CN110727563B - Cloud service alarm method and device for preset customers - Google Patents
Cloud service alarm method and device for preset customers Download PDFInfo
- Publication number
- CN110727563B CN110727563B CN201910966878.3A CN201910966878A CN110727563B CN 110727563 B CN110727563 B CN 110727563B CN 201910966878 A CN201910966878 A CN 201910966878A CN 110727563 B CN110727563 B CN 110727563B
- Authority
- CN
- China
- Prior art keywords
- abnormal data
- preset
- cloud service
- customer
- response
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/32—Monitoring with visual or acoustical indication of the functioning of the machine
- G06F11/324—Display of status information
- G06F11/327—Alarm or error message display
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/079—Root cause analysis, i.e. error or fault diagnosis
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/0793—Remedial or corrective actions
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Alarm Systems (AREA)
- Debugging And Monitoring (AREA)
Abstract
本申请实施例公开了一种预设客户的云服务报警方法，首先，获取云服务运行过程中产生的异常数据；然后，基于异常数据，识别产生异常数据的云服务所服务的客户，并确定客户是否属于预设客户类型；然后，响应于确定客户属于预设客户类型，并且响应于确定在第一预设时间段内，云服务产生的异常数据的次数超过预设次数阈值，发送报警信息。本公开的实施例可应用于云计算领域，通过采集线上异常数据，针对服务预设客户类型的云服务产生的异常数据，计算其发生频率，实现了对预设客户类型的云服务线上故障的主动、快速地感知，以便云服务商进行快速修复和及时止损。
The embodiment of the present application discloses a cloud service alarm method for preset customers. 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 the 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 abnormal data generated by the cloud service exceeds the preset number threshold within the first preset time period, an alarm message is sent. The embodiment of the present disclosure can be applied to the field of cloud computing. By collecting online abnormal data, the abnormal data generated by the cloud service that serves the preset customer type is calculated, and its occurrence frequency is calculated, so as to realize the active and rapid perception of online faults of the cloud service of the preset customer type, so that the cloud service provider can quickly repair and stop the loss in time.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a cloud service alarm method and device for preset clients.
Background
The large clients are also called key clients, main clients and the like, and refer to key clients which have high product or service consumption frequency, large consumption amount and high client profit margin and can have a certain influence on enterprise operation performance. The large clients are main users of the products on the cloud, and contribute most of revenue for cloud service providers. Cloud service providers need to provide stable online services for customers, especially large customers, to meet the customer's service needs. However, in some cases, such as disconnection of a machine room or internal abnormality of cloud service, a large amount of online abnormal data may be generated, and service unavailability may occur. Currently, when the situation occurs, the cloud service provider generally obtains the abnormal situation by waiting for the large client to perform fault feedback through the work order system or telephone contact.
Based on the mode of knowing the abnormal condition by the feedback of the large client, the cloud service provider cannot timely sense the service abnormal problem so as to quickly follow up the problem and stop the damage, thereby easily causing serious consequences and seriously affecting the client experience.
Disclosure of Invention
The embodiment of the application provides a cloud service alarm method and a cloud service alarm system for a preset client.
The embodiment of the application provides a cloud service alarm method for a preset client, wherein the method comprises the steps of obtaining abnormal data generated in the cloud service operation process, identifying the client served by the cloud service generating the abnormal data based on the abnormal data, determining whether the client belongs to a preset client type, responding to the fact that the client belongs to the preset client type, responding to the fact that the number of times of the abnormal data generated by the cloud service exceeds a preset number threshold in a first preset time period, and sending alarm information.
In some embodiments, the method comprises the steps of responding to the fact that the client belongs to a preset client type, responding to the fact that the number of times of abnormal data generated by cloud service exceeds a preset number threshold value within a first preset time period, and sending alarm information, wherein the alarm channel is used for representing a communication mode of sending the alarm information to a receiver, and the time stamp of the abnormal data is stored in an abnormal data list corresponding to the cloud service of the service client based on the fact that the client belongs to the preset client type.
In some embodiments, before identifying a customer served by the cloud service that generated the anomaly data based on the anomaly data and determining whether the customer belongs to a preset customer type, the method further includes deleting the acquired anomaly data in response to identifying that the anomaly data belongs to the preset anomaly data.
In some embodiments, the method for identifying the client served by the cloud service generating the abnormal data based on the abnormal data and determining whether the client belongs to the preset client type comprises the steps of acquiring client identification information in the abnormal data based on the abnormal data and determining whether the client belongs to the preset client type based on a matching result of the client identification information and the client identification information in the preset client type.
