Traffic control method and device, electronic equipment and storage medium

Abstract

The application discloses a flow control method and device, electronic equipment and storage medium. The method comprises the following steps: determining a plurality of processing nodes deployed in the back end; acquiring a first type processing architecture and a second type processing architecture corresponding to each type of processing node in the plurality of processing nodes; receiving flow distribution expectation parameters of a front-end application, wherein the flow distribution expectation parameters are used to indicate a processing system receiving flow data and a ratio of the flow data size received by each processing system to the total flow data size; and distributing the flow data to a first processing system corresponding to the first type processing architecture and a second processing system corresponding to the second type processing architecture according to a first ratio and a second ratio respectively. The application solves the technical problem that the related art cannot realize cross-platform mixed management and control of flow resources due to the single processor architecture-based server node resource arrangement task.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a flow control method, device, electronic device, and storage medium. Background Art

[0002] The cloud computing industry has experienced rapid growth in recent years. For decades, the X86 microprocessor architecture, represented by Intel, has dominated the processor market for personal computers and enterprise servers. However, with the advancement of chip technology, ARM processors, with their low cost, high performance, and low power consumption, have gradually emerged in the international chip market. The evolution of mobile internet technology has accelerated the deployment and use of ARM architecture processors in the mobile market. Since the launch of the domestic development of trusted computing in China in 2016, major domestic server manufacturers have been conducting innovative experiments using both the X86 and ARM instruction set architectures, applying them to IaaS infrastructure environments for both internet public clouds and enterprise-level private clouds. However, related technologies, such as those for resource scheduling, can only address resource orchestration tasks for server nodes within a single processor architecture and cannot achieve cross-platform hybrid management and control.

[0003] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0004] The embodiments of the present application provide a flow control method, device, electronic device and storage medium to at least solve the technical problem in the related art that cross-platform hybrid management and control of flow resources cannot be achieved due to the server node resource orchestration tasks that can only be based on a single processor architecture.

[0005] According to one aspect of an embodiment of the present application, a flow control method is provided, which is applied to a hybrid cloud computing processing system, the hybrid cloud computing processing system including multiple processing nodes, and each processing node supports multiple processor architectures, including: determining multiple processing nodes deployed on the back end, wherein the node types of the multiple processing nodes are different; obtaining a first type of processing architecture and a second type of processing architecture corresponding to each type of processing node in the multiple processing nodes, the architecture types of the first type of processing architecture and the second type of processing architecture being different; receiving expected flow distribution parameters of a front-end application, wherein the expected flow distribution parameters are used to indicate a processing system receiving flow data and a ratio of the flow data size received by each processing system to the total flow data size, wherein the ratio includes: a first ratio of the flow data size that the first processing system corresponding to the first type of processing architecture should receive to the total flow data size, and a second ratio of the flow data size that the second processing system corresponding to the second type of processing architecture should receive to the total flow data size; and distributing the flow data to a first processing system corresponding to the first type of processing architecture and a second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio, respectively.

[0006] Optionally, the traffic data is distributed to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio, respectively, including: obtaining the first health status of all processing nodes corresponding to the first type of processing architecture, and the second health status of all processing nodes corresponding to the second type of processing architecture; based on the first health status and the second health status, determining whether to distribute the traffic data to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio, respectively.

[0007] Optionally, based on the first health state and the second health state, determine whether to distribute the traffic data to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio, respectively, including: when the first health state and the second health state are both in normal states, distribute the traffic data to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio, respectively.

[0008] Optionally, based on the first health state and the second health state, determine whether to distribute the traffic data to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio respectively, including: when the first health state is in an abnormal state, refuse to distribute the traffic according to the ratio, and send all traffic data to the second processing system corresponding to the second processing architecture in the processing node; when the second health state is in an abnormal state, refuse to distribute the traffic according to the ratio, and send all traffic data to the first processing system corresponding to the first processing architecture in the processing node.

[0009] Optionally, before distributing the traffic data to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio respectively, the method also includes: determining whether the traffic data belongs to encryption and decryption business traffic data, and when the judgment result indicates that the traffic data belongs to encryption and decryption business traffic data, obtaining the resource pool routing information of the required encryption and decryption algorithm, and forwarding the traffic data to the load balancing server; the load balancing server forwards the data to multiple processing nodes deployed in the back end, calls the encryption and decryption algorithm according to the routing path indicated by the resource pool routing information, and encrypts and decrypts the business data in the processing node.

[0010] Optionally, the traffic data is distributed to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio, respectively, including: detecting the first processing system corresponding to the first processing architecture deployed by each processing node among multiple processing nodes, and determining the first load level corresponding to all first processing systems, wherein the load level is used to indicate the processing resource occupancy rate of each processing system; detecting the second processing system corresponding to the second processing architecture deployed by each processing node among multiple processing nodes, and determining the second load level corresponding to all second processing systems; dynamically adjusting according to the comparison value between the first load level and the second load level, and distributing the traffic data to the first processing system corresponding to the first processing architecture and the second processing system corresponding to the second processing architecture according to the dynamically adjusted ratio.

