Dynamic Job Scheduling Framework for Resource Utilization
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Solution Overview
Problem
Current job scheduling systems face inefficiencies in resource allocation, particularly due to assumptions of workload consistency and rigidity, leading to suboptimal utilization of hardware resources and increased costs, as they struggle to adapt to fluctuating workload demands and varying resource availability.
Innovation Solution
The proposed solution employs a scheduling framework that predicts idle and available contiguous resources using divide-and-conquer techniques, combining machine learning models like LSTM and polynomial regression to optimize resource allocation, and applies adaptive model management to ensure responsive adaptation to changing conditions, thereby improving resource utilization and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional job scheduling systems assume workload consistency and use rigid allocation methods, then system simplicity is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The patent implements dynamic job scheduling that adapts to fluctuating workload demands in real-time, replacing rigid static allocation with flexible dynamic adjustment mechanisms that respond to changing system conditions and resource availability
Solution Approach 2:
The system changes scheduling parameters dynamically based on workload characteristics, resource status, and job priorities, allowing the scheduler to adjust allocation strategies according to current system state rather than relying on fixed assumptions
2Adaptability or versatility
If rigid scheduling methods are used to maintain system simplicity, then ease of operation is preserved, but adaptability to fluctuating workloads deteriorates
Solution Approach 1:
The scheduling system transitions from static rigid allocation to dynamic adaptive scheduling that automatically adjusts to workload fluctuations, maintaining operational simplicity through automated decision-making based on real-time system state monitoring
3Loss of time
If traditional scheduling systems are used without predictive modeling, then device complexity is reduced, but job waiting times increase
Solution Approach 1:
The system performs preliminary predictive modeling to forecast future resource availability and job completion times, enabling proactive scheduling decisions that reduce job waiting times by preparing allocation strategies in advance based on predicted system state
4Productivity
If static resource allocation is used to simplify management, then ease of operation is maintained, but resource utilization efficiency deteriorates
Solution Approach 1:
The scheduling system implements self-service mechanisms where the scheduler automatically monitors system state, predicts resource availability, and adjusts job allocations without manual intervention, maintaining operational simplicity while achieving dynamic optimization of resource utilization
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture to improve job scheduling efficiency are disclosed. An example apparatus includes a feature generator to import default values of features corresponding to a first model type, a label trainer to train labels corresponding to the first model type, and a model evaluator to determine an accuracy metric of the first model type based on a first prediction corresponding to the default features, and update the features from the default values to updated values when the accuracy metric does not satisfy an accuracy threshold.