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Distributed storage usage capacity estimation method and device

A distributed storage and capacity technology, applied in instruments, biological neural network models, error detection/correction, etc., can solve the problems of estimation process complexity, time variability, nonlinearity, etc., to improve user satisfaction and avoid insufficient capacity , optimize the effect of resource application

Inactive Publication Date: 2020-06-30
INSPUR SUZHOU INTELLIGENT TECH CO LTD
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Problems solved by technology

[0002] The usage capacity of distributed storage plays a guiding role in the purchase of cluster nodes by users or the capacity alarm of distributed storage software itself. However, the estimation process of usage capacity of distributed storage has the characteristics of complexity, nonlinearity, and time-varying. Its accurate prediction has always been an industry Difficulties, causing inconvenience to users

Method used

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  • Distributed storage usage capacity estimation method and device
  • Distributed storage usage capacity estimation method and device
  • Distributed storage usage capacity estimation method and device

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Embodiment 1

[0037] The RBF neural network is a three-layer forward network, including an input layer, a hidden layer and an output layer. The input layer is composed of signal source nodes; the second layer is the hidden layer, the number of hidden units depends on the needs of the described problem, and the transformation function RBF of the hidden unit is a non-negative nonlinear function that is radially symmetrical to the center point and decays; The third layer is the output layer, which responds to the action of the input pattern. The transformation from input space to hidden layer space is nonlinear, while the transformation from hidden layer space to output layer space is linear. The basic idea of ​​the RBF network is: use RBF as the "base" of the hidden unit to form the hidden layer space, so that the input vector can be directly mapped to the hidden space. When the central point of the RBF is determined, the mapping relationship is also determined. The mapping from the hidden ...

Embodiment 2

[0055] This embodiment provides a device for estimating the usage capacity of distributed storage, which is used for users to train curves and estimate the usage capacity of distributed storage at a certain point in the future.

[0056] like figure 2 As shown, the device includes an estimation curve obtaining module 1 and a capacity estimation module 2 .

[0057] Among them, the estimated curve acquisition module 1 collects data samples, and uses the RBF neural network to obtain the distributed storage usage capacity estimation curve; the capacity estimation module 2 receives input parameters, and uses the distributed storage usage capacity estimation curve to estimate future distributed storage usage capacity.

[0058] The estimated curve obtaining module 1 includes a data acquisition unit 11 , a step size setting unit 12 and a curve estimation unit 13 . The data acquisition unit 11 executes the collection of data samples; the step size setting unit 12 allows the user to s...

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Abstract

The invention discloses a distributed storage usage capacity estimation method and device. The method comprises the steps of collecting a data sample; selecting a proper step length; inputting the data sample into an RBF neural network for learning to obtain a distributed storage usage capacity estimation curve, wherein the collected data samples are multiple groups of distributed storage parameters which are collected according to a time sequence and are related to the distributed storage use capacity at the next moment. According to the method, distributed storage parameters related to the distributed storage use capacity at the next moment are collected according to a time sequence; and the parameters are taken as the input vector to be input into an RBF (Radial Basis Function) neural network for learning to obtain a distributed storage use capacity estimation curve, related parameters are input by a user, and the short-term change trend of the distributed storage use capacity is predicted by utilizing the distributed storage use capacity estimation curve to obtain the distributed storage use capacity at a certain moment in the future.

Description

technical field [0001] The present invention relates to the field of distributed storage usage capacity, in particular to a method and device for estimating the usage capacity of distributed storage. Background technique [0002] The usage capacity of distributed storage plays a guiding role in the purchase of cluster nodes by users or the capacity alarm of distributed storage software itself. However, the estimation process of usage capacity of distributed storage has the characteristics of complexity, nonlinearity, and time-varying. Its accurate prediction has always been an industry Problems that cause inconvenience to users. Contents of the invention [0003] In order to solve the above problems, the present invention provides a method and device for estimating the usage capacity of distributed storage, which utilizes RBF neural network to estimate the usage capacity of distributed storage, which has better estimation effect and is convenient for users to use. [0004...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/34G06F11/30G06N3/04
CPCG06F11/3476G06F11/3447G06F11/3034G06F11/3006G06N3/045
Inventor 曹涛
Owner INSPUR SUZHOU INTELLIGENT TECH CO LTD
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