A method for detecting call chain exception in OpenStack cloud environment

Abstract

The application relates to the technical field of cloud computing, and discloses a detection method for an abnormal calling chain in an OpenStack cloud environment, which comprises the following steps: obtaining a service request in the OpenStack cloud environment, and generating a corresponding global tracking identifier according to the service request; constructing an actual calling chain of all components in the service request based on the global tracking identifier, and storing the calling time between each component in a two-dimensional adjacency matrix based on the actual calling chain; comparing the two-dimensional adjacency matrix distance between the actual calling chain and a preset normal calling chain with a threshold distance after calculating the two-dimensional adjacency matrix distance; and determining that the actual calling chain is abnormal if the two-dimensional adjacency matrix distance obtained by calculation is greater than the threshold distance. The method provided by the application can quickly locate faults and detect abnormalities without manual intervention, reduces the cost and risk caused by manual intervention, and improves the availability and reliability of the cloud computing environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cloud computing, and particularly relates to a detection method for call chain exception in OpenStack cloud environment. BACKGROUND

[0002] OpenStack is an open-source cloud computing management platform, which is widely used in public cloud and private cloud or hybrid cloud environment, and aims to provide infrastructure as a service (IaaS) solution. OpenStack allows users to be able to establish and manage infrastructure in data center, so as to realize virtualization of computing, storage and network resources. With the rapid development of cloud computing field, more and more enterprises deploy core business systems in the cloud, and the cloud platform is the core infrastructure of many businesses. Once a failure occurs, it may cause service interruption and reduce availability. In recent years, more and more intelligent operation and maintenance technologies are applied to the operation and maintenance of cloud platform, aiming to improve the availability and reliability of cloud computing environment, and reduce the cost and risk brought by failure and manual intervention. SUMMARY

[0003] Therefore, the present application provides a detection method for call chain exception in OpenStack cloud environment to solve the problem of large structural difference between different business request call chains.

[0004] In a first aspect, the present application provides a detection method for call chain exception in OpenStack cloud environment, comprising: obtaining a business request in OpenStack cloud environment, and generating a corresponding global tracking identifier according to the business request; constructing an actual call chain of all components in the business request based on the global tracking identifier, and storing the call time between each component in a two-dimensional adjacency matrix based on the actual call chain; comparing the two-dimensional adjacency matrix distance between the actual call chain and a preset normal call chain after calculating the two-dimensional adjacency matrix distance based on the two-dimensional adjacency matrix; and if the calculated two-dimensional adjacency matrix distance is greater than the threshold distance, determining that the actual call chain is abnormal.

[0005] The method provided by the present application introduces a global tracking identifier in each component corresponding to a specific business request, and constructs the call chain between all components according to the tracking identifier, which screens the problem of large structural difference between different business request call chains. When there are multiple different task requests in OpenStack cloud environment, each task request corresponds to a unique call chain, so when the call chain is abnormal, the corresponding business request can be quickly located, which is targeted, accurate, fast and suitable for real-time exception detection in large-scale cloud environment.

[0006] In an optional implementation, the process of obtaining the service request in the OpenStack cloud environment comprises: obtaining the service request and all components to be invoked by the service request; and creating the calling relationship between the components according to the code structure of the OpenStack cloud environment.

[0007] In an optional implementation, the process of generating the corresponding global tracking identifier according to the service request comprises: generating the global tracking identifier inside each component and outside each component based on the function calling scenario of each component.

[0008] In an optional implementation, the process of storing the calling time consumption between the components in the two-dimensional adjacency matrix based on the actual calling chain comprises: using a directed graph data structure to describe the actual calling chain, and storing the directed graph data in the two-dimensional adjacency matrix; and the numerical value in the two-dimensional adjacency matrix represents the calling time consumption between the components.

[0009] The method provided by the application can more accurately obtain the commonality of the abnormal calling chain by obtaining and analyzing a plurality of historical abnormal calling chains, thereby improving the accuracy of the threshold distance and reducing random errors.

[0010] In an optional implementation, after the process of determining whether the actual calling chain is abnormal, the process further comprises: after the actual calling chain is marked as an abnormal calling chain, storing the abnormal calling chain and the corresponding two-dimensional adjacency matrix.

