Container application migration method, device, electronic device and computer readable medium

Abstract

The embodiments of the present disclosure disclose a container application migration method, device, electronic device and computer-readable medium. A specific implementation of the method includes: based on the real-time operation monitoring information of the target node server, generating operation status information of a predetermined future time period, wherein the above operation status information is used to characterize that the operation load of the above target node server is normal or the operation load of the above target node server is abnormal; in response to determining that the above operation status information characterizes that the operation load of the above target node server is abnormal, selecting a container application from the container application set deployed in the above target node server as the target container application; and performing migration processing on the above target container application deployed in the above target node server. This implementation improves the timeliness of the migration processing of the container application deployed in the node server and reduces the number of load abnormalities of the node server during operation. This improves the stability of the container application cluster.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to a container application migration method, device, electronic device, and computer-readable medium. Background Art

[0002] With the development of computer technology, the data generated by various applications has increased dramatically. The node servers of container application clusters (for example, Kubernetes) can deploy container applications. At present, when managing container application clusters, the commonly used method is to configure resources when the node server first deploys the container application to manage the container application cluster.

[0003] However, when using the above method to manage container application clusters, the following technical problems often occur:

[0004] First, the container applications deployed in the node servers were not managed according to the running status of the node servers in the future time period, resulting in the untimely migration of the container applications deployed in the node servers, causing a large number of abnormal loads on the node servers during operation, thereby reducing the stability of the container application cluster;

[0005] Second, when resources are configured during the initial deployment of container applications, it is difficult to manage the allocated resources based on the resource usage of the node servers when the node servers are running, resulting in low resource utilization of the devices in the container application cluster. Summary of the invention

[0006] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0007] Some embodiments of the present disclosure propose a container application migration method, apparatus, electronic device, and computer-readable medium to solve one or more of the technical problems mentioned in the above background technology section.

[0008] In a first aspect, some embodiments of the present disclosure provide a container application migration method, the method comprising: generating operation status information for a predetermined future time period based on real-time operation monitoring information of a target node server, wherein the operation status information is used to characterize that the operation load of the target node server is normal or that the operation load of the target node server is abnormal; in response to determining that the operation status information characterizes that the operation load of the target node server is abnormal, selecting a container application from a set of container applications deployed in the target node server as a target container application; and performing migration processing on the target container application deployed in the target node server.

[0009] In a second aspect, some embodiments of the present disclosure provide a container application migration device, the device comprising: a generation unit, configured to generate operation status information of a predetermined future time period based on real-time operation monitoring information of a target node server, wherein the above operation status information is used to characterize that the operation load of the above target node server is normal or that the operation load of the above target node server is abnormal; a selection unit, configured to select a container application from a set of container applications deployed in the above target node server as a target container application in response to the above operation status information characterizing that the operation load of the above target node server is abnormal; and a processing unit, configured to perform migration processing on the above target container application deployed in the above target node server.

[0010] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner of the above-mentioned first aspect.

[0011] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method described in any implementation manner of the above-mentioned first aspect is implemented.

[0012] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the container application migration method of some embodiments of the present disclosure, the stability of the container application cluster is improved. Specifically, the reason for the reduced stability of the container application cluster is that the various container applications deployed in the node server are not managed according to the running status of the node server in the future time period, resulting in the low timeliness of the migration processing of the container applications deployed in the node server, resulting in a large number of abnormal loads of the node server during operation. Based on this, the container application migration method of some embodiments of the present disclosure first generates the running status information of the predetermined future time period based on the real-time running monitoring information of the target node server. Among them, the above-mentioned running status information is used to characterize that the running load of the above-mentioned target node server is normal or the running load of the above-mentioned target node server is abnormal. Thus, the running status of the target node server in the predetermined future time period can be predicted. Then, in response to determining that the above-mentioned running status information characterizes that the running load of the above-mentioned target node server is abnormal, a container application is selected from the container application set deployed in the above-mentioned target node server as the target container application. Thus, the container application that needs to be migrated can be determined from the container application set. Finally, the above-mentioned target container application deployed in the above-mentioned target node server is migrated. Thus, the container applications that need to be migrated can be migrated to manage the container application cluster. Also, because the running status information of the node server in the predetermined future time period is generated, the container applications deployed in each node server included in the container application cluster can be managed according to the running status of the node server in the future time period. This can improve the timeliness of the migration processing of the container applications deployed in the node server and reduce the number of abnormal loads of the node server during operation. This in turn improves the stability of the container application cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0014] Figure 1 is a schematic diagram of an application scenario of a container application migration method according to some embodiments of the present disclosure;

[0015] Figure 2 is a flow chart of some embodiments of the container application migration method according to the present disclosure;

[0016] Figure 3 is a correspondence table of the container application migration method according to the present disclosure;

[0017] Figure 4are flow charts of other embodiments of the container application migration method according to the present disclosure;

[0018] Figure 5 are flow charts of further embodiments of the container application migration method according to the present disclosure;

[0019] Figure 6 is a schematic diagram of the structure of some embodiments of the container application migration device according to the present disclosure;

[0020] Figure 7 It is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. DETAILED DESCRIPTION

[0021] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0022] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0023] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0024] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0025] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0026] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0027] Figure 1 It is a schematic diagram of an application scenario of the container application migration method according to some embodiments of the present disclosure.

