Cloud service alarm method and device for preset customers

Abstract

An embodiment of the present application discloses a cloud service alarm method for a preset customer. First, abnormal data generated during the operation of the cloud service is obtained; then, based on the abnormal data, the customer served by the cloud service that generates the abnormal data is identified, and it is determined whether the customer belongs to a preset customer type; then, in response to determining that the customer belongs to the preset customer type and in response to determining that the number of times of abnormal data generated by the cloud service exceeds a preset number threshold within a first preset time period, an alarm message is sent. The embodiments of the present disclosure can be applied to the field of cloud computing. By collecting online abnormal data and calculating the occurrence frequency of abnormal data generated by cloud services serving preset customer types, it realizes the active and rapid perception of online faults of cloud services of preset customer types, so that cloud service providers can perform rapid repair and timely stop losses.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of computer technologies, and particularly to a method and device for cloud service alarm for preset customers. Background Art

[0002] Large customers, also known as key customers, major customers, etc., refer to key customers who have a high consumption frequency, large consumption volume, and high customer profit margin for products or services and can have a certain impact on the business performance of an enterprise. Large customers are the main users of cloud products and contribute most of the revenue to cloud service providers. Cloud service providers need to provide stable online services for customers, especially large customers, to meet their service requirements. However, in some cases, such as network disconnection in the computer room or internal anomalies in cloud services, a large amount of online anomaly data will be generated, and the service may become unavailable. Currently, when the above situation occurs, cloud service providers generally learn about the anomaly by waiting for large customers to feedback the faults through the work order system or by phone.

[0003] Based on the method of learning about anomalies from large customer feedback, cloud service providers cannot timely perceive service anomaly problems to quickly follow up on problems and stop losses, which is likely to cause serious consequences and seriously affect the customer experience. Summary of the Invention

[0004] The embodiments of the present application propose a method and system for cloud service alarm for preset customers.

[0005] In a first aspect, the embodiments of the present application provide a method for cloud service alarm for preset customers. The method includes: obtaining anomaly data generated during the operation of the cloud service; based on the anomaly data, identifying the customers served by the cloud service that generates the anomaly data and determining whether the customers belong to a preset customer type; in response to determining that the customers belong to the preset customer type and in response to determining that within a first preset time period, the number of times of the anomaly data generated by the cloud service exceeds a preset number threshold, sending an alarm message.

[0006] In some embodiments, the step of in response to determining that the customers belong to the preset customer type and in response to determining that within a first preset time period, the number of times of the anomaly data generated by the cloud service exceeds a preset number threshold, sending an alarm message includes: based on the customers belonging to the preset customer type, storing the time stamps of the generated anomaly data into an anomaly data list corresponding to the cloud service serving the customers; in response to determining that within the first preset time period, the numerical value of the number of time stamps stored in the anomaly data list exceeds a preset quantity threshold, sending an alarm message through a preset alarm channel, where the alarm channel is used to represent the communication method for sending the alarm message to the receiving party.

[0007] In some embodiments, before identifying the customers served by the cloud service that generates the abnormal data based on the abnormal data and determining whether the customers belong to a preset customer type, the method further includes: in response to identifying that the abnormal data belongs to preset abnormal data, deleting the acquired abnormal data.

[0008] In some embodiments, identifying the customers served by the cloud service that generates the abnormal data based on the abnormal data and determining whether the customers belong to a preset customer type includes: based on the abnormal data, acquiring customer identification information in the abnormal data; and determining whether the customers belong to the preset customer type based on the matching result between the customer identification information and the customer identification information in the preset customer type.

[0009] In some embodiments, the method further includes: based on the abnormal data, determining whether it is the first time to acquire the abnormal data within a second preset time period; the first acquisition is used to indicate that the same abnormal data has not been acquired before acquiring the abnormal data; and in response to determining that it is the first time to acquire the abnormal data, sending an abnormal prompt signal.