[0011] In an optional implementation, the process of obtaining the threshold distance comprises: obtaining all the historical abnormal calling chains; and setting the threshold distance according to the two-dimensional adjacency matrix distance distribution of each abnormal calling chain and the normal calling chain.

[0012] In a second aspect, the application provides a device for detecting calling chain abnormality in an OpenStack cloud environment, comprising: an obtaining module, configured to obtain a service request in the OpenStack cloud environment and generate a corresponding global tracking identifier according to the service request; a storage module, configured to construct an actual calling chain of all components in the service request based on the global tracking identifier, and store the calling time consumption between the components in a two-dimensional adjacency matrix based on the actual calling chain; a calculation module, configured to calculate the two-dimensional adjacency matrix distance between the actual calling chain and a preset normal calling chain based on the two-dimensional adjacency matrix, and compare the calculated two-dimensional adjacency matrix distance with a threshold distance; and a judgment module, configured to determine that the calling chain is abnormal if the calculated two-dimensional adjacency matrix distance is greater than the threshold distance.

[0013] In a third aspect, the present application provides a computer device, comprising a memory and a processor, which are connected with each other in communication, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for detecting calling chain exception in OpenStack cloud environment according to the first aspect or any one of the corresponding embodiments.

[0014] In a fourth aspect, the present application provides a computer readable storage medium, which stores computer instructions for making a computer execute the method for detecting calling chain exception in OpenStack cloud environment according to the first aspect or any one of the corresponding embodiments.

[0015] In a fifth aspect, the present application provides a computer program product, which comprises computer instructions for making a computer execute the method for detecting calling chain exception in OpenStack cloud environment according to the first aspect or any one of the corresponding embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the specific embodiments of the present application or the technical solutions in the prior art, the drawings needed in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0017] Figure 1 is a calling request schematic diagram of the nova component in the related art;

[0018] Figure 2 is a flow schematic diagram of the method for detecting calling chain exception in OpenStack cloud environment according to the embodiment of the present application;

[0019] Figure 3 is a flow schematic diagram of the method for detecting calling chain exception in OpenStack cloud environment according to the embodiment of the present application;

[0020] Figure 4 is a calling request schematic diagram of the nova component according to the embodiment of the present application;

[0021] Figure 5 is a schematic diagram of the actual calling chain of the virtual machine according to the embodiment of the present application;

[0022] Figure 6 is a flow schematic diagram of the method for detecting calling chain exception in OpenStack cloud environment according to the embodiment of the present application;

[0023] Figure 7is a schematic diagram of a recording adjacency matrix of a calling chain time consumption between groups of components according to an embodiment of the present application;

[0024] Figure 8 is a flow schematic diagram of a method for detecting a calling chain exception in an OpenStack cloud environment according to an embodiment of the present application;

[0025] Figure 9 is a structural block diagram of a device for detecting a calling chain exception in an OpenStack cloud environment according to an embodiment of the present application;

[0026] Figure 10 is a hardware structure schematic diagram of a computer device according to an embodiment of the present application. DETAILED DESCRIPTION

[0027] In order to make the objects, technical solutions and advantages of embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0028] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as there is no conflict.

[0029] An OpenStack cloud environment contains many modules, and a component provides a REST API interface to a business management platform for calling, and the internal components are called through a remote procedure call (RPC). Taking a nova component as an example, as shown in Figure 1 , the nova-api component receives an external API request, and internally calls nova-scheduler, nova-conductor and nova-compute components, and the nova component also calls API interfaces of cinder and neutron to create a volume and a network card required by a virtual machine. Each business request corresponds to a generated request-id, and the request-id request identifier is transmitted into other internal components of nova, such as nova-conductor, nova-scheduler and nova-compute, but is not transmitted into other external components, such as cinder and neutron components, so that a full-link calling chain containing an entire calling period cannot be generated.

[0030] According to the embodiment of the present application, a method for detecting call chain exception in OpenStack cloud environment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in a different order.

[0031] In the present embodiment, a method for detecting call chain exception in OpenStack cloud environment is provided, Figure 2 The flowchart of the method for detecting call chain exception in OpenStack cloud environment according to the embodiment of the present application is shown in Figure 2 The flowchart includes the following steps:

[0032] Step S1: obtaining a service request in the OpenStack cloud environment, and generating a corresponding global tracking identifier according to the service request.