[0011] Optionally, dynamic adjustment is performed based on the comparison value between the first load level and the second load level, and the traffic data is distributed to the first processing system corresponding to the first processing architecture and the second processing system corresponding to the second processing architecture according to the dynamically adjusted ratio, including: determining the difference between the first load level and the second load level, and when the difference is greater than a first preset threshold, allocating all traffic data to the first processing system corresponding to the first processing architecture in each processing node for processing; when the difference is less than the first preset threshold and greater than the second preset threshold, determining the sum of the first ratio and the difference to obtain a third ratio, and allocating traffic data to the first processing system based on the third ratio, wherein the first preset threshold is greater than the second preset threshold.

[0012] Optionally, dynamic adjustment is performed based on the comparison value between the first load level and the second load level, and the traffic data is distributed to the first processing system corresponding to the first processing architecture and the second processing system corresponding to the second processing architecture according to the dynamically adjusted ratio, including: determining the difference between the second load level and the first load level, and when the difference is greater than a first preset threshold, allocating all traffic data to the second processing system corresponding to the second processing architecture in each processing node for processing; when the difference is less than the first preset threshold and greater than the second preset threshold, determining the sum of the first ratio and the difference to obtain a fourth ratio, and allocating traffic data to the second processing system based on the fourth ratio, wherein the first preset threshold is greater than the second preset threshold.

[0013] Optionally, the first processing architecture includes: ARM architecture, the second processing architecture includes: X86 architecture, and the multiple processing nodes include: containers, virtual machines and bare metal.

[0014] According to another aspect of an embodiment of the present application, a flow control device is further provided, which is applied to a hybrid cloud computing processing system, the hybrid cloud computing processing system including multiple processing nodes, and each processing node supports multiple processor architectures, including: a determination module, used to determine multiple processing nodes deployed on the back end, wherein the node types of the multiple processing nodes are different; an acquisition module, used to obtain a first type of processing architecture and a second type of processing architecture corresponding to each type of processing node in the multiple processing nodes, the architecture types of the first type of processing architecture and the second type of processing architecture being different; a receiving module, used to receive expected flow distribution parameters of a front-end application, wherein the expected flow distribution parameters are used to indicate a processing system receiving flow data and a ratio of the flow data size received by each processing system to the total flow data size, wherein the ratio includes: a first ratio of the flow data size that the first processing system corresponding to the first type of processing architecture should receive to the total flow data size, and a second ratio of the flow data size that the second processing system corresponding to the second type of processing architecture should receive to the total flow data size; and a distribution module, used to distribute the flow data to a first processing system corresponding to the first type of processing architecture and a second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio, respectively.

[0015] According to another aspect of an embodiment of the present application, a non-volatile storage medium is further provided, the storage medium including a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute any one of the flow control methods.

[0016] According to another aspect of an embodiment of the present application, an electronic device is further provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the instructions to implement any one of the flow control methods.

[0017] In an embodiment of the present application, a method of pulling through the physical barriers of each processing node and distributing traffic to different processing systems is adopted, by determining multiple processing nodes deployed on the back end, wherein the node types of the multiple processing nodes are different; obtaining a first type of processing architecture and a second type of processing architecture corresponding to each type of processing node in the multiple processing nodes, wherein the architecture types of the first type of processing architecture and the second type of processing architecture are different; receiving the traffic distribution expected parameters of the front-end application, wherein the traffic distribution expected parameters are used to indicate the processing system that receives the traffic data and the ratio of the traffic data size received by each processing system to the total traffic data size, wherein the ratio includes: the first type of processing architecture for A first ratio of the size of the traffic data to be received by the first processing system to the size of the total traffic data, and a second ratio of the size of the traffic data to be received by the second processing system corresponding to the second type of processing architecture to the size of the total traffic data; the traffic data is distributed to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio respectively, thereby achieving the technical effect of cross-platform distribution of traffic resources and hybrid control of traffic resources based on different processor structures, thereby solving the technical problem in the related technology that the server node resource orchestration tasks can only be based on a single processor architecture and cannot realize cross-platform hybrid control of traffic resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 is a flow chart of a flow control method according to an embodiment of the present application;

[0020] Figure 2 This is a system structure diagram in one embodiment of the present application;

[0021] Figure 3 This is a schematic diagram of a specific process of external access in an embodiment of the present application;

[0022] Figure 4 This is a schematic diagram of the architecture of a hybrid cloud intelligent traffic controller in one embodiment of the present application;

[0023] Figure 5 is a structural schematic diagram of a flow control device according to an embodiment of the present application;

[0024] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present application is shown. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0026] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] The Kubernetes orchestration management system within the cloud-native architecture introduced the concept of workloads after version 1.2. This concept provides pods, the smallest runtime unit for software applications, as runtime carriers, providing external application services in a multi-replica mode. Containers are launched within pods to implement unitized services. Pods provide policies for workloads, including replica count, health checks, and rolling updates. This allows for rolling updates of application releases, dynamic scaling of services, and runtime health checks of applications.

[0028] The pods orchestrated and managed by the Kubernetes orchestration management system are actually container carriers. Its workload targets undifferentiated resources, which are only container resources based on the LXC technology system. As a mature container orchestration manager, it implements scheduling management and traffic control for multiple copies of the same container in an environment with a processor computing instruction set.

