Cloud computing cluster data synchronization method and device, equipment and storage medium

Abstract

The invention discloses a cloud computing cluster data synchronization method and device, equipment and a storage medium, and relates to the technical field of distributed computing, and the method comprises the steps: receiving a data synchronization request of each working node in a target cloud computing cluster, distributing a working node IP to each working node according to the data synchronization request and the load state of each working node, segmenting the target to-be-synchronized data into data fragments; searching a preset mapping table to obtain a target IP of a target storage node corresponding to each working node, and constructing a network connection between each working node and each target storage node by using the working node IP and the target IP; and dynamically determining a target routing path according to a network state corresponding to the network connection, and sending each data fragment to each target storage node by using each working node and the target routing path. According to the invention, the to-be-synchronized data is fragmented, and the routing path for data synchronization is determined according to the network state, so that the data synchronization efficiency is improved, and the synchronization delay is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of distributed computing, and particularly relates to a data synchronization method, device, equipment and storage medium for a cloud computing cluster. Background Art

[0002] In the field of cloud computing, data synchronization is a key technology to ensure data consistency and high availability in a distributed system. With the wide application of cloud computing services, the increase in the number of cluster nodes and the explosive growth of data volume, traditional data synchronization methods are facing severe challenges.

[0003] Currently, the commonly used data synchronization methods in a cloud computing cluster are data synchronization based on timestamps or data synchronization based on version vectors. Such data synchronization methods have problems of low data synchronization efficiency and high latency in a large-scale and highly dynamic cloud computing environment. Therefore, how to perform high-efficiency and low-latency data synchronization in a cloud computing cluster has become a technical problem to be solved at present. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a data synchronization method, device, equipment and storage medium for a cloud computing cluster, which can shard the data to be synchronized, determine the routing path of data synchronization according to the network state, and use the determined routing path to synchronize the data fragments, improving the data synchronization efficiency and reducing the synchronization latency. The specific solutions are as follows:

[0005] In the first aspect, the present application provides a data synchronization method for a cloud computing cluster, including:

[0006] Receiving data synchronization requests from each working node in the target cloud computing cluster, allocating working node IPs to each working node according to the data synchronization requests and the load status of each working node, and splitting the target data to be synchronized in the target cloud computing cluster into data fragments;

[0007] Searching a preset mapping table to obtain the target IPs of the target storage nodes corresponding to each working node, and using the working node IPs and the target IPs to establish network connections between each working node and each target storage node;

[0008] Dynamically determining a target routing path according to the network state corresponding to the network connection, and using each working node and the target routing path to send each data fragment to each target storage node, so that each target storage node can obtain the target data to be synchronized.

[0009] Optionally, the step of using each working node and the target routing path to send each data fragment to each target storage node includes:

[0010] Encrypt each of the data segments using the target encryption algorithm to obtain corresponding encrypted segments, and send each of the encrypted segments to each of the target storage nodes by using each of the worker nodes and the target routing path.

[0011] Optionally, the cloud computing cluster data synchronization method further includes:

[0012] Compress each of the data segments corresponding to the target data to be synchronized using the target compression algorithm to obtain corresponding compressed data;

[0013] Synchronize each of the compressed data to each of the target storage nodes by using each of the worker nodes and the target routing path, so that each of the target storage nodes decompresses each of the compressed data and obtains each of the data segments.

[0014] Optionally, the cloud computing cluster data synchronization method further includes:

[0015] Monitor the data change situation in the target cloud computing cluster using the target data detection technology to obtain the target data that has changed;

[0016] Synchronize the target data to each of the target storage nodes by using each of the worker nodes and the target routing path.

[0017] Optionally, the cloud computing cluster data synchronization method further includes:

[0018] Monitor the data synchronization process in real time. If an abnormal state is detected, record the error log corresponding to the abnormal state for troubleshooting using the error log.

[0019] Optionally, after sending each of the data segments to each of the target storage nodes by using each of the worker nodes and the target routing path, it further includes:

[0020] Recycle the IPs of each of the worker nodes, so as to allocate the IPs of each of the worker nodes according to the new load status corresponding to each of the worker nodes during the next data synchronization.