In some embodiments, the method further comprises determining whether the first acquisition of the anomaly data is within a second preset time period based on the anomaly data, first acquisition of the anomaly data to indicate that the same anomaly data has not been acquired before the acquisition of the anomaly data, and transmitting an anomaly prompt signal in response to determining that the anomaly data has been acquired for the first time.
In a second aspect, an embodiment of the present application provides a cloud service alarm device for a preset client, where the device includes an acquisition unit configured to acquire abnormal data generated during operation of a cloud service, an identification unit configured to identify a client served by the cloud service generating the abnormal data based on the abnormal data and determine whether the client belongs to the preset client type, and an alarm unit configured to send alarm information in response to determining that the client belongs to the preset client type and in response to determining that a number of times of the abnormal data generated by the cloud service exceeds a preset number of times threshold within a first preset time period.
In some embodiments, the alarm unit is further configured to store a timestamp of generating the abnormal data to an abnormal data list corresponding to the cloud service of the service client based on the client belonging to the preset client type, and send the alarm information through a preset alarm channel in response to determining that the number value of the timestamps stored in the abnormal data list exceeds a preset number threshold in a first preset time period, wherein the alarm channel is used for representing a communication mode of sending the alarm information to the receiver.
In some embodiments, the apparatus further comprises a filtering unit configured to delete the acquired anomaly data in response to identifying that the anomaly data belongs to the preset anomaly data before identifying a customer served by the cloud service that generated the anomaly data based on the anomaly data and determining whether the customer belongs to the preset customer type.
In some embodiments, the identification unit is further configured to obtain the client identification information in the anomaly data based on the anomaly data, and determine whether the client belongs to the preset client type based on a matching result of the client identification information and the client identification information in the preset client type.
In some embodiments, the alarm unit is further configured to determine, based on the anomaly data, whether the anomaly data is first acquired within a second preset time period, first acquire the anomaly data to indicate that the same anomaly data has not been acquired before the anomaly data is acquired, and send an anomaly prompt signal in response to determining that the anomaly data is first acquired.
In a third aspect, embodiments of the present application provide a computer readable medium having a computer program stored thereon, wherein the program when executed by a processor implements a method as described in any of the implementations of the first aspect.
In a fourth aspect, an embodiment of the present application provides an electronic device, including one or more processors, and a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement a method as described in any of the implementations of the first aspect.
The cloud service alarm method and system for the preset client provided by the embodiment of the application comprise the steps of firstly obtaining abnormal data generated in the cloud service operation process, then identifying the client served by the cloud service generating the abnormal data based on the abnormal data, determining whether the client belongs to the preset client type, then responding to the determination that the client belongs to the preset client type, responding to the determination that the number of times of the abnormal data generated by the cloud service exceeds a preset number threshold value in a first preset time period, and sending alarm information. According to the technical scheme of cloud service alarm, through collecting online abnormal data and aiming at the abnormal data generated by cloud service of a preset customer type, the occurrence frequency of the abnormal data is calculated, so that active and rapid sensing of faults on the cloud service line of the preset customer type is realized, cloud service providers can repair the faults rapidly and stop the faults timely, uniform fault sensing capability of cloud service is realized, and online fault sensing capability can be provided for cloud service systems of all cloud service providers.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which an embodiment of the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a cloud service alerting method of a preset customer in accordance with the present application;
fig. 3 is a schematic diagram of an application scenario of a cloud service alerting method of a preset client according to the present embodiment;
FIG. 4 is a flow chart of yet another embodiment of a cloud service alerting method of a preset customer in accordance with the present application;
FIG. 5 is a block diagram of one embodiment of a cloud service alerting device of a preset customer in accordance with the present application;
FIG. 6 is a schematic diagram of a computer system suitable for use in implementing embodiments of the present application.
Detailed Description
The application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be noted that, for convenience of description, only the portions related to the present application are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other. The application will be described in detail below with reference to the drawings in connection with embodiments.
Fig. 1 illustrates an exemplary architecture 100 of a cloud service alerting method or alerting device of a preset customer to which the present application may be applied.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide 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, among others.
