CN114661247B - Automatic capacity expansion method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses an automatic capacity expansion method, a device, electronic equipment and a storage medium, wherein the method comprises the steps of acquiring a service to be processed by a client and a source storage child node of a data node currently mounted in a management node when the client is detected to access the data node, and determining a target child node still required for processing the service from a plurality of candidate child nodes in the management node according to the service, so that the capacity expansion processing can be performed on the source storage child node based on the target child node, the capacity expansion of the source storage child node for processing the service is completed when a user performs the service processing, the capacity expansion of the node is completed while the service processing is performed, the situation that the capacity expansion operation is performed when the service is found to be insufficient after the service is operated for a period of time is avoided, and the influence of the capacity expansion operation on the normal operation of the service is effectively reduced.
Description
Technical Field
The present invention relates to the field of automatic capacity expansion technologies, and in particular, to an automatic capacity expansion method and apparatus, an electronic device, and a storage medium.
Background
With the continuous development of information technology, the maturity of storage resources and computing resources and the continuous reduction of storage cost, the enterprise development, deployment and service provision are more convenient. With the benefit, for medium and small enterprises developing at a high speed, the ever-increasing data can be dealt with by continuously piling up machines and increasing storage.
However, in order to expand the storage capacity by a manual stacking machine, there are problems that it is inconvenient to perform the expansion operation by a professional, and automatic dynamic expansion cannot be realized.
Therefore, there is a need to provide a method and related apparatus that can solve the above problems.
Disclosure of Invention
An embodiment of the present invention provides an automatic capacity expansion method, an automatic capacity expansion device, an electronic device, and a storage medium, so as to solve the technical problem of the capacity expansion method in the background art.
In a first aspect, to achieve the above object, an embodiment of the present invention provides an automatic capacity expansion method, which is applied to a management node in a business service system, where the business service system further includes a data node and a client, where the data node is in communication connection with the management node and the client, respectively, and the automatic capacity expansion method includes:
when the client is detected to access the data node, acquiring a service to be processed by the client and a source storage child node of the data node currently mounted in the management node;
according to the service, determining a target sub-node which is needed for processing the service from a plurality of candidate sub-nodes in the management node;
and carrying out capacity expansion processing on the source storage child node based on the target child node.
Further, the determining, according to the service, a target child node that is still needed for processing the service from a plurality of candidate child nodes in the management node includes:
and inputting the service, a plurality of candidate child nodes in the management node and node information of each candidate child node into a trained prediction model so as to predict a target child node required for processing the service from the candidate child nodes, wherein the node information comprises attributes, loads and processing capacities of the corresponding child nodes.
Further, the performing capacity expansion processing on the source storage child node based on the target child node includes:
de-mounting storage units on the target child node;
and mounting the storage unit to the source storage child node.
Further, the performing capacity expansion processing on the source storage child node based on the target child node includes:
and merging the target child node and the source storage child node.
Further, before the step of obtaining the service to be processed by the client and the source storage child node currently mounted on the data node in the management node, the automatic capacity expansion method further includes:
inputting the service and all child nodes in the management node into a trained recognition model so as to identify a matching child node matched with the service from a plurality of child nodes;
and mounting the matched sub-nodes to the data nodes.
Further, the automatic capacity expansion method further includes:
when the storage utilization rate of a storage unit of the source storage sub-node mounted by the data node is detected to meet a preset condition, a storage unit switching instruction is generated, wherein the storage unit switching instruction comprises an expansion instruction and a capacity reduction instruction;
when the storage unit switching instruction is an expansion instruction, merging at least one candidate child node and the source storage child node;
and when the storage unit switching instruction is a capacity reduction instruction, determining a capacity reduction sub-node with a storage space smaller than that of the source storage sub-node from the candidate sub-nodes, and exchanging the IP address of the capacity reduction sub-node with the IP address of the source storage sub-node.
Further, before the step of determining, according to the service, a target child node that is still needed for processing the service from a plurality of candidate child nodes in the management node, the automatic capacity expansion method further includes:
and removing the source storage child node from all child nodes of the management node, and taking the rest child nodes as a plurality of candidate child nodes.
In a second aspect, to solve the same technical problem, an embodiment of the present invention provides an automatic capacity expansion device, which is disposed in a management node in a service system, where the service system further includes a data node and a client, the data node is in communication connection with the management node and the client, respectively, and the automatic capacity expansion device includes:
an obtaining module, configured to obtain, when the client accesses the data node, a service to be processed by the client and a source storage child node on which the data node is currently mounted in the management node;
a determining module, configured to determine, according to the service, a target child node that is required to process the service from a plurality of candidate child nodes in the management node;
and the capacity expansion module is used for carrying out capacity expansion processing on the source storage child node based on the target child node.
