CN114297019A - Method, system, terminal and storage medium for predicting enumerated object performance - Google Patents
Method, system, terminal and storage medium for predicting enumerated object performance Download PDFInfo
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Abstract
The invention relates to the technical field of object storage, in particular to a method, a system, a terminal and a storage medium for predicting enumerated object performance, wherein the method comprises the following steps: adding a pre-constructed extreme learning machine in an object storage cluster; monitoring operation parameters stored by an object and inputting the operation parameters into an extreme learning machine to obtain prediction data of the enumerated object performance; and comparing the prediction data with a set standard range, and generating performance data alarm information if the prediction data is not in the standard range. The invention predicts the cluster performance occupied by the list function according to the configuration parameters of the cluster, and can facilitate business personnel to optimize the list function and detect faults.
Description
Technical Field
The invention relates to the technical field of object storage, in particular to a method, a system, a terminal and a storage medium for predicting enumerated object performance.
Background
With the development of the internet, communication technology and storage technology, various industries generate massive data. The Ceph system is a distributed open-source storage system which is popular at present, and an object storage cluster in the Ceph system is widely applied to industries such as banks and communication operators due to good sharing performance and transmission speed. An enumeration object function (list) is used as a core component of the object storage cluster, and when the object storage is used for viewing the object operation, the enumeration object function occupies the performance of the object storage cluster, and may cause the failure of the object storage cluster when the object storage is serious. There is currently no effective method of supervising the functions of enumerated objects.
Disclosure of Invention
In view of the above-mentioned shortcomings in the prior art, the present invention provides a method, a system, a terminal and a storage medium for predicting the performance of an enumeration object, so as to solve the above-mentioned technical problems.
In a first aspect, the present invention provides a prediction method for enumerating object performances, including:
adding a pre-constructed extreme learning machine in an object storage cluster;
monitoring operation parameters stored by an object and inputting the operation parameters into the extreme learning machine to obtain prediction data of enumerated object performance;
and comparing the prediction data with a set standard range, and generating performance data alarm information if the prediction data is not in the standard range.
Further, the construction method of the extreme learning machine comprises the following steps:
setting the number of neurons of an input layer, the number of neurons of a hidden layer and the number of neurons of an output layer of the extreme learning machine, and constructing a topological structure of the extreme learning machine;
setting input layer parameters of the extreme learning machine as object storage processing speed, concurrent enumeration barrel fragment number and barrel fragment number, and setting output layer parameters as enumeration object performance parameters;
training the extreme learning machine by utilizing a pre-prepared training set, and optimizing the input weight and the output weight of each hidden layer of the extreme learning machine and the offset of each hidden layer to obtain the trained extreme learning machine;
the training set comprises historical processing speed, historical concurrent enumeration barrel fragment number, historical barrel fragment number and corresponding historical enumeration object performance data of object storage;
and performing reliability verification on the trained extreme learning machine by using a ten-fold cross verification method.
Further, monitoring the operating parameters stored by the object and inputting the operating parameters into the extreme learning machine to obtain the prediction data of the enumerated object performance, comprising:
establishing a monitoring process, and periodically acquiring operation parameters stored by an object by using the monitoring process, wherein the operation parameters comprise an object storage processing speed, a concurrent enumeration barrel fragment number and a barrel fragment number;
and inputting the operation parameters output by the monitoring process into the extreme learning machine to obtain the prediction data of the enumerated object performance.
Further, the predicted data is compared with a set standard range, and if the predicted data is not in the standard range, after the performance data alarm information is generated, the method further comprises the following steps:
optimizing the operation parameters according to the alarm information until the alarm information is removed;
and storing the prediction data for generating the alarm information into an enumeration object performance log, and outputting the enumeration object performance log as a fault analysis factor after monitoring that the object storage cluster has faults.
In a second aspect, the present invention provides a prediction system for enumerating object properties, comprising:
the model setting unit is used for additionally arranging a pre-constructed extreme learning machine in the object storage cluster;
the data prediction unit is used for monitoring the operation parameters stored by the object and inputting the operation parameters into the extreme learning machine to obtain the prediction data of the enumerated object performance;
and the data analysis unit is used for comparing the predicted data with a set standard range, and generating performance data alarm information if the predicted data is not in the standard range.