[0028] exist Figure 1In the application scenario, first, the computing device 101 can generate the operation status information 103 of the predetermined future time period based on the real-time operation monitoring information 102 of the target node server. Among them, the above-mentioned operation status information 103 is used to characterize that the operation load of the above-mentioned target node server is normal or the operation load of the above-mentioned target node server is abnormal. Then, in response to determining that the above-mentioned operation status information 103 characterizes that the operation load of the above-mentioned target node server is abnormal, the computing device 101 can select a container application as the target container application 105 from the container application set 104 deployed in the above-mentioned target node server. Finally, the computing device 101 can perform migration processing on the above-mentioned target container application 105 deployed in the above-mentioned target node server. Optionally, the computing device 101 can send the above-mentioned operation status information 103 to a device 106 with a display function associated with the above-mentioned target node server.

[0029] It should be noted that the computing device 101 can be hardware or software. When the computing device is hardware, it can be implemented as a distributed cluster consisting of multiple servers or terminal devices, or as a single server or a single terminal device. When the computing device is embodied as software, it can be installed in the hardware devices listed above. It can be implemented as multiple software or software modules for providing distributed services, or as a single software or software module. No specific limitation is made here.

[0030] It should be understood that Figure 1 The number of computing devices in the embodiment is only illustrative. Any number of computing devices may be provided according to implementation requirements.

[0031] Continue to refer Figure 2 , shows a process 200 of some embodiments of the container application migration method according to the present disclosure. The container application migration method includes the following steps:

[0032] Step 201: Generate operation status information for a predetermined future time period based on real-time operation monitoring information of the target node server.

[0033] In some embodiments, an execution subject (eg, Figure 1The computing device 101 shown can generate the operation status information of the predetermined future time period based on the real-time operation monitoring information of the target node server. Among them, the above-mentioned operation status information can be used to characterize that the operation load of the above-mentioned target node server is normal or the operation load of the above-mentioned target node server is abnormal. The above-mentioned target node server can be a server of a node in a container application cluster (for example, Kubernetes). The above-mentioned real-time operation monitoring information can be the resource usage information of the above-mentioned target node server at runtime. The above-mentioned real-time operation monitoring information can include but is not limited to at least one of the following: node central processing unit load rate, node memory load rate, node query rate per second. Among them, the above-mentioned node central processing unit load rate can be the usage ratio of the central processing unit when the above-mentioned target node server is running. The above-mentioned node memory load rate can be the usage ratio of the memory when the above-mentioned target node server is running. The above-mentioned node query rate per second can be the number of queries processed by the above-mentioned target node server per second. Among them, the above-mentioned predetermined future time period can be a predetermined time period after the current moment.

[0034] In practice, the execution subject can use a corresponding relationship table (such as Figure 3 As shown), the operation status information of the predetermined future time period is generated. The above-mentioned correspondence table may be a table pre-stored in the above-mentioned execution subject. The above-mentioned correspondence table may also be a table obtained by the above-mentioned execution subject from a terminal storing the above-mentioned correspondence table. Specifically, the above-mentioned execution subject may query the operation status information of the predetermined future time period corresponding to the above-mentioned real-time operation monitoring information from the above-mentioned correspondence table to obtain the operation status information.

[0035] As an example, the above real-time operation monitoring information may be [400 times]. The above "[400 times]" is the query rate per second of the node. The above correspondence table may be as follows: Figure 3 The above execution subject can be Figure 3 The operation status information corresponding to the above real-time operation monitoring information [400 times] is queried in the corresponding relationship table of the example, and the operation status information [abnormal] is obtained. The above “abnormal” indicates that the operation load of the above target node server is abnormal.

[0036] In practice, the above-mentioned execution subject can also use the load score for the target node server to generate the operation status information of the predetermined future time period. Specifically, first, the above-mentioned execution subject can obtain the load score corresponding to the above-mentioned real-time operation monitoring information of the above-mentioned target node server from the terminal through a wired connection method or a wireless connection method. Then, the above-mentioned execution subject can generate the operation status information characterizing the abnormal operation load of the above-mentioned target node server in response to determining that the above-mentioned load score meets the predetermined abnormal condition. Among them, the above-mentioned load score can be a quantitative representation of the load situation of the above-mentioned execution subject. The above-mentioned predetermined abnormal condition can be a predetermined condition for determining whether the operation status of the above-mentioned target node server in the predetermined future time period is abnormal. The above-mentioned predetermined abnormal condition can be "the load score is greater than or equal to a predetermined threshold value". Here, there is no limitation on the setting of the predetermined threshold value. Thus, the operation status of the target node server in the predetermined future time period can be predicted.

[0037] Step 202 : In response to determining that the running status information indicates that the running load of the target node server is abnormal, a container application is selected from the container application set deployed in the target node server as a target container application.

