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CN110727560A - Cloud service alarm method and device - Google Patents

Cloud service alarm method and device Download PDF

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Publication number
CN110727560A
CN110727560A CN201910966336.6A CN201910966336A CN110727560A CN 110727560 A CN110727560 A CN 110727560A CN 201910966336 A CN201910966336 A CN 201910966336A CN 110727560 A CN110727560 A CN 110727560A
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China
Prior art keywords
abnormal data
cloud service
preset
time period
service type
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CN201910966336.6A
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Chinese (zh)
Inventor
刘曾超前
董灵芝
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Beijing Baidu Netcom Science and Technology Co Ltd
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Beijing Baidu Netcom Science and Technology Co Ltd
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Priority to CN201910966336.6A priority Critical patent/CN110727560A/en
Publication of CN110727560A publication Critical patent/CN110727560A/en
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/3003Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3006Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system is distributed, e.g. networked systems, clusters, multiprocessor systems
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/3055Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/3089Monitoring arrangements determined by the means or processing involved in sensing the monitored data, e.g. interfaces, connectors, sensors, probes, agents

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  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • Quality & Reliability (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Debugging And Monitoring (AREA)

Abstract

The embodiment of the application discloses a cloud service alarm method, which comprises the steps of firstly, acquiring abnormal data generated in the cloud service operation process; then, identifying the cloud service type to which the abnormal data belongs based on a data interface generating the abnormal data; then, in response to determining that the number of abnormal data generated by the cloud service type exceeds a preset number threshold within a first preset time period, alarm information is sent. The embodiment of the disclosure can be applied to the field of cloud computing, and active and fast sensing of faults on a cloud service line is realized by acquiring abnormal data on the line and performing frequency analysis on the abnormal data, so that a cloud service provider can perform fast repair and timely loss stopping; moreover, the unified fault perception capability of the cloud service is realized, and the on-line fault perception capability can be provided for the cloud service system of each cloud service provider.

Description

Cloud service alarm method and device
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a cloud service alarm method and device.
Background
Cloud services providers need to provide stable online services to meet customer service needs. However, under certain conditions, for example, a computer room is disconnected from a network or internal abnormality occurs in cloud service, a large amount of online abnormal data is generated, and a situation that the service is unavailable may occur. Currently, when the above situation occurs, the abnormal situation is generally known by the following ways: waiting for the user to perform fault feedback through a work order system or telephone contact; and (3) respectively establishing a monitoring system for each service on the cloud, and monitoring each online fault in real time.
Based on the mode that the user feeds back to know the abnormal condition, the cloud service provider cannot timely sense the abnormal service problem so as to quickly follow up the problem and stop the loss, and the user experience is seriously influenced. The abnormal situation learning mode of the monitoring system is established based on respective services, corresponding research and development work and relevant mechanism establishment are required to be carried out by each team, a large amount of repeated work exists among the teams, and the cost of manpower and material resources is wasted.
Disclosure of Invention
The embodiment of the application provides a cloud service alarm method and system.
In a first aspect, an embodiment of the present application provides a cloud service alarm method, where the method includes: acquiring abnormal data generated in the running process of the cloud service; identifying the cloud service type to which the abnormal data belongs based on the data interface generating the abnormal data; and in response to determining that the quantity of abnormal data generated by the cloud service type exceeds a preset quantity threshold value within a first preset time period, sending alarm information.
In some embodiments, the sending alarm information in response to determining that the quantity value of the abnormal data generated by the cloud service type exceeds the preset quantity threshold value in the first preset time period includes:
storing the timestamp for generating the abnormal data into an abnormal data list corresponding to the cloud service type to which the abnormal data belongs; and in response to the fact that the quantity value of the timestamps stored in the abnormal data list exceeds a preset quantity threshold value within a first preset time period, sending alarm information through a preset alarm channel, wherein the alarm channel is used for representing a communication mode for sending the alarm information to a receiving party.
In some embodiments, before the identifying the cloud service type to which the abnormal data belongs based on the data interface generating the abnormal data, the method further includes: and in response to identifying that the abnormal data belongs to the preset abnormal type, deleting the acquired abnormal data.