Further, the construction method of the extreme learning machine comprises the following steps:
setting the number of neurons of an input layer, the number of neurons of a hidden layer and the number of neurons of an output layer of the extreme learning machine, and constructing a topological structure of the extreme learning machine;
setting input layer parameters of the extreme learning machine as object storage processing speed, concurrent enumeration barrel fragment number and barrel fragment number, and setting output layer parameters as enumeration object performance parameters;
training the extreme learning machine by utilizing a pre-prepared training set, and optimizing the input weight and the output weight of each hidden layer of the extreme learning machine and the offset of each hidden layer to obtain the trained extreme learning machine;
the training set comprises historical processing speed, historical concurrent enumeration barrel fragment number, historical barrel fragment number and corresponding historical enumeration object performance data of object storage;
and performing reliability verification on the trained extreme learning machine by using a ten-fold cross verification method.
Further, the data prediction unit includes:
the system comprises a process creation module, a monitoring module and a storage module, wherein the process creation module is used for creating a monitoring process and periodically acquiring operation parameters stored by an object by using the monitoring process, and the operation parameters comprise an object storage processing speed, a concurrent enumeration barrel fragment number and a barrel fragment number;
and the data forwarding module is used for inputting the operation parameters output by the monitoring process into the extreme learning machine to obtain the prediction data of the enumerated object performance.
Further, the data analysis unit includes:
the parameter optimization module is used for optimizing the operation parameters according to the alarm information until the alarm information is removed;
and the fault analysis module is used for storing the prediction data for generating the alarm information into an enumerated object performance log and outputting the enumerated object performance log as a fault analysis factor after monitoring that the object storage cluster has faults.
In a third aspect, a terminal is provided, including:
a processor, a memory, wherein,
the memory is used for storing a computer program which,
the processor is used for calling and running the computer program from the memory so as to make the terminal execute the method of the terminal.
In a fourth aspect, a computer storage medium is provided having stored therein instructions that, when executed on a computer, cause the computer to perform the method of the above aspects.
The method, the system, the terminal and the storage medium for predicting the enumerated object performance have the advantages that the performance of the enumerated object function is predicted by additionally arranging the extreme learning machine in the object storage cluster, and the operation parameters are optimized and the fault is analyzed according to the predicted data, so that the enumerated object function is effectively monitored. The invention predicts the cluster performance occupied by the list function according to the configuration parameters of the cluster, and can facilitate business personnel to optimize the list function and detect faults.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention.
FIG. 2 is a diagram of the topology of an extreme learning machine of the method of one embodiment of the present invention.
FIG. 3 is a schematic block diagram of a system of one embodiment of the present invention.
Fig. 4 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The following explains key terms appearing in the present invention.
Extreme Learning Machines (ELMs) or "ultralimit Learning machines" are a class of Machine Learning systems or methods constructed based on feed Forward Neural Networks (FNNs), and are suitable for supervised Learning and unsupervised Learning problems. ELM is considered as a special FNN in research, or is an improvement on FNN and a back propagation algorithm thereof, and is characterized in that the weight of hidden layer nodes is randomly or artificially given and does not need to be updated, and the learning process only calculates the output weight. Conventional ELMs have a single hidden layer and are considered to be potentially advantageous in terms of learning rate and generalization capability when compared to other shallow learning systems, such as single layer perceptrons (SVMs) and Support Vector Machines (SVMs). Some improved versions of ELM achieve depth structure by introducing self-encoder building or stacking hidden layers, enabling characterization learning. Applications of ELM include computer vision and bioinformatics, and are also applied to some regression problems in earth science and environmental science.
Cross validation by ten folds, called 10-fold cross-validation by English name, is used for testing the accuracy of the algorithm. Is a commonly used test method. The data set was divided into ten parts, and 9 parts of the data set were used as training data and 1 part of the data set was used as test data in turn for the experiments. Each trial will yield a corresponding accuracy (or error rate). The average of the accuracy (or error rate) of the 10 results is used as an estimate of the accuracy of the algorithm, and generally 10-fold cross validation is performed multiple times (for example, 10 times of 10-fold cross validation), and then the average is obtained as an estimate of the accuracy of the algorithm. Ten fold cross validation was chosen to divide the dataset into 10 because through extensive experimentation with a large number of datasets using different learning techniques, 10 fold was shown to be the proper choice to obtain the best error estimate, and there are some theories to justify this. But this is not the final diagnosis and disputes still exist. It also appears that the results obtained by the 5-fold or 20-fold and 10-fold are comparable.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention. The execution subject in fig. 1 may be a prediction system that enumerates the performance of objects.
