Backup Optimizer Prioritizing Data Streams by Time Estimates
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Solution Overview
Problem
Existing backup applications for distributed systems inefficiently back up data streams from multiple computers due to random queuing, leading to increased time requirements for data object restoration.
Innovation Solution
Implementing a backup optimizer that queries multiple computers to identify data streams scheduled for backup, creates a ranked relational sequence based on backup time estimates, and instructs them to provide data streams according to this sequence, optimizing the concurrent backup process within a concurrency limit.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data streams are queued randomly for backup, then the backup process is simple to implement, but the total backup time increases and resource utilization decreases
Solution Approach 1:
The system performs preliminary actions by querying multiple computers to identify data streams scheduled for backup and estimating their backup times before the actual backup process. This advance preparation enables the creation of an optimized backup sequence, allowing the system to process larger data streams first and minimize total backup time without adding operational complexity during execution.
2Loss of time
If multiple data streams are backed up concurrently without prioritization, then the backup process is straightforward, but the total time required for backing up all data streams increases
Solution Approach 1:
The system changes the parameter of backup prioritization by introducing backup time estimates as a new criterion. Instead of treating all data streams equally or using simple FIFO queuing, the system estimates the time required to back up each data stream and uses this parameter to prioritize larger data streams for concurrent processing, thereby reducing total backup time while maintaining operational simplicity through automated estimation and sequencing.
3Productivity
If the backup application processes data streams in random order, then resource allocation is simple, but resource utilization efficiency decreases
Solution Approach 1:
The system implements feedback by querying multiple computers to obtain data stream information and backup time estimates before creating the backup sequence. This feedback mechanism allows the backup optimizer to make informed decisions about prioritization, ensuring that resources are allocated efficiently to larger data streams first. The feedback loop continues during backup execution, monitoring progress and dynamically adjusting resource allocation to maintain optimal utilization.
Data Source
AI summary
Concurrently backing up data streams of multiple computers based on backup time estimates is described. A system queries multiple computers to identify multiple data streams scheduled for backup. The system queries the multiple computers to identify multiple backup time estimates corresponding to the multiple data streams. The system creates a ranked relational sequence based on the multiple backup time estimates. The system instructs the multiple computers to provide the multiple data streams based on the ranked relational sequence. The system concurrently backs up the multiple data streams based on a concurrency limit and the ranked relational sequence.