Compute Farm Resource Policy Enforcement via Automated Job Classification
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Solution Overview
Problem
In compute farms, resources are often misallocated due to incorrect job categorization by users, leading to waste and performance issues, and some users bypass queuing systems, causing further performance problems.
Innovation Solution
A system that enforces a resource-usage policy by categorizing job slots into different types, applying etiquette and corrective-action rules to monitor and correct resource usage violations, including notifications and termination of offending jobs or processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users manually categorize jobs into different job slots, then the system can allocate resources based on job type, but users may incorrectly categorize jobs leading to resource waste and performance problems
Solution Approach 1:
The system automatically monitors job execution behavior and self-determines job categorization without relying on user input. The queuing system analyzes actual resource usage patterns, execution time, and resource consumption to automatically classify jobs into appropriate slots, eliminating the need for manual categorization and its associated errors.
Solution Approach 2:
The system continuously monitors job execution and uses this feedback to automatically adjust job categorization. By observing actual resource consumption and execution characteristics, the system refines its classification decisions in real-time, creating a closed-loop system that adapts to actual job behavior rather than relying on potentially incorrect user declarations.
2Speed
If users bypass the queuing system to directly execute jobs on compute farm nodes, then job execution speed increases, but performance problems occur due to unmanaged resource allocation
Solution Approach 1:
The system introduces a monitoring intermediary layer that observes all job execution regardless of whether users bypass the queuing system. This intermediary monitors resource usage patterns and identifies unauthorized direct executions, allowing the system to maintain compliance while preserving user freedom of execution methods.
Solution Approach 2:
The system monitors resource usage patterns and provides feedback to identify when users bypass the queuing system. By analyzing execution behavior and resource consumption patterns, the system can detect unauthorized direct job execution and take appropriate corrective actions while maintaining normal operation.
3Reliability
If the system monitors all job and process resource usage, then resource-usage violations can be detected, but system complexity increases
Solution Approach 1:
The queuing system performs multiple functions: it manages job scheduling, monitors resource usage, detects violations, and executes corrective actions. By making the queuing system multi-functional, the patent avoids adding separate complex monitoring infrastructure while still achieving comprehensive resource usage monitoring and enforcement.
Solution Approach 2:
The system combines monitoring, detection, and enforcement functions into a unified queuing system. Rather than separate independent components, the monitoring of resource usage is integrated with the scheduling and management functions, reducing overall system complexity while maintaining effective policy enforcement.
Data Source
AI summary
One embodiment of the present invention provides a system that enforces a resource-usage policy in a compute farm. During operation, the system can receive etiquette rules which include resource-usage rules and corrective-action rules. Resource-usage rules can specify situations in which the resource-usage policy is violated, and corrective-action rules can specify situations in which a corrective action needs to be performed. Next, the system can receive resource-usage information which includes job monitoring data and process monitoring data. The system can then determine a resource-usage violation by applying the resource-usage rules to the resource-usage information. Next, the system can store the resource-usage violation in a violation database. The system can then determine a corrective action by applying corrective-action rules to a series of violations stored in the violation database. Next, the system can perform the corrective action, thereby enforcing the resource-usage policy in the compute farm.