As shown in fig. 1, the method includes:
and step 130, comparing the prediction data with a set standard range, and if the prediction data is not in the standard range, generating performance data alarm information.
In order to facilitate understanding of the present invention, the method for predicting the performance of the enumerated object provided by the present invention is further described below by using the principle of the method for predicting the performance of the enumerated object of the present invention and combining the process of predicting the performance of the enumerated object in the embodiment.
Specifically, the method for predicting the performance of the enumerated object includes:
and S1, adding a pre-constructed extreme learning machine in the object storage cluster.
Setting the number of neurons of an input layer, the number of neurons of a hidden layer and the number of neurons of an output layer of the extreme learning machine, and constructing a topological structure of the extreme learning machine; setting input layer parameters of the extreme learning machine as object storage processing speed, concurrent enumeration barrel fragment number and barrel fragment number, and setting output layer parameters as enumeration object performance parameters; training the extreme learning machine by utilizing a pre-prepared training set, and optimizing the input weight and the output weight of each hidden layer of the extreme learning machine and the offset of each hidden layer to obtain the trained extreme learning machine; the training set comprises historical processing speed, historical concurrent enumeration barrel fragment number, historical barrel fragment number and corresponding historical enumeration object performance data of object storage; and performing reliability verification on the trained extreme learning machine by using a ten-fold cross verification method.
Compared with the current popular artificial neural network, the ELM has obvious advantages in 3 aspects, 1) the model can be obtained by one-time learning without iteration, 2) the setting of random parameters can generally have better generalization performance, and 3) the learning speed is high. Specifically, please refer to fig. 2 for the topology of the extreme learning machine. The parameters of the input layer are osd processing speed (perf), the concurrent enumeration bucket fragmentation number (bucket _ index _ max _ aio) and the bucket fragmentation number (bucket _ index _ max _ shares), and the value of the output layer is list performance. Wherein, wj＝[wj1,wj2,...,wjn]TAnd betaj＝[βj1,βj2,...,βjm]TRepresenting the weight of the jth hidden node to the input node and the output node, bjIndicating the offset of the jth hidden node. The ELM model is constructed by adopting the following steps:
step 1: and collecting data. Recording input parameters stored by an object, namely osd processing speed, concurrent enumeration of barrel fragment number and barrel fragment number, acquiring corresponding performance data, acquiring 1000 groups of experimental data, and forming a training set;
step 2: and (5) constructing an ELM model. ELM randomly generates input weight and calculates output weight from hidden layer to output. Giving the number L of hidden layer nodes and an activation function g (x), and inputting a weight wiOffset from the hidden layer biIt is randomly generated. And solving a hidden layer output matrix H. And (3) solving the connection weight between the hidden layer and the output layer according to the formula (1).
Step 3: and (5) verifying the model. And verifying whether the constructed model is in accordance with the requirements or not by using a 10-fold cross validation mode based on the training set.
And adding the finally obtained extreme learning machine into the object storage cluster.
And S2, monitoring the operation parameters stored by the object and inputting the operation parameters into the extreme learning machine to obtain the prediction data of the enumerated object performance.
Establishing a monitoring process, and periodically acquiring operation parameters stored by an object by using the monitoring process, wherein the operation parameters comprise an object storage processing speed, a concurrent enumeration barrel fragment number and a barrel fragment number; and inputting the operation parameters output by the monitoring process into the extreme learning machine to obtain the prediction data of the enumerated object performance.
Setting the monitoring period of the monitoring process to be 5min, collecting operation parameters every 5min, and directly outputting the operation parameters to the extreme learning machine so as to obtain the monitored prediction data of enumerated object performance.
And S3, comparing the prediction data with a set standard range, and if the prediction data is not in the standard range, generating performance data alarm information.
Optimizing the operation parameters according to the alarm information until the alarm information is removed; and storing the prediction data for generating the alarm information into an enumeration object performance log, and outputting the enumeration object performance log as a fault analysis factor after monitoring that the object storage cluster has faults.
The standard range can be set according to the user requirement, but it must be ensured that the object storage cluster is not failed. If the predicted performance data is in the standard range, no processing is performed; and if the alarm information is not in the standard range, generating alarm information. The object storage system optimizes the operation parameters after finding the alarm information, and the operation parameters can be optimized manually or automatically by the existing tools of the object storage system. In addition, prediction data for generating alarm information, which is an important analysis target in performing failure analysis, is stored in an enumeration target performance log.
