CN116723111B

CN116723111B - Service request processing method, system and electronic equipment

Info

Publication number: CN116723111B
Application number: CN202310997362.1A
Authority: CN
Inventors: 黄冠; 陈劢; 雷浪声
Original assignee: Shenzhen Shifang Ronghai Technology Co ltd
Current assignee: Shenzhen Shifang Ronghai Technology Co ltd
Priority date: 2023-08-09
Filing date: 2023-08-09
Publication date: 2023-10-20
Anticipated expiration: 2043-08-09
Also published as: CN116723111A

Abstract

The disclosure provides a service request processing method, a service request processing system and electronic equipment, and relates to the technical field of resource scheduling. The specific implementation scheme is as follows: receiving a service request sent by a capacity open platform, and determining a plurality of virtual capacity network elements; establishing communication between a computing network brain and each virtual capacity network element, sending request information with a designated message format, and acquiring first data returned by each virtual capacity network element; screening the virtual capacity network elements according to the attribute information of each virtual capacity network element to obtain a first virtual capacity network element; determining the priority order of each first virtual capacity unit according to the calculation force information and the second data; acquiring each target virtual capacity network element from each first virtual capacity network element based on the target number and the priority order; and calling each target virtual capacity network element to process the service corresponding to the service request. The service request is processed by scheduling a plurality of virtual capacity network elements, so that the overall processing speed and throughput are improved.

Description

Service request processing method, system and electronic equipment

Technical Field

The disclosure relates to the technical field of data processing, in particular to a service request processing method, a service request processing system and electronic equipment.

Background

In the current technical environment, the efficiency and resource utilization of business processing are one of the important points of each enterprise. However, in conventional system architectures, there are several problems that are difficult to solve. For a receiving capability open platform, the platform needs to handle scheduling issues from multiple service requests. Each service request may require reliance on a different virtualization capability node to complete processing. The current use of conventional communication protocols and system architecture modes often fails to meet the requirements of such dynamic scheduling. For example, the conventional HTTP protocol is used for communication, so that service request processing with high real-time requirements cannot be effectively realized, and the problem of insufficient resource utilization is faced. In addition, the types of service requests are various, and the conventional static configuration is not good enough in terms of screening virtual nodes.

Thus, how to flexibly, efficiently and effectively schedule resources to meet the needs of service requests is a problem that needs to be solved at present.

Disclosure of Invention

The disclosure provides a service request processing method and a service request processing system.

According to an aspect of the present disclosure, there is provided a method for processing a service request, including:

Receiving a service request sent by a capacity open platform, and determining a plurality of virtual capacity network elements to be scheduled for the service request according to the service request, wherein the virtual capacity network elements have corresponding processing speed indexes, and the processing speed indexes at least comprise throughput, response time, concurrent connection number, data packet loss rate and CPU utilization rate;

establishing communication between a computing network brain and each virtual capability network element based on a WebSocket protocol, sending request information with a specified message format to each virtual capability network element in a resource pool, and acquiring first data returned by each virtual capability network element in an API (application program interface) endpoint, wherein the first data comprises second data corresponding to the processing speed index, computing power information of the virtual capability network element and attribute information of the virtual capability network element;

based on the service type contained in the service request, screening each virtual capacity network element according to the attribute information of each virtual capacity network element to obtain each first virtual capacity network element;

determining the priority order of each first virtual capacity unit according to the calculation force information and the second data;

Acquiring target quantity corresponding to the service type, and acquiring each target virtual capacity network element from each first virtual capacity network element based on the target quantity and the priority order;

and calling each target virtual capacity network element to process the service corresponding to the service request through an atomic API/composite API.

According to still another aspect of the present disclosure, there is provided a processing system for a service request, including:

the system comprises a receiving module, a processing module and a processing module, wherein the receiving module is used for receiving a service request sent by a capacity open platform, and determining a plurality of virtual capacity network elements which need to be scheduled for the service request according to the service request, wherein the virtual capacity network elements have corresponding processing speed indexes, and the processing speed indexes at least comprise throughput, response time, concurrent connection number, data packet loss rate and CPU utilization rate;

the communication module is used for establishing communication between a computing network brain and each virtual capability network element based on a WebSocket protocol, sending request information with a specified message format to each virtual capability network element in a resource pool, and acquiring first data returned by each virtual capability network element in an API endpoint, wherein the first data comprises second data corresponding to the processing speed index, computing power information of the virtual capability network element and attribute information of the virtual capability network element;

The screening module is used for screening each virtual capacity network element according to the attribute information of each virtual capacity network element based on the service type contained in the service request so as to obtain each first virtual capacity network element;

the determining module is used for determining the priority order of each first virtual capacity unit according to the computing power information and the second data;

the acquisition module is used for acquiring the target quantity corresponding to the service type and acquiring each target virtual capacity network element from each first virtual capacity network element based on the target quantity and the priority order;

and the calling module is used for calling each target virtual capacity network element to process the service corresponding to the service request through an atomic API/composite API.