The terminal devices 101, 102, 103 may be hardware devices or software supporting network connections to provide various network services. When the terminal device 101, 102, 103 is hardware, it may be various electronic devices supporting network functions such as information interaction, network connection functions, etc., including but not limited to smart phones, tablet computers, electronic book readers, laptop and desktop computers, etc. When the terminal devices 101, 102, 103 are software, they can be installed in the above-listed electronic devices. It may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.
The server 105 may be a server providing various cloud services, such as a server providing cloud storage and cloud computing services to the terminal devices 101, 102, 103. The server can store or process the received various data and feed back the processing result to the terminal equipment.
It should be noted that, the cloud service alarm method of the preset client provided by the embodiment of the present disclosure may be executed by the server 105. Accordingly, the cloud service alerting device may be provided in the server 105. The present invention is not particularly limited herein.
It should be noted that, the server may be hardware, or may be software. When the server is hardware, the server may be implemented as a distributed server cluster formed by a plurality of servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of software or software modules, for example, for providing distributed services, or as a single software or software module. The present invention is not particularly limited herein.
It should be understood that the number of terminal devices and servers in fig. 1 is merely illustrative. There may be any number of terminal devices and servers, as desired for implementation.
With continued reference to fig. 2, a flow 200 of one embodiment of a cloud service alerting method in accordance with the present application is shown, comprising the steps of:
step 201, acquiring abnormal data generated in the cloud service operation process.
In this embodiment, cloud services are an addition, use and interaction model of internet-based related services, generally involving providing dynamically extensible and often virtualized resources over the internet. The Cloud service may be various types of Cloud services provided by Cloud service providers for the Cloud service providers based on requirements of customer storage, computing and the like, including but not limited to Public clouds (Public clouds) and Private clouds (Private clouds).
Public cloud is the most basic service, a plurality of clients can share the system resource of a cloud service provider, and can enjoy professional internet technical service without erecting any equipment and equipment management personnel, which is certainly a good method for reducing cost for general creators and middle and small enterprises. Public clouds can also be subdivided into 3 categories, including SaaS (Software-as-a-Service), paaS (Platform-as-a-Service), iaaS (Infrastructure-as-a-Service).
The private cloud is a private cloud network erected by a large enterprise for considering privacy of industries (such as finance and insurance industries) and privacy of clients, and the enterprise needs to design a data center, a network and storage equipment by itself so as to have enough resources to ensure that the private cloud operates normally.
In this embodiment, the abnormal data is abnormal data generated by a fault in the operation process of the cloud service, for example, when a machine room providing the cloud service is disconnected from the network, a large amount of abnormal data such as network connection failure, data request failure, data storage failure and the like may occur when a client uses the cloud service. The abnormal data includes, but is not limited to, URL (Uniform Resource Locator ) of the abnormal data, line number and column number of the abnormal data, data interface for generating the abnormal data and stack information of the abnormal data.
In this embodiment, the gateway, browser and APP (Application program) interact directly with the cloud service client, so that most of abnormal data generated in the cloud service running process can be collected. The execution body (such as the server in fig. 1) of the embodiment may acquire, by adopting an exception reporting manner, exception data generated in the cloud service running process through a gateway, a browser and an APP in a terminal device applied by a client, where the gateway includes a console gateway and an API (Application Programming Interface ) gateway. When abnormal data is generated in the cloud service running process, the gateway, the browser, the APP and the like can receive the abnormal data of the cloud service, request the abnormal processing service in an asynchronous mode and report the abnormality to the execution body. After the gateway, the browser, the APP and the like report the abnormality, the execution main body acquires the abnormal data in the cloud service running process.
Step 202, identifying clients served by the cloud service generating the abnormal data based on the abnormal data, and determining whether the clients belong to a preset client type.
In this embodiment, the preset client type is a client type preset by the cloud service according to its own needs, and may be an important client contributing most of revenue for the cloud service provider.
In this embodiment, the abnormal data includes a data interface for generating the abnormal data and customer identification information of a customer served by the cloud service. The client identification information is used for uniquely identifying clients served by the cloud service provider, the clients corresponding to the client identification information can be obtained according to the client identification information, and the cloud service type of the abnormal data can be obtained through a data interface for generating the abnormal data. The cloud service types may be classified based on cloud service products proposed by a cloud service provider, such as product types of virtual machines, network EIP (ENTERPRISE INFORMATION PORTAL ) and the like proposed by the cloud service provider.