[0021] Optionally, after sending each of the data segments to each of the target storage nodes by using each of the worker nodes and the target routing path, it further includes:

[0022] Perform data consistency verification on the target data to be synchronized in each of the target storage nodes using the target synchronization primitive to obtain corresponding verification results;

[0023] If the verification result indicates that the target data to be synchronized in each of the target storage nodes is inconsistent, delete the target data to be synchronized in each of the target storage nodes and use each of the working nodes and the target routing path to resend each data segment to each of the target storage nodes.

[0024] In a second aspect, the present application provides a cloud computing cluster data synchronization device, including:

[0025] A synchronization request receiving module, configured to receive data synchronization requests from each working node in a target cloud computing cluster, allocate working node IPs to each working node according to the data synchronization requests and the load status of each working node, and divide the target data to be synchronized in the target cloud computing cluster into data segments;

[0026] A network connection building module, configured to search a preset mapping table to obtain the target IPs of the target storage nodes corresponding to each working node, and build network connections between each working node and each target storage node by using the working node IPs and the target IPs;

[0027] A data synchronization module, configured to dynamically determine a target routing path according to the network status corresponding to the network connection, and use each working node and the target routing path to send each data segment to each target storage node, so that each target storage node can obtain the target data to be synchronized.

[0028] In a third aspect, the present application provides an electronic device, including:

[0029] A memory, configured to store a computer program;

[0030] A processor, configured to execute the computer program to implement the foregoing cloud computing cluster data synchronization method.

[0031] In a fourth aspect, the present application provides a computer-readable storage medium, configured to store a computer program, and when the computer program is executed by a processor, the foregoing cloud computing cluster data synchronization method is implemented.

[0032] In this application, first, data synchronization requests from each working node in the target cloud computing cluster need to be received. Working node IPs are allocated to each working node according to the data synchronization requests and the load status of each working node, and the target data to be synchronized in the target cloud computing cluster is segmented into data fragments. Then, a preset mapping table is searched to obtain the target IPs of the target storage nodes corresponding to each working node, and network connections between each working node and each target storage node are established using the working node IPs and the target IPs. Finally, a target routing path is dynamically determined according to the network status corresponding to the network connections, and each data fragment is sent to each target storage node using each working node and the target routing path, so that each target storage node can obtain the target data to be synchronized. It can be seen that in this application, by splitting the data to be synchronized into multiple data fragments and using multiple working nodes to synchronize the data fragments, the load of a single working node is reduced, thereby improving the data synchronization efficiency and reducing the synchronization delay; by dynamically determining the target routing path for data synchronization according to the network connection status, it is ensured that the data is synchronized along the optimal path, improving the data synchronization efficiency; by searching the mapping table, the IP addresses of the storage nodes corresponding to the working nodes can be quickly obtained, so that the network connections between the nodes can be quickly established, thereby improving the data synchronization efficiency and reducing the delay in the data synchronization process. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0034] Figure 1 It is a flowchart of a method for synchronizing data in a cloud computing cluster disclosed in this application;

[0035] Figure 2 It is a schematic diagram of the process of a method for synchronizing data in a cloud computing cluster disclosed in this application;

[0036] Figure 3 It is a schematic diagram of the process of full data synchronization disclosed in this application;

[0037] Figure 4 It is a schematic diagram of the process of incremental data synchronization disclosed in this application;

[0038] Figure 5 It is a flowchart of a specific method for synchronizing data in a cloud computing cluster disclosed in this application;

[0039] Figure 6Structural schematic diagram of a cloud computing cluster data synchronization device disclosed in this application;

[0040] Figure 7 Structural diagram of an electronic device disclosed in this application. Detailed implementation manners

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0042] Currently, the data synchronization methods in cloud computing clusters are usually timestamp-based data synchronization or version vector-based data synchronization. These two data synchronization methods have problems of low data synchronization efficiency and high latency in large-scale and highly dynamic cloud computing environments. For this reason, this application provides a cloud computing cluster data synchronization method, which slices the data to be synchronized and determines the routing path of data synchronization according to the network status, improving the data synchronization efficiency and reducing the synchronization latency.