[0038] In some embodiments, the execution subject may select a container application from the container application set deployed in the target node server as the target container application in response to determining that the operation status information indicates that the operation load of the target node server is abnormal. In practice, first, the execution subject may obtain the application monitoring information of each container application in the container application set from the target node server through a wired connection or a wireless connection to obtain a set of application monitoring information. Among them, the application monitoring information may be information about the use of resources of the target node server by the container application when the target node server is running. The application monitoring information may include application central processor occupancy, application memory occupancy, and application request rate per second. The application central processor occupancy may be the ratio of the central processor of the target node server occupied by running the container application. The application memory occupancy may be the ratio of the memory of the target node server occupied by running the container application. The application request rate per second may be the number of queries processed per second when the container application is running in the target node server. Then, the execution entity may generate a resource occupancy score of the container application corresponding to the application monitoring information based on the application CPU occupancy, application memory occupancy and application request rate per second included in each application monitoring information in the application monitoring information set, and obtain a resource occupancy score set.

[0039] In practice, the execution subject may generate the resource occupancy score of the container application corresponding to the application monitoring information through the following steps:

[0040] The first step is to determine the ratio of the application request rate per second of the container application corresponding to the application monitoring information to the sum of the application request rates per second of the container applications in the container application set.

[0041] In a second step, the application CPU occupancy rate, the application memory occupancy rate and the average of the above ratios of the container application corresponding to the above application monitoring information are determined as the resource occupancy score of the above container application.

[0042] As an example, the CPU occupancy rate of the above application can be [70%]. The memory occupancy rate of the above application can be [20%]. The request rate per second of the above application can be [200 times]. The request rates per second of each application included in the above application monitoring information set can be [100 times, 200 times, 50 times, 50 times]. First, the above execution subject can determine the ratio [0.5] of the request rate per second of the above application [200 times] and the sum of the request rates per second of each application [100 times, 200 times, 50 times, 50 times]. Then, the above execution subject can determine the average [0.47] of the CPU occupancy rate of the above application [70%], the memory occupancy rate of the above application [20%] and the above ratio [0.5] as the resource occupancy score of the above container application. Here, the value of the above average can be retained to two significant figures after the decimal point.

[0043] In practice, the execution subject may also generate a resource occupancy score of the container application corresponding to the above application monitoring information based on the application CPU occupancy rate, application memory occupancy rate and application request rate per second included in each application monitoring information in the above application monitoring information set by using the following formula:

[0044]

[0045] Among them, AScore represents the resource occupancy score of the above container application. ACPUper represents the central processor occupancy rate of the above application. AStoreper represents the memory occupancy rate of the above application. AQPSper represents the request rate per second of the above application. N represents the number of application monitoring information included in the above application monitoring information set. i represents the sequence number of the application monitoring information in the above application monitoring information set. AQPSperi represents the request rate per second of the application included in the i-th application monitoring information. Therefore, the resource occupancy score of the container application corresponding to the application monitoring information can be generated according to the application central processor occupancy rate, application memory occupancy rate and application request rate per second included in the application monitoring information, so as to quantitatively represent the resource status of the target node server used by the container application during operation. The above formula respectively uses the ratio of the application central processor occupancy rate, the application memory occupancy rate and the application request rate per second to the sum of the request rates per second of each application as the adjustment factor of the resource occupancy score, so that the resource occupancy score increases with the increase of the application central processor occupancy rate, the application memory occupancy rate and the above ratio. At the same time, the application central processor occupancy rate, the application memory occupancy rate and the above ratio can be used as adjustment factors at different levels, and the adjustment range of the resource occupancy score is different. Specifically, the adjustment range of each adjustment factor can be: ratio> application CPU occupancy> application memory occupancy. Since the higher the above ratio, the higher the application request rate per second, the worse the stability of the target node server, the application request rate per second has a greater impact on the stability of the target node server, followed by the application CPU occupancy and application memory occupancy. Therefore, using the adjustment range of each adjustment factor in the above formula, a more objective and accurate resource occupancy score calculation can be achieved.

[0046] As an example, the CPU occupancy rate of the above application may be [70%]. The memory occupancy rate of the above application may be [20%]. The request rate per second of the above application may be [200 times]. The request rates per second of each application included in the above application monitoring information set may be [100 times, 200 times, 50 times, 50 times]. The above execution subject may generate the resource occupancy score of the container application corresponding to the above application monitoring information through the above formula: Here, the value of the resource occupancy score can be retained to two significant figures after the decimal point.

[0047] Afterwards, the execution subject may determine the resource occupancy score that satisfies the predetermined score condition in the resource occupancy score set as the target resource occupancy score. The predetermined score condition may be "the resource occupancy score is the maximum value". Finally, the execution subject may determine the container application corresponding to the target resource occupancy score as the target container application. Thus, the container application that needs to be migrated may be determined from the container application set.

[0048] As an example, the resource occupancy score set may be [0.49, 0.51, 0.32, 0.21]. First, the execution entity may determine the resource occupancy score [0.51] that satisfies the predetermined score condition "the resource occupancy score is the maximum value" in the resource occupancy score set [0.49, 0.51, 0.32, 0.21] as the target resource occupancy score. Then, the execution entity may determine the container application "container application A" corresponding to the target resource occupancy score [0.51] as the target container application.

[0049] Step 203: Migrate the target container application deployed in the target node server.

[0050] In some embodiments, the execution subject may perform migration processing on the target container application deployed in the target node server. In practice, the migration processing may be to uninstall the application of the target container application from the target node server, and to deploy the target container application in other node servers in the container application cluster corresponding to the target node server. Thus, the container application that needs to be migrated may be migrated to manage the container application cluster.