[0029] Under relevant technology systems, application systems primarily operate on bare metal and virtual machines. After evolving through two eras of physical bare metal and virtualized private cloud infrastructure, the application delivery environment has maintained a fundamental requirement for independent operating systems. IaaS-layer devices are divided into delivery support environments based on different chips and processor instruction sets. The deployment, orchestration, and delivery of application software require categorized management across different underlying architectures. Even within virtualized private cloud systems, virtualization technology tends to isolate the underlying architecture and lacks consistent traffic processing for upper-layer applications, making it impossible to achieve unified traffic access for applications and service levels. Furthermore, while existing technologies achieve isolation between the operating system and the underlying infrastructure, computing resource waste and over-subscription remain significant issues plaguing business system delivery. In the context of the prevalence of microservices and distributed application architectures, traditional cloud computing models struggle to support scenarios such as grayscale releases, traffic allocation, and circuit breaking and downgrades. Furthermore, the application and promotion of domestically produced chips presents challenges in verifying the coexistence of multiple chips, the sharing of multiple versions, and differentiated traffic control.

[0030] Based on LXC container technology, the Kubernetes orchestration management system implements scheduling, configuration, and flow control for container runtime services on IaaS server nodes using the same processor computing instruction set. However, when IaaS nodes use both X86 and Arm processor architectures, static image files for container runtimes must be processed separately. Due to the different ways the two CPU architectures handle compiled images, in such scenarios, two sets of application images are often required for a single application.

[0031] At the same time, when different images are released on IaaS nodes with different processor architectures, application service maintenance personnel often need to fully master Kubernetes orchestration management technology. Dynamic scaling, traffic management, and version releases all require scheduling and flow control based on node affinity configurations for different processors and internal and external load balancing mechanisms. This makes it difficult for application operations personnel to master application releases, configuration management, and emergency response. The reason for this is that while Kubernetes provides a container-based orchestration management mechanism, it cannot address container runtime resource management across processor architectures. It also lacks intuitive application-oriented management features such as multiple replicas, non-differentiated releases, grayscale releases, and rolling updates.

[0032] On the other hand, when moving away from container technology and into a traditional virtualized private cloud environment, when launching cross-processor architecture application service releases and runtime management, problems more difficult than Kubernetes orchestration management will arise. The application operation and maintenance side needs to independently develop an orchestration management system based on a virtual machine private cloud. This system can only solve the server node resource orchestration tasks of a single processor architecture, and cross-platform hybrid management and control cannot be achieved.

[0033] In summary, when enterprise IT organizations are faced with hybrid cloud deployment based on container cloud and infrastructure private cloud, and when there is a mixed use of X86 and ARM in the IaaS layer, they are unable to provide effective management capabilities for the application side.

[0034] This application mainly solves the problems of flow control management of hybrid cloud application services such as container cloud and infrastructure cloud under cross-processor architecture. According to an embodiment of the present application, an embodiment of a flow control method 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 a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0035] Figure 1 The flow control method according to the embodiment of the present application is applied to a hybrid cloud computing processing system, which includes multiple processing nodes, and each processing node supports multiple processor architectures, such as Figure 1 As shown, the method includes the following steps:

[0036] Step S102, determining multiple processing nodes deployed on the backend, wherein the multiple processing nodes are of different node types;

[0037] Step S104, obtaining a first type of processing architecture and a second type of processing architecture corresponding to each type of processing node in the plurality of processing nodes, where the first type of processing architecture and the second type of processing architecture are different in architecture type;

[0038] Step S106: Receive the expected traffic distribution parameters of the front-end application, wherein the expected traffic distribution parameters are used to indicate the processing system that receives the traffic data and the ratio of the traffic data size received by each processing system to the total traffic data size, wherein the ratio includes: a first ratio of the traffic data size that should be received by the first processing system corresponding to the first type of processing architecture to the total traffic data size, and a second ratio of the traffic data size that should be received by the second processing system corresponding to the second type of processing architecture to the total traffic data size;

[0039] In step S108 , the flow data is distributed to a first processing system corresponding to the first type of processing architecture and a second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio, respectively.

[0040] It can be understood that through the technical solutions of steps S102 to S108, the physical barriers between different processing nodes can be eliminated, and the traffic data can be distributed to different types of processing nodes for processing. At the same time, it can also be distributed to different processing architectures in different processing nodes for processing.

[0041] In the flow control method, the flow is distributed to different processing systems by pulling through the physical barriers of each processing node, and the multiple processing nodes deployed at the back end are determined, wherein the node types of the multiple processing nodes are different; a first type of processing architecture and a second type of processing architecture corresponding to each type of processing node in the multiple processing nodes are obtained, and the architecture types of the first type of processing architecture and the second type of processing architecture are different; the flow distribution expected parameters of the front-end application are received, wherein the flow distribution expected parameters are used to indicate the processing system that receives the flow data and the ratio of the flow data size received by each processing system to the total flow data size, wherein the ratio includes: the first type of processing architecture The invention discloses a method for distributing traffic resources across platforms and performing hybrid control of traffic resources based on different processor structures, thereby solving the technical problem that in related technologies, hybrid control of traffic resources cannot be achieved due to the server node resource orchestration tasks based on a single processor architecture.

[0042] It is easy to notice that this application is equivalent to providing a unified application-oriented traffic control management (method) tool across processor platforms; it also provides a set of hybrid cloud computing resource centralized management and non-differentiated cloud application support system; through the above method, it solves the resource problem of coexistence and sharing of ARM architecture processors and traditional X86 architecture processors, and differential treatment in the process of application software development, testing, and production operation, decouples the dependency between application system operation and IaaS layer resources, and realizes multi-dimensional management and intelligent control of application services.