[0043] See Figure 1 As shown, an embodiment of the present invention discloses a cloud computing cluster data synchronization method, including:

[0044] Step S11: Receive the data synchronization requests of each working node in the target cloud computing cluster, allocate working node IPs to each working node according to the data synchronization requests and the load status of each working node, and divide the target data to be synchronized in the target cloud computing cluster into data segments.

[0045] In this embodiment, the target cloud computing cluster mainly consists of a master node, worker nodes, and storage nodes. Among them, the master node is responsible for cluster management, task scheduling, resource allocation, and monitoring. The architecture of the master node: It adopts a highly available architecture to ensure the stable operation of the master node; the worker nodes are responsible for executing data synchronization tasks, processing data transmission and verification. The architecture of the worker nodes: It adopts an extensible design to dynamically increase or decrease nodes according to task requirements; the storage nodes are responsible for providing data storage services and supporting fast data reading and writing. The architecture of the storage nodes: It adopts distributed storage to improve data reliability and access speed. Moreover, the target cloud computing cluster in this embodiment adopts a flat network topology structure and an efficient network communication protocol, such as Internet communication protocols like TCP (Transmission Control Protocol), UDP (User Datagram Protocol), HTTP / 2 (Hypertext Transfer Protocol 2.0), etc.

[0046] In this embodiment, first, it is necessary to allocate worker node IPs (Internet Protocol addresses) to each worker node according to the load status of each worker node in the cluster. That is, elastic IPs are dynamically allocated according to the load and task requirements of the worker nodes. Before IP allocation, a prediction algorithm can also be used to pre-allocate IP resources to reduce allocation latency. Specifically, in a specific implementation, IPs can be allocated to each worker node according to the load status of each worker node in the cluster; in another specific implementation, elastic IPs can be dynamically allocated according to the task requirements corresponding to the data synchronization task. In addition, in this embodiment, the data to be synchronized in the cluster also needs to be divided into several data segments. The data to be synchronized with a data volume not less than a preset threshold is divided into segments to achieve block synchronization; the above preset threshold can be dynamically determined according to the actual situation of the cluster and the data synchronization requirements, and will not be specifically limited here; and the number of segments for the data to be synchronized can also be dynamically determined according to the data synchronization requirements. For example, the data to be synchronized A can be divided into 10 data segments, and the data to be synchronized B can be divided into 5 data segments, and different worker nodes are used to synchronize the data segments. It should be noted that as Figure 2 shown, in this embodiment, it is possible to select to perform incremental synchronization or full synchronization on the data according to the actual situation, so as to use each worker node to synchronize the data to different cloud computing clusters. Among them, full synchronization means performing data synchronization operations on all data, while incremental synchronization means only synchronizing the data that has changed. In a specific implementation, as Figure 3As shown, the data in the target cloud computing cluster can be synchronized to Cluster A and Cluster B in a full synchronization manner; in another specific implementation, as Figure 4 shown, incremental synchronization can be performed on the data in the target cloud computing cluster. In this embodiment, by splitting the data, data synchronization can be performed in the form of data fragments, reducing the load on the nodes; by using different worker nodes to synchronize the data fragments, the problem of high synchronization latency caused by a single large data to be synchronized is avoided; by allocating IPs to the worker nodes according to the load status of the worker nodes, the system load balance is ensured, and the situation where some nodes are overloaded while other nodes are idle is avoided.

[0047] Step S12: Search for a preset mapping table to obtain the target IPs of the target storage nodes corresponding to the worker nodes, and use the worker node IPs and the target IPs to establish network connections between the worker nodes and the target storage nodes.

[0048] In this embodiment, after obtaining the IP address, the worker node can, according to the IP address assigned to itself, search for the IP address of the corresponding storage node through the mapping table in the cluster, and establish a communication connection using the IP corresponding to the storage node, so as to perform data synchronization operations. It can be understood that before the data synchronization process starts, a mapping table of elastic IPs and worker nodes needs to be established to achieve fast IP search, and during the data synchronization process, the mapping table needs to be updated in real time to ensure the consistency of the IP address and the node status; the mapping table records the relationship between the worker node and the storage node. The worker node searches for the IP address of the target storage node by querying the mapping table, and then synchronizes the data to be synchronized to the corresponding storage node through the obtained IP address. The mapping table in this embodiment is stored in the form of key-value pairs. The key can be the identifier of the worker node (such as the node name or ID), and the value is the corresponding worker node IP address. In addition, this embodiment also constructs an index for the mapping table; by establishing an index for the mapping table and using the index to query the mapping table, the relationship between the worker node and the IP can be quickly queried and updated, so as to achieve fast IP search and improve the efficiency of data synchronization.