[0033] Specifically, the OpenStack cloud environment includes a plurality of service requests, and the call chain structure between each service request is different. In order to improve the speed and accuracy of the call chain exception detection process, an accurate call chain needs to be constructed for each service request, as shown in Figure 3 The process of obtaining a service request in the OpenStack cloud environment and generating a corresponding global tracking identifier according to the service request also includes:

[0034] Step S11: obtaining a service request and all components that need to be called by the service request.

[0035] Step S12: creating a call relationship between each component according to the code structure of the OpenStack cloud environment.

[0036] Step S13: generating a global tracking identifier within each component and between each component based on the function call scenario of each component.

[0037] Specifically, as shown in Figure 4 Figure 4 On the basis of Figure 1 In the context of each component function call, a global tracking identifier trace_id is added to support the generation of global tracking identifier between each component and the component outside, so that all call processes of the service request can be tracked according to the trace_id, all call chain links in the entire service request life cycle can be constructed, and fast fault positioning and exception detection can be achieved.

[0038] Step S2: constructing an actual call chain of all components in the service request based on the global tracking identifier, and storing the call time between each component in a two-dimensional adjacency matrix based on the actual call chain.​

[0039] Exemplarily, due to the cloud platform business types are more, different business types have large differences in the underlying call chain. According to the specific business type, different kinds of call chains are constructed. For example, the common business types of the cloud platform are: creation, restart, migration, expansion, deletion, etc. of virtual machine. Taking the creation of virtual machine as an example, according to the OpenStack code structure, the component call relationship of virtual machine creation can be obtained, as shown in FIG. 1, the creation process of virtual machine needs to call components A-1, A-2, A-3, A-4, B, C and D, the calling order between each component is shown by the arrow in the figure, then the component type and the call relationship in FIG. 1 constitute the actual call chain of the virtual machine creation process. Figure 5 Figure 5

[0040] Optionally, as shown in FIG. 2, the process of storing the calling time consumption between each component in the two-dimensional adjacency matrix based on the actual call chain, includes: Figure 6

[0041] Step S21: the actual call chain is described by using the directed graph data structure, and the directed graph data is stored in the two-dimensional adjacency matrix.

[0042] Step S22: the numerical value in the two-dimensional adjacency matrix represents the calling time consumption between each component.

[0043] Specifically, according to FIG. 1, the calling relationship between each component is marked with the calling time consumption: the calling time consumption between component A-1 and component B is D1, the calling time consumption between component A-1 and component A-2 is D2, the calling time consumption between component A-2 and component A-3 is D3, the calling time consumption between component A-2 and component A-4 is D4, the calling time consumption between component A-4 and component C is D5, and the calling time consumption between component A-4 and component D is D6. The marked calling time consumption D1-D6 is taken as the matrix element, and the two-dimensional adjacency matrix as shown in FIG. 2 is generated according to the calling relationship of each component. The element 0 in the two-dimensional adjacency matrix represents that there is no calling relationship between two components. Figure 6 Figure 7

[0044] Step S3: based on the two-dimensional adjacency matrix, the two-dimensional adjacency matrix distance between the actual call chain and the preset normal call chain is calculated, and then the calculated two-dimensional adjacency matrix distance is compared with the threshold distance.

[0045] Specifically, the calculation formula of the matrix distance Distance is as follows:

[0046]

[0047] ​​​​​The two-dimensional adjacency matrix X stores the call time between each component of the actual call chain, and the two-dimensional adjacency matrix Y stores the call time between each component of the preset normal call chain. X[i,j] is the element in the i-th row and j-th column of the two-dimensional adjacency matrix X, and Y[i,j] is the element in the i-th row and j-th column of the two-dimensional adjacency matrix Y. After obtaining the two-dimensional adjacency matrix distance Distance by measuring the difference between the actual call chain and the normal call chain through matrix distance, the matrix distance Distance is compared with the threshold distance.

[0048] Step S4: If the calculated distance of the two-dimensional adjacency matrix is ​​greater than the threshold distance, then the actual call chain is determined to be abnormal.

[0049] Specifically, if the calculated matrix distance Distance is less than the threshold distance, it indicates that the actual call chain is normal; otherwise, it is determined that the actual call chain is abnormal.

[0050] Optionally, after determining that the actual call chain is abnormal, the process further includes: marking the actual call chain as an abnormal call chain and storing the abnormal call chain and its corresponding two-dimensional adjacency matrix.