In some embodiments, 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 an ending time; the method further comprises the following steps: in response to determining that the quantity of abnormal data generated by the cloud service type does not exceed a preset quantity threshold value within a first preset time period, deleting the abnormal data outside the first preset time period.
In some embodiments, after the above sending alarm information in response to determining that the number of abnormal data generated by the cloud service type exceeds the preset number threshold within the first preset time period, the method further includes: and deleting all abnormal data generated by the acquired cloud service type.
In some embodiments, the above method further comprises: determining whether abnormal data are acquired for the first time within a second preset time period or not based on the abnormal data; acquiring abnormal data for representing that the same abnormal data is not acquired before the abnormal data is acquired for the first time; in response to determining to acquire the anomalous data for the first time, an anomaly prompting signal is sent.
In some embodiments, the method further comprises: and updating the preset quantity threshold value based on the access quantity of the cloud service type in response to the preset updating time.
In a second aspect, an embodiment of the present application provides a cloud service alarm device, where the device includes: the acquisition unit is configured to acquire abnormal data generated in the cloud service operation process; the identification unit is configured to identify a cloud service type to which the abnormal data belongs based on a data interface which generates the abnormal data; the alarm unit is configured to send alarm information in response to the fact that the number of abnormal data generated by the cloud service type exceeds a preset number threshold in a first preset time period.
In some embodiments, the alarm unit is further configured to store a timestamp for generating the abnormal data to an abnormal data list corresponding to a cloud service type to which the abnormal data belongs; and in response to the fact that the quantity value of the timestamps stored in the abnormal data list exceeds a preset quantity threshold value within a first preset time period, sending alarm information through a preset alarm channel, wherein the alarm channel is used for representing a communication mode for sending the alarm information to a receiving party.
In some embodiments, the apparatus further comprises: and the filtering unit is configured to delete the acquired abnormal data in response to recognizing that the abnormal data belongs to a preset abnormal type before the cloud service type to which the abnormal data belongs is recognized based on the data interface generating the abnormal data.
In some embodiments, 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 an ending time; the device also includes: the deleting unit is configured to delete the abnormal data outside the first preset time period in response to the fact that the number of the abnormal data generated by the cloud service type does not exceed the preset number threshold within the first preset time period.
In some embodiments, the deleting unit is further configured to delete all the acquired abnormal data generated by the cloud service type after sending the alarm information in response to determining that the number of the abnormal data generated by the cloud service type exceeds the preset number threshold within the first preset time period.
In some embodiments, the alarm unit is further configured to determine whether to acquire the abnormal data for the first time within a second preset time period based on the abnormal data; acquiring abnormal data for representing that the same abnormal data is not acquired before the abnormal data is acquired for the first time; in response to determining to acquire the anomalous data for the first time, an anomaly prompting signal is sent.
In some embodiments, the apparatus further comprises: and the updating unit is configured to update the preset number threshold value based on the access amount of the cloud service type in response to reaching the preset updating time.
In a third aspect, the present application provides a computer-readable medium, on which a computer program is stored, where the program, when executed by a processor, implements the method as described in any implementation manner of the first aspect.
In a fourth aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement a method as described in any implementation of the first aspect.
According to the cloud service alarm method and system provided by the embodiment of the application, firstly, abnormal data generated in the cloud service operation process are obtained; then, identifying the cloud service type to which the abnormal data belongs based on a data interface generating the abnormal data; then, in response to determining that the number of abnormal data generated by the cloud service type exceeds a preset number threshold within a first preset time period, alarm information is sent. According to the technical scheme of the cloud service alarm, the on-line abnormal data are collected and frequency analysis is carried out, so that the fault on the cloud service line is actively and quickly sensed, and a cloud service provider can quickly repair and stop loss in time; moreover, the unified fault perception capability of the cloud service is realized, and the on-line fault perception capability can be provided for the cloud service system of each cloud service provider.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a cloud service alert method according to the present application;
fig. 3 is a schematic diagram of an application scenario of the cloud service alert method according to the present embodiment;
FIG. 4 is a flow diagram of yet another embodiment of a cloud service alert method according to the present application;
FIG. 5 is a block diagram of one embodiment of a cloud service alert device according to the present application;
FIG. 6 is a block diagram of a computer system suitable for use in implementing embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
In a typical configuration of the present application, the terminal device and the server each include one or more processors (CPUs), input/output interfaces, network interfaces, and memories. The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, which include both non-transitory and non-transitory, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include non-transitory computer readable media (transient media), such as modulated data signals and carrier waves.