[0010] In a second aspect, an embodiment of the present application provides a cloud service alarm device for preset customers, where the device includes: an acquisition unit configured to acquire abnormal data generated during the operation of the cloud service; an identification unit configured to identify the customers served by the cloud service that generates the abnormal data based on the abnormal data and determine whether the customers belong to a preset customer type; and an alarm unit configured to send an alarm message in response to determining that the customers belong to the preset customer type and in response to determining that the number of times of the abnormal data generated by the cloud service exceeds a preset number threshold within a first preset time period.

[0011] In some embodiments, the alarm unit is further configured to store the time stamp of generating the abnormal data in an abnormal data list corresponding to the cloud service serving the customer based on the fact that the customer belongs to the preset customer type; and in response to determining that the numerical value of the number of time stamps stored in the abnormal data list exceeds a preset number threshold within a first preset time period, send an alarm message through a preset alarm channel, where the alarm channel is used to represent the communication method for sending the alarm message to the recipient.

[0012] In some embodiments, the device further includes: a filtering unit configured to delete the acquired abnormal data in response to identifying that the abnormal data belongs to preset abnormal data before identifying the customers served by the cloud service that generates the abnormal data based on the abnormal data and determining whether the customers belong to a preset customer type.

[0013] In some embodiments, the identification unit is further configured to obtain customer identification information in the abnormal data based on the abnormal data; and determine whether the customer belongs to a preset customer type based on the matching result between the customer identification information and the customer identification information in the preset customer type.

[0014] In some embodiments, the alarm unit is further configured to determine, based on the abnormal data, whether the abnormal data is obtained for the first time within a second preset time period; the first acquisition is used to indicate that the same abnormal data has not been obtained before obtaining the abnormal data; and in response to determining that the abnormal data is obtained for the first time, an abnormal prompt signal is sent.

[0015] In a third aspect, an embodiment of the present application provides a computer-readable medium, on which a computer program is stored, wherein when the program is executed by a processor, the method described in any implementation manner of the first aspect is implemented.

[0016] In a fourth aspect, an embodiment of the present application provides an electronic device, including: 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 first aspect.

[0017] For the cloud service alarm method and system for preset customers provided by the embodiments of the present application, first, abnormal data generated during the operation of the cloud service is obtained; then, based on the abnormal data, the customer served by the cloud service generating the abnormal data is identified, and it is determined whether the customer belongs to a preset customer type; then, in response to determining that the customer belongs to a preset customer type, and in response to determining that the number of times of abnormal data generated by the cloud service within a first preset time period exceeds a preset number threshold, an alarm message is sent. The technical solution of cloud service alarm provided by the present disclosure realizes the active and rapid perception of online faults of cloud services for preset customer types by collecting online abnormal data and calculating the occurrence frequency of abnormal data generated by cloud services serving preset customer types, so that cloud service providers can perform rapid repair and timely stop losses; moreover, it realizes the unified fault perception ability of cloud services, and can provide the online fault perception ability for the cloud service systems of each cloud service provider. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects, and advantages of the present application will become more apparent:

[0019] Figure 1 is an exemplary system architecture diagram to which an embodiment of the present application can be applied;

[0020] Figure 2 is a flowchart of an embodiment of the cloud service alarm method for preset customers according to the present application;

[0021] Figure 3 is a schematic diagram of an application scenario of a cloud service alarm method for a preset customer according to this embodiment;

[0022] Figure 4 is a flowchart of another embodiment of a cloud service alarm method for a preset customer according to this application;

[0023] Figure 5 is a structural diagram of an embodiment of a cloud service alarm device for a preset customer according to this application;

[0024] Figure 6 is a schematic structural diagram of a computer system suitable for implementing the embodiments of this application. Detailed implementation manners

[0025] The following further elaborates on this application with reference to the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are merely for explaining the relevant invention and not for limiting the invention. Additionally, it should be noted that for ease of description, only parts related to the relevant invention are shown in the drawings.