According to another aspect of the present disclosure, there is provided an electronic device including:

at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the above embodiments.

According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method according to the above-described embodiments.

According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method described in the above embodiments.

In the embodiment of the disclosure, firstly, a service request sent by a capability open platform is received, and a plurality of virtual capability network elements to be scheduled for the service request are determined according to the service request, wherein the virtual capability network elements have corresponding processing speed indexes, the processing speed indexes at least comprise throughput capacity, response time, concurrent connection number, data packet loss rate and CPU utilization rate, then, based on WebSocket protocol, communication between a computing network brain and each virtual capability network element is established, request information with a designated message format is sent to each virtual capability network element in a resource pool, first data returned by each virtual capability network element is acquired in an API endpoint, the first data comprises second data corresponding to the processing speed indexes, the computing capability information of each virtual capability network element and the attribute information of the virtual capability network elements, then, based on service types contained in the service request, each virtual capability network element is screened according to the attribute information of each virtual capability network element, so as to obtain each first virtual capability network element, request information and the first virtual capability network element are acquired according to the priority order, and the target number of the target virtual capability network elements is acquired from the API, and the target priority is determined, and finally, the target number of the target virtual capability network elements is acquired. Therefore, the service request is processed by scheduling a plurality of virtual capacity network elements, the processing capacity of each virtual capacity network element can be utilized, the overall processing speed and throughput can be improved, the virtual capacity network element which is most suitable for processing the current service request can be screened out based on the attribute information and the calculation capacity information of the virtual capacity network element, the system resource can be utilized more effectively, the priority order of each virtual capacity unit is determined according to the calculation capacity information and the second data (the data corresponding to the processing speed index) of the virtual capacity network element, so that the request with high priority can be ensured to be responded and processed more quickly, different service types can be processed flexibly, the virtual capacity network element which is suitable for processing the specific type of service can be screened out according to the service type and the attribute information of the virtual capacity network element in the service request, the service request with different types can be processed flexibly, the atomic API/composite API can be supported, the service request can be processed correspondingly by calling the atomic API or the composite API of the target virtual capacity network element, and the service requirement can be met.

It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.

Drawings

The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:

fig. 1 is a flow chart of a method for processing a service request according to an embodiment of the disclosure;

FIG. 2 is a schematic structural diagram of another service request processing system according to an embodiment of the present disclosure;

fig. 3 is a block diagram of an electronic device for implementing a method of processing a service request according to an embodiment of the present disclosure.

Description of the embodiments

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

The following describes a service request processing method, a service request processing system and an electronic device in detail with reference to the accompanying drawings.

Fig. 1 is a flow chart of a method for processing a service request according to an embodiment of the disclosure.

As shown in fig. 1, the method includes:

step 101, receiving a service request sent by a capability open platform, and determining a plurality of virtual capability network elements to be scheduled for the service request according to the service request, wherein the virtual capability network elements have corresponding processing speed indexes, and the processing speed indexes at least comprise throughput, response time, concurrent connection number, data packet loss rate and CPU utilization rate.

The service request may be used to process any service, such as by way of example, an AI voice notification capability. The AI voice notification capability is the capability of intelligent outbound provided for a third party application, the third party application establishes voice call connection between a calling party (AI voice) and a called party (mobile phone or fixed phone) through a capability opening platform, the AI voice plays voice notification content to the called party, and performs multi-round dialogue with the called party according to semantic analysis, and the whole call process is recorded.

The service request may be a service request for an AI voice notification capability, so as to obtain each virtual capability network element capable of implementing the AI voice notification capability.

The capability open platform (Open Capability Platform) is a platform that provides open interfaces and tools that enable developers to build and integrate various functions. The method allows a third party developer to utilize the existing and resources on the platform and realize innovative application and service through interface calling and data interaction.

A virtual capability network element (Virtual Capability Network Element) refers to a network element with virtualization technology and programmable capabilities. It is part of network function virtualization (Network Function Virtualization, NFV) that converts traditional physical network devices and functions into software-defined virtual instances. Virtual capability network elements are capable of providing network functions and services such as routing, switching, firewalls, etc., in a flexible, extensible manner, and may be dynamically deployed and managed in a cloud environment.

The processing speed index of the virtual function network element can evaluate the efficiency and performance of the virtual function network element when processing network traffic and service requests. The method reflects the strength of the processing capacity of the virtual function network element, and the indexes including the data packet forwarding speed, the service response time and the processing concurrent request can be used for measuring the performance level of the virtual function network element and the reliability of the virtual function network element in meeting the network requirements.

Throughput: the number of requests or data volume that can be handled by the virtual capability network element per second.

