Virtual Storage Upgrade Timing via Workload Monitoring
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Solution Overview
Problem
Existing virtual storage systems lack an efficient method to determine when upgrades are needed and to select the optimal time for upgrades, leading to potential disruptions and increased costs.
Innovation Solution
A method that involves monitoring the use status of the storage space and determining whether an upgrade is necessary based on this status. Once the need for an upgrade is established, the method identifies the optimal time for the upgrade by considering the workload of the virtual storage system, ensuring minimal disruption to operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the virtual storage system is upgraded frequently to meet growing storage needs, then the storage capacity is improved, but the system stability and operational continuity deteriorate due to repeated upgrade disruptions
Solution Approach 1:
The system performs preliminary assessment of upgrade necessity by monitoring storage use status against thresholds, and preliminary selection of optimal upgrade timing by analyzing workload patterns before actual upgrade execution. This allows planning upgrades in advance during low-utilization periods, minimizing disruption to system stability while meeting growing storage capacity requirements
2Quantity of substance
If the virtual storage system is upgraded during high workload periods to ensure timely capacity expansion, then the storage capacity is improved, but the productivity and user experience deteriorate due to service interruptions
Solution Approach 1:
The system continuously monitors workload metrics and storage use status, using this feedback to dynamically determine optimal upgrade timing. When workload exceeds thresholds, the system delays upgrade execution until workload decreases, ensuring capacity expansion occurs during low-activity periods when impact on productivity and user experience is minimized
3Device complexity
If manual assessment methods are used to determine upgrade timing, then the upgrade process is simple, but the measurement precision of upgrade timing optimization deteriorates
Solution Approach 1:
The system automatically monitors its own storage use status and workload metrics, performs self-assessment of upgrade necessity, and autonomously selects optimal upgrade timing based on predefined thresholds and patterns. This self-service approach eliminates manual intervention while achieving high precision in upgrade timing optimization through continuous automated monitoring and analysis
Data Source
AI summary
The present disclosure relates to a method that includes acquiring a use status of a storage space of the virtual storage system. The method further includes determining whether to upgrade the virtual storage system based on the use status. The method further includes determining a targeted instant based on the workload of the virtual storage system in response to the determination to upgrade the virtual storage system. The method further includes upgrading the virtual storage system at the targeted instant. In this way, it can be accurately determined whether to upgrade the virtual storage system based on the storage status of the virtual storage system, and when upgrade is needed, the optimum upgrade time is selected for upgrade of the system based on the workload of the virtual storage system, thereby effectively ensuring normal use of the virtual storage system.