[0043] In some embodiments of the present application, allocating traffic data to a first processing system corresponding to a first type of processing architecture and a second processing system corresponding to a second type of processing architecture according to a first ratio and a second ratio, respectively, can be achieved in the following manner: obtaining a first health status of all processing nodes corresponding to the first type of processing architecture and a second health status of all processing nodes corresponding to the second type of processing architecture; and determining, based on the first health status and the second health status, whether to allocate traffic data to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture, respectively, according to the first health status and the second health status.

[0044] As an optional embodiment, based on the first health state and the second health state, it is determined whether to distribute the flow data to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio respectively, including: when the first health state and the second health state are both in normal states, the flow data is distributed to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio respectively.

[0045] As another optional embodiment, based on the first health state and the second health state, it is determined whether to distribute the flow data to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio respectively. It is also possible that when the first health state is in an abnormal state, the flow distribution according to the ratio is refused, and all flow data (i.e., the ratio is 100%) is sent to the second processing system corresponding to the second processing architecture in the processing node; when the second health state is in an abnormal state, the flow distribution according to the ratio is refused, and all flow data is sent to the first processing system corresponding to the first processing architecture in the processing node.

[0046] In some embodiments of the present application, before distributing traffic data to a first processing system corresponding to a first type of processing architecture and a second processing system corresponding to a second type of processing architecture according to a first ratio and a second ratio, respectively, it can be determined whether the traffic data belongs to encryption and decryption business traffic data. If the judgment result indicates that the traffic data belongs to encryption and decryption business traffic data, the resource pool routing information of the required encryption and decryption algorithm is obtained, and the traffic data is forwarded to a load balancing server; the load balancing server forwards the data to multiple processing nodes deployed in the back end, and the encryption and decryption algorithm is called according to the routing path indicated by the resource pool routing information, and the business data is encrypted and decrypted in the processing node.

[0047] In an exemplary embodiment of the present application, traffic data is distributed to a first processing system corresponding to a first type of processing architecture and a second processing system corresponding to a second type of processing architecture according to a first ratio and a second ratio, respectively, including: detecting the first processing system corresponding to the first processing architecture deployed by each processing node among a plurality of processing nodes, and determining a first load level corresponding to all first processing systems, wherein the load level is used to indicate the processing resource occupancy rate of each processing system; detecting the second processing system corresponding to the second processing architecture deployed by each processing node among a plurality of processing nodes, and determining a second load level corresponding to all second processing systems; dynamically adjusting according to a comparison value between the first load level and the second load level, and distributing the traffic data to the first processing system corresponding to the first processing architecture and the second processing system corresponding to the second processing architecture according to the dynamically adjusted ratio.

[0048] As an optional implementation, dynamic adjustment is performed based on the comparison value between the first load level and the second load level, and the traffic data is distributed to the first processing system corresponding to the first processing architecture and the second processing system corresponding to the second processing architecture according to the dynamically adjusted ratio. The difference between the first load level and the second load level can be determined. When the difference is greater than a first preset threshold, all traffic data is distributed to the first processing system corresponding to the first processing architecture in each processing node for processing; when the difference is less than the first preset threshold and greater than the second preset threshold, a third ratio is determined as the sum of the first ratio and the difference, and traffic data is distributed to the first processing system based on the third ratio, wherein the first preset threshold is greater than the second preset threshold.

[0049] As another optional implementation, dynamic adjustment is performed based on the comparison value between the first load level and the second load level, and the flow data is distributed to the first processing system corresponding to the first processing architecture and the second processing system corresponding to the second processing architecture according to the dynamically adjusted ratio. It is also possible to determine the difference between the second load level and the first load level. When the difference is greater than a first preset threshold, all flow data are distributed to the second processing system corresponding to the second processing architecture in each processing node for processing; when the difference is less than the first preset threshold and greater than the second preset threshold, the sum of the first ratio and the difference is determined to obtain a fourth ratio, and the flow data is distributed to the second processing system based on the fourth ratio. It should be noted that the first preset threshold is greater than the second preset threshold.

[0050] It should be noted that the above-mentioned first processing architecture includes: ARM architecture, the second processing architecture includes: X86 architecture, and the above-mentioned multiple processing nodes include: containers, virtual machines, bare metal and virtualized private clouds, etc.

[0051] Figure 2 This is a system structure diagram in one embodiment of the present application. Figure 2As shown, the application server can distribute the traffic of different APPs to the container cloud and virtualized private cloud. Figure 3 This is a specific flow chart of external access, such as Figure 3 As shown, it can integrate the POD processing units of the container cloud, the virtual machine units under virtualization technology, the physical bare metal server units, and the special equipment server units, open up the SDN network routing and various layers of communication, build the basic service registration module of the DNS domain name resolution server and LoadBalance load balancer, embed the independently developed multi-protocol service registration center, and comprehensively integrate the full-service registration and service discovery of the multi-layer network.