In some alternative implementations of the present embodiment, the determining whether the client belongs to the preset client type may include obtaining client identification information in the exception data based on the exception data, and determining whether the client belongs to the preset client type based on a result of matching the client identification information with the client identification information in the preset client type. When the client identification information matches the client identification information in the preset client type, it may be determined that the client belongs to the preset client type.
Step 203, in response to determining that the client belongs to a preset client type, and in response to determining that the number of times of abnormal data generated by the cloud service exceeds a preset number threshold value in a first preset time period, alarm information is sent.
In this embodiment, the first preset time period is a first preset time period with a certain historical time as a starting time and a current time as a ending time, and the time length is specifically set according to the cloud service type, which is not limited herein. In some alternative embodiments, the first preset time period may be set in the manner of a sliding time window. Specifically, a preset duration may be taken as the duration of the first preset time period, and the current time is taken as the cut-off time, so as to determine the starting time of the sliding time window. For example, the time length of the sliding time window for the cloud service type a is set to 100s, and the minimum time unit identifiable by the sliding time window is set to seconds, if the current time is 2019 9 month 17 day 11 time 08 minutes 40 seconds, the cut-off time of the sliding time window is 2019 month 17 day 11 time 08 minutes 50 seconds, the start time thereof is 2019 month 17 day 11 time 07 minutes 10 seconds, and as the current time becomes 2019 month 17 day 11 time 18 minutes 50 seconds, the cut-off time of the sliding time window becomes 2019 month 17 day 11 time 18 minutes 50 seconds, and the start time thereof becomes 2019 month 17 day 11 time 17 minutes 10 seconds. Thus, the execution subject can calculate the occurrence frequency of the abnormal data in real time.
In this embodiment, the preset number threshold may be specifically set according to a cloud service type of a preset customer type of service and an online access amount of the cloud service type, which is not limited herein. For example, the average online access amount a of the cloud service type a is greater than the average online access amount B of the cloud service type B, and accordingly, the preset number threshold for the cloud service type a may be greater than the preset number threshold for the cloud service type B. The preset quantity threshold is a reference threshold for sending alarm information, so that an accurate alarm with a higher reference value is realized by setting a corresponding alarm reference threshold for the online access quantity of the cloud service.
In some optional implementations of the present embodiment, after the step of sending the alarm information in response to determining that the client belongs to the preset client type and in response to determining that the number of times of the abnormal data generated by the cloud service exceeds the preset number threshold in the first preset period of time, the method of the present embodiment may further include updating the preset number threshold based on a change in the access amount of the cloud service type in response to reaching a preset update time.
In the cloud service operation process, the service expansion and the service volume increase of the client may cause the variation of the access volume on the cloud service line. At this time, by updating the preset number threshold for the cloud service type, the updated preset number threshold can be more matched with the current online access amount.
In this embodiment, the alarm information may be sent in a preset alarm format, and the alarm information may include, for example, but not limited to, information for indicating at least one of an alarm information receiving party, an abnormal occurrence time period, the number of abnormal occurrences, and abnormal data information last acquired in a first preset time period.
The executing body of the embodiment counts abnormal data generated by a certain cloud service type of a service preset client type based on the cloud service type, and calculates occurrence frequency analysis of the abnormal data generated by the cloud service type according to a first preset time period, namely, determines that the number of the abnormal data generated by the cloud service type exceeds a preset number threshold in the first preset time period, and the executing body sends alarm information to a receiving party of the alarm information, wherein a receiving party of the alarm information can be maintenance personnel of a cloud service provider.
In some optional implementations of the present embodiment, the occurrence frequency of the abnormal data may be calculated by presetting an abnormal data list corresponding to a cloud service type of a service preset client type based on the cloud service type of the service preset client type. The execution main body stores the time stamp for generating the abnormal data into an abnormal data list corresponding to the cloud service type to which the abnormal data belongs according to the time schedule, and the number of the time stamps in the abnormal data list is the number of the abnormal data generated by the cloud service type corresponding to the abnormal data list. And in response to determining that the number value of the timestamps stored in the abnormal data list exceeds a preset number threshold in a first preset time period, sending alarm information through a preset alarm channel, wherein the alarm channel is used for representing communication modes of sending the alarm information to a receiver, such as communication modes of various real-time communication application programs, mails, short messages, telephones and the like.
In this embodiment, after the alarm information is sent, the corresponding maintainer of the cloud service provider may perform exception processing on the alarm information, and the obtained exception data has no use value, so that all exception data generated by the obtained cloud service type may be deleted in consideration of saving storage space and improving running performance.