In a third aspect, to solve the same technical problem, an embodiment of the present invention provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, where the memory is coupled to the processor, and the processor implements the steps in the automatic capacity expansion method when executing the computer program.
In a fourth aspect, to solve the same technical problem, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, where, when the computer program runs, a device in which the computer-readable storage medium is located is controlled to execute any one of the steps in the automatic capacity expansion method.
The embodiment of the invention provides an automatic capacity expansion method, a device, electronic equipment and a storage medium, wherein the method comprises the steps of obtaining a service to be processed at a client and a source storage child node of a data node which is currently mounted in a management node, and determining a target child node which is still needed for processing the service from a plurality of candidate child nodes in the management node according to the service, so that capacity expansion processing can be carried out on the source storage child node based on the target child node, the capacity expansion of the source storage child node for processing the service is completed when a user carries out service processing, the capacity expansion of the node is completed while the service processing is carried out, the capacity expansion operation is prevented from being carried out when the service is found to be insufficient after the service is operated for a period of time, and the influence of the capacity expansion operation on the normal operation of the service is effectively reduced.
Drawings
Fig. 1 is a schematic structural diagram of a business service system provided by an embodiment of the present invention;
FIG. 2 is a schematic flow chart of an automatic capacity expansion method according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a recognition model training method according to an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a predictive model training method according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an automatic expansion device according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
fig. 7 is another schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
In the related art, facing the current growing data, it is mainly dealt with by continuously piling up machines to increase storage. And the mode of increasing storage to realize dilatation through the piling machine can not accomplish automatic dilatation, and the convenience is not high. Meanwhile, when the capacity expansion operation is carried out, professional workers are required to operate, and the common user is easy to operate, so that misoperation is easy to occur, and the capacity expansion effect is influenced. Moreover, as the size of the enterprise increases, the servers of the enterprise will be distributed in a plurality of remote places, and when the storage capacity of a certain server is insufficient, the storage needs to be increased by transferring workers from the remote places, thereby wasting manpower and material resources seriously.
In order to solve the above technical problem, an embodiment of the present invention provides an automatic capacity expansion method, please refer to fig. 1, where fig. 1 is a schematic structural diagram of a service system provided in an embodiment of the present invention, where the automatic capacity expansion method provided in an embodiment of the present invention is mainly applied to a management node in the service system, the service system further includes a data node and a client, and the data node is in communication connection with the management node and the client, respectively. Specifically, please refer to fig. 2 at the same time, fig. 2 is a schematic flow chart of the automatic capacity expansion method according to the embodiment of the present invention, and as shown in fig. 2, the automatic capacity expansion method according to the embodiment of the present invention includes steps 201 to 203, which are as follows:
In this embodiment, the management node is mainly responsible for managing the storage condition of the service system, such as monitoring, managing, and allocating the storage resource of the service system. The data node is mainly used for a user to access data through a client and for the user to perform corresponding service processing. Specifically, the data node is mounted in advance on one storage child node in the management node, and this embodiment uses the storage child node as a source storage child node. Therefore, the source storage child node in the management node mainly provides the storage resource required by the user for service processing. The client is mainly used for verifying the user information of the user and acquiring the service to be processed by the user.
It should be noted that the management node provided in this embodiment includes a plurality of child nodes, and each child node may be the same or different. Specifically, the setting of each child node may be set according to a user-defined setting, or may be set according to a corresponding application scenario, which is not limited herein.
In some embodiments, since a plurality of child nodes in a management node may be different, different child nodes will have different impacts on the operation of the service in the service system, for example, storage resources will be wasted when an excessive storage space is mounted for a data node; and the normal operation of the service is influenced when the data node is mounted with an excessively small storage space. Therefore, in order to avoid the above problems, the automatic capacity expansion method provided in this embodiment further includes, before the step of obtaining the service to be processed by the client and the source storage child node currently mounted on the data node in the management node, a step of: inputting the service and all child nodes in the management node into a trained recognition model so as to identify a matching child node matched with the service from a plurality of child nodes; and mounting the matched sub-nodes to the data nodes.
In this embodiment, the trained recognition model mainly identifies the service to be processed by the user to determine the storage resource condition specifically required by the service to be processed by the user, and determines the matching child node matched with the storage resource required by the service to be processed by the user from all child nodes in the management node, so that the determined matching child node can be used as a source storage child node of the data node to be mounted on the data node, so that the service to be processed by the user can normally operate, and the storage resource of the service system cannot be wasted.