Fig. 1 illustrates an exemplary architecture 100 to which the cloud service alert system of the present application may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The terminal devices 101, 102, 103 may be hardware devices or software that support network connectivity to provide various network services. When the terminal devices 101, 102, and 103 are hardware, they may be various electronic devices supporting network functions such as information interaction and network connection functions, including but not limited to smart phones, tablet computers, e-book readers, laptop portable computers, desktop computers, and the like. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented, for example, as multiple software or software modules to provide distributed services, or as a single software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various cloud services, such as a server that provides cloud storage and cloud computing services to the terminal devices 101, 102, 103. The server can store or process various received data and feed back the processing result to the terminal equipment.
It should be noted that the cloud service alarm method provided by the embodiment of the present disclosure may be executed by the server 105. Accordingly, a cloud service alert device may be provided in the server 105. And is not particularly limited herein.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules, for example, to provide distributed services, or as a single piece of software or software module. And 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 alert method according to the present application is shown, including the steps of:
step 201: and acquiring abnormal data generated in the cloud service operation process.
In this embodiment, cloud services are an augmentation, usage, and interaction model of internet-based related services, typically involving the provision of dynamically scalable and often virtualized resources over the internet. The Cloud service may be various types of Cloud services provided by a Cloud service provider based on the requirements of user storage, computing and the like, including but not limited to Public Cloud (Public Cloud) and Private Cloud (Private Cloud).
The public cloud is the most basic service, a plurality of users can share the system resources of a cloud service provider, and the users can enjoy professional internet technical service without erecting any equipment and allocating management personnel, which is undoubtedly a good method for reducing the cost for general creators and medium-sized and small enterprises. Public clouds can also be subdivided into 3 categories, including SaaS (Software-as-a-Service), PaaS (Platform-as-a-Service), and IaaS (Infrastructure-as-a-Service).
The private cloud is a private cloud network established by large enterprises for considering both privacy of industries (such as finance and insurance industries) and privacy of users, and the enterprises need to design data centers, networks and storage devices by themselves so as to have enough resources to ensure normal operation of the private cloud.
In this embodiment, the abnormal data is abnormal data generated by a fault occurring in the operation process of the cloud service, for example, when a machine room providing the cloud service is disconnected, a user may generate a large amount of abnormal data such as network connection failure, data request failure, data storage failure, and the like when using the cloud service. The abnormal data includes, but is not limited to: the URL (Uniform Resource Locator) of the exception data, the row number and column number of the exception data, the data interface for generating the exception data, and the stack information of the exception data.
In this embodiment, the gateway, the browser, and the APP (Application) directly interact with the cloud service user, and may collect most of abnormal data generated during the cloud service operation. An execution main body (for example, the server in fig. 1) in this embodiment may obtain, in an exception reporting manner, exception data generated in the cloud service operation process through a gateway, a browser, and an APP in a terminal device applied by a user, where the gateway includes a console gateway and an API (Application Programming Interface) gateway. When abnormal data are generated in the running process of the cloud service, 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 abnormal data to the execution main body. And after the gateway, the browser, the APP and the like report the abnormity, the execution main body acquires abnormal data in the cloud service operation process.
Step 202: and identifying the cloud service type to which the abnormal data belongs based on the data interface generating the abnormal data.
In this embodiment, the cloud service types may be divided based on cloud service products delivered by a cloud service provider, for example, product types such as a virtual machine and a network EIP (Enterprise Information Portal) delivered by the cloud service provider.