[0026] It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other. The following will elaborate on this application in detail with reference to the drawings and embodiments.

[0027] Figure 1 Illustrates an exemplary architecture 100 to which the cloud service alarm method or alarm device for a preset customer of this application can be applied.

[0028] As Figure 1 shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0029] The terminal devices 101, 102, 103 may be hardware devices or software that support network connections to provide various network services. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices that support network functions such as information interaction and network connection functions, including but not limited to smart phones, tablet computers, e-book readers, laptop computers, and desktop computers, etc. When the terminal devices 101, 102, 103 are software, they may be installed in the above-listed electronic devices. It may be implemented as, for example, multiple software or software modules for providing distributed services, or may also be implemented as a single software or software module. No specific limitation is made here.

[0030] Server 105 may be a server that provides various cloud services. For example, it is a server that provides cloud storage and cloud computing services to terminal devices 101, 102, and 103. The server can store or process various received data and feedback the processing results to the terminal devices.

[0031] It should be noted that the cloud service alarm method for a preset customer provided by the embodiments of the present disclosure can be executed by server 105. Correspondingly, the cloud service alarm device can be set in server 105. No specific limitation is made here.

[0032] It should be noted that the server can be hardware or software. When the server is hardware, it can be implemented as a distributed server cluster composed of multiple servers or as a single server. When the server is software, 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.

[0033] It should be understood, Figure 1 the numbers of the terminal devices and servers in are only illustrative. According to the implementation requirements, there can be any number of terminal devices and servers.

[0034] Continuing to refer to Figure 2 , a flowchart 200 of an embodiment of the cloud service alarm method according to the present application is shown, including the following steps:

[0035] Step 201: Obtain abnormal data generated during the operation of the cloud service.

[0036] In this embodiment, the cloud service is an increasing, using, and interacting mode of related services based on the Internet, and usually involves providing dynamically scalable and often virtualized resources through the Internet. Its cloud services can be various types of cloud services provided by cloud service providers based on customers' storage, computing, etc. requirements, including but not limited to public cloud and private cloud.

[0037] The public cloud is the most basic service. Multiple customers can share the system resources of a cloud service provider. They do not need to set up any equipment and allocate management personnel, and can enjoy professional Internet technology services. This is undoubtedly a good way to reduce costs for general entrepreneurs and small and medium-sized enterprises. The public cloud can be further divided into 3 categories, including SaaS (Software-as-a-Service), PaaS (Platform-as-a-Service), and IaaS (Infrastructure-as-a-Service).

[0038] A private cloud is a private cloud network established by large enterprises to balance industry privacy (such as in the finance and insurance industries) and customer privacy. Enterprises need to design data centers, networks, and storage devices themselves to have sufficient resources to ensure the normal operation of the private cloud.

[0039] In this embodiment, abnormal data is the abnormal data generated during the operation of cloud services due to failures. For example, when the network in the computer room providing cloud services is disconnected, a large amount of abnormal data such as network connection failures, data request failures, and data storage failures will occur when customers use the cloud service. Among them, the abnormal data includes but is not limited to: the URL (Uniform Resource Locator) of the abnormal data, the line number and column number of the abnormal data, the data interface that generates the abnormal data, and the stack information of the abnormal data.

[0040] In this embodiment, the gateway, browser, and APP (Application) directly interact with cloud service customers and can collect most of the abnormal data generated during the operation of cloud services. The execution entity of this embodiment (such as Figure 1 the server) can obtain the abnormal data generated during the operation of cloud services through the gateway, browser, and APP in the terminal device used by the customer in the form of abnormal reporting. Among them, the gateway includes a console gateway and an API (Application Programming Interface) gateway. When abnormal data is generated during the operation of cloud services, the gateway, browser, APP, etc. can receive the abnormal data of the cloud service, request the abnormal handling service asynchronously, and report the abnormality to the execution entity. After the gateway, browser, APP, etc. report the abnormality, the execution entity obtains the abnormal data during the operation of cloud services.