Referring to fig. 3, fig. 3 is a schematic flow chart of a recognition model training method according to an embodiment of the present invention, and as shown in fig. 3, the recognition model training method according to the embodiment includes steps 301 to 303.
In this embodiment, the service includes a service in a windows system and a service in a linux system. Correspondingly, a plurality of nodes contained in the management node in the business service system also correspond to different operating systems and provide storage service for the different operating systems independently, so that the influence on the normal operation of the business caused by mixed use among the different operating systems is avoided.
In this embodiment, because different child nodes correspond to different service systems, in order to avoid mixed use among different operating systems and influence on normal operation of a service, the embodiment determines a child node matched with the service according to the type of the service, and if the service is a service under a windows system, determines a child node that provides a storage service only for the windows system as a matched child node; and when the service is the service under the linux system, determining the child node which only provides the storage service for the linux system as the matching child node.
In this embodiment, according to the loss generated after each training, the model parameters of the recognition model in the training process are optimized and fine-tuned, and when the loss generated after the training is smaller than a preset loss value, the recognition model is determined to be converged. Specifically, the preset loss value may be set according to an actual application scenario, such as 0.01, 0.001, or 0.0001.
Optionally, before the step of determining, according to the service, a target child node that is still needed for processing the service from a plurality of candidate child nodes in the management node, the automatic capacity expansion method further includes: and removing the source storage child node from all child nodes of the management node, and taking the rest child nodes as a plurality of candidate child nodes.
Since the source storage child node is mounted on the data node, in order to ensure that the service can normally operate for a long time in this embodiment, more storage space needs to be provided for the source storage child node, so as to avoid affecting the normal operation of the service. Specifically, the embodiment is to ensure that the service can have enough storage space for the service to continue to operate normally after the service operates for a preset time. The preset time may be 1 hour, 2 hours, or 3 hours, and the specific time may be set according to the actual requirement of the service, which is not limited herein.
In this embodiment, the step of determining, according to the service, a target child node that is still needed for processing the service from a plurality of candidate child nodes in the management node specifically includes: and inputting the service, a plurality of candidate sub-nodes in the management node and node information of each candidate sub-node into a trained prediction model so as to predict a target sub-node required for processing the service from the plurality of candidate sub-nodes.
Wherein the node information includes attributes, loads, and processing capacities of the corresponding child nodes. Specifically, the attribute of the node refers to a system such as a windows system and a linux system which is served by the node. Load refers to the storage space currently remaining by a node. Processing power refers to the response speed of a node.
Referring to fig. 4, fig. 4 is a flowchart illustrating a predictive model training method according to an embodiment of the present invention, and as shown in fig. 4, the recognition model training method according to the embodiment includes steps 401 to 404.
Step 401, obtaining a service supported by the service system, a plurality of candidate child nodes in the management node, and node information of each candidate child node.
In this embodiment, the service includes a service in a windows system and a service in a linux system. Correspondingly, a plurality of nodes contained in the management node in the business service system also correspond to different operating systems and provide storage service for the different operating systems independently, so that the influence on the normal operation of the business caused by mixed use among the different operating systems is avoided.
And determining a child node supporting the processing of the service, and taking the child node as a matching child node matched with the service supported by the service system.
In this embodiment, because different child nodes correspond to different service systems, in order to avoid mixed use among different operating systems and influence on normal operation of a service, the embodiment determines a child node matched with the service according to the type of the service, and if the service is a service under a windows system, determines a child node that provides a storage service only for the windows system as a matched child node; and when the service is the service under the linux system, determining the child node which only provides the storage service for the linux system as the matching child node.
In some embodiments, the candidate child node whose storage space and the target storage space required by the service meet the preset storage condition may be used as the target child node matched with the service.
The preset storage condition refers to that the storage space is larger than a target storage space required by the service. In order to enable the service to operate normally and not waste the storage resource of the service system, the preset storage condition provided by this embodiment may be a certain preset value, which may be 5MB, 10MB or 15MB, greater than the storage space required by the service supported by the service system. Specifically, the size of the preset data may be set according to the size of the storage space of the business service system, or may be set by a user through self-definition, which is not specifically limited herein.
In other embodiments, the matching process of the target child node may specifically be as follows: firstly, determining a system type to which the service belongs, for example, determining that the service is a linux service, and then, firstly, screening out a node only serving the linux system from a plurality of candidate child nodes to obtain a first candidate child node. Then, the priority of each first candidate child node is determined based on the node information (load and processing capacity) of each first candidate child node. And then taking the first candidate child node with the highest priority as a target child node.