In this embodiment, the abnormal data includes a data interface that generates the abnormal data, and the cloud service type to which the abnormal data belongs can be known through the data interface that generates the abnormal data.
Step 203: and in response to determining that the quantity of abnormal data generated by the cloud service type exceeds a preset quantity threshold value within a first preset time period, sending alarm information.
In this embodiment, the first preset time period is a first preset time period in which a certain historical time is a starting time and a current time is an ending time, and a time length of the first preset time period is specifically set according to a cloud service type, which is not limited herein. In some alternative embodiments, the first preset time period may be set in a manner of a sliding time window. Specifically, a preset time period may be used as the time period of the first preset time period, and the current time may be used as the ending 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 recognizable by the sliding time window is set to second; if the current time is 08 minutes 40 seconds at 11 hours at 9, 17 and 2019, the ending time of the sliding time window is 08 minutes 50 seconds at 11 hours at 17, 11 hours at 9, 17 and 2019, and the starting time is 07 minutes 10 seconds at 11 hours at 9, 17 and 2019; as the current time becomes 18 minutes 50 seconds at 11 days 11 at 9/17 in 2019, the cutoff time of the sliding time window becomes 18 minutes 50 seconds at 11 days 11 at 9/17 in 2019, and the start time thereof becomes 17 minutes 10 seconds at 11 days 11 at 9/17 in 2019. In this way, the execution subject can perform frequency analysis on the acquired abnormal data in real time.
In this embodiment, the preset number threshold may be specifically set according to the cloud service type and the online access amount of the cloud service type, which is not limited herein. For example, the average online access amount a for cloud service type a is greater than the average online access amount B for cloud service type B, then the quantity threshold for cloud service type a may be greater than the quantity threshold for cloud service type B accordingly. The preset quantity threshold is a reference threshold for sending alarm information, so that accurate and reference-value alarm is realized by setting a corresponding alarm reference threshold for the online access amount of the cloud service.
In some optional implementations of this embodiment, after the step of determining that the time period is within the first preset time period, the method of this embodiment may further include: and updating the preset number threshold value based on the change of the access amount of the cloud service type in response to the preset updating time.
In the cloud service operation process, due to the service expansion and the increase of the service volume of the user, the change of the access volume on the cloud service line may be caused. At this time, by updating the preset number threshold for the cloud service type, the updated threshold can be more matched with the current online access amount.
In this embodiment, the alarm information may be sent through a preset alarm format, and the alarm information may include, but is not limited to, information for indicating at least one of the following: the alarm information receiving personnel, the abnormal occurrence time period, the abnormal occurrence frequency and the abnormal data information which is generated at the moment closest to the moment of sending the alarm message.
The execution main body of the embodiment counts abnormal data generated by a certain cloud service type based on the cloud service type, performs frequency analysis on the abnormal data generated by the cloud service type according to a first preset time period, that is, determines that the quantity of the abnormal data generated by the cloud service type exceeds a preset quantity threshold value in the first preset time period, and sends alarm information to a receiver of the alarm information, wherein the receiver of the alarm information can be a maintainer of a cloud service provider.
In some optional implementations of this embodiment, the frequency analysis may be performed by: and presetting a corresponding abnormal data list based on the cloud service type. The execution main body stores the time stamps for generating the abnormal data to an abnormal data list corresponding to the cloud service type to which the abnormal data belong according to the time schedule, so that 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 the fact that the quantity value of the timestamps stored in the abnormal data list exceeds a preset quantity threshold value within a first preset time period, sending alarm information through a preset alarm channel, wherein the alarm channel is used for representing communication modes for sending the alarm information to a receiver, such as various real-time communication application programs, mails, short messages, telephones and the like.
