Asynchronous Storage Copy with Predictive VM Performance Adjustment
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Solution Overview
Problem
Existing methods fail to consider future processing performance needs, leading to suboptimal performance in asynchronous data copy operations between multiple sites.
Innovation Solution
A storage system with a processing performance adjustment unit that anticipates and adjusts the processing performance of a virtual computer in a cloud environment based on current input and output processing states in an on-premises environment, ensuring higher performance levels are maintained through proactive adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If processing performance is adjusted based on current state only, then current performance requirements are met, but future performance requirements cannot be prepared in advance
Solution Approach 1:
The system performs preliminary actions by predicting future I/O processing states and adjusting processing performance in advance before the predicted high-load period occurs. The management server calculates future I/O processing states based on historical data and trends, then proactively adjusts the virtual computer's performance parameters before the actual high-demand moment arrives, eliminating performance degradation that would occur with reactive adjustments.
Solution Approach 2:
The system implements dynamic performance adjustment by continuously monitoring current I/O processing states, predicting future states, and adaptively changing virtual computer performance parameters. The management server dynamically modifies CPU allocation, memory allocation, and storage I/O priorities based on predicted workload patterns, allowing the system to flexibly respond to changing demands while maintaining optimal performance.
2Reliability
If processing performance is increased in advance, then future performance requirements are met, but current resources are over-provisioned
Solution Approach 1:
The system performs preliminary actions by predicting future I/O processing states and adjusting processing performance in advance before the predicted high-load period occurs. The management server calculates future I/O processing states based on historical data and trends, then proactively adjusts the virtual computer's performance parameters before the actual high-demand moment arrives, eliminating performance degradation that would occur with reactive adjustments.
Solution Approach 2:
The system changes performance parameters dynamically based on predicted workload. The management server adjusts specific parameters such as CPU allocation ratios, memory allocation sizes, and storage I/O priority levels according to the predicted I/O processing state. This allows precise control over resource provisioning, allocating additional resources only when and where they are predicted to be needed, rather than maintaining constant over-provisioning.
3Reliability
If processing performance is adjusted frequently, then performance requirements are met, but system complexity increases
Solution Approach 1:
The system implements a feedback mechanism where the management server continuously monitors actual I/O processing states and compares them with predicted states. Based on this feedback, the system adjusts performance parameters to correct deviations. The feedback loop includes monitoring current performance metrics, comparing with predictions, and making corrective adjustments to keep actual performance aligned with desired performance levels.
Solution Approach 2:
The system performs preliminary actions by predicting future I/O processing states and adjusting processing performance in advance before the predicted high-load period occurs. The management server calculates future I/O processing states based on historical data and trends, then proactively adjusts the virtual computer's performance parameters before the actual high-demand moment arrives, eliminating performance degradation that would occur with reactive adjustments.
Data Source
AI summary
In a storage system including another storage device, in which processing performance of a virtual computer that executes asynchronous copy processing of asynchronously copying data with one storage device that executes input and output processing of data in response to a request from a host device may be changed, a processing performance adjustment unit changes in advance processing performance required at a second time point temporally later than a first time point by the virtual computer of the other storage device before the second time point when the asynchronous copy processing is executed at the second time point according to a state of the input and output processing at the first time point in the one storage device.