As shown in fig. 3, the system 300 includes:
a model setting unit 310, configured to add a pre-constructed extreme learning machine in the object storage cluster;
the data prediction unit 320 is used for monitoring the operation parameters stored by the objects and inputting the operation parameters into the extreme learning machine to obtain the prediction data of the enumerated object performance;
and the data analysis unit 330 is configured to compare the predicted data with a set standard range, and if the predicted data is not within the standard range, generate performance data alarm information.
Optionally, as an embodiment of the present invention, the method for constructing the extreme learning machine includes:
setting the number of neurons of an input layer, the number of neurons of a hidden layer and the number of neurons of an output layer of the extreme learning machine, and constructing a topological structure of the extreme learning machine;
setting input layer parameters of the extreme learning machine as object storage processing speed, concurrent enumeration barrel fragment number and barrel fragment number, and setting output layer parameters as enumeration object performance parameters;
training the extreme learning machine by utilizing a pre-prepared training set, and optimizing the input weight and the output weight of each hidden layer of the extreme learning machine and the offset of each hidden layer to obtain the trained extreme learning machine;
the training set comprises historical processing speed, historical concurrent enumeration barrel fragment number, historical barrel fragment number and corresponding historical enumeration object performance data of object storage;
and performing reliability verification on the trained extreme learning machine by using a ten-fold cross verification method.
Optionally, as an embodiment of the present invention, the data prediction unit includes:
the system comprises a process creation module, a monitoring module and a storage module, wherein the process creation module is used for creating a monitoring process and periodically acquiring operation parameters stored by an object by using the monitoring process, and the operation parameters comprise an object storage processing speed, a concurrent enumeration barrel fragment number and a barrel fragment number;
and the data forwarding module is used for inputting the operation parameters output by the monitoring process into the extreme learning machine to obtain the prediction data of the enumerated object performance.
Optionally, as an embodiment of the present invention, the data analysis unit includes:
the parameter optimization module is used for optimizing the operation parameters according to the alarm information until the alarm information is removed;
and the fault analysis module is used for storing the prediction data for generating the alarm information into an enumerated object performance log and outputting the enumerated object performance log as a fault analysis factor after monitoring that the object storage cluster has faults.
Fig. 4 is a schematic structural diagram of a terminal 400 according to an embodiment of the present invention, where the terminal 400 may be configured to execute the method for predicting the enumerated object performance according to the embodiment of the present invention.
Among them, the terminal 400 may include: a processor 410, a memory 420, and a communication unit 430. The components communicate via one or more buses, and those skilled in the art will appreciate that the architecture of the servers shown in the figures is not intended to be limiting, and may be a bus architecture, a star architecture, a combination of more or less components than those shown, or a different arrangement of components.
The memory 420 may be used for storing instructions executed by the processor 410, and the memory 420 may be implemented by any type of volatile or non-volatile storage terminal or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The executable instructions in memory 420, when executed by processor 410, enable terminal 400 to perform some or all of the steps in the method embodiments described below.
The processor 410 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by operating or executing software programs and/or modules stored in the memory 420 and calling data stored in the memory. The processor may be composed of an Integrated Circuit (IC), for example, a single packaged IC, or a plurality of packaged ICs connected with the same or different functions. For example, the processor 410 may include only a Central Processing Unit (CPU). In the embodiment of the present invention, the CPU may be a single operation core, or may include multiple operation cores.
A communication unit 430, configured to establish a communication channel so that the storage terminal can communicate with other terminals. And receiving user data sent by other terminals or sending the user data to other terminals.
The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and the program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Therefore, the invention realizes effective supervision of the enumerated object function by adding the extreme learning machine in the object storage cluster to predict the performance of the enumerated object function, and optimizing the operation parameters and analyzing the faults according to the predicted data. The cluster performance occupied by the list function is predicted according to the configuration parameters of the cluster, so that business personnel can optimize the list function and detect faults conveniently.
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of a software product, where the computer software product is stored in a storage medium, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, and the storage medium can store program codes, and includes instructions for enabling a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, and the like) to perform all or part of the steps of the method in the embodiments of the present invention.
The same and similar parts in the various embodiments in this specification may be referred to each other. Especially, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant points can be referred to the description in the method embodiment.