[0041] Step 202: Based on the abnormal data, identify the customers served by the cloud service that generates the abnormal data, and determine whether the customers belong to a preset customer type.

[0042] In this embodiment, the preset customer type is the customer type preset by the cloud service according to its own needs, and it can be important customers who contribute most of the revenue to the cloud service provider.

[0043] In this embodiment, the abnormal data includes the data interface that generates the abnormal data and the customer identification information of the customer served by the cloud service. The customer identification information is used to uniquely identify the customer served by the cloud service provider. Based on the customer identification information, the customer corresponding to the customer identification information can be obtained. Through the data interface that generates the abnormal data, the type of cloud service to which the abnormal data belongs can be obtained. Among them, the cloud service type can be divided based on the cloud service products launched by the cloud service provider. For example, product types such as virtual machines and network EIPs (Enterprise Information Portals) launched by the cloud service provider.

[0044] In some alternative embodiments of this embodiment, the following method can be used to determine whether a customer belongs to a preset customer type: based on the abnormal data, obtain the customer identification information in the abnormal data; based on the matching result between the customer identification information and the customer identification information in the preset customer type, determine whether the customer belongs to the preset customer type. When the customer identification information matches the customer identification information in the preset customer type, it can be determined that the customer belongs to the preset customer type.

[0045] Step 203: In response to determining that the customer belongs to the preset customer type, and in response to determining that the number of times of abnormal data generated by the cloud service exceeds the preset number threshold within the first preset time period, send an alarm message.

[0046] In this embodiment, the first preset time period is a first preset time period starting from a certain historical moment and ending at the current moment. Its time length is specifically set according to the cloud service type and will not be limited here. In some alternative embodiments, the first preset time period can be set in the form of a sliding time window. Specifically, a preset duration can be used as the duration of the first preset time period, and the current moment is used as the cut-off moment to determine the start moment of the sliding time window. For example, the time length of the sliding time window for cloud service type A is set to 100 s, and the minimum time unit recognizable by the sliding time window is set to seconds; if the current moment is 11:08:40 on September 17, 2019, the cut-off moment of the sliding time window is 11:08:50 on September 17, 2019, and its start moment is 11:07:10 on September 17, 2019; as the current moment becomes 11:18:50 on September 17, 2019, the cut-off moment of the sliding time window becomes 11:18:50 on September 17, 2019, and its start moment becomes 11:17:10 on September 17, 2019. In this way, the execution entity can calculate the occurrence frequency of the abnormal data in real time.

[0047] In this embodiment, the preset quantity threshold can be specifically set according to the cloud service type for preset customer types of services and the online access volume of the cloud service type, and no limitation is imposed here. For example, if the average online access volume a of cloud service type A is greater than the average online access volume b of cloud service type B, then correspondingly, the preset quantity threshold for cloud service type A can be greater than the preset quantity threshold for cloud service type B. The preset quantity threshold is a reference threshold for sending alarm information. Therefore, by setting corresponding alarm reference thresholds for the online access volume of cloud services, accurate and more valuable alarm information can be achieved.

[0048] In some alternative embodiments of this embodiment, after the above step of sending an alarm message in response to determining that the customer belongs to a preset customer type and in response to determining that the number of abnormal data generated by the cloud service exceeds the preset number threshold within the first preset time period, the method of this embodiment may further include: in response to reaching the preset update time, updating the preset quantity threshold based on the change in the access volume of the cloud service type.

[0049] During the operation of the cloud service, due to the business expansion and the growth of the business volume of the customer, the online access volume of the cloud service may change. At this time, by updating the preset quantity threshold for this cloud service type, the updated preset quantity threshold can be made more compatible with the current online access volume.