Similarly, in response to determining that the number of abnormal data generated by the cloud service type does not exceed the preset number threshold in the first preset time period, if the setting of the first preset time period adopts a sliding time window mode, abnormal data outside the first preset time period may be deleted. Because the sliding time window always takes the current time as the cut-off time and slides along with the change of the current time, that is, the occurrence frequency of the abnormal data is calculated by the abnormal data outside the sliding time window, on the premise that the quantity of the abnormal data generated by the cloud service type does not exceed the preset quantity threshold value, the abnormal data outside the sliding time window has no utilization value.
In the embodiment, the execution main body acquires the abnormal data generated in the cloud service operation process in real time, calculates the occurrence frequency of the abnormal data in real time according to the preset client type served by the cloud service type generating the abnormal data, realizes the initiative and quick perception of the fault on the cloud service line of the preset client type, so that cloud service providers can repair the fault quickly and stop the damage timely, realizes the unified fault perception capability of the cloud service, and can provide the fault perception capability on the line for the cloud service systems of all cloud service providers.
Fig. 3 schematically shows an application scenario of the cloud service alerting method of the preset client according to the present embodiment. Cloud facilitator 301 provides cloud services for a number of customers, including customer 302 and customer 303, where customer 303 is a preset customer type for cloud facilitator 301. The cloud service type provided by the cloud service provider 301 for the client 302 is a virtual machine service, and the cloud service type provided for the client 303 is a virtual machine service and a network EIP service. The server of the cloud service provider 301 collects abnormal data generated in the running process of the cloud service in real time through a browser, a gateway and an application program used by the client 302 and the client 303 while providing services for the client 302 and the client 303, identifies that the cloud service type to which the abnormal data belongs is network EIP service for providing services for the client 303 through analysis of the abnormal data, determines that the client 303 is a preset client type, calculates occurrence frequency of the abnormal data generated for the network EIP service for serving the client 303 in a first preset time period, determines that the number of the abnormal data generated by the network EIP service exceeds a preset number threshold value in the first preset time period, and sends alarm information to maintainers 304 of the cloud service provider.
With continued reference to fig. 4, there is shown a schematic flow 400 of another embodiment of a cloud service alerting method according to the present application, comprising the steps of:
Step 401, obtaining abnormal data generated in the cloud service operation process.
In this embodiment, step 401 is performed in a similar manner to step 201, and will not be described here again.
And step 402, deleting the acquired abnormal data in response to the fact that the abnormal data are identified to belong to the preset abnormal type.
In this embodiment, the preset anomaly type is anomaly data for representing that statistics is not required in the cloud service alarm process. The preset exception types include, but are not limited to, exception data types generated due to incorrect input of parameters of the client, exception data types generated due to non-real-name authentication of the client, and exception data types generated due to non-opening of service rights of the client.
When the abnormal data belongs to a preset abnormal type, the abnormal data is not generated due to the abnormality of the cloud service, and the abnormal data is filtered when alarm analysis is performed. After the preset anomaly type filtering is carried out on the anomaly data, all the anomaly data for alarming are the anomaly data generated by the anomaly of the cloud service, so that the frequency calculation result of the anomaly data is more accurate, and the alarm information has more reference value.
Step 403, based on the abnormal data, identifying the customer served by the cloud service generating the abnormal data, and determining whether the customer belongs to a preset customer type.
In this embodiment, step 403 is performed in a similar manner to step 202, and will not be described here again.
Step 404, determining whether to acquire the abnormal data for the first time in a second preset time period based on the abnormal data, and transmitting an abnormal prompt signal in response to determining to acquire the abnormal data for the first time.
In this embodiment, the first acquisition is used to characterize that the same abnormal data has not been acquired before the abnormal data is acquired.
In this embodiment, the second preset time period is set according to the repair time of the recipient of the abnormal prompt signal to the abnormal data, and the setting of the time length of the second preset time period is specifically set according to the abnormal data and the required repair time thereof, which is not limited herein. After receiving the abnormal prompt signal, the receiver of the abnormal prompt signal should repair the abnormal signal. The repairing process needs a certain repairing time period, in the repairing time period, the abnormal prompting signal is sent to the receiving party when the abnormal data is acquired for the first time, and the abnormal prompting signal is not needed to be sent again for the subsequent received abnormal data which is not acquired for the first time.