In the embodiments provided in the present invention, it should be understood that the disclosed system and method can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A prediction method for enumerating object performances is characterized by comprising the following steps:
adding a pre-constructed extreme learning machine in an object storage cluster;
monitoring operation parameters stored by an object and inputting the operation parameters into the extreme learning machine to obtain prediction data of enumerated object performance;
and comparing the prediction data with a set standard range, and generating performance data alarm information if the prediction data is not in the standard range.
2. The method according to claim 1, wherein the construction method of the extreme learning machine comprises:
setting the number of neurons of an input layer, the number of neurons of a hidden layer and the number of neurons of an output layer of the extreme learning machine, and constructing a topological structure of the extreme learning machine;
setting input layer parameters of the extreme learning machine as object storage processing speed, concurrent enumeration barrel fragment number and barrel fragment number, and setting output layer parameters as enumeration object performance parameters;
training the extreme learning machine by utilizing a pre-prepared training set, and optimizing the input weight and the output weight of each hidden layer of the extreme learning machine and the offset of each hidden layer to obtain the trained extreme learning machine;
the training set comprises historical processing speed, historical concurrent enumeration barrel fragment number, historical barrel fragment number and corresponding historical enumeration object performance data of object storage;
and performing reliability verification on the trained extreme learning machine by using a ten-fold cross verification method.
3. The method of claim 1, wherein monitoring object stored operating parameters and inputting the operating parameters into the extreme learning machine to obtain predictive data enumerating object properties comprises:
establishing a monitoring process, and periodically acquiring operation parameters stored by an object by using the monitoring process, wherein the operation parameters comprise an object storage processing speed, a concurrent enumeration barrel fragment number and a barrel fragment number;
and inputting the operation parameters output by the monitoring process into the extreme learning machine to obtain the prediction data of the enumerated object performance.
4. The method of claim 1, wherein the predicted data is compared with a set standard range, and if the predicted data is not within the standard range, after generating performance data alarm information, the method further comprises:
optimizing the operation parameters according to the alarm information until the alarm information is removed;
and storing the prediction data for generating the alarm information into an enumeration object performance log, and outputting the enumeration object performance log as a fault analysis factor after monitoring that the object storage cluster has faults.
5. A predictive system for enumerating object properties, comprising:
the model setting unit is used for additionally arranging a pre-constructed extreme learning machine in the object storage cluster;
the data prediction unit is used for monitoring the operation parameters stored by the object and inputting the operation parameters into the extreme learning machine to obtain the prediction data of the enumerated object performance;
and the data analysis unit is used for comparing the predicted data with a set standard range, and generating performance data alarm information if the predicted data is not in the standard range.
6. The system according to claim 5, wherein the construction method of the extreme learning machine comprises the following steps:
setting the number of neurons of an input layer, the number of neurons of a hidden layer and the number of neurons of an output layer of the extreme learning machine, and constructing a topological structure of the extreme learning machine;
setting input layer parameters of the extreme learning machine as object storage processing speed, concurrent enumeration barrel fragment number and barrel fragment number, and setting output layer parameters as enumeration object performance parameters;
training the extreme learning machine by utilizing a pre-prepared training set, and optimizing the input weight and the output weight of each hidden layer of the extreme learning machine and the offset of each hidden layer to obtain the trained extreme learning machine;
the training set comprises historical processing speed, historical concurrent enumeration barrel fragment number, historical barrel fragment number and corresponding historical enumeration object performance data of object storage;
and performing reliability verification on the trained extreme learning machine by using a ten-fold cross verification method.
7. The system of claim 5, wherein the data prediction unit comprises:
the system comprises a process creation module, a monitoring module and a storage module, wherein the process creation module is used for creating a monitoring process and periodically acquiring operation parameters stored by an object by using the monitoring process, and the operation parameters comprise an object storage processing speed, a concurrent enumeration barrel fragment number and a barrel fragment number;
and the data forwarding module is used for inputting the operation parameters output by the monitoring process into the extreme learning machine to obtain the prediction data of the enumerated object performance.
8. The system of claim 5, wherein the data analysis unit comprises:
the parameter optimization module is used for optimizing the operation parameters according to the alarm information until the alarm information is removed;
and the fault analysis module is used for storing the prediction data for generating the alarm information into an enumerated object performance log and outputting the enumerated object performance log as a fault analysis factor after monitoring that the object storage cluster has faults.
9. A terminal, comprising:
a processor;
a memory for storing instructions for execution by the processor;
wherein the processor is configured to perform the method of any one of claims 1-4.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-4.
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