[0049] Step S13: Dynamically determine the target routing path according to the network status corresponding to the network connection, and use the worker nodes and the target routing path to send the data fragments to the target storage nodes, so that the target storage nodes can obtain the target data to be synchronized.

[0050] In this embodiment, first, a dynamic routing algorithm is adopted to dynamically determine the routing path for the worker node to perform data synchronization according to the network status. By dynamically adjusting the routing path, the load balance of the routing is ensured, and the situation of single-point routing overload is avoided.

[0051] The process of sending each data segment to each target storage node by using each working node and the target routing path may specifically include: encrypting each of the data segments by using a target encryption algorithm to obtain corresponding encrypted segments, and sending each of the encrypted segments to each of the target storage nodes by using each of the working nodes and the target routing path. Specifically, symmetric encryption and asymmetric encryption technologies can be used to ensure the security of data transmission, achieve end-to-end data encryption, and prevent data leakage and tampering.

[0052] In this embodiment, after sending each data segment to each target storage node by using each working node and the target routing path, it further includes: recycling the IPs of each working node so as to allocate the IPs of each working node according to the new load status corresponding to each working node during the next data synchronization; that is, when a working node completes a task or its load decreases, the elastic IP is automatically recycled, and through resource pool management, the recycling of IP addresses is achieved.

[0053] In this embodiment, after sending each data segment to each target storage node by using each working node and the target routing path, it further includes: performing data consistency verification on the target data to be synchronized in each target storage node by using a target synchronization primitive to obtain a corresponding verification result; if the verification result indicates that the target data to be synchronized in each target storage node is inconsistent, then deleting the target data to be synchronized in each target storage node and resending each data segment to each target storage node by using each working node and the target routing path. It can be understood that in this embodiment, a multi-thread synchronization method is used, and different working nodes are used to perform data synchronization simultaneously. During the synchronization process, errors may occur, resulting in data inconsistency between different nodes. Therefore, after the data synchronization is completed, synchronization primitives such as lock mechanisms and semaphores are also required to ensure the consistency and integrity of the data.

[0054] It should be noted that in this embodiment, a load balancer can also be deployed at the entrance of the cloud computing cluster, and load balancing algorithms such as round-robin, least connections, and source IP hashing are used to achieve global traffic balance; correspondingly, local load balancing is adopted inside the working nodes of the cluster to optimize resource utilization. In addition, this embodiment can also use a visual interface to monitor the data synchronization process in real time. If an abnormal state is detected, the error log corresponding to the abnormal state is recorded for troubleshooting using the error log, that is, errors in the synchronization process such as network interruption and data inconsistency are detected in real time during data synchronization, and the error log is recorded for troubleshooting and statistical analysis. For recoverable errors, an automatic retry mechanism is implemented. The retry times and intervals are set to avoid resource waste caused by frequent retries. Among them, the retry times interval and the number of message synchronization retries can be set according to requirements and are not specifically limited here. In addition, in this embodiment, the master node maintains a task queue, distributes tasks according to the synchronization policy, and at the same time supports the adjustment of task priorities and the insertion of urgent tasks. The monitoring data is displayed through a visual interface to facilitate the administrator to quickly understand the cluster status; by performing data consistency verification, the consistency of data in each storage node is ensured; by recycling the working node IPs, the IP addresses can be reused, ensuring that each time data synchronization is performed, the working node with the optimal comprehensive conditions is used, improving the data synchronization efficiency and reducing the latency.

[0055] It can be seen that this application reduces the data transmission volume and improves the execution speed of the synchronization operation by adopting advanced data sharding technology and intelligent synchronization strategies; by optimizing the data synchronization path, it reduces the waiting time of data during transmission to achieve fast synchronization; by performing data consistency verification and system fault detection functions, it ensures the accuracy and consistency of data in a multi-node environment, and improves the system's response ability and self-repair ability to abnormal situations.