[0050] In this embodiment, the alarm information can be sent in a preset alarm format, and the alarm information may include, for example, but not limited to, information indicating at least one of the following: the recipient of the alarm information, the abnormal occurrence time period, the number of abnormal occurrences, and the abnormal data information finally obtained within the first preset time period.

[0051] The execution entity of this embodiment will count the abnormal data generated by a certain cloud service type for preset customer types of services based on the cloud service type, and analyze the occurrence frequency of the abnormal data generated by the cloud service type according to the first preset time period, that is, determine that the number of abnormal data generated by the cloud service type exceeds the preset quantity threshold within the first preset time period, and the execution entity sends an alarm message to the recipient of the alarm information, where the recipient of the alarm information can be the maintenance personnel of the cloud service provider.

[0052] In some alternative embodiments of the present embodiment, the occurrence frequency of abnormal data can be calculated in the following manner: Based on the cloud service type serving the preset customer type, an abnormal data list corresponding to the cloud service type of the preset customer type is preset. The execution entity stores the timestamps of the generated abnormal data into the abnormal data list corresponding to the cloud service type to which the abnormal data belongs according to the time progress. Thus, the number of timestamps in the abnormal data list is the number of abnormal data generated by the cloud service type corresponding to the abnormal data list. In response to determining that within the first preset time period, the numerical value of the number of timestamps stored in the abnormal data list exceeds the preset quantity threshold, an alarm message is sent through a preset alarm channel, where the alarm channel is used to represent the communication method for sending the alarm message to the recipient, such as various real-time communication applications, emails, text messages, phone calls, and other communication methods.

[0053] In this embodiment, after sending the alarm message, the corresponding maintenance personnel of the cloud service provider can perform abnormal handling on the alarm message. Since the obtained abnormal data has no further use value, considering saving storage space and improving operation performance, all the abnormal data generated by the obtained cloud service type can be deleted.

[0054] Similarly, in response to determining that within the first preset time period, if the number of abnormal data generated by the cloud service type does not exceed the preset quantity threshold, and the first preset time period is set in the form of a sliding time window, the abnormal data outside the first preset time period can be deleted. Because the sliding time window always takes the current moment as the cut-off moment and slides with the change of the current moment, that is, the abnormal data outside the sliding time window has already had its occurrence frequency calculated. On the premise that the number of abnormal data generated by the cloud service type does not exceed the preset quantity threshold, the abnormal data outside the sliding time window has no further use value.

[0055] In this embodiment, the execution entity obtains the abnormal data generated during the operation of the cloud service in real time, and calculates the occurrence frequency of the abnormal data in real time for the preset customer type served by the cloud service type that generates the abnormal data, achieving the active and rapid perception of the online faults of the cloud service for the preset customer type, so that the cloud service provider can perform quick repair and timely stop losses; moreover, it realizes the unified fault perception ability of the cloud service, and can provide the online fault perception ability for the cloud service systems of each cloud service provider.

[0056] Figure 3An application scenario of the cloud service alarm method for a preset customer according to this embodiment is schematically shown. A cloud service provider 301 provides cloud services for many customers, including customer 302 and customer 303. Among them, customer 303 is a preset customer type of the cloud service provider 301. The cloud service type provided by the cloud service provider 301 for customer 302 is virtual machine service, and the cloud service types provided for customer 303 are virtual machine service and network EIP service. While the server of the cloud service provider 301 provides services for customer 302 and customer 303, it collects abnormal data generated during the operation of the cloud service in real time through the browsers, gateways, and application programs used by customer 302 and customer 303. Through the analysis of the abnormal data, it is identified that the cloud service type to which the abnormal data belongs is the network EIP service provided for customer 303, it is determined that customer 303 is a preset customer type, and the occurrence frequency of the abnormal data generated for the network EIP service of customer 303 within the first preset time period is calculated. It is determined that the number of abnormal data generated by the network EIP service within the first preset time period exceeds the preset quantity threshold, and an alarm message is sent to the maintenance staff 304 of the cloud service provider.