Response time: the time required for the virtual capability network element to complete the processing of the request.

Number of concurrent connections: the maximum number of connections simultaneously supported by the virtual capability network element.

Packet loss rate: the proportion of packet loss that may occur from input to output during processing.

CPU utilization: the CPU usage of the virtual capability network element represents the resource occupation condition of the virtual capability network element when processing the request.

Wherein, the multiple virtual capacity network elements to be scheduled for the service request are each virtual capacity network element in the resource pool, and then the virtual capacity network elements need to be screened.

Step 102, based on WebSocket protocol, establishing communication between the computing network brain and each virtual capability network element, sending request information with a designated message format to each virtual capability network element in a resource pool, and acquiring first data returned by each virtual capability network element in an API endpoint, wherein the first data comprises second data corresponding to a processing speed index, computing power information of the virtual capability network element and attribute information of the virtual capability network element.

WebSocket is a TCP-based protocol that provides a bi-directional communication path that establishes a real-time, persistent communication connection between the computing network brain and various virtual capability network elements. When WebSocket is used for communication, a request message can be sent to each virtual capability network element, and the message format can be defined according to specific requirements. The request information may be sent to each virtual capability network element in the resource pool and then the first data returned by each virtual capability network element is monitored at the API endpoint.

By means of real-time connection of WebSocket, efficient two-way communication can be supported, and the method is very useful for real-time data and scenes of interaction with virtual capacity network elements. The first data is returned by the virtual capacity network element and comprises second data, calculation force information and attribute information. The second data is data corresponding to the processing speed index, the calculation force information may be used to evaluate the calculation force of the virtual capability network element, and the attribute information may be a capability label of the virtual capability network element, which is not limited herein.

Communication between the computing network brain and the virtual capacity network element is established based on the WebSocket protocol, real-time two-way communication can be realized, and efficiency and stability between the computing network brain and the virtual capacity network element are improved. The request information with the appointed message format is sent to each virtual capacity network element in the resource pool, so that the dynamic scheduling and management of the virtual capacity network elements can be realized, and the resource utilization efficiency is improved. The data returned by each virtual capacity network element is obtained from the API end point, so that the monitoring and the management of the virtual capacity network elements can be realized, the problems can be found and solved in time, and the reliability and the safety of the system are improved. The first data comprises second data corresponding to the processing speed index, so that task scheduling and optimization of a computing network brain can be facilitated, and the efficiency and accuracy of task processing are improved. The first data contains the computing power information and attribute information of the virtual capacity network element, so that the computing network brain can be helped to carry out resource scheduling and management, and the resource utilization efficiency and the expandability of the system are improved.

When the designated message format can be JSON, XML, protobuf, factors such as simplicity, readability, expandability and the like of the message format are considered, so that confidentiality and integrity of the message are ensured.

Step 103, based on the service type contained in the service request, screening each virtual capacity network element according to the attribute information of each virtual capacity network element to obtain each first virtual capacity network element.

Optionally, service type information and service processing level may be extracted from the service request, a plurality of target functions corresponding to the service type information may be determined, then function labels, cost information and availability information corresponding to each virtual capability network element are determined according to attribute information of each virtual capability network element, then second virtual capability network elements are obtained from each virtual capability network element according to the target functions and each function label, then cost levels and availability levels corresponding to the service processing level are determined based on a preset mapping relationship, then whether each second virtual capability network element meets the cost levels and the availability levels according to the cost information and the availability information corresponding to each second virtual capability network element, and finally the second virtual capability network elements meeting the cost levels and the availability levels are regarded as the first virtual capability network elements.

Specifically, the service type information and the service processing level are extracted from the service request, and regular expression and character string interception can be used. The attribute information of each virtual capability network element is determined, and metadata management techniques may be used, such as storing the attribute information of the virtual capability network element in a database, and obtaining the attribute information by querying the database. The attribute information includes cost information, availability information and function labels of the virtual capability network element, which are not limited herein.

The cost information and the function label are information which is input in advance, and the availability information is determined according to the processing condition of the virtual capacity network element, for example, if the current resource occupancy rate of the virtual capacity network element is higher, the memory is smaller and the virtual capacity network element is fully occupied, the virtual capacity network element is unavailable.

Specifically, the failure rate of the system may be determined by analyzing past failure data and monitoring indicators of the virtual capability network element. A lower failure rate indicates higher availability. And redundancy and backup can be considered, whether the virtual capacity network element is available is judged in advance by analyzing redundancy and backup mechanisms implemented by the system, such as hot backup, cold backup or cloud-based fault tolerance, or whether complete system testing and fault recovery testing are performed can be known through testing and failure recovery strategies, and the influence of the testing on the availability is evaluated.

