Bidirectional adaptive method and system for computing network and service SLA (Service Level Agreement) based on cloud edge collaboration

Abstract

The invention provides a cloud edge collaborative computing network and service SLA bidirectional self-adaption method and system, electronic equipment and a storage medium, and aims to solve the technical problem that differentiated and flexible SLA guarantee cannot be provided. The method comprises the following steps: carrying out demand portraying on a service scene, and describing a demand as a service SLA index; mapping the service SLA index into a computing network SLA index according to a preset translation algorithm, and configuring a computing network resource strategy for the system according to a strategy mapping method; issuing the information to each terminal in the system, and enabling the system to operate normally; monitoring service state information of the system; predicting the change trend of the computing network resources on the edge side; and according to the monitored service state information and the change trend of the computing network resources, adjusting a computing network resource strategy or adjusting the service operation state of the terminal side. According to the invention, agile adjustment and optimization of the computing network and service adaptation of the computing network can be realized, and the effect of network industry collaboration is achieved.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a cloud-edge collaborative computing network and service SLA bidirectional adaptive method, a cloud-edge collaborative computing network and service SLA bidirectional adaptive system, an electronic device and a computer-readable storage medium. Background Art

[0002] In the 5G era, the further improvement of mobile communication technology capabilities is expected to help vertical industrial fields reconsider how to wirelessly interconnect machines and production lines, thereby promoting the digital transformation of the entire society. Mobile communication services have changed from user-oriented (2C) types to industry-oriented (2B) types, and the terminals are mainly machines, sensors, robots, etc. For traditional 2C services, users have a much higher tolerance for service interruptions than machines. For example, when the mobile network connection quality is poor or the service is interrupted, 2C users can try to redial later, but for 2B business users, it may cause the production line to be shut down, resulting in loss of production efficiency. SLA (Service Level Agreement), also known as service level assurance, is a contract between a service provider and a customer that aims to establish a common understanding of services, priorities, responsibilities, etc. For industry customers, SLA includes business SLA,

[0003] The network SLA consists of two parts. The business SLA starts from the industry business flow and defines indicators such as the business flow interruption duration and the amount of data transmitted. The network SLA starts from the computing network perspective and defines indicators such as the rate, latency, and computing power of the network connection.

[0004] Industry-oriented SLA services usually involve users selecting a network service level, and then the service provider provides network services to users based on that level. As industry needs become more diversified, customized, and complex, providing only one fixed SLA service to industry users is no longer enough to meet the needs. How to provide industry users with a better computing network service experience and provide differentiated and flexible SLA guarantees for different industrial scenarios is an urgent problem that needs to be solved in the process of computing network services for the industry. Summary of the invention

[0005] In order to at least solve the technical problem existing in the prior art of how to provide differentiated and flexible SLA guarantees for different industrial scenario requirements, the present disclosure provides a cloud-edge collaborative computing network and business SLA bidirectional adaptive method, a cloud-edge collaborative computing network and business SLA bidirectional adaptive system, an electronic device and a computer-readable storage medium, which can realize agile tuning of the computing network according to business perception and demand changes, ensure business experience, and realize computing network adaptation to business; on the other hand, based on computing network information such as network coverage quality or available bandwidth prediction, business adaptation to the computing network is realized, so as to achieve the effect of network-industry collaboration and ensure controllable business quality.

[0006] In a first aspect, the present disclosure provides a cloud-edge collaborative computing network and service SLA bidirectional adaptive method, the method comprising:

[0007] Profiling business scenarios and describing business requirements as business SLA indicators;

[0008] According to the preset translation algorithm of the business SLA indicator to the computing network SLA indicator, the business SLA indicator is mapped to the computing network SLA indicator, and according to the strategy mapping method of the computing network SLA indicator to the computing network resource configuration, the computing network resource strategy is configured for the system;

[0009] Distribute the computing network resource strategy to each terminal in the system to ensure the normal operation of the system;

[0010] Monitoring system business status information;

[0011] Predict the changing trend of computing network resources through the preset prediction model on the edge side;

[0012] Adjust the computing network resource strategy or the terminal-side business operation status based on the monitored business status information and the changing trend of computing network resources.