[0057] Continuing to refer to Figure 4 , a schematic process 400 of another embodiment of the cloud service alarm method according to this application is shown, including the following steps:

[0058] Step 401: Obtain the abnormal data generated during the operation of the cloud service.

[0059] In this embodiment, step 401 is executed in a manner similar to step 201, and will not be elaborated here.

[0060] Step 402: In response to identifying that the abnormal data belongs to a preset abnormal type, delete the obtained abnormal data.

[0061] In this embodiment, the preset abnormal type is used to represent the abnormal data that does not need to be counted during the cloud service alarm process. The preset abnormal type includes but is not limited to the abnormal data type generated due to incorrect customer parameter input, the abnormal data type generated due to the customer not undergoing real-name authentication, and the abnormal data type generated due to the customer not having service permission enabled.

[0062] When the abnormal data belongs to the preset abnormal type, it indicates that the abnormal data is not generated due to the abnormality of the cloud service itself. Such abnormal data should be filtered during the alarm analysis. After filtering the abnormal data by the preset abnormal type, all the abnormal data for which alarms are made are the abnormal data generated due to the abnormality of the cloud service itself. In this way, the calculation result of the frequency of the abnormal data is more accurate, and the alarm message is more valuable for reference.

[0063] Step 403: Based on the abnormal data, identify the customers served by the cloud service that generated the abnormal data, and determine whether the customers belong to a preset customer type.

[0064] In this embodiment, Step 403 is executed in a manner similar to Step 202, which will not be elaborated here.

[0065] Step 404: Based on the abnormal data, determine whether it is the first time to obtain the abnormal data within a second preset time period; in response to determining that it is the first time to obtain the abnormal data, send an abnormal prompt signal.

[0066] In this embodiment, the first acquisition is used to indicate that the same abnormal data has not been obtained before obtaining the abnormal data.

[0067] In this embodiment, the second preset time period is set according to the repair time of the abnormal data by the recipient of the abnormal prompt signal. The time length of the second preset time period is specifically set according to the abnormal data and the required repair time, which will not be limited here. After receiving the abnormal prompt signal, the recipient of the abnormal prompt signal should repair the abnormal signal. The repair process requires a certain repair time period. During the repair time period, an abnormal prompt signal has been sent to the recipient when the abnormal data is first obtained, and there is no need to send an abnormal prompt signal for the subsequent received non-first-obtained abnormal data.

[0068] In some alternative embodiments, an abnormal database can be established based on the first-obtained abnormal data. The first-obtained abnormal data is stored in the abnormal database, and it is determined whether it is the first time to obtain the abnormal data according to the comparison result between the obtained abnormal data and the abnormal data in the abnormal database.

[0069] Step 405: In response to determining that the customer belongs to a preset customer type, and in response to determining that the number of times of abnormal data generated by the cloud service exceeds a preset number threshold within a first preset time period, send an alarm message.

[0070] In this embodiment, Step 405 is executed in a manner similar to Step 203, which will not be elaborated here.

[0071] From Figure 4 it can be seen that compared with the corresponding embodiment of Figure 2 the process 400 of the cloud service alarm method in this embodiment specifically describes the filtering of abnormal data before identifying the cloud service type for the abnormal data, and the abnormal prompt when the abnormal data is first obtained. After filtering the abnormal data by the preset abnormal type, all the abnormal data for which an alarm is made is the abnormal data generated due to the abnormality of the cloud service itself. In this way, the calculation result of the frequency of the abnormal data is more accurate, and the alarm message is more valuable for reference. The abnormal prompt when the abnormal data is first obtained facilitates timely discovery of problems for quick repair.

[0072] Continue to refer to Figure 5 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a cloud service alarm device, which corresponds to the method embodiment shown in Figure 2 and can be specifically applied to various electronic devices.