The cost is divided into hardware cost and software cost: the required hardware and software resources are analyzed and their corresponding costs, including procurement, maintenance and level fees, are assessed. Energy consumption: and evaluating the energy consumption of the virtual capacity network element and calculating the energy composition related to the virtual capacity network element. Maintenance and support costs: personnel and resource costs associated with virtual capability network element maintenance, management and support are assessed. Additional cost: other costs associated with virtual capability network element deployment and operation, such as training fees, license fees, etc., are considered. After analyzing the availability and cost, a hierarchical system may be used to assign an appropriate level to each virtual capability network element. For example, with a high-to-low level index, virtual capability network elements may be classified into high availability, medium availability, and low-similarly, according to their availability, virtual capability network elements may be classified into high-cost, medium-cost, and low-cost levels, according to a cost index.

It should be noted that, one service type information may correspond to a plurality of functions, for example, if the service type is AI voice notification capability, the audio/video capability network element interfacing capability, IMS network processing capability, recording capability, and audio processing capability may be used as target functions. Further, the function labels corresponding to each virtual capability network element can be compared, so that a second virtual capability network element of the target function is obtained. For example, if the service type is AI voice notification capability, each of the first virtual capability network elements is W1, W2, W3, W4, W5, and W6, and the function labels corresponding to W3, W4, W5, and W6 are audio/video capability network element interfacing capability, IMS network processing capability, recording capability, and audio processing capability, respectively, then W3, W4, W5, and W6 may be used as the second virtual capability network element.

Further, the corresponding cost level and availability level may be determined according to the cost information and availability information of the second virtual capability network element. In determining the cost level and availability level corresponding to the traffic processing level, hierarchical management techniques may be used, such as dividing the processing level into higher, middle, and lower levels, corresponding to different cost levels and availability levels, respectively. The cost level and the availability level corresponding to different service processing levels are also different.

For example, if the service processing level is relatively low, a second virtual capability network element with a low cost level and a low availability level may be used, and if the service processing level is relatively high, a second virtual capability network element with a high cost level and a high availability level may be used. Further, respective cost levels and availability levels corresponding to each business capability level may be established.

For example, the cost class is classified into A1, A2, A3, A4, A5, A6, wherein the cost class of A1 is lowest, the cost class of A6 is highest, the cost class of A1, A2, A3, A4, A5, A6 is sequentially increased, the availability class is classified into B1, B2, B3, B4, B5, B6, wherein the cost class of B1 is lowest, the cost class of B6 is highest, the cost class of B1, B2, B3, B4, B5, B6 is sequentially increased, the business process class is classified into C1, C2, C3, wherein the class of C1 is lowest, the class of C3 is highest, thus it is determined that the cost class corresponding to C1 is A1, A2, the corresponding availability class is B1, B2, the cost class corresponding to C2 is A3, A4, the corresponding availability class is B3, B4, the cost class corresponding to C3, A5, A6, and the corresponding availability class is B5, B6. Further, a cost level and an availability level corresponding to each second virtual capability network element may be determined, and whether the cost level and the availability level match the service processing level may be determined. For example, if the service processing level corresponding to the AI voice notification capability is the highest level C3, the second virtual capability network element with the cost level of A1, A2, A3, A4 and the second virtual capability network element with the availability level of B1, B2, B3, B4 are not in compliance. And finally, taking the second virtual capacity network element meeting the cost grade and the availability grade as the first virtual capacity network element, and caching information of the virtual capacity network element meeting the requirements into a memory by using a caching technology, for example, so as to improve the query efficiency.

Step 104, determining the priority order of each first virtual capability unit according to the calculation force information and the second data.

Optionally, the second data may be parsed to determine the throughput, response time, concurrent connection number, packet loss rate and CPU utilization corresponding to each of the first virtual capability network elements.

And determining that the processing speed score corresponding to the first virtual capacity network element is the highest score under the conditions that the throughput corresponding to the first virtual capacity network element is larger than a first threshold, the response time is smaller than a second threshold, the concurrent connection number is larger than a third threshold, the data packet loss rate is smaller than a fourth threshold and the CPU utilization rate is smaller than a fifth threshold.

Wherein, the virtual capability network element refers to a device or node executing a network function in a virtualized environment. Such network elements may carry network traffic.

The throughput refers to the amount of data passing through the network element in a unit time. Higher throughput generally means higher processing speed because network elements can process and forward data packets faster.

Where the response time is the time at which the network element responds to the request. A lower response time means faster processing speed because the network element can process the received request and send the response faster.

The concurrent connection number refers to the number of connections that are simultaneously established and maintained. A higher number of concurrent connections may affect the processing speed because each connection requires processing and management by the network element.

The data packet loss rate refers to the proportion of the data packets lost in the transmission process to the total transmitted data packets. Lower packet loss rates help to increase processing speed because the network element does not need to retransmit or process lost packets.

