The system performs preliminary simulation of task preemption by evaluating system state scores before actually evicting tasks. The preemption simulator generates simulated system states by preempting different combinations of running tasks, and the system state evaluator calculates scores for each simulated state to predict the outcome of preemption actions in advance, allowing the scheduler to make informed decisions about which tasks to evict for optimal resource utilization.
2Productivity
If the scheduler preempts running tasks to schedule higher priority tasks, then system performance improves, but the complexity of scheduling decisions increases
Solution Approach 1:
The system creates simulated copies of the current system state to evaluate different preemption scenarios. The preemption simulator generates multiple simulated system states by preempting different combinations of running tasks, and the system state evaluator calculates scores for each simulated state. This copying approach allows the scheduler to evaluate multiple scenarios without actually committing to any preemption action until the best option is identified, reducing the complexity of making scheduling decisions.
3Measurement precision
If the scheduler evaluates all possible preemption scenarios, then optimal task scheduling is achieved, but the time required for scheduling decisions increases
Solution Approach 1:
The system changes the parameter of evaluation from exhaustive scenario analysis to score-based ranking. The system state evaluator calculates a system state score for each simulated state based on priority and resource utilization metrics, allowing the scheduler to identify optimal preemption decisions through score comparison rather than exhaustive analysis. This parameter change from comprehensive evaluation to scored selection reduces the time required for scheduling decisions while maintaining optimality.
Data Source
AI summary
Apparatuses, systems, and techniques to schedule one or more workloads to one or more computers by comparing one or more performance metrics of the one or more workloads to be performed using one or more computers with one or more performance metrics of the one or more workloads to be performed using a simulation of the one or more computers.