Storage Service Action Optimization via Priority Ranking
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Solution Overview
Problem
Current methods for managing service actions in storage systems lack efficiency in scheduling and prioritization, leading to suboptimal resource allocation and increased service times due to inadequate consideration of event severity, location, and duration.
Innovation Solution
A system that monitors storage systems for service events, ranks pending service actions based on criteria such as event type, severity, location, and duration, and optimizes the assignment of these actions to scheduled service windows using algorithms like the prize collecting traveling salesman problem to minimize service time and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If service actions are scheduled without optimization algorithms, then scheduling simplicity is maintained, but service time and resource allocation efficiency deteriorate
Solution Approach 1:
The patent introduces an optimization algorithm as an intermediary component between service event detection and service window assignment. This algorithm processes multiple service actions, evaluates them against criteria (severity, duration, location), and generates optimized schedules. The intermediary handles the computational complexity internally while presenting simplified interfaces for service window assignment, thus improving productivity without requiring users to directly manage the complexity.
Solution Approach 2:
The scheduling system performs self-optimization by automatically evaluating service actions against predefined criteria and adjusting service window assignments without external intervention. The system autonomously ranks service actions, determines optimal sequences, and reallocates service windows based on changing conditions, enabling self-service improvement of scheduling efficiency while maintaining system simplicity for users.
2Reliability
If service actions are prioritized by multiple criteria (severity, location, duration), then service action quality improves, but scheduling complexity increases
Solution Approach 1:
The patent transforms multiple prioritization criteria (event severity, location distance, service duration) into a unified ranking system by assigning weights and scores to each parameter. The system changes the state of these parameters from raw values to normalized scores, then combines them into an overall priority metric. This parameter transformation enables accurate multi-criteria prioritization while managing complexity through standardized evaluation functions.
Solution Approach 2:
The ranking system is segmented into distinct evaluation modules, each handling a specific criterion (severity evaluation, location calculation, duration assessment). Each module independently processes its aspect and contributes to the overall ranking. This segmentation allows the system to maintain high prioritization accuracy through comprehensive criteria while managing complexity by organizing evaluations into separate, manageable components.
3Loss of time
If service windows are dynamically adjusted based on service action duration, then time allocation efficiency improves, but scheduling flexibility deteriorates
Solution Approach 1:
The patent implements dynamic service window adjustment where window durations and assignments are automatically modified based on actual service action requirements. The system calculates optimal window lengths based on service action durations and reassigns windows dynamically as service events are detected and prioritized. This dynamic adaptation improves time utilization efficiency while maintaining flexibility through automated reconfiguration rather than rigid fixed schedules.
Solution Approach 2:
The system performs preliminary evaluation of service actions and pre-calculates optimal service window assignments before actual service execution. By anticipating service duration requirements and pre-optimizing window allocations, the system improves time utilization efficiency in advance while maintaining flexibility to adjust predictions as more information becomes available, thus balancing efficiency gains with adaptability.
4Productivity
If multiple service actions are assigned to single service window, then resource utilization improves, but service action completion quality deteriorates
Solution Approach 1:
The patent implements a selection mechanism where only the most critical service actions (those with highest priority scores based on severity and other criteria) are assigned to each service window, even when capacity allows for more. This partial assignment approach ensures that limited service windows are dedicated to high-priority tasks, maintaining completion quality for critical services while still achieving improved resource utilization compared to assigning all detected service actions.
Solution Approach 2:
The system applies different assignment strategies to different service windows based on local conditions such as service window duration, technician availability, and service action priorities. Each service window receives a customized set of assigned actions tailored to its specific characteristics and capacity, rather than applying a uniform assignment rule. This local optimization maintains high completion quality for each window's assigned tasks while improving overall resource utilization across the system.
Data Source
AI summary
Systems and methods for improving efficiency in performing service actions for one or more storage systems are described. In one embodiment, the systems and methods include detecting a service event on at least one of one or more storage systems based at least in part on monitoring events on the one or more storage systems, creating a service action based at least in part on detecting the service event, adding the service action to a list of pending service actions associated with the one or more storage systems, and assigning one or more service actions from the list of pending service actions to a service window. In some cases, each service event in the list of pending service events represents an adverse condition awaiting repair in relation to at least one of the one or more storage systems.