The CPU utilization rate refers to the utilization rate of CPU resources when executing tasks. A higher CPU utilization may have an impact on processing speed because a high load may reduce processing capacity and thus a lower CPU utilization corresponds to a greater processing speed.

Optionally, first, based on a predetermined mapping relationship between a section and a score, determining a score corresponding to each target section according to the throughput, response time, concurrent connection number, packet loss rate and CPU utilization rate corresponding to each first virtual capability network element, and taking a sum of the scores corresponding to each target section as a processing speed score corresponding to the first virtual capability network element.

The larger the number of the target section is, the larger the corresponding score is, and the smaller the number of the target section is, the larger the corresponding score is, for the response time, the packet loss rate, and the CPU utilization.

Further, the computing power information may be parsed to determine a unit power consumption, a network processing capability, a storage resource, a network resource utilization rate, and an operational capability corresponding to each of the first virtual capability network elements.

And judging whether the unit power consumption, the network processing capacity, the storage resource, the network resource utilization rate and the computing capacity corresponding to each first virtual capacity network element meet corresponding preset conditions according to the computing capacity demand information contained in the service request, and determining that the first virtual capacity network element, the unit power consumption, the network processing capacity, the storage resource, the network resource utilization rate and the computing capacity of which meet the corresponding preset conditions, is a third virtual capacity network element.

The preset condition may be a corresponding target numerical range.

Determining the service processing capacity of each first virtual capacity network element under a plurality of levels of service volume and the computing power demand information corresponding to the service processing capacity according to the historical data;

Determining the current level traffic of each first virtual capacity network element;

determining computing power demand information corresponding to the current level of traffic;

and determining the preset conditions respectively corresponding to the unit power consumption, the network processing capacity, the storage resource, the network resource utilization rate and the computing capacity according to the computing power demand information.

Specifically, relevant data of each first virtual capacity network element under a plurality of levels of traffic can be collected, including service processing capacity and corresponding calculation power demand information. Such data may come from system logs, monitoring tools, or other resources. The historical data can then be analyzed: and processing and analyzing the historical data by utilizing a data analysis technology to determine the service processing capacity of the first virtual capacity network element under different levels of service volume and corresponding calculation power demand information. The current level traffic may then be measured: and obtaining the traffic data of the current level by monitoring the traffic of each first virtual capacity network element in the current system. This may be accomplished by a real-time monitoring system or other performance monitoring tool. The power demand information may then be determined: and according to the current-level traffic data, combining the historical data results obtained by previous analysis, and determining the calculation power demand information corresponding to the current-level traffic. This may require estimating parameters of the computational force demand by interpolation, regression or other correlation methods. Finally, the preset conditions can be determined: and determining preset conditions such as unit power consumption, network processing capacity, storage resources, network resource utilization rate, computing capacity and the like according to the obtained computing power demand information.

Further, the priority order of the third virtual capacity network elements may be determined according to the processing speed score corresponding to each third virtual capacity network element, where the first virtual capacity network element that does not meet the preset condition is at the lowest priority.

Wherein, the higher the processing speed score, the higher the priority, and the lower the processing speed score, the lower the priority.

Optionally, a first score corresponding to the computing power information corresponding to the third virtual capability network element may be determined, and the priority order may be adjusted based on the first score.

Wherein the first score is used to evaluate the computational power of the third virtual capability network element. The specific calculation mode of the first score can refer to the processing speed score.

Specifically, the first score may be added to the processing speed score to obtain a second score, so that the priority order is determined according to the magnitude of the second score.

Step 105, obtaining the target number corresponding to the service type, and obtaining each target virtual capacity network element from each first virtual capacity network element based on the target number and the priority order.

Specifically, the first virtual capacity network elements with the target number and the front priority can be obtained as target virtual capacity network elements according to the priority sequence corresponding to each first virtual capacity network element.

The target number may be a number to be acquired corresponding to the service type. The number of targets corresponding to different traffic types is also different. For example, if the service type is a, the target number is 3, and the first virtual capability network element is ranked as S1, S2, S3, S4 according to the priority from high to low, S1, S2, S3 may be used as the target virtual capability network element.

Specifically, under the condition that the number of the first virtual capacity network elements is smaller than the target number, fault repair is required, and then the target virtual capacity network elements are acquired.

And collecting target operation parameters of each first virtual capacity network element based on the computing network brain so as to judge whether a fault virtual capacity network element exists in each first virtual capacity network element, and if so, carrying out fault recovery on the fault virtual capacity network element based on Kubernetes and VMware vSphere.

And determining the fault grade of the fault virtual capacity network element, isolating the fault virtual capacity network element if the fault grade is greater than a preset grade, and sending early warning information to the capacity open platform and simultaneously starting a preset optimization strategy.