It should be noted that the priority of the child node with high load capacity (largest remaining storage space) and strong processing capacity (fastest response speed) may be set to be the highest, and the priority of the child node with high load capacity and strong processing capacity is the second. Different weights can be set for the load capacity and the processing capacity, so that the load capacity and the processing capacity are weighted and summed (the strength of the capacity can be characterized by a value of 0-1), and the result of weighted summation is taken as the priority of each child node, wherein the weights of the load capacity and the processing capacity are both set to be 1 in the embodiment. The setting method of the specific weight can be set according to the actual requirements of the service, for example, the service which tends to be processed in real time, and the weight of the processing capacity can be set to be larger than the weight of the load capacity; traffic that is prone to data storage may be weighted more heavily for load capacity than for processing capacity.
In this embodiment, according to the loss generated after each training, the model parameters of the prediction model in the training process are optimized and fine-tuned, and when the loss generated after the training is smaller than a preset loss value, the prediction model is determined to be converged. Specifically, the preset loss value may be set according to an actual application scenario, such as 0.01, 0.001, or 0.0001.
And 203, performing capacity expansion processing on the source storage child node based on the target child node.
It should be noted that the management node of the service system includes a plurality of child nodes, and each child node is correspondingly mounted with a storage unit. When the source storage child node needs to be subjected to capacity expansion processing, the storage units in other child nodes of the management node can be mounted on the source storage child node, so that the storage space of the source storage child node is increased, and the capacity expansion of the source storage child node is realized.
In an embodiment, the step of performing capacity expansion processing on the source storage child node based on the target child node may specifically be: de-mounting storage units on the target child node; and mounting the storage unit to the source storage child node.
Since each child node is correspondingly mounted with a storage unit, when the source storage child node needs to be expanded, this embodiment may remove the storage unit mounted on the determined target child node, and mount the removed storage unit on the source storage child node, so as to implement expansion of the source storage child node.
In another embodiment, the step of performing capacity expansion processing on the source storage child node based on the target child node may specifically be: and merging the target child node and the source storage child node.
Since each child node is correspondingly mounted with a storage unit, when the source storage child node needs to be expanded, the embodiment may also directly merge the determined target child node and the source storage child node to implement the expansion of the source storage child node.
It should be noted that the Storage unit provided in this embodiment may be iSCSI (Internet Small Computer System Interface), also referred to as IP-SAN, which is a novel Storage technology based on IP Storage theory, and the technology combines SCSI Interface technology widely used in the Storage industry with IP network technology, and may construct SAN on an IP network. In short, iSCSI is a network storage technology that runs the SCSI protocol over IP networks. The embodiment adopts iSCSI storage to realize capacity expansion of the storage node mounted by the remote data node through the characteristics of rapid capacity expansion, remote mounting and the like of the iSCSI storage, and avoids the increase of storage caused by the transfer of a worker from a remote place, thereby saving a large amount of manpower and material resources.
It can be understood that the management node provided in this embodiment may be another storage disk that can be mounted remotely, the storage unit may be one disk partition of the storage disk, and the management node monitors and manages the disk partition of the storage disk, so as to implement expansion of different disk partitions.
As an optional embodiment, the automatic capacity expansion method provided in this embodiment further includes: when the storage utilization rate of a storage unit of a source storage child node mounted by the data node is detected to meet a preset condition, a storage unit switching instruction is generated, wherein the storage unit switching instruction comprises an expansion instruction and a reduction instruction; when the storage unit switching instruction is an expansion instruction, merging at least one candidate child node and the source storage child node; and when the storage unit switching instruction is a capacity reduction instruction, determining a capacity reduction sub-node with a storage space smaller than that of the source storage sub-node from the candidate sub-nodes, and exchanging the IP address of the capacity reduction sub-node with the IP address of the source storage sub-node.
In this embodiment, by adding an update _ time field to the configured iSCSI storage, the pdate _ time field is modified each time storage is performed, so as to achieve the purpose of obtaining the storage utilization rate of the iSCSI storage in real time.
It should be noted that the preset conditions include an expansion condition that the storage utilization rate is greater than the first utilization rate and a reduction condition that the storage utilization rate is less than the second utilization rate. Specifically, the first utilization rate may be 80%, 85%, or 90%, and the second utilization rate may be 50%, 45%, or 40%. When the storage utilization rate of the iSCSI storage is greater than the first utilization rate, generating a capacity expansion instruction; and when the storage utilization rate of the iSCSI storage is less than the second utilization rate, generating a capacity reduction instruction.