[0051] Optionally, such as Figure 8 As shown, the process of obtaining the threshold distance includes:

[0052] Step S31: Obtain all exception call chains stored in the history.

[0053] Step S32: Set the threshold distance based on the distance distribution of the two-dimensional adjacency matrix between each abnormal call chain and the normal call chain.

[0054] Specifically, the threshold is obtained by statistically analyzing historical call chain data. By calculating and analyzing the distribution of matrix distance values ​​between historical abnormal call chains and normal call chains, an appropriate threshold is set for anomaly detection.

[0055] The method provided in this embodiment introduces a global tracing identifier in each component corresponding to each specific business request, and constructs the call chain between all components based on the tracing identifier. This masks the problem of large structural differences between call chains of different business requests. When there are multiple different task requests in the OpenStack cloud environment, each task request corresponds to a unique call chain. Therefore, when the call chain is abnormal, the corresponding business request can be quickly located. It is highly targeted, accurate, and fast in detection, and is suitable for real-time anomaly detection in large-scale cloud environments.

[0056] In the embodiment, a device for detecting call chain exception in OpenStack cloud environment is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and will not be described herein again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, implementation of hardware, or a combination of software and hardware, is also possible and contemplated.

[0057] The embodiment provides a device for detecting call chain exception in OpenStack cloud environment, as shown in Figure 9 , comprising:

[0058] The acquisition module 901 is configured to acquire a service request in the OpenStack cloud environment, and generate a corresponding global tracking identifier according to the service request.

[0059] The storage module 902 is configured to construct an actual call chain of all components in the service request based on the global tracking identifier, and store the call time between each component in a two-dimensional adjacency matrix based on the actual call chain.

[0060] The calculation module 903 is configured to calculate the two-dimensional adjacency matrix distance between the actual call chain and the preset normal call chain based on the two-dimensional adjacency matrix, and compare the calculated two-dimensional adjacency matrix distance with a threshold distance.

[0061] The judgment module 904 is configured to determine that the call chain is abnormal if the calculated two-dimensional adjacency matrix distance is greater than the threshold distance.

[0062] Further function descriptions of the above-mentioned modules and units are the same as those of the corresponding embodiments, and will not be described herein again.

[0063] The device for detecting call chain exception in OpenStack cloud environment in the embodiment is presented in the form of functional units, and the units herein refer to ASIC (Application Specific Integrated Circuit) circuits, processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0064] The embodiment of the present application also provides a computer device with the device for detecting call chain exception in OpenStack cloud environment as shown in Figure 9 .

[0065] Please refer to Figure 10 , Figure 10 which is a structural schematic diagram of a computer device provided in an optional embodiment of the present application, as shown in Figure 10As shown, the computer device includes one or more processors 10, memory 20, and interfaces 30 for the various components to communicate with one another. The various components communicate through one or more buses, and can be mounted on a common motherboard or in other manners as appropriate. The processor 10 can execute instructions, for example, stored in the memory 20 to display graphical information for a GUI on an external input / output device, such as a display device coupled to the interface. In some optional implementations, multiple processors and / or multiple buses can be employed as appropriate, such as about the memory 20. Also, various components can be distributed, such as over a network to provide for parallel processing. Figure 10 The processor 10 is taken as an example.

[0066] The processor 10 can be a central processing unit, a network processor, or a combination thereof. The processor 10 can further include a hardware chip. The hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device can be a complex programmable logic device, a field programmable logic device, a general array logic, or any combination thereof.

[0067] The memory 20 stores instructions that are executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods illustrated in the above embodiments.

[0068] The memory 20 can include a program storage area and a data storage area. The program storage area can store an operating system, application programs required by at least one function, and the like. The data storage area can store data created by the computer device, and the like. In addition, the memory 20 can include a high-speed random access memory, and can further include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid state memory device. In some optional implementations, the memory 20 can optionally include a memory that is remotely located with respect to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0069] The memory 20 can include a volatile memory, such as a random access memory, and can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid state disk. The memory 20 can also include a combination of the above-mentioned types of memories.