In this embodiment, the execution main body may further determine whether to acquire the abnormal data for the first time within a second preset time period based on the abnormal data; acquiring abnormal data for representing that the same abnormal data is not acquired before the abnormal data is acquired for the first time; and sending an exception prompt signal to the receiving party in response to determining that the exception data is acquired for the first time. The recipient may be a maintenance person of a cloud facilitator that provides the cloud service. After receiving the abnormal prompt signal, the maintainer can take corresponding repair measures, and the abnormal prompt signal does not need to be sent again within the repair time, so that the second preset time period is set, and the abnormal prompt signal can be sent once within the second preset time period. In some optional embodiments, an exception database may be established based on exception data acquired for the first time, the exception database stores the exception data acquired for the first time, and whether the exception data is acquired for the first time is determined according to a comparison result between the acquired exception data and the exception data in the exception database.
In the embodiment, the execution main body acquires abnormal data generated in the cloud service operation process in real time, and performs frequency analysis on the abnormal data in real time based on the cloud service type generating the abnormal data, so that the fault on the cloud service line is actively and quickly sensed, and a cloud service provider can quickly repair and stop loss in time; moreover, the unified fault perception capability of the cloud service is realized, and the on-line fault perception capability can be provided for the cloud service system of each cloud service provider.
Fig. 3 schematically shows an application scenario of the cloud service alarm method according to the present embodiment. The cloud service provider 301 provides cloud services for a plurality of users, wherein the users include a user 302 and a user 303. The cloud service type provided by the cloud service provider 301 for the user 302 is a virtual machine service, and the cloud service type provided for the user 303 is a virtual machine service and a network EIP service. The method comprises the steps that while the server of the cloud service provider 301 provides services for a user 302 and a user 303, abnormal data generated in the running process of the cloud service are collected in real time through a browser, a gateway and an application program used by the user 302 and the user 303, the cloud service type to which the abnormal data belong is identified to be the network EIP service through analysis of a data interface generating the abnormal data, frequency analysis is carried out on the abnormal data in a first preset time period, the number of the abnormal data generated by the network EIP service in the first preset time period is determined to exceed a preset number threshold, and alarm information is sent to a maintenance worker 304 of the cloud service provider.
With continuing reference to fig. 4, a schematic flow chart 400 illustrating another embodiment of a cloud service alert method according to the present application is shown including the steps of:
step 401: and acquiring abnormal data generated in the cloud service operation process.
In this embodiment, step 401 is performed in a manner similar to step 201, and is not described herein again.
Step 402: and in response to identifying that the abnormal data belongs to the preset abnormal type, deleting the acquired abnormal data.
In this embodiment, the preset exception type is exception data used for representing that statistics is not required in the cloud service alarm process. The preset exception types include, but are not limited to, an exception data type generated due to a user parameter input error, an exception data type generated due to a user not performing real-name authentication, and an exception data type generated due to a user not opening a service right.
When the abnormal data belong to the preset abnormal type, the abnormal data are not generated due to the abnormality of the cloud service, and the abnormal data are filtered when alarm analysis is carried out. After the preset abnormal type filtering is carried out on the abnormal data, all the abnormal data for alarming are abnormal data generated due to the self abnormality of the cloud service, so that the frequency analysis result of the abnormal data is more accurate, and the alarm information has more reference value.
Step 403: and identifying the cloud service type to which the abnormal data belongs based on the data interface generating the abnormal data.
In this embodiment, step 403 is performed in a manner similar to step 202, and is not described herein again.
Step 404: and in response to determining that the quantity of abnormal data generated by the cloud service type exceeds a preset quantity threshold value within a first preset time period, sending alarm information.
In this embodiment, step 404 is performed in a manner similar to step 203, and is not described herein again.
Step 405: and deleting all abnormal data generated by the acquired cloud service type.
In this embodiment, after the alarm information is sent, a maintenance person corresponding to the cloud service provider is required to perform exception handling on the alarm information, the obtained exception data has no value in utilization, and all exception data generated by the obtained cloud service type can be deleted in consideration of saving storage space and improving operation performance.
Similarly, in response to determining that the amount of the abnormal data generated by the cloud service type does not exceed the preset amount threshold within the first preset time period, and when the setting of the first preset time period adopts a sliding time window manner, the abnormal data outside the first preset time period may be deleted. Because the sliding time window always takes the current time as the cutoff time and slides along with the change of the current time, that is, the abnormal data outside the sliding time window is subjected to frequency analysis of the abnormal data, and 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 value in utilization.