[0051] As an example, the target node server may be "node server A". The target container application may be "container application A". First, the execution subject may uninstall the application of the target container application "container application A" from the target node server "node server A". Then, the target container application "container application A" may be deployed on the node server "node server B" in the container application cluster corresponding to the target node server "node server A".

[0052] In some optional implementations of some embodiments, the execution subject may send the operation status information to a device with a display function associated with the target node server. Thus, the operation status of the target node server may be visually monitored by displaying the operation status information on the device.

[0053] The above-mentioned various embodiments of the present disclosure have the following beneficial effects: through the container application migration method of some embodiments of the present disclosure, the stability of the container application cluster is improved. Specifically, the reason for the reduced stability of the container application cluster is that the various container applications deployed in the node server are not managed according to the running status of the node server in the future time period, resulting in the low timeliness of the migration processing of the container applications deployed in the node server, resulting in a large number of abnormal loads of the node server during operation. Based on this, the container application migration method of some embodiments of the present disclosure first generates the running status information of the predetermined future time period based on the real-time running monitoring information of the target node server. Among them, the above-mentioned running status information is used to characterize that the running load of the above-mentioned target node server is normal or the running load of the above-mentioned target node server is abnormal. Thus, the running status of the target node server in the predetermined future time period can be predicted. Then, in response to determining that the above-mentioned running status information characterizes that the running load of the above-mentioned target node server is abnormal, a container application is selected from the container application set deployed in the above-mentioned target node server as the target container application. Thus, the container application that needs to be migrated can be determined from the container application set. Finally, the above-mentioned target container application deployed in the above-mentioned target node server is migrated. Thus, the container applications that need to be migrated can be migrated to manage the container application cluster. Also, because the running status information of the node server in the predetermined future time period is generated, the container applications deployed in each node server included in the container application cluster can be managed according to the running status of the node server in the future time period. This can improve the timeliness of the migration processing of the container applications deployed in the node server and reduce the number of abnormal loads of the node server during operation. This in turn improves the stability of the container application cluster.

[0054] Further references Figure 4 , which shows a process 400 of another embodiment of the container application migration method. The process 400 of the container application migration method includes the following steps:

[0055] Step 401 , input the real-time operation monitoring information into a pre-trained operation status prediction model to obtain operation status information.

[0056] In some embodiments, an execution subject (eg, Figure 1 The computing device 101 shown in the figure can input the above real-time operation monitoring information into a pre-trained operation status prediction model to obtain operation status information. The above operation status prediction model can be trained by the following steps:

[0057] The first step is to obtain a set of historical monitoring information of the target node server as a sample set. The sample may include historical operation monitoring information and sample historical operation status information of the predetermined future time period corresponding to the historical operation monitoring information. The historical operation monitoring information may be the operation monitoring information of the target node server at a historical time point. The operation monitoring information may be the resource utilization information of the target node server at runtime. For example, the historical operation monitoring information may include but is not limited to at least one of the following: a historical node CPU load rate, a historical node memory load rate, and a historical node query rate per second. The historical node CPU load rate may be the usage ratio of the CPU when the target node server is running at a historical time point. The historical node memory load rate may be the usage ratio of the memory when the target node server is running at a historical time point. The historical node query rate per second may be the number of queries processed per second by the target node server at a historical time point. The sample historical operation status information may be the operation status information of the target node server at a preset time period after the historical time point corresponding to the historical operation monitoring information. The preset time period is the same as the preset future time period. The sample historical operation status information may be used to indicate that the operation load of the target node server is normal or that the operation load of the target node server is abnormal.

[0058] In the second step, the following training steps are performed based on the sample set:

[0059] In the first training sub-step, the historical operation monitoring information of at least one sample in the sample set is input into the initial neural network to obtain the historical operation status information corresponding to each sample in the at least one sample. In practice, the initial neural network can be any neural network that can obtain the historical operation status information based on the historical operation monitoring information, such as a convolutional neural network, a deep neural network, etc.

[0060] The second training sub-step is to compare the historical operation status information corresponding to each sample in the at least one sample with the historical operation status information of the corresponding sample. In practice, when the historical operation status information corresponding to a sample is the same as the historical operation status information of the corresponding sample, the historical operation status information is considered to be accurate.

[0061] The third training sub-step is to determine whether the initial neural network has achieved a preset optimization target based on the comparison result. In practice, for the comparison result obtained by the second training sub-step, the optimization target may refer to the accuracy of the historical operating status information generated by the initial neural network being greater than a preset accuracy threshold.

[0062] In a fourth training sub-step, in response to determining that the initial neural network has achieved the above optimization goal, the initial neural network is used as a trained running state prediction model.

[0063] The third step is to adjust the network parameters of the initial neural network in response to determining that the initial neural network does not achieve the above optimization goal. And use unused samples to form a sample set, use the adjusted initial neural network as the initial neural network, and perform the above training steps again. In practice, the BP algorithm (Back Propgation Algorithm) and the Batch Gradient Descent method can be used to adjust the network parameters of the above initial neural network.

[0064] Through step 401, relatively accurate operation status information can be generated according to the real-time operation monitoring information through the pre-trained operation status prediction model.

[0065] Step 402 : In response to determining that the running status information indicates that the running load of the target node server is abnormal, a container application is selected from a set of container applications deployed in the target node server as a target container application.