[0073] As shown in Figure 5 , the cloud service alarm device includes: an acquisition unit 501, a filtering unit 502, an identification unit 503, and an alarm unit 504.

[0074] The acquisition unit 501 is configured to acquire abnormal data generated during the operation of the cloud service. The filtering unit 502 is configured to delete the acquired abnormal data in response to identifying that the abnormal data belongs to a preset abnormal type. The identification unit 503 is configured to identify the customer served by the cloud service that generates the abnormal data based on the abnormal data, and determine whether the customer belongs to a preset customer type. The alarm unit 504 is configured to send an alarm message in response to determining that the customer belongs to a preset customer type and in response to determining that the number of times of abnormal data generated by the cloud service exceeds a preset number threshold within a first preset time period.

[0075] In this embodiment, the alarm unit 504 is further configured to store the timestamp of the generated abnormal data in an abnormal data list corresponding to the cloud service serving the customer based on the fact that the customer belongs to a preset customer type; in response to determining that the numerical value of the timestamps stored in the abnormal data list exceeds a preset number threshold within a first preset time period, send an alarm message through a preset alarm channel, where the alarm channel is used to represent the communication method for sending the alarm message to the recipient.

[0076] In this embodiment, the alarm unit 504 is also configured to determine whether it is the first time to acquire abnormal data within a second preset time period based on the abnormal data; the first acquisition is used to represent that the same abnormal data has not been acquired before acquiring the abnormal data; in response to determining that it is the first time to acquire abnormal data, send an abnormal prompt signal.

[0077] In this embodiment, the identification unit 503 is further configured to acquire customer identification information in the abnormal data based on the abnormal data; determine whether the customer belongs to a preset customer type based on the matching result between the customer identification information and the customer identification information in the preset customer type

[0078] Next, refer to Figure 6 , which shows a schematic structural diagram of a computer system 600 of a device (such as the devices 101, 102, 103, 105 shown in Figure 1 ) suitable for implementing the embodiments of the present application. Figure 6The device shown is only an example and should not impose any limitations on the functions and scope of use of the embodiments of the present application.

[0079] As Figure 6 shown, the computer system 600 includes a processor (e.g., CPU, central processing unit) 601, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 602 or the program loaded from the storage section 608 into the random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The processor 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. The input / output (I / O) interface 605 is also connected to the bus 604.

[0080] The following components are connected to the I / O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as needed. A removable medium 611, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 610 as needed so that a computer program read from it can be installed into the storage section 608 as needed.

[0081] Specifically, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes program code for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network via the communication section 609 and / or installed from the removable medium 611. When the computer program is executed by the processor 601, the above functions defined in the method of the present application are executed.

[0082] It should be noted that the computer-readable medium of the present application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can 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 the present application, the computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can 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 can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0083] The computer program code for performing the operations of the present application can be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages - such as Java, Smalltalk, C++, and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code can be executed entirely on the client computer, partially on the client computer, executed as an independent software package, partially on the client computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the client computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or, alternatively, can be connected to an external computer (e.g., by using an Internet service provider to connect through the Internet).

[0084] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of apparatuses, methods, and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, or they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0085] The units described in the embodiments of the present application can be implemented in software or in hardware. The described units can also be provided in a processor. For example, it can be described as a processor including an acquisition unit, a filtering unit, an identification unit, and an alarm unit. Among them, the names of these units do not constitute a limitation on the units themselves in some cases. For example, the acquisition unit can also be described as a unit for "acquiring abnormal data generated during the operation of the cloud service".

[0086] On the other hand, the present application also provides a computer-readable medium, which can be included in the device described in the above embodiments; or can exist separately without being assembled into the device. The above computer-readable medium carries one or more programs. When the one or more programs are executed by the device, the computer device is caused to: acquire abnormal data generated during the operation of the cloud service; based on the abnormal data, identify the customers served by the cloud service that generated the abnormal data, and determine whether the customers belong to a preset customer type; in response to determining that the customers belong to the preset customer type, and in response to determining that the number of times of abnormal data generated by the cloud service exceeds a preset number threshold within a first preset time period, send an alarm message.