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 cloud service type identification is performed on the abnormal data, and deleting the abnormal data that has no more value in utilization in consideration of saving storage space and improving operation performance. After the preset abnormal type filtering is carried out on the abnormal data, all the abnormal data for alarming are abnormal data generated due to the self abnormality of the cloud service, so that the frequency analysis result of the abnormal data is more accurate, and the alarm information has more reference value.
With continued reference to fig. 5, as an implementation of the methods shown in the above-mentioned 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 apparatus includes: an acquisition unit 501, a filtering unit 502, an identification unit 503, an alarm unit 504, a deletion unit 505 and an update unit 506.
The obtaining unit 501 is configured to obtain 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 cloud service type to which the abnormal data belongs based on a data interface generating the abnormal data. The alarm unit 504 is configured to send alarm information in response to determining that the number of abnormal data generated by the cloud service type exceeds a preset number threshold within a first preset time period. The deleting unit 505 is configured to delete the abnormal data outside the first preset time period in response to determining that the amount of the abnormal data generated by the cloud service type does not exceed the preset amount threshold within the first preset time period; and deleting all the acquired abnormal data generated by the cloud service type after sending alarm information in response to the fact that the number of the abnormal data generated by the cloud service type exceeds a preset number threshold within a first preset time period. The updating unit 506 is configured to update the preset number threshold based on the access amount of the cloud service type in response to reaching the preset update time.
In this embodiment, the alarm unit 504 is further configured to store a timestamp for generating the abnormal data to an abnormal data list corresponding to a cloud service type to which the abnormal data belongs; and in response to the fact that the quantity value of the timestamps stored in the abnormal data list exceeds a preset quantity threshold value within a first preset time period, sending alarm information through a preset alarm channel, wherein the alarm channel is used for representing a communication mode for sending the alarm information to a receiving party.
In this embodiment, the alarm unit 504 is further configured to determine whether to acquire the abnormal data for the first time within a second preset time period based on the abnormal data; acquiring abnormal data for representing that the same abnormal data is not acquired before the abnormal data is acquired for the first time; in response to determining to acquire the anomalous data for the first time, an anomaly prompting signal is sent.
Referring now to FIG. 6, shown is a block diagram of a computer system 600 suitable for use in implementing devices of embodiments of the present application (e.g., devices 101, 102, 103, 105 shown in FIG. 1). The apparatus shown in fig. 6 is only an example, and should not bring any limitation to the function and use range of the embodiments of the present application.
As shown in fig. 6, the computer system 600 includes a processor (e.g., CPU, central processing unit) 601 that 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 necessary for the operation of the system 600 are also stored. The processor 601, the ROM602, and the RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output portion 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; 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, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 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, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the 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 illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program, when executed by the processor 601, performs the above-described functions defined in the method of the present application.
It should be noted that the computer readable medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the 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 present application, 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 this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects 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 + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart 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 described in the embodiments of the present application may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor comprises an acquisition unit, a filtering unit, an identification unit, an alarm unit, a deletion unit and an updating unit. The names of the units do not form a limitation on the units themselves in some cases, and for example, the acquiring unit may also be described as a unit for acquiring abnormal data generated during the operation of the cloud service.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by the apparatus, cause the computer device to: acquiring abnormal data generated in the running process of the cloud service; identifying the cloud service type to which the abnormal data belongs based on the data interface generating the abnormal data; and in response to determining that the quantity of abnormal data generated by the cloud service type exceeds a preset quantity threshold value within a first preset time period, sending alarm information.
The above description is only a preferred embodiment of the application and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention herein disclosed is not limited to the particular combination of features described above, but also encompasses other arrangements formed by any combination of the above features or their equivalents without departing from the spirit of the invention. For example, the above features may be replaced with (but not limited to) features having similar functions disclosed in the present application.