It should be noted that, the target operation parameters may be collected first, that is, the target operation parameters of each first virtual capacity network element including performance indexes, state information, and the like may be collected through the computing network brain, then fault detection is performed, and fault detection is performed on each first virtual capacity network element using the collected target operation parameters. And judging whether the fault virtual capacity network element exists or not by analyzing the abnormal condition of the parameter, the numerical value exceeding the threshold value or the error state. If a failed virtual capability network element exists, the failure can be recovered by using the container orchestration tool Kubernetes and the virtualization platform VMware vSphere. The specific operation comprises the following steps:

The affected virtual machines or container instances are redeployed in Kubernetes, ensuring that they restart on the host node of the failed virtual capability network element. With the recovery function provided by VMware vSphere, virtual machine migration, recovery snapshot, or other operations may be required to ensure that the failed virtual capability network element is repaired at the physical level. Further, fault level assessment may be performed: and carrying out fault grade evaluation on the fault virtual capacity network element to determine the severity of the fault virtual capacity network element. The evaluation may be determined based on the extent to which the fault affects system function, performance, and stability. Finally, fault processing can be performed, and if the fault level is greater than the preset level, the following operations are executed:

the faulty virtual capacity network element is isolated from further influences on other parts.

And sending early warning information to the capacity opening platform, informing related personnel about fault conditions, and providing necessary detailed information.

Specifically, an optimization strategy may also be started, that is, corresponding measures may be started to cope with the failure according to a preset optimization strategy, and specifically, load balancing adjustment, container/virtual machine redistribution, failover, or other optimization means may be included, so as to maintain performance and stability of the system to the greatest extent.

Thus, automatic fault detection and recovery can be performed: the calculation network brain collects the target operation parameters of each first virtual capacity network element, so that whether the fault virtual capacity network element exists or not can be judged, and the fault recovery is carried out by utilizing Kubernetes and VMware vSphere. This will reduce human intervention and quick response to system failures, improving the reliability and usability of the system. Fault level determination and handling: by judging the fault grade of the fault virtual capacity network element, corresponding processing can be carried out according to the preset grade. If the fault level is greater than the preset level, the system automatically isolates the fault virtual capacity network element and sends early warning information, and a preset optimization strategy is started. This minimizes the impact of the fault and takes timely action to avoid greater losses. System stability and performance are improved: by automatic fault recovery and initiation of the optimization strategy, the system is able to quickly recover to normal conditions and maintain a high level of performance. This will reduce the impact of faults on user experience and business continuity, improving the stability and performance of the system. In general, the technical scheme can realize intelligent fault detection, recovery and optimization, and has remarkable advantages in improving the reliability, stability and performance of the system.

And 106, calling each target virtual capacity network element to process the service corresponding to the service request through the atomic API/composite API.

The atomic API and the composite API are two API design modes for modularization and combination of services. Specifically, to the network elements with virtualization capability, each network element can be called through the APIs to execute corresponding service request processing. For atomic APIs, they typically represent a single function or capability that can be invoked and used separately. For example, assuming an atomic API is used to send a short message, the API may be used to send the short message to a specified cell phone number. The composite API is composed of multiple atomic APIs, providing a higher level of functionality or service. Through the composite API, a series of atomic APIs can be used to implement more complex business processes. For example, a composite API may be created to handle user registration procedures, which includes raw API calls for multiple steps of validating user names, checking, saving user data, and the like. In summary, the atomic API and the composite API may be used to call different network elements with virtualization capabilities, so as to perform corresponding processing on the service request. By appropriate design and combination of these APIs, a flexible, extensible, and reusable system architecture can be constructed.