After the capacity expansion instruction is generated, the management node can automatically merge at least one candidate child node with the source storage child node. Specifically, one or more candidate child nodes are merged with the source storage child node, so that the storage utilization rate of the merged source storage child node is not greater than the first utilization rate and not less than the second utilization rate. It is to be understood that the storage units of one or more candidate child nodes may also be mounted on the source storage child node to implement capacity expansion, and for a specific mounting manner, please refer to the flow steps provided in the foregoing embodiments, which are not described herein again.
After the capacity reduction instruction is generated, the management node automatically determines a capacity reduction sub-node with a storage space smaller than that of the source storage sub-node from the candidate sub-nodes, and exchanges the IP address of the capacity reduction sub-node with the IP address of the source storage sub-node, so that the capacity reduction sub-node with a smaller storage space is used as the source storage sub-node, and waste of storage resources is avoided. Specifically, a capacity reduction sub-node with a storage utilization rate not less than the second utilization rate and a storage utilization rate not greater than the first utilization rate is selected and exchanged with the IP address of the source storage sub-node, so that storage resources in the business service system are effectively utilized, and waste of the storage resources is further avoided.
In summary, the automatic capacity expansion method provided in the embodiment of the present invention includes, when it is detected that a client accesses a data node, acquiring a service to be processed by the client and a source storage child node on which the data node is currently mounted in a management node, and determining a target child node that is still needed for processing the service from a plurality of candidate child nodes in the management node according to the service, so that capacity expansion processing can be performed on the source storage child node based on the target child node, so as to complete capacity expansion on the source storage child node that processes the service when a user performs service processing, thereby completing capacity expansion on the node while performing service processing, avoiding performing capacity expansion operation when the service finds that a storage space is insufficient after the service is operated for a period of time, and effectively reducing an influence of the capacity expansion operation on normal operation of the service.
According to the method described in the foregoing embodiment, this embodiment will be further described from the perspective of an automatic capacity expansion device, where the automatic capacity expansion device may be specifically implemented as an independent entity, or may be implemented by being integrated in an electronic device, such as a terminal, and the terminal may include a mobile phone, a tablet computer, and the like.
Referring to fig. 5, fig. 5 is a schematic structural diagram of an automatic capacity expansion apparatus according to an embodiment of the present invention, as shown in fig. 5, an automatic capacity expansion apparatus 500 according to an embodiment of the present invention is configured as a management node in a service system, where the service system further includes a data node and a client, the data node is in communication connection with the management node and the client, respectively, and the automatic capacity expansion apparatus 500 includes: the device comprises an acquisition module 501, a determination module 502 and a capacity expansion module 503.
The obtaining module 501 is configured to obtain, when detecting that the client accesses the data node, a service to be processed by the client and a source storage child node currently mounted on the management node by the data node.
In this embodiment, in order to determine which child node the source storage child node currently mounted on the management node is, please refer to fig. 5, the automatic capacity expansion apparatus 500 according to the embodiment of the present invention further includes: an identification module 504 and a mounting module 505.
The identification module 504 is configured to input the service and all child nodes in the management node into a trained identification model to identify a matching child node matching the service from the plurality of child nodes.
The mounting module 505 is configured to mount the matching child node on the data node.
As can be seen from fig. 5, the recognition module 504 and the mounting module 505 are applied before the obtaining module 501.
The determining module 502 is configured to determine, according to the service, a target child node that is needed to process the service from a plurality of candidate child nodes in the management node.
In this embodiment, the determining module 502 is specifically configured to: and inputting the service, a plurality of candidate sub-nodes in the management node and node information of each candidate sub-node into a trained prediction model so as to predict a target sub-node required for processing the service from the plurality of candidate sub-nodes.
Wherein the node information includes attributes, loads, and processing capacities of the corresponding child nodes.
Optionally, in order to determine candidate child nodes in the management node, please refer to fig. 5, where the automatic capacity expansion apparatus 500 according to the embodiment of the present invention further includes: 506 the module is removed.
The removing module 506 is configured to remove the source storage child node from all child nodes of the management node, and use the remaining child nodes as a plurality of candidate child nodes.
It should be noted that the removing module 506 is prior to the functioning and determining module 502, so as to be able to determine a plurality of candidate child nodes in the management node.
The capacity expansion module 503 is configured to perform capacity expansion processing on the source storage child node based on the target child node.
In some embodiments, the capacity expansion module 503 is specifically configured to: de-mounting storage units on the target child node; and mounting the storage unit to the source storage child node.
In other embodiments, the capacity expansion module 503 is further specifically configured to: and merging the target child node and the source storage child node.
Optionally, with continued reference to fig. 5, the automatic expansion device 500 provided in the embodiment of the present invention further includes: a detection module 507, a merging module 508 and a capacity reduction module 509.