[0056] Based on the foregoing embodiments, this application describes the overall process of using working nodes for data synchronization in a cloud computing cluster. Next, this application will elaborate in detail on how to further reduce the data transmission volume and improve the data synchronization efficiency. Refer to Figure 5 As shown, an embodiment of the present invention discloses a specific cloud computing cluster data synchronization method, including:

[0057] Step S21: Use target data detection technology to monitor the data change situation in the target cloud computing cluster to obtain the target data that has changed, divide the target data into data segments, and compress the data segments to obtain corresponding compressed data;

[0058] In this embodiment, technologies such as timestamps, log records, and version control, that is, the above-mentioned target data detection technology, can be used to detect data changes, and the changed data is determined as the target data; that is, in this embodiment, only the changed data is synchronized. In addition, in this embodiment, the data fragments are compressed. Specifically, the target compression algorithm, such as the gzip, zlib, etc. algorithms, is used to compress each of the data fragments corresponding to the target data to be synchronized, and optimization is performed for specific data types to obtain the corresponding compressed data. By monitoring the data change situation in the cloud computing cluster and determining the changed data as the data to be synchronized, the amount of data synchronization is reduced, thereby reducing the time required for data synchronization and improving the data synchronization efficiency in the cloud computing cluster; by compressing the data fragments of the data to be synchronized, the data volume is further reduced, the load on the worker nodes and the router is alleviated, thereby reducing the latency of data synchronization and improving the data synchronization efficiency.

[0059] Step S22: Search for a preset mapping table to obtain the target IPs of the target storage nodes corresponding to each worker node in the target cloud computing cluster, and use the target IPs to establish network connections between each worker node and each target storage node;

[0060] Step S23: Dynamically determine the target routing path according to the network state corresponding to the network connection, and use each worker node and the target routing path to send each compressed data to each target storage node, so that each target storage node can obtain the target data based on the compressed data.

[0061] Among them, the specific processes of the above steps S22 and S23 can refer to the corresponding content disclosed in the foregoing embodiments, and will not be elaborated herein.

[0062] It can be seen that in this application, by splitting the data to be synchronized into multiple data fragments and using multiple worker nodes to synchronize the data fragments, the load on a single worker node is reduced, thereby improving the data synchronization efficiency and reducing the synchronization latency; by dynamically determining the target routing path for data synchronization according to the network connection status, it is ensured that the data is synchronized along the optimal path, improving the data synchronization efficiency; by searching the mapping table, the IP addresses of the storage nodes corresponding to the worker nodes can be quickly obtained, so that the network connections between the nodes can be quickly established, thereby improving the data synchronization efficiency and reducing the latency during the data synchronization process.

[0063] See Figure 6 As shown, an embodiment of the present invention discloses a cloud computing cluster data synchronization device, including:

[0064] A synchronous request receiving module 11, configured to receive data synchronization requests from each working node in a target cloud computing cluster, allocate working node IPs to each working node according to the data synchronization requests and the load status of each working node, and divide target data to be synchronized in the target cloud computing cluster into data segments;

[0065] A network connection building module 12, configured to look up a preset mapping table to obtain target IPs of target storage nodes corresponding to each working node, and build network connections between each working node and each target storage node by using the working node IPs and the target IPs;

[0066] A data synchronization module 13, configured to dynamically determine a target routing path according to a network status corresponding to the network connection, and send each data segment to each target storage node by using each working node and the target routing path, so that each target storage node can obtain the target data to be synchronized.

[0067] It can be seen that in this application, by splitting the data to be synchronized into multiple data segments and using multiple working nodes to synchronize the data segments, the load of a single working node is reduced, thereby improving the data synchronization efficiency and reducing the synchronization latency; by dynamically determining the target routing path of data synchronization according to the network connection status, it is ensured that the data is synchronized along the optimal path, improving the data synchronization efficiency; by looking up the mapping table, the IP address of the storage node corresponding to the working node can be quickly obtained, so that the network connection between nodes can be quickly established, thereby improving the data synchronization efficiency and reducing the latency in the data synchronization process.