[0066] Step 403: Migrate the target container application deployed in the target node server.

[0067] In some embodiments, the specific implementation of steps 402-403 and the technical effects brought about can be referred to Figure 2 The corresponding steps 202-203 in the embodiments are not described in detail here.

[0068] from Figure 4 It can be seen that Figure 2 Compared with the description of some corresponding embodiments, Figure 4 The process 400 of the container application migration method in some corresponding embodiments embodies the step of expanding the generation of the operation status information of the predetermined future time period. Therefore, the scheme described in these embodiments can generate more accurate operation status information according to the real-time operation monitoring information through the pre-trained operation status prediction model.

[0069] Further references Figure 5 , which shows a process 500 of another embodiment of the container application migration method. The process 500 of the container application migration method includes the following steps:

[0070] Step 501: Generate operation status information for a predetermined future time period based on real-time operation monitoring information of the target node server.

[0071] Step 502 : In response to determining that the running status information indicates that the running load of the target node server is abnormal, a container application is selected from a set of container applications deployed in the target node server as a target container application.

[0072] In some embodiments, the specific implementation of steps 501-502 and the technical effects brought about can be referred to Figure 2 The corresponding steps 201-202 in the embodiments are not described in detail here.

[0073] Step 503: determine the container deployment permission identifier of the target node server as the first identifier to lock the device resources of the target node server.

[0074] In some embodiments, an execution subject (eg, Figure 1 The computing device 101 shown in the figure can determine the container deployment permission identifier of the target node server as the first identifier to lock the device resources of the target node server in response to determining that the above-mentioned operating status information indicates that the operating load of the target node server is abnormal. The above-mentioned container deployment permission identifier can be an identifier in text or digital form that indicates whether the device resources of the node server are available. The above-mentioned first identifier can indicate that the device resources of the node server are in a locked state. As an example, the above-mentioned execution subject can determine the container deployment permission identifier of the target node server as the first identifier "0" to lock the device resources of the target node server in response to determining that the above-mentioned operating status information indicates that the operating load of the target node server is abnormal. In this way, the device resources of the node server with abnormal operating load can be locked, so that the node server no longer processes requests to deploy container applications.

[0075] Step 504: Migrate the target container application deployed in the target node server.

[0076] In some embodiments, the specific implementation of step 504 and the technical effects brought about can be referred to Figure 2 The corresponding step 203 in the embodiments will not be described in detail here.

[0077] Step 505: Acquire real-time operation monitoring information from the target node server as target real-time operation monitoring information.

[0078] In some embodiments, the execution subject may obtain the real-time operation monitoring information from the target node server as the target real-time operation monitoring information through a wired connection or a wireless connection. The step of obtaining the real-time operation monitoring information may be a step of obtaining the real-time operation monitoring information after determining that the operation load of the target node server is abnormal.

[0079] Step 506: Generate a node load score based on the target real-time operation monitoring information.

[0080] In some embodiments, the execution subject may generate a node load score based on the target real-time operation monitoring information. The target real-time operation monitoring information may include a node CPU load rate, a node memory load rate, and a node query rate per second. The node CPU load rate may be a usage ratio of a CPU when the target node server is running. The node memory load rate may be a usage ratio of a memory when the target node server is running. The node query rate per second may be the number of queries processed by the target node server per second.

[0081] In practice, the above-mentioned execution subject may generate a node load score based on the above-mentioned node CPU load rate, the above-mentioned node memory load rate and the above-mentioned node query rate per second. Specifically, first, in response to determining that the above-mentioned node query rate per second is greater than or equal to the preset node query rate per second, the above-mentioned execution subject may determine the difference between the above-mentioned node query rate per second and the preset node query rate per second and the ratio of the above-mentioned preset node query rate per second as the node query rate per second excess rate. In response to determining that the above-mentioned node query rate per second is less than the above-mentioned preset node query rate per second, the above-mentioned execution subject may determine a predetermined value as the node query rate per second excess rate. Here, there is no limitation on the setting of the preset node query rate per second and the predetermined value.

[0082] As an example, the node query rate per second may be [150 times]. The preset node query rate per second may be [180 times]. The preset value may be [0]. In response to determining that the node query rate per second [150 times] is less than the preset node query rate per second [180 times], the execution subject may determine the preset value [0] as the node query rate per second excess rate.

[0083] As another example, the node query rate per second may be [190 times]. The preset node query rate per second may be [180 times]. In response to determining that the node query rate per second [190 times] is greater than the preset node query rate per second [180 times], the execution entity may determine the node query rate per second excess rate as the difference [10 times] between the node query rate per second [190 times] and the preset node query rate per second [180 times] and the ratio [1 / 18] of the preset node query rate per second [180 times].

[0084] Then, the execution subject may generate a node load score based on the node CPU load rate, the node memory load rate and the node query rate per second excess rate. In practice, the execution subject may determine the node load score as the average of the node CPU load rate, the node memory load rate and the node query rate per second excess rate.

[0085] As an example, the node CPU load rate may be [30%]. The node memory load rate may be [15%]. The node query rate per second excess rate may be [1 / 18]. The execution entity may determine the node load score as the average value [0.17] of the node CPU load rate [30%], the node memory load rate [15%], and the node query rate per second excess rate [1 / 18]. Here, the average value may be rounded to two significant digits after the decimal point.

