Continuous Cloud Resource Scheduling for Uptime Alignment
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Solution Overview
Problem
Existing cloud resource management systems fail to efficiently align cloud resource states with scheduled downtime and uptime due to discrete scheduling approaches, leading to unnecessary resource consumption and misconfigurations, especially when scans are missed or syntax is incompatible with database resources.
Innovation Solution
A continuous scheduling system that aligns cloud resource states with a fully realized schedule at any point in time, using compatible metadata syntax to manage uptime and downtime windows, and allows for temporary overrides and buffers to ensure resources are correctly suspended or resumed based on a continuous schedule.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If discrete scheduling approaches are used to manage cloud resources, then implementation simplicity is maintained, but resource alignment accuracy deteriorates leading to unnecessary consumption and misconfigurations
Solution Approach 1:
The patent implements continuous scheduling that constantly monitors and adjusts cloud resource states based on recurring schedules, ensuring resources are always aligned with their intended uptime/downtime windows. This continuous approach eliminates the gaps and misalignments inherent in discrete scheduling, preventing unnecessary resource consumption while maintaining implementation feasibility through automated remediation actions.
Solution Approach 2:
The system continuously scans cloud resource states and compares them against the recurring schedule, then automatically performs remediation actions (suspend/resume) to correct any misalignments. This feedback loop ensures high resource alignment accuracy by detecting and correcting deviations in real-time, while the automation maintains implementation simplicity without requiring complex manual intervention.
2Manufacturing precision
If continuous scheduling is implemented to improve resource alignment accuracy, then resource consumption is optimized, but system complexity increases
Solution Approach 1:
The continuous scheduling system performs automated self-service by autonomously scanning resource states, determining schedule compliance, and executing remediation actions without human intervention. This self-service capability achieves high resource alignment accuracy while managing system complexity through automation rather than requiring complex manual management procedures.
Solution Approach 2:
The system performs preliminary actions by pre-defining recurring schedules with uptime and downtime windows before execution. These pre-configured schedules guide the continuous scheduling process, enabling accurate resource alignment while simplifying the overall system structure through upfront planning rather than complex real-time decision-making.
3Ease of operation
If scheduling scans are performed at fixed intervals, then system operation simplicity is maintained, but scheduling reliability deteriorates when scans are missed
Solution Approach 1:
The system implements continuous feedback through recurring scans that persistently monitor cloud resource states against the schedule. This continuous feedback mechanism ensures scheduling reliability by detecting and correcting misalignments regardless of individual scan timing, while maintaining operational simplicity through automated remediation that doesn't require complex scan coordination or manual intervention.
Data Source
AI summary
In some implementations, a scheduling system for automated suspension and resumption of cloud resources is described. The scheduling system receives a scheduling tag defining custom uptime or downtime windows for a cloud resource over a scheduling period. A continuous schedule with recurring uptime and downtime windows is determined, and, at periodic scans, the scheduling system aligns the resource's current state with a target state based on this schedule. The scheduling system may support custom syntax for database cloud resources, global or resource-specific overrides to temporarily modify the schedule without changing the scheduling tag, and buffer periods before uptime windows to account for delays in resource start-up.