[0068] In some specific embodiments, the data synchronization module 13 may specifically include:

[0069] A data encryption unit, configured to encrypt each data segment by using a target encryption algorithm to obtain corresponding encrypted segments, and send each encrypted segment to each target storage node by using each working node and the target routing path.

[0070] In some specific embodiments, the cloud computing cluster data synchronization device further includes:

[0071] A data compression module, configured to compress each data segment corresponding to the target data to be synchronized by using a target compression algorithm to obtain corresponding compressed data;

[0072] A first data sending module, configured to synchronize each compressed data to each target storage node by using each working node and the target routing path, so that each target storage node can decompress each compressed data and obtain each data segment.

[0073] In some specific embodiments, the cloud computing cluster data synchronization device further includes:

[0074] A data monitoring module, configured to monitor the data change situation in the target cloud computing cluster by using target data detection technology to obtain the target data that has changed;

[0075] A second data sending module, configured to synchronize the target data to each of the target storage nodes by using each of the working nodes and the target routing path.

[0076] In some specific embodiments, the cloud computing cluster data synchronization device further includes:

[0077] An abnormal state monitoring module, configured to monitor the data synchronization process in real time. If an abnormal state is detected, record the error log corresponding to the abnormal state for troubleshooting by using the error log.

[0078] In some specific embodiments, the data synchronization module 13 further includes:

[0079] An IP recycling unit, configured to recycle the IPs of each of the working nodes, so as to allocate the IPs of each of the working nodes according to the new load status corresponding to each of the working nodes during the next data synchronization.

[0080] In some specific embodiments, the data synchronization module 13 further includes:

[0081] A data verification unit, configured to perform data consistency verification on the target data to be synchronized in each of the target storage nodes by using a target synchronization primitive to obtain a corresponding verification result;

[0082] A data deletion unit, configured to, if the verification result indicates that the target data to be synchronized in each of the target storage nodes is inconsistent, delete the target data to be synchronized in each of the target storage nodes and re-send each data segment to each of the target storage nodes by using each of the working nodes and the target routing path.

[0083] Furthermore, the embodiment of the present application also discloses an electronic device, Figure 7 which is a structural diagram of the electronic device 20 shown according to an exemplary embodiment. The content in the figure should not be regarded as any limitation on the scope of use of the present application.

[0084] Figure 7Schematic diagram of the structure of an electronic device 20 provided by an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input / output interface 25, and a communication bus 26. Among them, the memory 22 is used to store a computer program, and the computer program is loaded and executed by the processor 21 to implement the relevant steps in the cloud computing cluster data synchronization method disclosed in any of the foregoing embodiments. Additionally, the electronic device 20 in this embodiment may specifically be an electronic computer.

[0085] In this embodiment, the power supply 23 is used to provide working voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and external devices, and the communication protocol it follows is any communication protocol applicable to the technical solution of the present application, and no specific limitation is imposed on it here; the input / output interface 25 is used to obtain external input data or output data to the outside, and its specific interface type can be selected according to specific application requirements, and no specific limitation is made here.

[0086] In addition, as a carrier for resource storage, the memory 22 may be a read-only memory, a random access memory, a disk, or an optical disc, etc., and the resources stored thereon may include an operating system 221, a computer program 222, etc., and the storage method may be temporary storage or permanent storage.

[0087] Among them, the operating system 221 is used to manage and control each hardware device and the computer program 222 on the electronic device 20, and it may be Windows Server, Netware, Unix, Linux, etc. The computer program 222 may further include a computer program capable of completing other specific tasks in addition to the computer program capable of implementing the cloud computing cluster data synchronization method executed by the electronic device 20 disclosed in any of the foregoing embodiments.

[0088] Furthermore, the present application also discloses a computer-readable storage medium for storing a computer program; wherein, when the computer program is executed by a processor, it implements the cloud computing cluster data synchronization method disclosed above. For the specific steps of this method, reference may be made to the corresponding content disclosed in the foregoing embodiments, and details will not be repeated here.

[0089] In this specification, the various embodiments are described in a progressive manner, and the key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the various embodiments may be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the relevant parts may be referred to the description of the method part.