[0086] In practice, the above execution entity can also generate the node load score through the following formula:

[0087]

[0088] Among them, NScore represents the node load score. NCPUper represents the central processing unit load rate of the above node. NStoreper represents the memory load rate of the above node. NQPSOver represents the excess rate of the query rate per second of the above node. Therefore, the node load score of the target node server can be generated according to the node central processing unit load rate, node memory load rate and node query rate per second included in the target real-time application monitoring information, so as to quantitatively represent the resource usage status of the target node server at runtime.

[0089] As an example, the CPU load rate of the node can be [30%]. The memory load rate of the node can be [15%]. The query rate per second excess rate of the node can be [1 / 18]. The execution entity can generate the node load score through the formula Here, the value of the node load score can be retained to two significant figures after the decimal point.

[0090] Step 507 : In response to determining that the node load score satisfies a predetermined condition, the container deployment authority identifier is determined as a second identifier for deploying the container application on the target node server.

[0091] In some embodiments, the execution entity may determine the container deployment permission identifier as a second identifier for deploying container applications on the target node server in response to determining that the node load score satisfies a predetermined condition. The predetermined condition may be "the node load score is less than a predetermined value". The second identifier may indicate that the device resources of the node server are in an available state. Here, there is no limitation on the setting of the predetermined value. Thus, the device resources of the node server whose node load score satisfies the predetermined condition may be unlocked for continued deployment of container applications on the node server.

[0092] As an example, the node load score may be [0.28]. The predetermined value may be [0.5]. In response to determining that the node load score [0.28] satisfies the predetermined condition “the node load score is less than 0.5”, the execution subject may determine the container deployment permission identifier as the second identifier “1” for deploying the container application on the target node server.

[0093] The above technical solution is an inventive point of an embodiment of the present disclosure, which solves the second technical problem mentioned in the background technology: "When the container application is deployed for the first time, it is difficult to manage the allocated resources according to the resource usage status of the node server when the node server is running, resulting in low resource utilization of the equipment of the container application cluster". The factors that lead to low resource utilization of the equipment of the container application cluster are often as follows: when the container application is deployed for the first time, it is difficult to manage the allocated resources according to the resource usage status of the node server when the node server is running. If the above factors are solved, the effect of improving the resource utilization of the equipment of the container application cluster can be achieved. In order to achieve this effect, the present disclosure first responds to determining that the above-mentioned running status information represents the abnormal running load of the above-mentioned target node server, and determines the container deployment permission identifier of the above-mentioned target node server as the first identifier to lock the device resources of the above-mentioned target node server. In this way, the device resources of the node server with abnormal running load can be locked, so that the node server no longer processes the request to deploy the container application. Then, after the above-mentioned target container application deployed in the above-mentioned target node server is migrated, the real-time running monitoring information is obtained from the above-mentioned target node server as the target real-time running monitoring information. Afterwards, a node load score is generated based on the above-mentioned target real-time operation monitoring information. Thus, the node load score of the target node server can be generated according to the node CPU load rate, node memory load rate and node query rate per second included in the real-time application monitoring information, so as to quantitatively represent the resource usage of the target node server at runtime. The formula in step 506 uses the node CPU load rate, node memory load rate and node query rate per second excess rate as adjustment factors of the node load score, so that the node load score can increase with the increase of the node CPU load rate, node memory load rate and node query rate per second excess rate. At the same time, the node CPU load rate, node memory load rate and node query rate per second excess rate can be used as adjustment factors of different levels, and the adjustment range of the node load score is different. Specifically, the adjustment range size relationship of each adjustment factor can be: node query rate per second excess rate>node CPU load rate>node memory load rate. Since the higher the node query rate per second excess rate, the higher the node query rate per second, the worse the stability of the target node server, the node query rate per second has a greater impact on the stability of the target node server, followed by the node CPU load rate and the node memory load rate, therefore, using the adjustment range of each adjustment factor in the above formula, a more objective and accurate node load score calculation can be achieved. Finally, in response to determining that the above node load score meets the predetermined conditions, the above container deployment permission identifier is determined as the second identifier for deploying container applications on the above target node server.Thus, the device resources of the node server whose node load score meets the above predetermined conditions can be unlocked to continue to deploy container applications on the node server. Therefore, resource configuration can be performed after the initial deployment of the container application, and the allocated resources can be managed according to the resource usage status of the node server when the node server is running. In turn, the resource utilization rate of the device of the container application cluster can be improved.

[0094] from Figure 5 It can be seen that Figure 2 Compared with the description of some corresponding embodiments, Figure 5 The process 500 of the container application migration method in some corresponding embodiments embodies the steps related to the extended container deployment permission identification. Therefore, the schemes described in these embodiments can perform resource configuration after the initial deployment of the container application, and manage the allocated resources according to the resource usage status of the node server when the node server is running. In turn, the resource utilization rate of the device of the container application cluster can be improved.

