Workload Scheduler Proactive Intervention for System Event Impacts
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Solution Overview
Problem
Workload schedulers face challenges in timely and efficient problem resolution due to the time-consuming and resource-intensive nature of diagnostic activities, especially when multiple issues occur simultaneously, which can lead to missed deadlines and consequent system or business outages.
Innovation Solution
A method that retrieves historical system and work unit data to estimate the impacts of system events on workload plans, predicts potential problems, and establishes an intervention program to address these issues proactively, prioritizing resource allocation based on predicted impacts and available time to prevent deadline misses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diagnostic activities are performed manually to identify and fix system events, then problem resolution accuracy is improved, but time consumption and resource requirements increase significantly
Solution Approach 1:
The system performs preliminary diagnostic actions by automatically detecting system events and predicting their impact on workload plans before manual intervention is needed. Historical data is analyzed in advance to establish patterns, enabling the system to proactively identify potential problems and their root causes, thereby reducing both time consumption and maintaining accuracy.
Solution Approach 2:
The system enables self-service diagnostics by automatically detecting system events, analyzing historical data, predicting impacts, and generating intervention programs without requiring manual diagnostic activities. The workload scheduler autonomously identifies problems, determines their causes, and prioritizes interventions, freeing human resources while maintaining high accuracy through systematic analysis.
2Reliability
If multiple diagnostic activities are performed simultaneously for multiple system events, then comprehensive problem coverage is improved, but resource requirements and complexity increase
Solution Approach 1:
The system merges multiple diagnostic activities into a unified automated process. Instead of performing separate manual diagnostics for each system event, the workload scheduler combines event detection, historical data analysis, impact prediction, and intervention prioritization into an integrated system that handles multiple events simultaneously, maintaining comprehensive coverage while reducing operational complexity.
Solution Approach 2:
The workload scheduler is designed as a universal system that can handle multiple types of system events and workload plans through a single automated diagnostic framework. It universally applies historical data analysis and impact prediction across different event types, eliminating the need for separate diagnostic procedures for each event while maintaining comprehensive problem coverage.
3Adaptability or versatility
If manual resource allocation is used for diagnostic activities, then flexibility in handling different problem types is improved, but response time and efficiency decrease
Solution Approach 1:
The system implements dynamic resource allocation through automated intervention prioritization. Instead of static manual allocation, the workload scheduler dynamically determines which system events require immediate attention based on real-time impact analysis on workload plans. Resources are automatically directed to the most critical events first, providing both flexibility in handling different problem types and high response efficiency through systematic prioritization.
Solution Approach 2:
The system uses feedback from historical data analysis and impact prediction to continuously optimize resource allocation. The workload scheduler monitors system events, analyzes their impact on workload deadlines, and adjusts intervention priorities based on this feedback. This closed-loop approach maintains flexibility in handling diverse problem types while significantly improving response efficiency through data-driven decision-making.
Data Source
AI summary
A method and associated system. Expected problems in a workload plan are predicted in response to any current occurrences of impacting system events according to the workload plan and corresponding expected impacts of the impacting system events of system events on execution of impacted work units of work units. Predicting the expected problems includes: identifying workload deadlines relating to the impacted work units in the workload plan; estimating intervention times for addressing the expected problems before missing the corresponding workload deadlines; determining corresponding minimum times required to fulfill the workload deadlines according to a progress of the workload plan; and estimating each of the intervention times further according to the minimum time of the corresponding workload deadline. An intervention program of system interventions on the computing system is established. The system interventions address the current occurrences of impacting system events according to the corresponding expected problems.