[0013] Further, adjusting the computing network resource strategy or adjusting the terminal side business operation state according to the monitored business state information and the change trend of the computing network resources includes:

[0014] When the fluctuation of business status information is monitored or the change trend of the predicted computing network resources meets the preset conditions, the computing network SLA and computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information are recalculated on the edge side, and the calculation result is compared with the predicted computing network resources for the next stage;

[0015] If the predicted computing network resources for the next stage match the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information, the edge side sends the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal;

[0016] If the predicted computing network resources for the next stage do not match the computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information, the business SLA indicators will be reversely calculated on the edge side based on the predicted computing network resources for the next stage, and the business operation status on the terminal side will be adjusted to a corresponding lower level based on the calculated business SLA indicators.

[0017] Furthermore, the method further comprises:

[0018] Based on the predicted computing network resources for the next stage, before the edge side reversely calculates the service SLA indicators, the edge side initiates a computing network resource scheduling request to the cloud platform to apply for scheduling computing network resources;

[0019] Determine whether the computing network resource scheduling is successful;

[0020] If the scheduling is successful, the edge side will send the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal;

[0021] If the scheduling fails, the step of reversely calculating the service SLA indicators on the edge side is executed based on the predicted computing network resources for the next stage.

[0022] Furthermore, the fluctuation of the service status information or the predicted change trend of the computing network resources meets the preset conditions, including:

[0023] The computing network resources are reduced; or,

[0024] The business demand corresponding to the business status information becomes larger and will exceed the current indicator range, and the duration of the business demand change indicator exceeds the first time.

[0025] Furthermore, the method further comprises:

[0026] Monitoring system computing network status information;

[0027] Upload the monitored system's business status information and computing network status information, as well as the computing network SLA and computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information calculated by the edge side, and the business SLA indicators reversely calculated based on the predicted computing network resources in the next stage to the cloud platform to enrich the training set of the cloud prediction model and train the prediction model;

[0028] Update the prediction model regularly and send the updated prediction model to the edge side.

[0029] In a second aspect, the present disclosure provides a cloud-edge collaborative computing network and service SLA bidirectional adaptive system, the system comprising:

[0030] The portrait module is configured to perform demand portraits on business scenarios and describe business requirements as business SLA indicators;

[0031] A mapping and configuration module, which is configured to map the business SLA indicators to the computing network SLA indicators according to a preset translation algorithm from the business SLA indicators to the computing network SLA indicators, and configure the computing network resource strategy for the system according to a strategy mapping method from the computing network SLA indicators to the computing network resource configuration;

[0032] The distribution module is configured to distribute the computing network resource strategy to each terminal in the system to enable the system to operate normally;

[0033] A monitoring module configured to monitor the service status information of the system;

[0034] A prediction module, which is configured to predict the change trend of computing network resources through a preset prediction model on the edge side;

[0035] The adjustment module is configured to adjust the computing network resource strategy or the terminal side business operation status according to the monitored business status information and the change trend of the computing network resources.

[0036] Furthermore, the adjustment module is specifically configured as follows:

[0037] When the monitoring module detects fluctuations in the business status information or the change trend of the computing network resources predicted by the prediction module meets the preset conditions, the computing network SLA and computing network resource configuration strategy corresponding to the business SLA indicator of the current business status information are recalculated on the edge side, and the calculation result is compared with the predicted computing network resources for the next stage;

[0038] If the predicted computing network resources for the next stage can match the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information, the sending module sends the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal from the edge side;

[0039] If the predicted computing network resources for the next stage do not match the computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information, the business SLA indicators will be reversely calculated on the edge side based on the predicted computing network resources for the next stage, and the business operation status on the terminal side will be adjusted to a corresponding lower level based on the calculated business SLA indicators.