[0070] The computer device further includes a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0071] The embodiments of the present application further provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded to a local storage medium through network, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned memories. It can be understood that the computer, the processor, the microprocessor controller, or the programmable hardware includes a storage component that can store or receive software or computer code, when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0072] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, through the operation of the computer, the method and / or technical solutions according to the present application can be called or provided. Those skilled in the art should understand that the form of computer program instructions in a computer readable medium includes but is not limited to source files, executable files, installation package files, etc. Correspondingly, the way of executing computer program instructions by computer includes but is not limited to: the computer directly executes the instructions, or the computer compiles the instructions and then executes the corresponding compiled program, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installed program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0073] Although the embodiments of the present application are described in conjunction with the accompanying drawings, various modifications and changes can be made by those skilled in the art without departing from the spirit and scope of the present application, and such modifications and changes fall within the scope defined by the appended claims.

Claims

1. A method for detecting a call chain exception in an OpenStack cloud environment, characterized in that, The application comprises the following steps: acquiring a service request in an OpenStack cloud environment and generating a corresponding global tracking identifier according to the service request; constructing an actual call chain of all components in the service request based on the global tracking identifier, and storing the call time between each component in a two-dimensional adjacency matrix based on the actual call chain; comparing the two-dimensional adjacency matrix distance between the actual call chain and a preset normal call chain after calculating the two-dimensional adjacency matrix distance; if the calculated two-dimensional adjacency matrix distance is greater than the threshold distance, determining that the actual call chain is abnormal; the process of generating a corresponding global tracking identifier according to the service request comprises the following steps: generating a global tracking identifier inside and outside each component based on the function call scene of each component; the process of storing the call time between each component in a two-dimensional adjacency matrix based on the actual call chain comprises the following steps: using a directed graph data structure to describe the actual call chain, and storing the directed graph data in the two-dimensional adjacency matrix; the numerical value in the two-dimensional adjacency matrix represents the call time between each component; the process of obtaining the threshold distance comprises the following steps: obtaining all abnormal call chains stored in history; setting the threshold distance according to the two-dimensional adjacency matrix distance distribution between each abnormal call chain and a normal call chain.

2. The method of claim 1, wherein, the process of acquiring a service request in an OpenStack cloud environment comprises the following steps: acquiring a service request and all components that need to be called by the service request; creating the call relationship between each component according to the code structure of the OpenStack cloud environment.

3. The method of claim 1, wherein, after the process of determining that the actual call chain is abnormal, the process further comprises the following steps: after marking the actual call chain as an abnormal call chain, storing the abnormal call chain and its corresponding two-dimensional adjacency matrix.

4. A device for detecting call chain anomalies in an OpenStack cloud environment, characterized in that, The application comprises the following steps: an acquisition module is configured to acquire a service request in an OpenStack cloud environment and generate a corresponding global tracking identifier according to the service request; the process of generating a corresponding global tracking identifier according to the service request comprises the following steps: generating a global tracking identifier inside and outside each component based on the function call scene of each component; a storage module is configured to construct an actual call chain of all components in the service request based on the global tracking identifier, and store the call time between each component in a two-dimensional adjacency matrix based on the actual call chain; the process of storing the call time between each component in a two-dimensional adjacency matrix based on the actual call chain comprises the following steps: using a directed graph data structure to describe the actual call chain, and storing the directed graph data in the two-dimensional adjacency matrix; the numerical value in the two-dimensional adjacency matrix represents the call time between each component; The computing module is configured to calculate a two-dimensional adjacency matrix distance between the actual calling chain and the preset normal calling chain based on the two-dimensional adjacency matrix, and compare the obtained two-dimensional adjacency matrix distance with a threshold distance; the threshold distance is obtained by: obtaining all the historical stored abnormal calling chains; and setting the threshold distance according to the two-dimensional adjacency matrix distance distribution of each abnormal calling chain and the normal calling chain. The judging module is configured to determine that the calling chain is abnormal if the obtained two-dimensional adjacency matrix distance is greater than the threshold distance.

5. A computer device, comprising: The method comprises the following steps: A memory and a processor are connected in communication with each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for detecting calling chain abnormality in an OpenStack cloud environment according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the method for detecting calling chain abnormality in an OpenStack cloud environment according to any one of claims 1 to 3.

7. A computer program product, characterised in that, The computer instructions are used to make the computer execute the method for detecting calling chain abnormality in an OpenStack cloud environment according to any one of claims 1 to 3.

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