Weight-Based Backup Scheduling for Replication Sessions
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Solution Overview
Problem
The First-In-First-Out (FIFO) policy in data backup systems leads to long processing times for early replication sessions, causing later sessions to miss recovery points, resource overconsumption, and increased data inconsistency, ultimately leading to system slowdowns and memory issues.
Innovation Solution
Determine parameters and weights for replication sessions in a waiting queue, ranking them based on these weights to prioritize execution in a running queue, considering factors like waiting time, processing time, and RPO levels to optimize backup efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If FIFO policy is used for backup tasks, then processing order is simple and fair, but later replication sessions miss recovery points and data inconsistency increases
Solution Approach 1:
The patent changes the scheduling parameter from simple FIFO ordering to a weight-based priority system. Each replication session is assigned a weight based on multiple parameters including waiting time, RPO requirements, and data change frequency. This parameter change allows the system to dynamically adjust scheduling priorities to meet RPO requirements while maintaining operational simplicity through automated weight calculation.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring replication session performance metrics such as waiting time, RPO compliance status, and data consistency levels. These metrics feed back into the weight calculation system, allowing the scheduler to dynamically adjust priorities and ensure that sessions at risk of missing RPO points are prioritized in subsequent scheduling cycles.
2Stability of the object's composition
If early replication sessions are processed first, then processing order is maintained, but resource consumption increases and system slows down
Solution Approach 1:
The patent introduces dynamic scheduling where replication session priorities are not fixed but change over time based on current system state and session characteristics. Sessions that have been waiting longer or have stricter RPO requirements automatically gain higher priority weights. This dynamic approach maintains processing order stability through consistent weight-based ordering while improving productivity by preventing resource starvation and optimizing throughput.
3Quantity of substance
If more backup tasks are queued, then more data is protected, but memory consumption increases and system performance degrades
Solution Approach 1:
The patent extracts and prioritizes only the most critical replication sessions for immediate processing based on calculated weights, rather than processing all queued tasks equally. By identifying and extracting high-priority sessions (those with longest waiting time or strictest RPO requirements), the system processes a smaller subset of tasks that provides the greatest protection value, thereby reducing memory consumption and improving performance while still protecting the most vulnerable data.
Data Source
AI summary
Techniques for backup involve determining parameters of replication sessions in a waiting queue in response to a backup execution command being triggered; and such techniques further involve determining weights of the replication sessions based on the parameters of the replication sessions. Such techniques further involve selecting at least one replication session from the replication sessions to enter a running queue based on the weights of the replication sessions. Such techniques further involve performing a backup task with respect to the at least one replication session in the running queue. Accordingly, each replication session is ranked based on priority, and parameters are taken into account in the ranking to improve backup efficiency, thereby reducing the number of waiting and failed tasks and better protecting data, which enhances the replication capability of the system and increases the replication limit number to prevent the system from running out of memory.