[0052] Figure 4 It is a hybrid cloud intelligent traffic controller in one embodiment of the present application. In this hybrid cloud intelligent traffic controller, for resource management, in containerized cloud resource management, an independent Controller controller program can be written to regulate POD resources, workloads, service discovery, Configmap configuration volumes and other resource objects through research and analysis of the Kubernetes orchestration service API interface. The program presets a model orchestration for resources created by the hybrid cloud computing processing system, in which custom information items are implemented in a parameterized manner, and an independent visualization page is designed on the front end to support manual editing of custom information items. In addition to the Controller, a Watch monitor service program can be designed and developed. All predefined model orchestrations are regarded as expected standards by the Watch program for change monitoring to ensure that resources are created normally and maintain the expected operating state. For virtualized and bare metal general-purpose servers, a control program based on the Controller model is also designed. The program can obtain virtual machine resource configuration and user authentication information from the virtualization platform, and manage virtual machines as a type of basic resource in the hybrid cloud infrastructure. Bare metal servers directly enter the basic information of the Linux operating system (including IP address, user password, etc.) in the management platform of the processing system. The management platform will directly issue the controller agent program to complete the management of edge services. Special equipment such as encryption machines, SSL servers, etc., take over the basic resources of the equipment according to the industry standard interface in the special equipment management unit, and also use the Controller program to monitor and control the special cloud encryption resource pool and cloud certificate resource pool on the system server side.

[0053] Traffic control and traffic allocation can be based on a DNS + load balancing + application service registration center mechanism. The application service registration center provides registration functions for various protocol programs and microservices to the application system, and the registration method is performed in the form of DNS domain names. When deploying a set of business applications and allocating traffic in a cross-processor structure, an integrated integration is achieved by considering the following two major differences:

[0054] (1) Application services implement flow control under the two instruction set structures of X86 and ARM64. First, the problem of application structure differences between application versions under different operating system environments of X86 and ARM64 processors is solved. During the compilation phase, the application selects JVM resources under different processor structures for compilation and construction. A set of application services outputs product packages and image products in the two operating system environments of X86 and ARM64. The index information of this set of product groups is uploaded during the configuration of the release phase. The release and runtime parameter arrangement of the application are carried out on the deployment page. Specifically, the deployment mechanism, traffic distribution ratio, runtime status expectations, resource configuration requirements, etc. are written. Finally, the release arrangement and product information are published to the Controller. The hybrid cloud computing processing system allocates container resources, virtual machine resources or bare metal resources with good status and idle resources according to the intelligent algorithm during the application release phase. Application R&D and operation and maintenance personnel do not need to pay attention to which underlying environment their services are published. After business traffic enters the channel, it is forwarded via DNS and a load balancer to the hybrid cloud computing processing system's gateway service. The gateway service then locates the application service's backend node based on the service registry's routing table. An intelligent analysis module assesses the health of the backend node's replica cluster in real time and, based on the controller's orchestration expectations, distributes traffic to the target nodes. Consider the following two examples: A: Application traffic distribution expectations are 30% to x86 backend nodes and 70% to ARM64 backend nodes, in which case traffic distribution is executed accordingly. B: Application traffic distribution expectations are 30% to x86 backend nodes and 70% to ARM64 backend nodes. However, if all backend node services running on ARM64 resources are unhealthy, traffic is dispatched to x86 backend service nodes based on availability-first scheduling. In both examples, the underlying infrastructure for backend node service scheduling does not differentiate between containers, virtual machines, or bare metal devices; all implement IaaS resource pooling.

[0055] (2) Traffic distribution and control mechanism for special equipment. An intelligent flow control model for non-standard processor equipment is designed and developed in the hybrid cloud computing processing system. The controller dedicated to the encryption security resource pool registers encryption security devices in the form of members to the service registration center, and different types of encryption security devices are pooled and processed in blocks. The encryption and decryption business traffic is processed through the business application interface. The business application obtains the resource pool routing information of the required encryption algorithm from the service registration center, forwards the encryption and decryption message to the corresponding internal load balancing address, and then distributes the load to the back-end node for specific encryption and decryption security processing. The health status, traffic load, and algorithm type of each member node in the resource pool are managed by the controller according to the orchestration expectations.

[0056] It should be noted that the IaaS infrastructure layer can manage devices with X86 and ARM64 processor chips, deploy the Linux operating system as a general-purpose operating system environment, and abstract computing resources, storage resources, and cache resources into atomic capabilities of the IaaS basic environment in the hybrid cloud platform. After being abstracted into resources, they are intelligently packaged into different types of resource objects such as PODs, virtual machines, or bare metal servers according to specific usage scenarios, and application characteristics are applied to the scenario implementation of resource objects in the form of orchestration files. This type of environmental resource orchestration file can apply control requirements such as dynamic traffic allocation, circuit breaking and degradation, and grayscale release to the release process of various business systems, thereby breaking away from the limitations of different underlying processor architectures and achieving backward compatibility of PaaS capabilities. This is called "PaaS-" capability release.

[0057] The corresponding "PaaS-" capability realizes the design of cross-processor architecture, and "PaaS+" gets rid of the release of indifferent traffic control capabilities in different business demand scenarios. In the embodiment of this application, application-level service registration and service discovery mechanisms such as Nacos, Dubbo, and Zookeeper are provided, and the registration discovery of microservices is combined with the global service discovery mechanism of multiple data centers and multiple domains by relying on the linkage mechanism of the hybrid cloud platform, and the application-level service discovery mechanism is combined with the basic service discovery mechanism of DNS. After the fusion of multiple mechanisms, it develops upward to construct intelligent traffic control that supports different applications, and provides plug-and-play modeling capabilities for various application systems to realize traffic allocation in a cross-processor hybrid cloud environment. The platform provides a visual unified operation interface, including basic resource supply, service resource supply, traffic policy configuration and other capabilities.

