Maintenance Scheduling for Distributed Data Processing
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Solution Overview
Problem
Selecting an optimal maintenance time for geographically distributed data processing systems is challenging due to varying operating hours across multiple time zones, leading to significant interference with operational activities.
Innovation Solution
A method and system that select a primary territory with associated operating hours, identify maintenance hours excluding these, and choose a maintenance time at the midpoint of those hours, minimizing interference with operations and allowing for consideration of business or executive factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If maintenance time is selected during low system activity periods, then maintenance interference with operational activities is reduced, but maintenance operations cannot be performed during business hours when needed
Solution Approach 1:
The patent segments the global system into multiple geographic territories with different operating hours, allowing maintenance to be scheduled during off-peak hours for each region. By dividing the system into territories with distinct operational time windows, the patent enables maintenance during periods of lowest overall activity while still providing regular maintenance cycles.
Solution Approach 2:
The patent dynamically adjusts maintenance scheduling based on real-time or historical activity patterns of different geographic territories. The system monitors operational activity levels and adapts maintenance timing accordingly, moving from static scheduled maintenance to dynamic activity-based scheduling that responds to actual system usage patterns.
2Reliability
If maintenance is scheduled during business hours to minimize downtime, then system availability is improved, but interference with operational activities increases
Solution Approach 1:
The patent implements periodic maintenance cycles that are spaced apart to allow system operations to continue uninterrupted. By scheduling maintenance periodically rather than continuously, the system provides regular maintenance opportunities while ensuring that operations can resume immediately afterward, balancing availability with minimal interference.
Solution Approach 2:
The patent introduces an intermediary scheduling mechanism that coordinates between maintenance requirements and operational needs. This intermediary layer analyzes both maintenance needs and operational patterns to find optimal timing windows, acting as a mediator that reconciles the conflicting requirements of maintenance scheduling and operational continuity.
3Adaptability or versatility
If ad hoc administrator selection is used for maintenance timing, then flexibility is maintained, but optimization based on business considerations is lost
Solution Approach 1:
The patent implements self-service scheduling where the system automatically analyzes its own operational patterns, activity levels, and maintenance needs to determine optimal maintenance timing. The system serves itself by collecting data on operational activity, identifying patterns, and autonomously selecting maintenance windows without requiring constant administrator intervention or ad hoc decision-making.
Solution Approach 2:
The patent incorporates feedback mechanisms where maintenance outcomes and system performance data are continuously monitored and fed back into the scheduling algorithm. This feedback loop allows the system to learn from past maintenance experiences and operational impacts, progressively optimizing maintenance timing decisions based on actual business considerations and operational patterns.
Data Source
AI summary
A system and method are provided for optimizing maintenance of a geographically distributed data processing system. The method comprises selecting a primary territory having associated operating hours, identifying maintenance hours that exclude the operating hours, and selecting a maintenance time within the available maintenance hours. The midpoint of the maintenance hours may be selected as the maintenance time, or activity distribution data may be analyzed to select a maintenance time corresponding to a low activity time.