Claims (16)

1. A cloud service alarm method, wherein the method comprises:
acquiring abnormal data generated in the running process of the cloud service;
identifying the cloud service type to which the abnormal data belongs based on the data interface generating the abnormal data;
and sending alarm information in response to the fact that the number of abnormal data generated by the cloud service type exceeds a preset number threshold within a first preset time period.
2. The method of claim 1, wherein the sending alarm information in response to determining that the quantity value of the abnormal data generated by the cloud service type exceeds a preset quantity threshold within a first preset time period comprises:
storing the timestamp for generating the abnormal data into an abnormal data list corresponding to the cloud service type to which the abnormal data belongs;
and in response to the fact that the quantity value of the timestamps stored in the abnormal data list exceeds a preset quantity threshold value within a first preset time period, sending alarm information through a preset alarm channel, wherein the alarm channel is used for representing a communication mode for sending the alarm information to a receiving party.
3. The method of claim 1, wherein prior to identifying the cloud service type to which the anomalous data belongs based on the data interface that generated the anomalous data, the method further comprises:
and in response to the fact that the abnormal data are recognized to belong to a preset abnormal type, deleting the acquired abnormal data.
4. The method according to claim 1, wherein 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 an ending time;
the method further comprises the following steps:
in response to determining that the quantity of abnormal data generated by the cloud service type does not exceed a preset quantity threshold value within a first preset time period, deleting the abnormal data outside the first preset time period.
5. The method of claim 4, wherein after the sending alarm information in response to determining that the amount of abnormal data generated by the cloud service type exceeds a preset amount threshold within a first preset time period, the method further comprises:
and deleting all the acquired abnormal data generated by the cloud service type.
6. The method of claim 1, wherein the method further comprises:
determining whether the abnormal data is acquired for the first time within a second preset time period or not based on the abnormal data; the first acquisition is used for representing that the same abnormal data is not acquired before the abnormal data is acquired;
and sending an exception prompt signal in response to determining that the exception data is acquired for the first time.
7. The method according to claim 1 or 2, wherein the method further comprises:
and updating the preset quantity threshold value based on the access amount of the cloud service type in response to reaching the preset updating time.
8. A cloud service alert apparatus, wherein the apparatus comprises:
the acquisition unit is configured to acquire abnormal data generated in the cloud service operation process;
the identification unit is configured to identify a cloud service type to which the abnormal data belongs based on a data interface generating the abnormal data;
the alarm unit is configured to send alarm information in response to the fact that the number of the abnormal data generated by the cloud service type exceeds a preset number threshold within a first preset time period.
9. The apparatus of claim 8, wherein,
the alarm unit is further configured to store a timestamp for generating the abnormal data in an abnormal data list corresponding to a cloud service type to which the abnormal data belongs; and in response to the fact that the quantity value of the timestamps stored in the abnormal data list exceeds a preset quantity threshold value within a first preset time period, sending alarm information through a preset alarm channel, wherein the alarm channel is used for representing a communication mode for sending the alarm information to a receiving party.
10. The apparatus of claim 8, wherein the apparatus further comprises:
the filtering unit is configured to delete the acquired abnormal data in response to the fact that the abnormal data is identified to belong to a preset abnormal type before the data interface which generates the abnormal data identifies the cloud service type to which the abnormal data belongs.
11. The apparatus according to claim 8, wherein 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 an ending time;
the device further comprises:
the deleting unit is configured to delete abnormal data outside a first preset time period in response to the fact that the quantity of the abnormal data generated by the cloud service type does not exceed a preset quantity threshold within the first preset time period.
12. The apparatus of claim 11, wherein,
the deleting unit is further configured to delete all the acquired abnormal data generated by the cloud service type after the alarm information is sent in response to the fact that the number of the abnormal data generated by the cloud service type exceeds a preset number threshold in a first preset time period.
13. The apparatus of claim 8, wherein,
the alarm unit is further configured to determine whether the abnormal data is acquired for the first time within a second preset time period based on the abnormal data; the first acquisition is used for representing that the same abnormal data is not acquired before the abnormal data is acquired; and sending an exception prompt signal in response to determining that the exception data is acquired for the first time.