[0058] In combination with the above embodiments, it can be seen that this application has the following beneficial effects by building a hybrid cloud management platform:

[0059] (1) By building a hybrid cloud management platform, different types of processing nodes such as container clouds, virtualized private clouds, and bare metal servers can be managed simultaneously.

[0060] (2) The service registration center of the hybrid cloud computing processing system can be different from the service configuration and service registration components of open source Nacos, Dubbo, Zookeeper, etc. The registration discovery mechanism deployment does not rely solely on the automatic registration mechanism of the HTTP protocol and GRPC protocol. Instead, it integrates dynamic registration with static registration and non-standard interface registration to design a registration discovery system that supports application services in different processor environments such as X86, ARM64, and special chips, while providing intelligent traffic distribution, health check, resource allocation and other control functions. In terms of specific technology, the registration method can be started through application release orchestration and code engineering, so that the controllers of various infrastructures can perceive the behavior of service registration and service access. Then, the health check and status detection of webhooks are used, and the pressure distribution of back-end nodes is perceived by network traffic monitoring, so that the computer can actively select resources with more idle time to carry transaction traffic, realizing intelligent traffic forwarding control.

[0061] (3) Hybrid cloud can manage processors of different frameworks such as X86 and ARM64 in a unified manner in the form of computing nodes, obtain computing resources, storage resources, and cache resources, collect user passwords and other access rights, and unify the authentication and control mechanism in a tenant manner.

[0062] (4) Innovate application orchestration capabilities, integrate multiple types of orchestration execution technologies that can be executed in container resources, virtual machine resources, and bare metal server resource objects, lower the operating threshold with a visual interface, model the traffic allocation mechanism of various application services, provide parameterized policy configuration functions, and statically adjust the traffic distribution strategy of different application service objects.

[0063] (5) Integrate traffic monitoring components to dynamically determine traffic ratios and application load levels, determine traffic change trends based on dynamic indicators, and use trend determination algorithms to adjust traffic distribution strategies based on timing status, thereby dynamically adjusting load control for different application service objects.

[0064] Figure 5 A flow control device according to an embodiment of the present application is applied to a hybrid cloud computing processing system, which includes multiple processing nodes, and each processing node supports multiple processor architectures, such as Figure 5 As shown, the flow control device includes:

[0065] A determination module 50 is configured to determine a plurality of processing nodes deployed at the back end, wherein the plurality of processing nodes are of different node types;

[0066] An acquisition module 52 is configured to acquire a first type of processing architecture and a second type of processing architecture corresponding to each type of processing node in the plurality of processing nodes, wherein the first type of processing architecture and the second type of processing architecture are different in architecture type;

[0067] A receiving module 54 is configured to receive a traffic distribution expectation parameter of a front-end application, wherein the traffic distribution expectation parameter is used to indicate a processing system that receives traffic data and a ratio of the traffic data size received by each processing system to the total traffic data size, wherein the ratio includes: a first ratio of the traffic data size that a first processing system corresponding to a first type of processing architecture should receive to the total traffic data size, and a second ratio of the traffic data size that a second processing system corresponding to a second type of processing architecture should receive to the total traffic data size;

[0068] The distribution module 56 is configured to distribute the traffic data to a first processing system corresponding to the first type of processing architecture and a second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio, respectively.

[0069] In the flow control device, a determination module 50 is used to determine multiple processing nodes deployed at the back end, wherein the node types of the multiple processing nodes are different; an acquisition module 52 is used to obtain a first type of processing architecture and a second type of processing architecture corresponding to each type of processing node in the multiple processing nodes, and the architecture types of the first type of processing architecture and the second type of processing architecture are different; a receiving module 54 is used to receive the flow distribution expected parameters of the front-end application, wherein the flow distribution expected parameters are used to indicate the processing system that receives the flow data and the ratio of the flow data size received by each processing system to the total flow data size, wherein the ratio includes: the first processing system corresponding to the first type of processing architecture A first ratio of the size of the traffic data to be received to the size of the total traffic data, and a second ratio of the size of the traffic data to be received by the second processing system corresponding to the second type of processing architecture to the size of the total traffic data; an allocation module 56, for allocating the traffic data to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio, respectively, achieving the technical effect of allocating traffic resources across platforms and performing hybrid control of traffic resources based on different processor structures, thereby solving the technical problem in the related technology that cross-platform hybrid control of traffic resources cannot be achieved due to the server node resource orchestration tasks that can only be based on a single processor architecture.

[0070] According to another aspect of an embodiment of the present application, a non-volatile storage medium is further provided, the storage medium including a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute any one of the flow control methods.