In some alternative embodiments, an anomaly database may be established based on the first acquired anomaly data, the first acquired anomaly data is stored in the anomaly database, and whether the first acquired anomaly data is determined according to a comparison result of the acquired anomaly data and the anomaly data in the anomaly database.
Step 405, in response to determining that the client belongs to a preset client type, and in response to determining that the number of times of abnormal data generated by the cloud service exceeds a preset number of times threshold value within a first preset time period, sending alarm information.
In this embodiment, step 405 is performed in a similar manner to step 203, and will not be described here again.
As can be seen from fig. 4, compared with the embodiment corresponding to fig. 2, the flow 400 of the cloud service alarm method in this embodiment specifically illustrates filtering of the abnormal data before the cloud service type identification is performed on the abnormal data, and performing the abnormal prompt when the abnormal data is acquired for the first time. After the preset anomaly type filtering is carried out on the anomaly data, all the anomaly data for alarming are the anomaly data generated by the anomaly of the cloud service, so that the frequency calculation result of the anomaly data is more accurate, and the alarm information has more reference value. The abnormal data is acquired for the first time to carry out abnormal prompt, so that the problem can be found out in time to carry out quick repair.
With continued reference to fig. 5, as an implementation of the method shown in the foregoing figures, the present disclosure provides an embodiment of a cloud service alarm apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 5, the cloud service alarm device includes an acquisition unit 501, a filtering unit 502, an identification unit 503, and an alarm unit 504.
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 identifying unit 503 is configured to identify a customer serviced by the cloud service that generated 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 alarm information in response to determining that the client belongs to a preset client type and in response to determining that the number of times of abnormal data generated by the cloud service exceeds a preset number of times threshold within a first preset time period.
In this embodiment, the alarm unit 504 is further configured to store, based on that the client belongs to a preset client type, a timestamp for generating the abnormal data to an abnormal data list corresponding to the cloud service serving the client, and send, in response to determining that the number value of the timestamps stored in the abnormal data list exceeds a preset number threshold in a first preset time period, alarm information through a preset alarm channel, where the alarm channel is used to characterize a communication manner of sending the alarm information to the receiver.
In this embodiment, the alarm unit 504 is further configured to determine, based on the abnormal data, whether the abnormal data is acquired for the first time within the second preset period of time, acquire for the first time to indicate that the same abnormal data has not been acquired before the abnormal data is acquired, and send an abnormality prompt signal in response to determining that the abnormal data is acquired for the first time.
In this embodiment, the identifying unit 503 is further configured to obtain the client identification information in the anomaly data based on the anomaly data, and determine whether the client belongs to the preset client type based on the matching result of the client identification information and the client identification information in the preset client type
Referring now to FIG. 6, there is illustrated a schematic diagram of a computer system 600 suitable for use with devices (e.g., devices 101, 102, 103, 105 shown in FIG. 1) implementing embodiments of the present application. The apparatus shown in fig. 6 is merely an example, and should not be construed as limiting the functionality and scope of use of embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a processor (e.g., CPU, central processing unit) 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data required for the operation of the system 600 are also stored. The processor 601, the ROM602, and the RAM603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Connected to the I/O interface 605 are an input section 606 including a keyboard, a mouse, and the like, an output section 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like, a storage section 608 including a hard disk, and the like, and a communication section 609 including a network interface card such as a LAN card, a modem, and the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the method of the application are performed when the computer program is executed by the processor 601.
The computer readable medium of the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the client computer, partly on the client computer, as a stand-alone software package, partly on the client computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the client computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units involved in the embodiments of the present application may be implemented in software or in hardware. The described units may also be provided in a processor, for example as a processor comprising an acquisition unit, a filtering unit, an identification unit and an alarm unit. The names of these units do not constitute a limitation on the unit itself in some cases, and for example, the acquisition unit may also be described as a unit that "acquires abnormal data generated during the operation of the cloud service".
