Systems And Methods For Generalized Adaptive Storage Endpoint Prediction
Systems and methods are provided that that may be implemented to perform generalized adaptive storage endpoint prediction. A system/storage management application gathers data samples pertaining to operation of a storage device (e.g., remaining rated write endurance, available spare blocks, remaining drive space), clusters the data samples (e.g., using DBSCAN algorithm), and approximates a polynomial function usable to predict an endpoint of the storage device (e.g., SSD) by using an artificial neural network that receives the data samples and clusters and in response generates the approximated polynomial function. The approximate polynomial function may be a combination of Gaussian functions corresponding to the clusters. A mean and variance associated with each cluster may be calculated that is a mean and variance of a corresponding Gaussian function. A feed forward artificial neural network having at least one hidden layer, constant bias, and Gaussian activation function may be employed.
1 . An information handling system, comprising:
a programmable integrated circuit;
a storage device; and
a system/storage management application executing on the programmable integrated circuit that:
gathers data samples pertaining to operation of the storage device;
clusters the data samples into a plurality of clusters; and
approximates a polynomial function usable to predict an endpoint of the storage device by using an artificial neural network that receives the data samples and clusters and in response generates the approximated polynomial function.
2 . The information handling system of claim 1 ,
where the storage device comprises a solid state drive (SSD).
3 . The information handling system of claim 1 ,
where each of the data samples comprises an information value and a time value;
where the information values are of a selected information type from a list of information types comprising:
remaining rated write endurance of the storage device;
available spare blocks of the storage device;
remaining drive space of the storage device; and
remaining storage capacity of all storage devices in the information handling system; and
where the predicted endpoint is a time at which the approximated polynomial function predicts an information value of the selected information type will drop below a predetermined value.
4 . The information handling system of claim 1 ,
where to cluster the data samples into the plurality of clusters the application employs a DBSCAN (density based spatial clustering of application with noise) algorithm.
5 . The information handling system of claim 1 ,
where the approximate polynomial function is a combination of a plurality of Gaussian functions corresponding to the plurality of clusters.
6 . The information handling system of claim 5 ,
where to cluster the data samples into the plurality of clusters the application calculates a mean and variance associated with each cluster of the plurality of clusters that is a mean and variance of the corresponding Gaussian function of the plurality of Gaussian functions.
7 . The information handling system of claim 1 ,
where the artificial neural network is a feed forward artificial neural network having at least one hidden layer, constant bias, and Gaussian activation function.
8 . The information handling system of claim 7 ,
where for each neuron of a plurality of neurons of the at least one hidden layer, the neuron has a radial function (x i −c j ) and a hyper-plane equation (w T x+b), where x j is a j-th data sample of the gathered data samples, c i is a center of an i-th cluster of the plurality of clusters, w T is a weight matrix of the neuron, and b is bias of the neuron.
9 . The information handling system of claim 8 ,
where an output of a single perceptron of the artificial neural network has equation
1
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where σ 1 is a variance of the i-th cluster.
10 . A method for use in an information handling system having a storage device, the method comprising:
gathering data samples pertaining to operation of the storage device;
clustering the data samples into a plurality of clusters; and
approximating a polynomial function usable to predict an endpoint of the storage device by using an artificial neural network that receives the data samples and clusters and in response generates the approximated polynomial function.
11 . The method of claim 10 ,
where the storage device comprises a solid state drive (SSD).
12 . The method of claim 10 ,
where each of the data samples comprises an information value and a time value;
where the information values are of a selected information type from a list of information types comprising:
remaining rated write endurance of the storage device;
available spare blocks of the storage device;
remaining drive space of the storage device; and
remaining storage capacity of all storage devices in the information handling system; and
where the predicted endpoint is a time at which the approximated polynomial function predicts an information value of the selected information type will drop below a predetermined value.
13 . The method of claim 10 ,
where said clustering the data samples into the plurality of clusters comprises employing a DBSCAN (density based spatial clustering of application with noise) algorithm.
14 . The method of claim 10 ,
where the approximate polynomial function is a combination of a plurality of Gaussian functions corresponding to the plurality of clusters.
15 . The method of claim 14 ,
where said clustering the data samples into the plurality of clusters comprises calculating a mean and variance associated with each cluster of the plurality of clusters that is a mean and variance of the corresponding Gaussian function of the plurality of Gaussian functions.
16 . The method of claim 10 ,
where the artificial neural network is a feed forward artificial neural network having at least one hidden layer, constant bias, and Gaussian activation function.
17 . The method of claim 16 ,
where for each neuron of a plurality of neurons of the at least one hidden layer, the neuron has a radial function (x j −c i ) and a hyper-plane equation (w T x+b), where x j is a j-th data sample of the gathered data samples, c i is a center of an i-th cluster of the plurality of clusters, w T is a weight matrix of the neuron, and b is bias of the neuron.
18 . The method of claim 17 ,
where an output of a single perceptron of the artificial neural network has equation
1
σ
i
2
π
e
-
{
(
w
T
(
x
j
-
C
i
)
+
b
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2
σ
2
}
,
where σ 1 is a variance of the i-th cluster.
19 . A non-transitory computer-readable medium having instructions stored thereon that are capable of causing or configuring an information handling system having at least one programmable integrated circuit and a storage device to perform operations comprising:
gathering data samples pertaining to operation of the storage device;
clustering the data samples into a plurality of clusters; and
approximating a polynomial function usable to predict an endpoint of the storage device by using an artificial neural network that receives the data samples and clusters and in response generates the approximated polynomial function.
20 . The non-transitory computer-readable medium of claim 19 , having instructions stored thereon that are capable of causing or configuring an information handling system having at least one programmable integrated circuit and a storage device to perform operations further comprising:
where said clustering the data samples into the plurality of clusters comprises employing a DBSCAN (density based spatial clustering of application with noise) algorithm;
where the approximate polynomial function is a combination of a plurality of Gaussian functions corresponding to the plurality of clusters; and
where said clustering the data samples into the plurality of clusters comprises calculating a mean and variance associated with each cluster of the plurality of clusters that is a mean and variance of the corresponding Gaussian function of the plurality of Gaussian functions.