[0071] Specifically, the above storage medium is used to store program instructions for the following functions to implement the following functions:

[0072] Determine a plurality of processing nodes of a backend deployment, wherein the plurality of processing nodes are of different node types; obtain a first type processing architecture and a second type processing architecture corresponding to each type of processing node of the plurality of processing nodes, wherein the first type processing architecture and the second type processing architecture are of different architecture types; receive traffic distribution expectation parameters of a frontend application, wherein the traffic distribution expectation parameters are used to indicate processing systems receiving traffic data and a ratio of sizes of traffic data received by each processing system to a total size of traffic data, wherein the ratio includes a first ratio of a size of traffic data that a first processing system corresponding to the first type processing architecture should receive to the total size of traffic data, and a second ratio of a size of traffic data that a second processing system corresponding to the second type processing architecture should receive to the total size of traffic data; and distribute the traffic data to the first processing system corresponding to the first type processing architecture and the second processing system corresponding to the second type processing architecture according to the first ratio and the second ratio, respectively.

[0073] Optionally, in an embodiment, the storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the storage medium would include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0074] In an example embodiment of the present application, a computer program product is also provided, comprising a computer program which, when executed by a processor, implements any of the above traffic control methods.

[0075] Optionally, the computer program, when executed by the processor, can implement the following steps:

[0076] Determine multiple processing nodes deployed on the back end, wherein the node types of the multiple processing nodes are different; obtain a first type of processing architecture and a second type of processing architecture corresponding to each type of processing node in the multiple processing nodes, the architecture types of the first type of processing architecture and the second type of processing architecture are different; receive the expected traffic distribution parameters of the front-end application, wherein the expected traffic distribution parameters are used to indicate the processing system that receives the traffic data and the ratio of the size of the traffic data received by each processing system to the total traffic data size, wherein the ratio includes: a first ratio of the size of the traffic data that the first processing system corresponding to the first type of processing architecture should receive to the total traffic data size, and a second ratio of the size of the traffic data that the second processing system corresponding to the second type of processing architecture should receive to the total traffic data size; distribute the traffic data to the first processing system corresponding to the first type of processing architecture and the second processing system corresponding to the second type of processing architecture according to the first ratio and the second ratio, respectively.

[0077] According to an embodiment of the present application, an electronic device is provided, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute any of the above-mentioned flow control methods.

[0078] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0079] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement an embodiment of the present application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0080] like Figure 6As shown, the device 600 includes a computing unit 601 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from the storage unit 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the device 600 can also be stored in the RAM 603. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0081] Various components in the device 600 are connected to the I / O interface 605, including an input unit 606 such as a keyboard, a mouse, etc., an output unit 607 such as various types of displays, speakers, etc., a storage unit 608 such as a magnetic disk, an optical disk, etc., and a communication unit 609 such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0082] The computing unit 601 can be various general and / or special purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 601 performs various methods and processes described above, such as the flow control method. For example, in some embodiments, the flow control method can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the flow control method described above can be performed. Alternatively, in other embodiments, the computing unit 601 can be configured to perform the flow control method by any other appropriate means, such as by means of firmware.

[0083] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0084] The program code for implementing the methods of the present application can be written in any combination of one or more programming languages. Such program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the program code is executed by the processor or controller, the functions / operations specified in the flow charts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0085] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0086] To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0087] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0088] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0089] The above-mentioned sequence numbers of the embodiments of the present application are only for description, and do not represent advantages or disadvantages of the embodiments.

[0090] In the above-mentioned embodiments of the present application, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0091] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0092] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0093] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0094] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0095] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A flow control method, characterized in that: The flow control method is applied to a hybrid cloud computing processing system, wherein the hybrid cloud computing processing system includes multiple processing nodes, and each processing node supports multiple processor architectures. The method includes: Determining the plurality of processing nodes deployed at the backend, wherein the plurality of processing nodes correspond to a plurality of node types; For each type of multiple processing nodes, obtaining a first type of processor architecture and a second type of processor architecture corresponding to the multiple processing nodes of the type, where the first type of processor architecture and the second type of processor architecture are different in architecture type; Receive expected traffic distribution parameters of a front-end application, wherein the expected traffic distribution parameters are used to indicate a processing system that receives traffic data and a ratio of a size of traffic data received by each processing system to a size of total traffic data, wherein the ratios include: a first ratio of a size of traffic data to be received by a first processing system corresponding to the first type of processor architecture to a size of total traffic data, and a second ratio of a size of traffic data to be received by a second processing system corresponding to the second type of processor architecture to a size of total traffic data, the first processing system including a plurality of processing nodes corresponding to the first type of processor architecture among the plurality of processing nodes of the type, and the second processing system including a plurality of processing nodes corresponding to the second type of processor architecture among the plurality of processing nodes of the type; The traffic data is distributed to a first processing system corresponding to the first type of processor architecture and a second processing system corresponding to the second type of processor architecture according to the first ratio and the second ratio respectively.

2. The method according to claim 1, characterized in that Distributing the traffic data to a first processing system corresponding to the first type of processor architecture and a second processing system corresponding to the second type of processor architecture according to the first ratio and the second ratio, respectively, includes: Obtaining a first health status of all processing nodes corresponding to the first type of processor architecture and a second health status of all processing nodes corresponding to the second type of processor architecture; According to the first health state and the second health state, it is determined whether to distribute the flow data to a first processing system corresponding to the first type of processor architecture and a second processing system corresponding to the second type of processor architecture according to the first ratio and the second ratio, respectively.

3. The method according to claim 2, characterized in that Determining, based on the first health state and the second health state, whether to distribute the flow data to a first processing system corresponding to the first type of processor architecture and a second processing system corresponding to the second type of processor architecture according to the first ratio and the second ratio, respectively, includes: When both the first health state and the second health state are in normal state, the flow data is distributed to a first processing system corresponding to the first type of processor architecture and a second processing system corresponding to the second type of processor architecture according to the first ratio and the second ratio respectively.