[0040] Furthermore, the system also includes an application module;

[0041] The application module is configured to initiate a network resource scheduling request to the cloud platform at the edge side to apply for scheduling network resources before the adjustment module reversely calculates the service SLA indicators at the edge side based on the predicted network resources of the next stage; and

[0042] Determine whether the computing network resource scheduling is successful;

[0043] If the scheduling is successful, the sending module sends the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal from the edge side;

[0044] If the scheduling fails, the adjustment module executes the step of reversely calculating the service SLA indicators on the edge side according to the predicted computing network resources for the next stage.

[0045] In a third aspect, the present disclosure provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the cloud-edge collaborative computing network and business SLA bidirectional adaptive method as described in any one of the first aspects.

[0046] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for bidirectional adaptation of cloud-edge collaborative computing network and business SLA as described in any one of the first aspects above is implemented.

[0047] Beneficial effects:

[0048] The cloud-edge collaborative computing network and business SLA bidirectional adaptive method, cloud-edge collaborative computing network and business SLA bidirectional adaptive system, electronic device and storage medium provided by the present invention first profile the industry business needs through the cloud platform, describe them as business SLA indicators, and obtain the computing network resource configuration plan through the computing network SLA indicator translation algorithm and strategy mapping method, and send this policy configuration to the terminal, and the system operates normally. After that, business status monitoring is achieved through the collection of key node indicators, business indicator information is visualized, and computing network resources are analyzed and predicted in real time. When changes in computing network resources and business needs are monitored, the computing network resource strategy or the terminal-side business operation status is adjusted based on the monitored business status information and the change trend of computing network resources on the edge side; two-way adjustment of policies on the business side and the computing network side is achieved. On the one hand, according to business perception and demand changes, agile tuning of the computing network is achieved to ensure business experience and adapt the computing network to the business; on the other hand, based on computing network information such as network coverage quality or available bandwidth prediction, the computing network capabilities are opened to upper-level businesses, the business layer calls on the computing network capabilities and optimizes the business layer, and the business adapts to the computing network, achieving the effect of network-industry collaboration and ensuring controllable business quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A flowchart of a bidirectional adaptive method for computing network and service SLA in cloud-edge collaboration provided in Embodiment 1 of the present disclosure;

[0050] Figure 2 A flowchart of a bidirectional adaptive method for computing network and service SLA in cloud-edge collaboration provided in Embodiment 2 of the present disclosure;

[0051] Figure 3An architecture diagram of a cloud-edge collaborative computing network and business SLA bidirectional adaptive system provided in Embodiment 3 of the present disclosure;

[0052] Figure 4 This is an architecture diagram of an electronic device provided in Embodiment 4 of the present disclosure. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, the present disclosure is further described in detail below in conjunction with the drawings and embodiments. It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention, rather than to limit the present invention.

[0054] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence; and, in the absence of conflict, the embodiments in the present disclosure and the features in the embodiments can be arbitrarily combined with each other.

[0055] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure. The singular forms of "a", "said" and "the" used in the embodiments of the present disclosure and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings.

[0056] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present disclosure, and have no specific meanings. Therefore, "module", "component" or "unit" can be used in a mixed manner.

[0057] Computing network services involve thousands of industries, and the business scenarios vary greatly, and the business scenarios also have different requirements for computing networks. How to provide industry users with a better computing network service experience and provide differentiated and flexible SLA guarantees for different industrial scenarios is an urgent problem to be solved in the process of computing network services for the industry. The continuous enrichment of computing network configuration, scheduling and other capabilities also makes it possible to provide better SLA services for industry users.

[0058] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems existing in the prior art are described in detail below with specific embodiments. It is understandable that in the embodiments of the present application, the execution subject can perform some or all of the steps in the embodiments of the present application, and these steps or operations are only examples. The embodiments of the present application can also perform other operations or deformations of various operations. In addition, each step can be performed in a different order presented in the embodiments of the present application, and it is possible not to perform all the operations in the embodiments of the present application. And, the following several specific embodiments can be combined with each other, and may not be repeated in some embodiments for the same or similar concepts or processes.