As a further aspect, the application also provides a computer readable medium which may be comprised in the device described in the above embodiments or may be present alone without being fitted into the device. The computer readable medium carries one or more programs that, when executed by the apparatus, cause the computer device to acquire anomaly data generated during operation of the cloud service, identify a customer serviced by the cloud service that generated the anomaly data based on the anomaly data, and determine whether the customer is of a preset customer type, and send alert information in response to determining that the customer is of the preset customer type and in response to determining that a number of times the anomaly data generated by the cloud service exceeds a preset number of times threshold within a first preset time period.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the application referred to in the present application is not limited to the specific combinations of the technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the inventive concept described above. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910966878.3A CN110727563B (en) | 2019-10-12 | 2019-10-12 | Cloud service alarm method and device for preset customers |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910966878.3A CN110727563B (en) | 2019-10-12 | 2019-10-12 | Cloud service alarm method and device for preset customers |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110727563A CN110727563A (en) | 2020-01-24 |
| CN110727563B true CN110727563B (en) | 2025-07-01 |
Family
ID=69220004
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910966878.3A Active CN110727563B (en) | 2019-10-12 | 2019-10-12 | Cloud service alarm method and device for preset customers |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110727563B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114089712B (en) * | 2020-08-07 | 2024-04-05 | 北京京东乾石科技有限公司 | A data processing method and device |
| CN113984119A (en) * | 2021-10-26 | 2022-01-28 | 江苏尚然饮品有限公司 | Bottled water detecting system |
| CN114190916B (en) * | 2021-12-07 | 2023-05-23 | 河南省儿童医院郑州儿童医院 | Fabric sensor-based child respiration monitoring method and system |
| CN115278214B (en) * | 2022-07-11 | 2025-09-09 | 北京达佳互联信息技术有限公司 | Abnormality determination method, abnormality determination device, server and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103580920A (en) * | 2013-11-07 | 2014-02-12 | 江南大学 | Method for detecting abnormal operation of information system based on cloud computing technology |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104348650B (en) * | 2013-08-05 | 2019-07-16 | 腾讯科技（深圳）有限公司 | Monitoring method, service apparatus and the system of website |
| CN104901955B (en) * | 2015-05-20 | 2017-09-26 | 腾讯科技（深圳）有限公司 | A kind of method for early warning, device and processing server |
| US10453009B2 (en) * | 2015-06-19 | 2019-10-22 | Walmart, Apollo, LLC | Method and apparatus for detecting and/or utilizing sales anomalies to improve store management |
| CN108880920B (en) * | 2017-05-08 | 2022-01-25 | 阿里巴巴集团控股有限公司 | Cloud service management method and device and electronic equipment |
| CN109257196A (en) * | 2017-07-12 | 2019-01-22 | 阿里巴巴集团控股有限公司 | A kind of abnormality eliminating method and equipment |
| EP3454280A1 (en) * | 2017-09-12 | 2019-03-13 | Nagravision SA | Content management system and method for operating same |
-
2019
- 2019-10-12 CN CN201910966878.3A patent/CN110727563B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103580920A (en) * | 2013-11-07 | 2014-02-12 | 江南大学 | Method for detecting abnormal operation of information system based on cloud computing technology |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110727563A (en) | 2020-01-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110727560B (en) | Cloud service alarm methods and devices | |
| CN111124819B (en) | Method and device for full link monitoring | |
| CN110727563B (en) | Cloud service alarm method and device for preset customers | |
| WO2019051948A1 (en) | Method, apparatus, server, and storage medium for processing monitoring data | |
| CN110928934A (en) | Data processing method and device for business analysis | |
| CN107295067A (en) | Across the method and apparatus of screen identification user | |
| CN110620699B (en) | Message arrival rate determination method, device, equipment and computer readable storage medium | |
| CN107179975A (en) | monitoring method and device | |
| CN114612212A (en) | Business processing method, device and system based on risk control | |
| CN112783677A (en) | Method and device for monitoring service abnormity | |
| CN109388546B (en) | Method, device and system for processing faults of application program | |
| CN115001967A (en) | Data acquisition method and device, electronic equipment and storage medium | |
| CN115629909A (en) | Service data processing method and device, electronic equipment and storage medium | |
| CN113238919A (en) | Statistical method, device and system for user access number | |
| CN109087097B (en) | Method and device for updating same identifier of chain code | |
| CN113778780A (en) | Application stability determination method, apparatus, electronic device and storage medium | |
| CN110554895B (en) | A method and device for data calling | |
| CN113722193A (en) | Method and device for detecting page abnormity | |
| CN118941300A (en) | Data processing method, device, electronic device and computer readable medium | |
| CN112788075A (en) | Business service monitoring method and device | |
| CN113761433B (en) | Service processing method and device | |
| CN111290873B (en) | Fault processing method and device | |
| CN112860292A (en) | Configuration management method and device based on application | |
| CN115396434B (en) | A message processing method and device | |
| CN114756540B (en) | Data processing method, device, electronic device and computer readable medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |