Prediction method, system and equipment for performance of storage equipment

Abstract

The invention provides a prediction method, a prediction system and prediction equipment for performance of storage equipment. The prediction method, the prediction system and the prediction equipmenthave the beneficial effects that the performance of the storage equipment is predicted through adopting an AdaBoost iterative algorithm model, various data samples obtained when the storage equipmentis developed and tested can be effectively utilized, the AdaBoost iterative algorithm model obtained after machine learning can be used for acquiring a better configuration scheme for a user to use the storage equipment, the workload of the developing and testing personnel for adjusting and optimizing the storage equipment is reduced, and the prediction method, the prediction system and the prediction equipment have high practicability.

Description

technical field [0001] The present invention relates to the technical field of storage device testing, and more specifically relates to a storage device performance prediction method, system and device. Background technique [0002] With the rapid development of scientific computing and various network applications, the amount of information generated by humans is increasing, which makes the storage of data more and more people's attention, thus making the status of storage components in the entire computer system More and more important, storage has changed from a single disk and tape to a disk array, and then developed to the current popular storage network. The demand for large-scale data applications is constantly emerging, and massive data and its applications have become a new development direction. Data storage has had a huge impact on people's work and life, and the improvement of the performance of the storage devices used is also Nature needs more and more attenti...

AI Technical Summary

Problems solved by technology

The performance value of the storage device is related to the number of selected links, the RAID level, the number of disks included in the RAID, the number of created LUNs, and the number of concurrent parameters under the condition that the hardware remains unchanged. In the process of storage device performance tuning , each parameter adjustment will affect the level of performance. In general, it is necessary to repeatedly debug and combine experience to select the optimal storage performance configuration. There is no direct theoretical basis and plan to refer to

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