[0090] Those skilled in the art may further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0091] The steps of the methods or algorithms described in combination with the embodiments disclosed herein can be directly implemented by hardware, software modules executed by a processor, or a combination of both. The software modules can be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0092] Finally, it should also be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0093] The technical solutions provided in this application have been introduced in detail above. Specific examples have been used in this article to elaborate on the principles and implementation manners of this application. The description of the above embodiments is only used to help understand the method and its core idea of this application; at the same time, for those of ordinary skill in the art, according to the idea of this application, there will be changes in the specific implementation manners and application scopes. In summary, the content of this specification should not be construed as a limitation to this application.

Claims

1. A cloud computing cluster data synchronization method, characterized in that: include: Receive data synchronization requests from each working node in the target cloud computing cluster, assign a working node IP to each working node according to the data synchronization request and the load status of each working node, and divide the target data to be synchronized in the target cloud computing cluster into data segments; Searching a preset mapping table to obtain the target IP of the target storage node corresponding to each of the working nodes, and using the working node IP and the target IP to establish a network connection between each of the working nodes and each of the target storage nodes; The target routing path is dynamically determined according to the network status corresponding to the network connection, and each of the data fragments is sent to each of the target storage nodes using each of the working nodes and the target routing path, so that each of the target storage nodes obtains the target data to be synchronized.

2. The cloud computing cluster data synchronization method according to claim 1, characterized in that: The sending each of the data fragments to each of the target storage nodes by using each of the working nodes and the target routing path includes: Each of the data fragments is encrypted using a target encryption algorithm to obtain a corresponding encrypted fragment, and each of the encrypted fragments is sent to each of the target storage nodes using each of the working nodes and the target routing path.

3. The cloud computing cluster data synchronization method according to claim 1, characterized in that: Also includes: Compressing each of the data segments corresponding to the target data to be synchronized using a target compression algorithm to obtain corresponding compressed data; Each of the compressed data is synchronized to each of the target storage nodes using each of the working nodes and the target routing path, so that each of the target storage nodes can decompress each of the compressed data and obtain each of the data fragments.

4. The cloud computing cluster data synchronization method according to claim 1, characterized in that: Also includes: Using target data detection technology to monitor data changes in the target cloud computing cluster to obtain changed target data; The target data is synchronized to each target storage node using each of the working nodes and the target routing path.

5. The cloud computing cluster data synchronization method according to claim 1, characterized in that: Also includes: The data synchronization process is monitored in real time. If an abnormal state is detected, an error log corresponding to the abnormal state is recorded so that the error log can be used for troubleshooting.

6. The cloud computing cluster data synchronization method according to claim 1, characterized in that: After sending each of the data fragments to each of the target storage nodes by using each of the working nodes and the target routing path, the method further includes: The IP addresses of the working nodes are recycled so that the IP addresses of the working nodes can be allocated according to the new load status corresponding to the working nodes during the next data synchronization.

7. The cloud computing cluster data synchronization method according to any one of claims 1 to 6, characterized in that: After sending each of the data fragments to each of the target storage nodes by using each of the working nodes and the target routing path, the method further includes: Using the target synchronization primitive, performing data consistency check on the target data to be synchronized in each target storage node to obtain a corresponding check result; If the verification result indicates that the target data to be synchronized in each target storage node is inconsistent, the target data to be synchronized in each target storage node is deleted and each data segment is resent to each target storage node using each working node and the target routing path.

8. A cloud computing cluster data synchronization device, characterized in that: include: A synchronization request receiving module, used to receive data synchronization requests from each working node in the target cloud computing cluster, allocate a working node IP to each working node according to the data synchronization request and the load status of each working node, and divide the target data to be synchronized in the target cloud computing cluster into data segments; A network connection building module is used to search a preset mapping table to obtain the target IP of the target storage node corresponding to each of the working nodes, and use the working node IP and the target IP to build a network connection between each of the working nodes and each of the target storage nodes; The data synchronization module is used to dynamically determine the target routing path according to the network status corresponding to the network connection, and use each working node and the target routing path to send each data fragment to each target storage node so that each target storage node obtains the target data to be synchronized.

9. An electronic device, characterized in that: include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the cloud computing cluster data synchronization method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: Used to store a computer program, which, when executed by a processor, implements the cloud computing cluster data synchronization method according to any one of claims 1 to 7.

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