Backup Workload Prediction for Storage Systems
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Solution Overview
Problem
Current data center storage systems face performance issues and failures due to peak workload scenarios caused by improper scheduling of backups and snapshot management, leading to crashes in management servers.
Innovation Solution
A method that predicts potential backup failures by comparing backup plan factors with threshold values, enabling early detection of extreme snapshot management scenarios and notifying entities to take preventive actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple snapshots are created and expired at the same time to maintain backup continuity, then backup reliability is improved, but storage system performance deteriorates and management servers crash
Solution Approach 1:
The system performs preliminary analysis of backup schedules and workload patterns to predict peak workload scenarios before they occur. By proactively identifying situations where multiple snapshots would be created or expired simultaneously, the system can take preventive actions such as redistributing backup operations across different time windows, thereby avoiding performance degradation and server crashes while maintaining backup reliability
Solution Approach 2:
The system implements a feedback mechanism that continuously monitors storage system performance metrics, backup job status, and snapshot operations. When the system detects patterns indicating potential peak workload scenarios, it adjusts backup scheduling in real-time to prevent performance deterioration. This closed-loop control ensures backup reliability is maintained while avoiding the harmful effects of simultaneous snapshot operations
2Reliability
If backup operations are scheduled to ensure data continuity, then data protection is improved, but extreme peak workloads occur causing management server crashes
Solution Approach 1:
The system performs preliminary analysis of backup schedules and workload patterns to predict peak workload scenarios before they occur. By proactively identifying situations where multiple snapshots would be created or expired simultaneously, the system can take preventive actions such as redistributing backup operations across different time windows, thereby avoiding performance degradation and server crashes while maintaining backup reliability
Solution Approach 2:
The system introduces an intermediary workload management layer that acts as a mediator between backup operations and the storage system. This intermediary analyzes upcoming backup jobs, predicts potential peak workloads, and adjusts scheduling to smooth out extreme variations. By inserting this intermediate control layer, the system maintains data protection while reducing the complexity and instability of direct backup scheduling
Data Source
AI summary
Techniques disclosed herein provide for improved backup copy management in an information processing system. For example, a method comprises obtaining a set of one or more values representing a backup plan factor respectively corresponding to a set of one or more storage systems upon which data associated with the execution of application programs on one or more host servers coupled to the set of storage systems is stored. The method compares the obtained set of one or more values with a set of one or more threshold values. The method predicts whether a scheduled backup plan corresponding to each of the set of one or more storage systems is likely to fail based on a result of comparing the obtained set of one or more values with the set of one or more threshold values.