In the embodiment of the disclosure, firstly, a service request sent by a capability open platform is received, and a plurality of virtual capability network elements to be scheduled for the service request are determined according to the service request, wherein the virtual capability network elements have corresponding processing speed indexes, the processing speed indexes at least comprise throughput capacity, response time, concurrent connection number, data packet loss rate and CPU utilization rate, then, based on WebSocket protocol, communication between a computing network brain and each virtual capability network element is established, request information with a designated message format is sent to each virtual capability network element in a resource pool, first data returned by each virtual capability network element is acquired in an API endpoint, the first data comprises second data corresponding to the processing speed indexes, the computing capability information of each virtual capability network element and the attribute information of the virtual capability network elements, then, based on service types contained in the service request, each virtual capability network element is screened according to the attribute information of each virtual capability network element, so as to obtain each first virtual capability network element, request information and the first virtual capability network element are acquired according to the priority order, and the target number of the target virtual capability network elements is acquired from the API, and the target priority is determined, and finally, the target number of the target virtual capability network elements is acquired. Therefore, the service request is processed by scheduling a plurality of virtual capacity network elements, the processing capacity of each virtual capacity network element can be utilized, the overall processing speed and throughput can be improved, the virtual capacity network element which is most suitable for processing the current service request can be screened out based on the attribute information and the calculation capacity information of the virtual capacity network element, the system resource can be utilized more effectively, the priority order of each virtual capacity unit is determined according to the calculation capacity information and the second data (the data corresponding to the processing speed index) of the virtual capacity network element, so that the request with high priority can be ensured to be responded and processed more quickly, different service types can be processed flexibly, the virtual capacity network element which is suitable for processing the specific type of service can be screened out according to the service type and the attribute information of the virtual capacity network element in the service request, the service request with different types can be processed flexibly, the atomic API/composite API can be supported, the service request can be processed correspondingly by calling the atomic API or the composite API of the target virtual capacity network element, and the service requirement can be met. The WebSocket protocol is introduced as a communication means, and a real-time communication mechanism between the computing network brain and each virtualization capability node is established. Through the WebSocket protocol, two-way communication can be realized, request information with a designated message format is transmitted in real time, and first data returned by each virtualization capability node is acquired. In addition, according to the type of the service request and the attribute information of each virtualization capability node, an intelligent screening algorithm is adopted to dynamically select a proper virtualization node to process the request. By analyzing the computational power information and the second data of the virtualization capability node, a priority order may be determined, ensuring faster response and processing of high priority requests. Finally, the processing of the service request is completed by calling an atomic API or a composite API of the target virtualization capability node. The scheme not only improves the efficiency and expandability of service processing, but also fully utilizes system resources and meets complex and changeable service demands.

In order to implement the above embodiment, the embodiment of the present disclosure further provides a service request processing system. Fig. 2 is a schematic structural diagram of a service request processing system according to an embodiment of the present disclosure.

As shown in fig. 2, the service request processing system 200 includes: the system comprises a receiving module 210, a communication module 220, a screening module 230, a determining module 240, an obtaining module 250 and a calling module 260.

Optionally, the screening module is specifically configured to:

extracting service type information and service processing grade from the service request, and determining target functions corresponding to the service type information, wherein the target functions are multiple;

determining function labels, cost information and availability information corresponding to each virtual capability network element according to the attribute information of each virtual capability network element;

Acquiring a second virtual capacity network element from each virtual capacity network element according to the target function and each function label;

determining a cost grade and an availability grade corresponding to the service processing grade based on a preset mapping relation;

judging whether each second virtual capacity network element meets the cost grade and the availability grade according to the cost information and the availability information corresponding to each second virtual capacity network element;

and taking the second virtual capacity network element meeting the cost grade and the availability grade as the first virtual capacity network element.

Optionally, the determining module includes:

the first analyzing unit is used for analyzing the second data to determine the throughput, response time, concurrent connection number, data packet loss rate and CPU utilization rate corresponding to each first virtual capacity network element;

the first determining unit is configured to determine a score corresponding to each target interval according to the throughput, response time, concurrent connection number, packet loss rate and CPU utilization rate corresponding to each first virtual capacity network element based on a mapping relationship between a predetermined interval and a score, and use a sum of the scores corresponding to each target interval as a processing speed score corresponding to the first virtual capacity network element;

The second analyzing unit is used for analyzing the computing power information to determine unit power consumption, network processing capacity, storage resources, network resource utilization rate and computing capacity corresponding to each first virtual capacity network element;

the judging unit is used for judging whether the unit power consumption, the network processing capacity, the storage resource, the network resource utilization rate and the computing capacity corresponding to each first virtual capacity network element meet corresponding preset conditions according to the computing power demand information contained in the service request, and determining that the first virtual capacity network element, the unit power consumption, the network processing capacity, the storage resource, the network resource utilization rate and the computing capacity meet the corresponding preset conditions, is a third virtual capacity network element;

a second determining unit, configured to determine a priority order of each third virtual capability network element according to a processing speed score corresponding to each third virtual capability network element, where the first virtual capability network element that does not meet the preset condition is at a lowest priority;

and the third determining unit is used for determining a first score corresponding to the computing power information corresponding to the third virtual capacity network element and adjusting the priority order based on the first score.

Optionally, the first parsing unit is further configured to:

Optionally, the judging unit is further configured to:

Optionally, the acquiring module is specifically configured to:

and under the condition that the number of the first virtual capacity network elements is smaller than the target number, collecting target operation parameters of the first virtual capacity network elements based on the computing network brain to judge whether fault virtual capacity network elements exist in the first virtual capacity network elements, and if so, carrying out fault recovery on the fault virtual capacity network elements based on Kubernetes and VMware vSphere.

Optionally, the acquiring module is further configured to:

According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

Fig. 3 illustrates a schematic block diagram of an example electronic device 600 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile systems, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing systems. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 3, the apparatus 600 includes a computing unit 601 that can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 602 or a computer program loaded from a storage unit 608 into a RAM (Random Access Memory ) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other by a bus 604. An I/O (Input/Output) interface 605 is also connected to bus 604.