[0095] Further references Figure 6 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of a container application migration device. These device embodiments are similar to Figure 6 Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0096] like Figure 6 As shown, the container application migration device 600 of some embodiments includes: a generation unit 601, a selection unit 602 and a processing unit 603. The generation unit 601 is configured to generate operation status information of a predetermined future time period based on the real-time operation monitoring information of the target node server, wherein the above operation status information is used to characterize that the operation load of the above target node server is normal or the operation load of the above target node server is abnormal; the selection unit 602 is configured to select a container application from the container application set deployed in the above target node server as a target container application in response to the above operation status information characterizing that the operation load of the above target node server is abnormal; the processing unit 603 is configured to perform migration processing on the above target container application deployed in the above target node server.

[0097] In an optional implementation of some embodiments, the generation unit 601 of the container application migration device 600 may be further configured to: input the above-mentioned real-time operation monitoring information into a pre-trained operation status prediction model to obtain the operation status information.

[0098] In an optional implementation of some embodiments, the above-mentioned operation status prediction model can be trained through the following steps: obtaining a set of historical monitoring information of the above-mentioned target node server as a sample set, wherein the above-mentioned sample includes historical operation monitoring information and sample historical operation status information of the above-mentioned predetermined future time period corresponding to the above-mentioned historical operation monitoring information; performing the following training steps based on the sample set: inputting the historical operation monitoring information of at least one sample in the sample set into the initial neural network respectively, and obtaining the historical operation status information corresponding to each sample in the above-mentioned at least one sample; comparing the historical operation status information corresponding to each sample in the above-mentioned at least one sample with the corresponding sample historical operation status information; determining whether the initial neural network has achieved a preset optimization target based on the comparison result; in response to determining that the initial neural network has achieved the above-mentioned optimization target, using the initial neural network as a trained operation status prediction model.

[0099] In an optional implementation of some embodiments, the step of training to obtain the above-mentioned operating status prediction model also includes: in response to determining that the initial neural network has not achieved the above-mentioned optimization goal, adjusting the network parameters of the initial neural network, and using unused samples to form a sample set, using the adjusted initial neural network as the initial neural network, and performing the above-mentioned training step again.

[0100] In an optional implementation of some embodiments, the selection unit 602 of the container application migration device 600 may include: a determination subunit (not shown in the figure), configured to determine the container deployment permission identifier of the above-mentioned target node server as the first identifier to lock the device resources of the above-mentioned target node server.

[0101] In an optional implementation of some embodiments, the container application migration device 600 further includes: an acquisition unit, a node load score generation unit, and a determination unit (not shown in the figure). Among them, the acquisition unit is configured to acquire real-time operation monitoring information from the above-mentioned target node server as the target real-time operation monitoring information. The node load score generation unit is configured to generate a node load score based on the above-mentioned target real-time operation monitoring information. The determination unit is configured to determine the above-mentioned container deployment permission identifier as a second identifier for deploying the container application on the above-mentioned target node server in response to determining that the above-mentioned node load score meets a predetermined condition.

[0102] In an optional implementation of some embodiments, the above-mentioned target real-time operation monitoring information includes the node central processing unit load rate, the node memory load rate and the node query rate per second.

[0103] In an optional implementation of some embodiments, the node load score generating unit is further configured to generate a node load score based on the node central processing unit load rate, the node memory load rate and the node query rate per second.

[0104] In an optional implementation of some embodiments, the container application migration device 600 further includes: a sending unit (not shown in the figure), configured to send the above-mentioned running status information to a device with a display function associated with the above-mentioned target node server.

[0105] It is understood that the units described in the device 600 are similar to those described in the reference Figure 2 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the device 600 and the units included therein, and will not be described in detail here.

[0106] Reference below Figure 7 , which shows an electronic device (eg, Figure 1 Schematic diagram of the structure of the computing device 101)700. Figure 7 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0107] like Figure 7 As shown, the electronic device 700 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703. In the RAM 703, various programs and data required for the operation of the electronic device 700 are also stored. The processing device 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0108] Typically, the following devices may be connected to the I / O interface 705: an input device 706 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 707 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 708 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 709. The communication device 709 may allow the electronic device 700 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 7 The electronic device 700 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 7 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0109] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 709, or installed from the storage device 708, or installed from the ROM 702. When the computer program is executed by the processing device 701, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0110] It should be noted that the computer-readable medium recorded in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0111] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0112] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: generates operation status information for a predetermined future time period based on the real-time operation monitoring information of the target node server, wherein the operation status information is used to characterize that the operation load of the target node server is normal or the operation load of the target node server is abnormal; in response to determining that the operation status information characterizes that the operation load of the target node server is abnormal, selects a container application from the container application set deployed in the target node server as the target container application; and performs migration processing on the target container application deployed in the target node server.

[0113] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0114] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0115] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The units described may also be provided in a processor, for example, may be described as: a processor including a generation unit, a selection unit, and a processing unit. The names of these units do not, in some cases, constitute limitations on the units themselves, for example, the generation unit may also be described as "a unit that generates operation status information for a predetermined future time period based on real-time operation monitoring information of a target node server".