[0087] The above description is only a preferred embodiment of the present application and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features having similar functions disclosed in the present application.

Claims

1. A cloud service alarm method for a preset customer, wherein, The method includes: Obtaining abnormal data generated during the operation of the cloud service; Based on the matching result between the customer identification information in the abnormal data and the customer identification information in the preset customer type, determining whether the customer served by the cloud service that generated the abnormal data belongs to the preset customer type; In response to determining that the customer belongs to the preset customer type, and in response to determining that within a first preset time period, the number of times of the abnormal data generated by the cloud service exceeds a preset number threshold, sending an alarm message.

2. The method according to claim 1, wherein The step of, in response to determining that the customer belongs to the preset customer type, and in response to determining that within a first preset time period, the number of times of the abnormal data generated by the cloud service exceeds a preset number threshold, sending an alarm message, includes: Based on the fact that the customer belongs to the preset customer type, storing the timestamp of the generated abnormal data into an abnormal data list corresponding to the cloud service serving the customer; In response to determining that within a first preset time period, the numerical value of the timestamps stored in the abnormal data list exceeds a preset quantity threshold, sending an alarm message through a preset alarm channel, where the alarm channel is used to represent the communication method for sending the alarm message to the receiving party.

3. The method according to claim 1, wherein, Before the step of, based on the abnormal data, identifying the customer served by the cloud service that generated the abnormal data and determining whether the customer belongs to the preset customer type, the method further includes: In response to identifying that the abnormal data belongs to the preset abnormal data, deleting the obtained abnormal data.

4. The method according to claim 1, wherein The method further includes: Based on the abnormal data, determining whether it is the first time to obtain the abnormal data within a second preset time period; the first obtaining is used to represent that the same abnormal data has not been obtained before obtaining the abnormal data; In response to determining that it is the first time to obtain the abnormal data, sending an abnormal prompt signal.

5. A cloud service alarm device for a preset customer, wherein, The device includes: An obtaining unit, configured to obtain abnormal data generated during the operation of the cloud service; An identifying unit, configured to determine whether the customer served by the cloud service that generated the abnormal data belongs to the preset customer type based on the matching result between the customer identification information in the abnormal data and the customer identification information in the preset customer type; An alarm unit, configured to send an alarm message in response to determining that the customer belongs to the preset customer type, and in response to determining that within a first preset time period, the number of times of the abnormal data generated by the cloud service exceeds a preset number threshold.

6. The device according to claim 5, wherein The alarm unit is further configured to store the timestamp of the generated abnormal data into an abnormal data list corresponding to the cloud service serving the customer based on the fact that the customer belongs to the preset customer type; In response to determining that within a first preset time period, the numerical value of the timestamps stored in the abnormal data list exceeds a preset quantity threshold, sending an alarm message through a preset alarm channel, where the alarm channel is used to represent the communication method for sending the alarm message to the receiving party.

7. The apparatus according to claim 5, wherein, The device further includes: A filtering unit, configured to delete the acquired abnormal data in response to identifying that the abnormal data belongs to preset abnormal data, before identifying, based on the abnormal data, a customer served by a cloud service that generates the abnormal data and determining whether the customer belongs to a preset customer type.

8. The apparatus according to claim 5, wherein the alarm unit is further configured to determine, based on the abnormal data, whether the abnormal data is acquired for the first time within a second preset time period; the first acquisition is used to indicate that the same abnormal data has not been acquired before the acquisition of the abnormal data; and in response to determining that the abnormal data is acquired for the first time, an abnormal prompt signal is sent.

9. 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 1-4 is implemented.

10. An electronic device, comprising: one or more processors; a storage device having stored thereon one or more programs, 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 1-4.

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