The detection module 507 is configured to generate a storage unit switching instruction when detecting that a storage utilization rate of a storage unit of a source storage child node mounted on the data node meets a preset condition, where the storage unit switching instruction includes a capacity expansion instruction and a capacity reduction instruction.
The merging module 508 is configured to merge at least one candidate child node with the source storage child node when the storage unit switching instruction is an expansion instruction.
The capacity reduction module 509 is configured to, when the storage unit switching instruction is a capacity reduction instruction, determine a capacity reduction sub-node from the plurality of candidate sub-nodes, where a storage space of the capacity reduction sub-node is smaller than that of the source storage sub-node, and exchange an IP address of the capacity reduction sub-node and an IP address of the source storage sub-node with each other.
In specific implementation, each module and/or unit may be implemented as an independent entity, or may be implemented as one or multiple entities by any combination, where the specific implementation of each module and/or unit may refer to the foregoing method embodiment, and specific achievable beneficial effects also refer to the beneficial effects in the foregoing method embodiment, which are not described herein again.
In addition, referring to fig. 6, fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, where the electronic device may be a mobile terminal such as a smart phone and a tablet computer. As shown in fig. 6, the electronic device 600 includes a processor 601, a memory 602. The processor 601 is electrically connected to the memory 602.
The processor 601 is a control center of the electronic device 600, connects various parts of the whole electronic device using various interfaces and lines, and performs various functions of the electronic device 600 and processes data by running or loading an application stored in the memory 602 and calling data stored in the memory 602, thereby performing overall monitoring of the electronic device 600.
In this embodiment, the processor 601 in the electronic device 600 loads instructions corresponding to processes of one or more application programs into the memory 602 according to the following steps, and the processor 601 runs the application programs stored in the memory 602, thereby implementing various functions:
when the client is detected to access the data node, acquiring a service to be processed by the client and a source storage child node of the data node currently mounted in the management node;
according to the service, determining a target sub-node which is needed for processing the service from a plurality of candidate sub-nodes in the management node;
and carrying out capacity expansion processing on the source storage child node based on the target child node.
The electronic device 600 can implement the steps in any embodiment of the automatic capacity expansion method provided in the embodiment of the present invention, and therefore, the beneficial effects that can be achieved by any automatic capacity expansion method provided in the embodiment of the present invention can be achieved, which are detailed in the foregoing embodiments and will not be described herein again.
Referring to fig. 7, fig. 7 is another schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 7, fig. 7 is a specific structural block diagram of the electronic device according to the embodiment of the present invention, where the electronic device may be used to implement the automatic capacity expansion method provided in the foregoing embodiment. The electronic device 700 may be a mobile terminal such as a smart phone or a notebook computer.
The RF circuit 710 is used for receiving and transmitting electromagnetic waves, and performing interconversion between the electromagnetic waves and electrical signals, thereby communicating with a communication network or other devices. The RF circuitry 710 may include various existing circuit elements for performing these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption/decryption chips, Subscriber Identity Module (SIM) cards, memory, and so forth. The RF circuit 710 may communicate with various networks such as the internet, an intranet, a wireless network, or with other devices over a wireless network. The wireless network may comprise a cellular telephone network, a wireless local area network, or a metropolitan area network. The Wireless network may use various Communication standards, protocols and technologies, including but not limited to Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Wireless Fidelity (Wi-Fi) (e.g., IEEE802.11 a, IEEE802.11 b, IEEE802.11g and/or IEEE802.11 n), Voice over Internet Protocol (VoIP), world wide Internet Protocol (Microwave Access for micro), and other short message protocols for instant messaging, as well as any other suitable communication protocols, and may even include those that have not yet been developed.
The memory 720 may be used to store software programs and modules, such as program instructions/modules corresponding to the automatic capacity expansion method in the foregoing embodiment, and the processor 780 executes various functional applications and performs automatic capacity expansion by running the software programs and modules stored in the memory 720, that is, the following functions are implemented:
when the client is detected to access the data node, acquiring a service to be processed by the client and a source storage child node of the data node currently mounted in the management node;
according to the service, determining a target sub-node which is needed for processing the service from a plurality of candidate sub-nodes in the management node;
and carrying out capacity expansion processing on the source storage child node based on the target child node.
