Automated Backup Scheduling via Early Deadline First Heuristics
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Solution Overview
Problem
Complex backup environments with multiple assets and targets pose challenges in scheduling backups to meet service level agreements (SLAs) while respecting hardware limitations, often resulting in inefficiencies or SLA violations due to the dynamic nature of these environments.
Innovation Solution
An automated backup plan generation system that uses smart data structures and heuristic approaches, such as the Early Deadline First algorithm, to determine optimal backup schedules for multiple assets across a single server, considering resource availability and SLA constraints like RPO, and dynamically adjusts to changes in the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human scheduling of backups is implemented in complex environments with hundreds or thousands of assets and targets, then backup scheduling can be performed, but it becomes cumbersome and inefficient
Solution Approach 1:
The system implements automated backup scheduling that performs scheduling decisions autonomously without human intervention. The scheduling engine automatically generates, evaluates, and adjusts backup schedules based on SLA requirements, resource availability, and environment dynamics, eliminating the need for manual scheduling operations in complex environments.
Solution Approach 2:
The patent replaces manual human scheduling operations with an automated computational scheduling engine. This substitution transforms the mechanical process of human decision-making into an automated system that uses algorithms to evaluate constraints, generate schedules, and optimize resource utilization across hundreds or thousands of assets and targets.
2Productivity
If automated backup scheduling is implemented to handle complex environments, then scheduling efficiency improves, but the system complexity increases
Solution Approach 1:
The scheduling system is divided into distinct functional modules including constraint evaluation components, schedule generation components, and optimization components. This segmentation allows each module to handle specific aspects of the scheduling problem independently, improving overall efficiency while managing system complexity through modular architecture.
Solution Approach 2:
The scheduling system is designed to be dynamic and adaptive, automatically adjusting schedules in response to changing environment conditions, resource availability, and SLA requirements. This dynamic capability enables the system to handle complexity efficiently by continuously optimizing schedules rather than relying on static pre-defined schedules.
3Ease of manufacture
If siloed information approach is used to deal with complex backup environments, then implementation becomes simpler, but backup server utilization efficiency decreases
Solution Approach 1:
The system merges previously siloed scheduling decisions into a unified global scheduling framework. The scheduling engine consolidates information from multiple assets, targets, and constraints into a single coherent schedule that optimizes overall backup server utilization, eliminating the inefficiencies of isolated scheduling decisions while maintaining implementability through automated generation.
4Device complexity
If manual backup scheduling is used in dynamic environments, then system complexity remains low, but the ability to timely revise schedules in response to changes deteriorates
Solution Approach 1:
The scheduling system incorporates feedback mechanisms that continuously monitor environment changes, resource availability, and SLA compliance. This feedback enables the system to automatically detect changes and trigger schedule revisions, maintaining high adaptability to dynamic conditions while managing complexity through automated response protocols.
Solution Approach 2:
The system performs preliminary evaluation of potential schedule changes and pre-calculates optimized schedules in anticipation of expected changes or during off-peak periods. This preliminary action enables rapid schedule revision when changes occur, improving adaptability without requiring complex real-time decision-making processes.
Data Source
AI summary
One example method includes identifying a group of asset backups to be performed, and each asset backup is associated with a respective asset and has an associated backup time and RPO, selecting an asset backup to run first, and the asset backup that will run first is chosen based on a start deadline of that asset backup relative to respective start deadlines of one or more other asset backups, and the start deadline falls within a time slot, selecting a stream from a group of streams for the selected asset backup, and the selected stream is a stream with a lowest value of first available time slot, and backing up the asset at a backup server by running the selected asset backup, and backup begins at a start time that is a time when the selected stream becomes available, and the asset backup runs on the selected stream.