14. The apparatus of claim 8 or 9, wherein the apparatus further comprises:
an updating unit configured to update the preset number threshold based on the access amount of the cloud service type in response to reaching a preset update time.
15. A computer-readable medium, on which a computer program is stored, wherein the program, when executed by a processor, implements the method of any one of claims 1-7.
16. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
CN201910966336.6A 2019-10-12 2019-10-12 Cloud service alarm method and device Pending CN110727560A (en)

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CN111586129A (en) * 2020-04-28 2020-08-25 北京奇艺世纪科技有限公司 Alarm method and device for data synchronization, electronic equipment and storage medium
CN112491858A (en) * 2020-11-20 2021-03-12 北京百度网讯科技有限公司 Method, device, equipment and storage medium for detecting abnormal information
CN112579399A (en) * 2020-12-28 2021-03-30 上海蓝云网络科技有限公司 Cloud service testing method and device, electronic equipment and computer storage medium
CN113551297A (en) * 2021-07-27 2021-10-26 工大科雅(天津)能源科技有限公司 Heat supply network abnormal water replenishing monitoring method and heat supply network monitoring terminal
CN113660510A (en) * 2021-08-19 2021-11-16 杭州时趣信息技术有限公司 Video processing cloud manufacturer configuration method, device and system
CN114024831A (en) * 2021-11-08 2022-02-08 中国工商银行股份有限公司 Abnormal event early warning method, device and system
CN114139936A (en) * 2021-11-29 2022-03-04 合肥安达创展科技股份有限公司 Cloud intelligent early warning and repair reporting system based on exhibition item interactive data
CN115550093A (en) * 2022-09-13 2022-12-30 海尔优家智能科技(北京)有限公司 Application research method, storage medium and electronic device
CN116882946A (en) * 2023-09-06 2023-10-13 南京南自华盾数字技术有限公司 Intelligent management system and method based on power generation enterprise data

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111586129A (en) * 2020-04-28 2020-08-25 北京奇艺世纪科技有限公司 Alarm method and device for data synchronization, electronic equipment and storage medium
CN112491858A (en) * 2020-11-20 2021-03-12 北京百度网讯科技有限公司 Method, device, equipment and storage medium for detecting abnormal information
CN112491858B (en) * 2020-11-20 2023-05-30 北京百度网讯科技有限公司 Method, device, equipment and storage medium for detecting abnormal information
CN112579399A (en) * 2020-12-28 2021-03-30 上海蓝云网络科技有限公司 Cloud service testing method and device, electronic equipment and computer storage medium
CN112579399B (en) * 2020-12-28 2024-04-09 上海蓝云网络科技有限公司 Cloud service testing method and device, electronic equipment and computer storage medium
CN113551297A (en) * 2021-07-27 2021-10-26 工大科雅(天津)能源科技有限公司 Heat supply network abnormal water replenishing monitoring method and heat supply network monitoring terminal
CN113660510A (en) * 2021-08-19 2021-11-16 杭州时趣信息技术有限公司 Video processing cloud manufacturer configuration method, device and system
CN114024831B (en) * 2021-11-08 2024-01-26 中国工商银行股份有限公司 Abnormal event early warning method, device and system
CN114024831A (en) * 2021-11-08 2022-02-08 中国工商银行股份有限公司 Abnormal event early warning method, device and system
CN114139936A (en) * 2021-11-29 2022-03-04 合肥安达创展科技股份有限公司 Cloud intelligent early warning and repair reporting system based on exhibition item interactive data
CN115550093A (en) * 2022-09-13 2022-12-30 海尔优家智能科技(北京)有限公司 Application research method, storage medium and electronic device
CN116882946B (en) * 2023-09-06 2024-01-19 南京南自华盾数字技术有限公司 Intelligent management system and method based on power generation enterprise data
CN116882946A (en) * 2023-09-06 2023-10-13 南京南自华盾数字技术有限公司 Intelligent management system and method based on power generation enterprise data

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