The memory 720 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 720 may further include memory located remotely from processor 780, which may be connected to electronic device 700 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input unit 730 may be used to receive input numeric or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, the input unit 730 may include a touch-sensitive surface 731 as well as other input devices 732. Touch-sensitive surface 731, also referred to as a touch display screen or touch pad, can collect touch operations by a user on or near touch-sensitive surface 731 (e.g., operations by a user on or near touch-sensitive surface 731 using a finger, stylus, or any other suitable object or attachment) and drive the corresponding connection device according to a predetermined program. Alternatively, the touch sensitive surface 731 may comprise two parts, a touch detection means and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts it to touch point coordinates, and sends the touch point coordinates to the processor 780, and can receive and execute commands from the processor 780. In addition, the touch sensitive surface 731 can be implemented using various types of resistive, capacitive, infrared, and surface acoustic waves. The input unit 730 may also include other input devices 732 in addition to the touch-sensitive surface 731. In particular, other input devices 732 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.
The display unit 740 may be used to display information input by or provided to the user and various graphical user interfaces of the electronic device 700, which may be made up of graphics, text, icons, video, and any combination thereof. The Display unit 740 may include a Display panel 741, and optionally, the Display panel 741 may be configured in the form of an LCD (Liquid Crystal Display), an OLED (Organic Light-Emitting Diode), or the like. Further, touch-sensitive surface 731 can overlay display panel 741, such that when touch-sensitive surface 731 detects a touch event thereon or nearby, processor 780 can determine the type of touch event, and processor 780 can then provide a corresponding visual output on display panel 741 based on the type of touch event. Although in the figure the touch sensitive surface 731 and the display panel 741 are shown as two separate components to implement input and output functions, in some embodiments the touch sensitive surface 731 and the display panel 741 may be integrated to implement input and output functions.
The electronic device 700 may also include at least one sensor 750, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel 741 according to the brightness of ambient light, and a proximity sensor that may generate an interrupt when the folder is closed or closed. As one of the motion sensors, the gravity acceleration sensor may detect the magnitude of acceleration in each direction (generally, three axes), detect the magnitude and direction of gravity when the mobile phone is stationary, and may be used for applications of recognizing gestures of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and tapping), and other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor that are further configured to the electronic device 700, and are not described herein again.
The audio circuit 760, speaker 761, and microphone 762 may provide an audio interface between a user and the electronic device 700. The audio circuit 760 may transmit the electrical signal converted from the received audio data to the speaker 761, and convert the electrical signal into an audio signal for output by the speaker 761; on the other hand, the microphone 762 converts the collected sound signal into an electric signal, converts the electric signal into audio data after being received by the audio circuit 760, processes the audio data by the audio data output processor 780, and transmits the processed audio data to, for example, another terminal via the RF circuit 710, or outputs the audio data to the memory 720 for further processing. The audio circuitry 760 may also include an earbud jack to provide communication of a peripheral headset with the electronic device 700.
The electronic device 700, via the transport module 770 (e.g., a Wi-Fi module), may assist the user in receiving requests, sending information, etc., which provides the user with wireless broadband internet access. Although the transmission module 770 is illustrated in the drawings, it is understood that it does not belong to the essential constitution of the electronic device 700 and may be omitted entirely within the scope not changing the essence of the invention as needed.
The processor 780 is a control center of the electronic device 700, connects various parts of the entire cellular phone using various interfaces and lines, and performs various functions of the electronic device 700 and processes data by operating or executing software programs and/or modules stored in the memory 720 and calling data stored in the memory 720, thereby integrally monitoring the electronic device. Optionally, processor 780 may include one or more processing cores; in some embodiments, processor 780 may integrate an application processor that handles primarily the operating system, user interface, applications, etc. and a modem processor that handles primarily wireless communications. It will be appreciated that the modem processor described above may not be integrated into processor 780.
The electronic device 700 also includes a power supply 790 (e.g., a battery) that provides power to various components, and in some embodiments may be logically coupled to the processor 780 via a power management system that may perform functions such as managing charging, discharging, and power consumption. The power supply 790 may also include any component including one or more dc or ac power sources, recharging systems, power failure detection circuitry, power converters or inverters, power status indicators, and the like.
Although not shown, the electronic device 700 further includes a camera (e.g., a front camera, a rear camera), a bluetooth module, and the like, which are not described in detail herein. Specifically, in this embodiment, the display unit of the electronic device is a touch screen display, the mobile terminal further includes a memory, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for:
when the client is detected to access the data node, acquiring a service to be processed by the client and a source storage child node of the data node currently mounted in the management node;
according to the service, determining a target sub-node which is needed for processing the service from a plurality of candidate sub-nodes in the management node;
and carrying out capacity expansion processing on the source storage child node based on the target child node.
In specific implementation, the above modules may be implemented as independent entities, or may be combined arbitrarily to be implemented as the same or several entities, and specific implementation of the above modules may refer to the foregoing method embodiments, which are not described herein again.