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

The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing units 601 include, but are not limited to, a CPU (Central Processing Unit ), a GPU (Graphic Processing Units, graphics processing unit), various dedicated AI (Artificial Intelligence ) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processor, digital signal processors), and any suitable processors, controllers, microcontrollers, and the like. The computing unit 601 performs the respective methods and processes described above, for example, a processing method of a service request. For example, in some embodiments, the method of processing a service request may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto 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 above-described processing method of service requests may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the processing method of the service request in any other suitable way (e.g. by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit System, FPGA (Field Programmable Gate Array ), ASIC (Application-Specific Integrated Circuit, application-specific integrated circuit), ASSP (Application Specific Standard Product, special-purpose standard product), SOC (System On Chip ), CPLD (Complex Programmable Logic Device, complex programmable logic device), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input system, and at least one output system.

Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing system such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any suitable combination of the preceding. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory, erasable programmable read-Only Memory) or flash Memory, an optical fiber, a CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display system (e.g., CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display ) monitor) for displaying information to a user; and a keyboard and pointing system (e.g., a mouse or trackball) through which a user can provide input to the computer. Other kinds of systems can also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a background 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 background, 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: LAN (Local Area Network ), WAN (Wide Area Network, wide area network), internet and blockchain networks.

The computer system may include a client and a server. The client and server are typically 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, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service (Virtual Private Server, virtual special servers) are overcome. The server may also be a server of a distributed system or a server that incorporates a blockchain.

According to an embodiment of the present disclosure, the present disclosure further provides a computer program product, which when executed by an instruction processor in the computer program product, performs the method for processing a service request set forth in the foregoing embodiment of the present disclosure.

It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.

The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.

Claims

1. A method for processing a service request, comprising:

determining the priority order of each first virtual capacity network element according to the calculation force information and the second data;

and calling each target virtual capacity network element to process the service corresponding to the service request through an atomic API or a composite API.

2. The method according to claim 1, wherein the screening the virtual capability network elements according to the service type included in the service request and the attribute information of each virtual capability network element to obtain each first virtual capability network element includes:

3. The method according to claim 1, wherein said determining a priority order of said respective first virtual capability network elements from said computing power information and said second data comprises:

analyzing the second data to determine the throughput, response time, concurrent connection number, data packet loss rate and CPU utilization rate corresponding to each first virtual capacity network element;

based on a mapping relation between a predetermined interval and a score, determining a score corresponding to each target interval according to the throughput, response time, concurrent connection number, data packet loss rate and CPU utilization rate corresponding to each first virtual capacity network element, and taking the sum of the scores corresponding to the target intervals as a processing speed score corresponding to the first virtual capacity network element;

analyzing the computing power information to determine unit power consumption, network processing capacity, storage resources, network resource utilization rate and computing capacity corresponding to each first virtual capacity network element;

judging whether the unit power consumption, the network processing capacity, the storage resource, the network resource utilization rate and the computing capacity corresponding to each first virtual capacity network element meet corresponding preset conditions according to the computing capacity demand information contained in the service request, and determining that the first virtual capacity network element, the unit power consumption, the network processing capacity, the storage resource, the network resource utilization rate and the computing capacity meet the corresponding preset conditions, is a third virtual capacity network element;

Determining the priority order of each third virtual capacity network element according to the processing speed score corresponding to each third virtual capacity network element, wherein the first virtual capacity network element which does not meet the preset condition is in the lowest priority;

and determining a first score corresponding to the computing power information corresponding to the third virtual capacity network element, and adjusting the priority order based on the first score.

4. The method of claim 3, further comprising, after said parsing said second data to determine said throughput, response time, number of concurrent connections, packet loss rate, and CPU utilization for each of said first virtual capability network elements:

5. The method according to claim 3, wherein before determining whether the unit power consumption, the network processing capability, the storage resource, the network resource utilization rate, and the computing capability corresponding to each of the first virtual capability network elements satisfy the corresponding preset conditions according to the computing power requirement information included in the service request, the method further includes:

6. The method of claim 1, further comprising, after the obtaining a target number corresponding to the service type and based on the target number and the priority order, obtaining each target virtual capability element from the each first virtual capability element:

in case the number of said respective first virtual capability network elements is smaller than said target number,

7. The method of claim 6, further comprising, after said collecting, based on said computing network brain, target operating parameters of said respective first virtual capability network element to determine whether a failed virtual capability network element exists in said respective first virtual capability network element:

8. A system for processing a service request, comprising:

the determining module is used for determining the priority order of each first virtual capacity network element according to the computing power information and the second data;

and the calling module is used for calling each target virtual capacity network element to process the service corresponding to the service request through an atomic API or a composite API.

9. The system according to claim 8, wherein the screening module is specifically configured to:

10. An electronic device, comprising:

at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.