4. The method according to claim 2, characterized in that Determining, based on the first health state and the second health state, whether to distribute the flow data to a first processing system corresponding to the first type of processor architecture and a second processing system corresponding to the second type of processor architecture according to the first ratio and the second ratio, respectively, includes: When the first health state is in an abnormal state, refusing to distribute the traffic according to the ratio, and sending all traffic data to a second processing system corresponding to a second processor architecture in the processing node; When the second health status is in an abnormal state, traffic distribution according to the ratio is refused, and all traffic data is sent to the first processing system corresponding to the first processor architecture in the processing node.

5. The method according to claim 1, wherein Before distributing the traffic data to a first processing system corresponding to the first type of processor architecture and a second processing system corresponding to the second type of processor architecture according to the first ratio and the second ratio, respectively, the method further includes: Determining whether the traffic data belongs to encryption and decryption service traffic data, and if the determination result indicates that the traffic data belongs to encryption and decryption service traffic data, obtaining resource pool routing information of the required encryption and decryption algorithm, and forwarding the traffic data to the load balancing server; The load balancing server forwards the traffic data to the multiple processing nodes deployed at the backend, calls the encryption and decryption algorithm according to the routing path indicated by the resource pool routing information, and encrypts and decrypts the traffic data in the processing nodes.

6. The method according to claim 1, characterized in that Distributing the traffic data to a first processing system corresponding to the first type of processor architecture and a second processing system corresponding to the second type of processor architecture according to the first ratio and the second ratio, respectively, includes: detecting a first processing system corresponding to a first processor architecture deployed in each of the plurality of processing nodes, and determining a first load level corresponding to all of the first processing systems, wherein the load level is used to indicate a processing resource occupancy rate of each processing system; detecting a second processing system corresponding to a second processor architecture deployed in each of the plurality of processing nodes, and determining a second load level corresponding to all of the second processing systems; The ratio is dynamically adjusted according to the first load level and the second load level, and the traffic data is distributed to a first processing system corresponding to the first processor architecture and a second processing system corresponding to the second processor architecture according to the dynamically adjusted ratio.

7. The method according to claim 6, characterized in that Dynamically adjusting the ratio according to the first load level and the second load level, and distributing the traffic data to a first processing system corresponding to the first processor architecture and a second processing system corresponding to the second processor architecture according to the dynamically adjusted ratio, comprising: determining a difference between the first load level and the second load level, and if the difference is greater than a first preset threshold, distributing all traffic data to a first processing system corresponding to a first processor architecture in each processing node for processing; When the difference is less than the first preset threshold and greater than the second preset threshold, a third ratio is determined as the sum of the first ratio and the difference, and flow data is allocated to the first processing system based on the third ratio, wherein the first preset threshold is greater than the second preset threshold.

8. The method according to claim 6, characterized in that Dynamically adjusting the ratio according to the first load level and the second load level, and distributing the traffic data to a first processing system corresponding to the first processor architecture and a second processing system corresponding to the second processor architecture according to the dynamically adjusted ratio, comprising: determining a difference between the second load level and the first load level, and if the difference is greater than a first preset threshold, distributing all traffic data to a second processing system corresponding to a second processor architecture in each processing node for processing; When the difference is smaller than the first preset threshold and larger than the second preset threshold, a fourth ratio is determined as the sum of the first ratio and the difference, and flow data is allocated to the second processing system based on the fourth ratio, wherein the first preset threshold is larger than the second preset threshold.

9. The method according to any one of claims 1 to 8, characterized in that The first processor architecture includes: ARM architecture, the second processor architecture includes: X86 architecture, and the multiple processing nodes include: containers, virtual machines and bare metal.

10. A flow control device, characterized in that: The flow control device is applied to a hybrid cloud computing processing system, which includes multiple processing nodes, and each processing node supports multiple processor architectures, including: a determination module, configured to determine the plurality of processing nodes deployed at the back end, wherein the plurality of processing nodes correspond to a plurality of node types; an acquisition module configured to acquire, for each type of multiple processing nodes, a first type of processor architecture and a second type of processor architecture corresponding to the multiple processing nodes of the type, wherein the first type of processor architecture and the second type of processor architecture are different in architecture type; a receiving module, configured to receive expected traffic distribution parameters of a front-end application, wherein the expected traffic distribution parameters are used to indicate a processing system that receives traffic data and a ratio of the size of traffic data received by each processing system to the size of the total traffic data, wherein the ratios include: a first ratio of the size of traffic data to be received by a first processing system corresponding to the first type of processor architecture to the size of the total traffic data, and a second ratio of the size of traffic data to be received by a second processing system corresponding to the second type of processor architecture to the size of the total traffic data, the first processing system including multiple processing nodes of the type corresponding to the first type of processor architecture, and the second processing system including multiple processing nodes of the type corresponding to the second type of processor architecture; A distribution module is used to distribute the traffic data to a first processing system corresponding to the first type of processor architecture and a second processing system corresponding to the second type of processor architecture according to the first ratio and the second ratio respectively.

11. A non-volatile storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the flow control method according to any one of claims 1 to 9.

12. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the flow control method according to any one of claims 1 to 9.

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