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor. To this end, an embodiment of the present invention provides a storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps of any embodiment of the automatic capacity expansion method provided in the embodiment of the present invention.
Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the instructions stored in the storage medium can execute the steps in any embodiment of the automatic capacity expansion method provided by the embodiment of the present invention, the beneficial effects that can be realized by any automatic capacity expansion method provided by the embodiment of the present invention can be realized, which are detailed in the foregoing embodiments and will not be described herein again.
The above detailed description is given to an automatic capacity expansion method, an automatic capacity expansion device, an electronic device, and a storage medium provided in the embodiments of the present application, and a specific example is applied in the present application to explain the principle and the implementation of the present application, and the description of the above embodiments is only used to help understanding the method and the core idea of the present application; meanwhile, for those skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application. Moreover, it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention, and such modifications and adaptations are intended to be within the scope of the invention.
Claims (9)
1. An automatic capacity expansion method is applied to a management node in a business service system, the business service system further comprises a data node and a client, the data node is respectively in communication connection with the management node and the client, and the automatic capacity expansion method comprises the following steps:
when the client is detected to access the data node, acquiring a service to be processed by the client and a source storage child node of the data node currently mounted in the management node;
inputting the service, a plurality of candidate sub-nodes in the management node, and node information of each candidate sub-node into a trained prediction model to predict a target sub-node required for processing the service from the plurality of candidate sub-nodes, where the node information includes a windows system and a linux system served by the corresponding sub-node, a load and a processing capacity, the load and the processing capacity are provided with corresponding weights, and the target sub-node is determined according to a weighted sum of the load and the processing capacity;
and carrying out capacity expansion processing on the source storage child node based on the target child node.
2. The automatic capacity expansion method of claim 1, wherein the capacity expansion processing of the source storage child node based on the target child node comprises:
de-mounting storage units on the target child node;
and mounting the storage unit to the source storage child node.
3. The automatic capacity expansion method of claim 1, wherein the capacity expansion processing of the source storage child node based on the target child node comprises:
and merging the target child node and the source storage child node.
4. The automatic capacity expansion method according to claim 1, wherein before the step of obtaining the pending traffic of the client and the source storage child node currently mounted on the data node in the management node, the automatic capacity expansion method further comprises:
inputting the service and all child nodes in the management node into a trained recognition model so as to identify a matching child node matched with the service from a plurality of child nodes;
and mounting the matched sub-nodes to the data nodes.
5. The automated capacity expansion method of claim 1, further comprising:
when the storage utilization rate of a storage unit of a source storage child node mounted by the data node is detected to meet a preset condition, a storage unit switching instruction is generated, wherein the storage unit switching instruction comprises an expansion instruction and a reduction instruction;
when the storage unit switching instruction is an expansion instruction, merging at least one candidate child node and the source storage child node;
and when the storage unit switching instruction is a capacity reduction instruction, determining a capacity reduction sub-node with a storage space smaller than that of the source storage sub-node from the candidate sub-nodes, and exchanging the IP address of the capacity reduction sub-node with the IP address of the source storage sub-node.
6. The automatic capacity expansion method according to claim 1, wherein before the step of determining, from among a plurality of candidate child nodes in the management node, a target child node that is further required for processing the service according to the service, the automatic capacity expansion method further includes:
and removing the source storage child node from all child nodes of the management node, and taking the rest child nodes as a plurality of candidate child nodes.
7. An automatic capacity expansion device is characterized in that a management node is arranged in a business service system, the business service system further comprises a data node and a client, the data node is respectively in communication connection with the management node and the client, and the automatic capacity expansion device comprises:
an obtaining module, configured to obtain, when the client accesses the data node, a service to be processed by the client and a source storage child node on which the data node is currently mounted in the management node;
a determining module, configured to input the service, a plurality of candidate sub-nodes in the management node, and node information of each candidate sub-node into a trained prediction model, so as to predict a target sub-node required for processing the service from the plurality of candidate sub-nodes, where the node information includes a windows system and a linux system served by the corresponding sub-node, a load, and a processing capability, where the load and the processing capability have corresponding weights, and the target sub-node is determined according to a weighted sum value of the load and the processing capability;
and the capacity expansion module is used for carrying out capacity expansion processing on the source storage child node based on the target child node.
8. An electronic device comprising a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, wherein the memory is coupled to the processor, and the processor executes the computer program to implement the steps of the automatic capacity expansion method according to any one of claims 1 to 6.
9. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and wherein when the computer program runs, the computer-readable storage medium controls a device in which the computer-readable storage medium is located to perform the steps in the automatic capacity expansion method according to any one of claims 1 to 6.
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