[0116] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0117] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A container application migration method, comprising: Based on the real-time operation monitoring information of the target node server, generating operation status information of a predetermined future time period, wherein the operation status information is used to indicate that the operation load of the target node server is normal or the operation load of the target node server is abnormal; In response to determining that the running status information indicates that the running load of the target node server is abnormal, selecting a container application from the container application set deployed in the target node server as the target container application, wherein the selecting a container application from the container application set deployed in the target node server as the target container application includes: Acquire application monitoring information of each container application in the container application set from the target node server to obtain an application monitoring information set, wherein the application monitoring information includes application central processing unit occupancy rate, application memory occupancy rate and application request rate per second; Based on the application central processor occupancy rate, application memory occupancy rate and application request rate per second included in each application monitoring information in the application monitoring information set, a resource occupancy score of the container application corresponding to the application monitoring information is generated to obtain a resource occupancy score set, wherein the generating the resource occupancy score of the container application corresponding to the application monitoring information includes: generating the resource occupancy score of the container application corresponding to the application monitoring information by the following formula: Wherein, AScore represents the resource occupancy score of the container application, ACPUper represents the CPU occupancy rate of the application, AStoreper represents the memory occupancy rate of the application, AQPSper represents the request rate per second of the application, N represents the number of application monitoring information included in the application monitoring information set, f represents the sequence number of the application monitoring information in the application monitoring information set, and AQPSper represents the number of application monitoring information in the application monitoring information set. i Indicates the application request rate per second included in the i-th application monitoring information; Determining a resource occupancy score that meets a predetermined score condition in the resource occupancy score set as a target resource occupancy score; Determine the container application corresponding to the target resource occupancy score as the target container application; Migrate the target container application deployed in the target node server.

2. The method according to claim 1, wherein: The generating of the operation status information of the predetermined future time period includes: The real-time operation monitoring information is input into a pre-trained operation status prediction model to obtain operation status information.

3. The method according to claim 2, wherein: The running status prediction model is trained by the following steps: Acquire a set of historical monitoring information of the target node server as a sample set, wherein the sample includes historical operation monitoring information and sample historical operation status information of the predetermined future time period corresponding to the historical operation monitoring information; Perform the following training steps based on the sample set: Inputting historical operation monitoring information of at least one sample in the sample set into the initial neural network respectively to obtain historical operation status information corresponding to each sample in the at least one sample; Comparing the historical running state information corresponding to each sample of the at least one sample with the corresponding sample historical running state information; Determine whether the initial neural network reaches the preset optimization goal according to the comparison result; In response to determining that the initial neural network achieves the optimization goal, the initial neural network is used as a trained operating state prediction model.

4. The method according to claim 3, wherein: The step of training the operating state prediction model also includes: In response to determining that the initial neural network does not achieve the optimization goal, the network parameters of the initial neural network are adjusted, and unused samples are used to form a sample set, and the adjusted initial neural network is used as the initial neural network to perform the training step again.

5. The method according to claim 1, wherein: The selecting a container application as a target container application from the container application set deployed in the target node server further includes: The container deployment permission identifier of the target node server is determined as the first identifier to lock the device resources of the target node server.

6. The method according to claim 5, wherein: The method further comprises: Acquire real-time operation monitoring information from the target node server as target real-time operation monitoring information; Generate a node load score based on the target real-time operation monitoring information; In response to determining that the node load score satisfies a predetermined condition, the container deployment authority identifier is determined as a second identifier for deploying a container application on the target node server.

7. The method according to claim 6, wherein: The target real-time operation monitoring information includes the node central processor load rate, the node memory load rate and the node query rate per second; as well as The generating a node load score based on the target real-time operation monitoring information includes: A node load score is generated based on the node central processing unit load rate, the node memory load rate and the node query rate per second.

8. The method according to claim 1, wherein: The method further comprises: The operation status information is sent to a device with a display function associated with the target node server.

9. A container application migration device, comprising: A generating unit is configured to generate operating status information of a predetermined future time period based on the real-time operating monitoring information of the target node server, wherein the operating status information is used to indicate that the operating load of the target node server is normal or the operating load of the target node server is abnormal; The selection unit is configured to select a container application as a target container application from the container application set deployed in the target node server in response to the operation status information characterizing that the operation load of the target node server is abnormal, wherein the selecting of the container application as the target container application from the container application set deployed in the target node server includes: acquiring application monitoring information of each container application in the container application set from the target node server to obtain an application monitoring information set, wherein the application monitoring information includes application central processing unit occupancy, application memory occupancy, and application request rate per second; based on the application central processing unit occupancy, application memory occupancy, and application request rate per second included in each application monitoring information in the application monitoring information set, generating a resource occupancy score of the container application corresponding to the application monitoring information to obtain a resource occupancy score set, wherein the generating of the resource occupancy score of the container application corresponding to the application monitoring information includes: generating the resource occupancy score of the container application corresponding to the application monitoring information by the following formula: Wherein, AScore represents the resource occupancy score of the container application, ACPUper represents the CPU occupancy rate of the application, AStoreper represents the memory occupancy rate of the application, AQPSper represents the request rate per second of the application, N represents the number of application monitoring information included in the application monitoring information set, l represents the sequence number of the application monitoring information in the application monitoring information set, and AQPSper represents the number of the application monitoring information in the application monitoring information set. i Indicates the application request rate per second included in the i-th application monitoring information; determines the resource occupation score that meets the predetermined score condition in the resource occupation score set as the target resource occupation score; determines the container application corresponding to the target resource occupation score as the target container application; The processing unit is configured to perform migration processing on the target container application deployed in the target node server.

10. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon; When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 8.

11. A computer readable medium having a computer program stored thereon, wherein: When the program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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