[0059] Figure 1 A flowchart of a method for bidirectional self-adaptation of computing network and business SLA for cloud-edge collaboration provided in the first embodiment of the present disclosure is shown in FIG. Figure 1 As shown, the method includes:

[0060] Step S101: Profiling business scenarios and describing business requirements as business SLA indicators;

[0061] Step S102: Mapping the business SLA indicator to the computing network SLA indicator according to the preset translation algorithm of the business SLA indicator to the computing network SLA indicator, and configuring the computing network resource strategy for the system according to the strategy mapping method of the computing network SLA indicator to the computing network resource configuration;

[0062] Step S103: Send the computing network resource strategy to each terminal in the system to make the system operate normally;

[0063] Step S104: monitoring system service status information;

[0064] Step S105: predicting the change trend of computing network resources by using a preset prediction model on the edge side;

[0065] Step S106: According to the monitored service status information and the change trend of the computing network resources, adjust the computing network resource strategy or adjust the terminal side service operation status.

[0066] The disclosed embodiment first uses the cloud platform to profile the industry business needs, describes them as business SLA indicators, and describes the industry business needs as specific business SLA indicators, such as transmission cycle, application data volume, etc. In this way, the industry needs are converted into specific business SLA indicators; then, according to the preset business SLA to computing network SLA indicator mapping rules, the business SLA indicators are mapped to computing network SLA indicators, such as user rate, latency, computing power, etc. Then, according to the mapped computing network SLA indicators, the computing network resource policy set is configured for the system through the preset computing network resource configuration rules, and the policy information includes network policy and computing power policy. The business SLA indicators and computing network SLA indicators applicable to this policy set are all within a certain range, not a specific value. The business SLA to computing network SLA indicator mapping rules and computing network resource configuration rules can be set according to actual conditions and historical experience.

[0067] Specifically, the business SLA indicators, computing network SLA indicators, and computing network resource strategies can be divided into different levels, and each level can be matched. Thus, a bidirectional mapping table of business SLA indicators, computing network SLA indicators, and computing network resource strategies for this business scenario is formed. After configuring the computing network resource strategy for the system, the computing network resource strategy is issued to each terminal in the system to enable the system to operate normally. Start to collect key node indicators of the system in real time and monitor the business status information of the system.

[0068] On the edge side, the preset prediction model is used to analyze and predict the changing trends of business demand and computing network resources in real time. The preset prediction model is a big data model established by existing technical means, which predicts the changing trends of the next stage through the recorded historical data and the currently monitored data. According to the monitored business status information and the changing trends of computing network resources, the computing network resource strategy or the terminal side business operation status is adjusted; if the business demand increases and the computing network resources are sufficient, more computing network resources can be allocated to the terminal, so as to realize the agile optimization of the computing network according to the business perception and demand changes, ensure the business experience, and realize the adaptation of the computing network to the business; if the computing network resources are insufficient, in order to ensure the quality of the business, the business operation status can be adjusted to a lower level on the terminal side, so as to realize the opening of the computing network capability to the upper-layer business based on the computing network information such as network coverage quality or available bandwidth prediction, realize the business adaptation of the computing network, achieve the effect of network industry collaboration, and ensure the controllable business quality.

[0069] Further, adjusting the computing network resource strategy or adjusting the terminal side business operation state according to the monitored business state information and the change trend of the computing network resources includes:

[0070] When the fluctuation of business status information is monitored or the change trend of the predicted computing network resources meets the preset conditions, the computing network SLA and computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information are recalculated on the edge side, and the calculation result is compared with the predicted computing network resources for the next stage;

[0071] If the predicted computing network resources for the next stage match the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information, the edge side sends the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal;

[0072] If the predicted computing network resources for the next stage do not match the computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information, the business SLA indicators will be reversely calculated on the edge side based on the predicted computing network resources for the next stage, and the business operation status on the terminal side will be adjusted to a corresponding lower level based on the calculated business SLA indicators.

[0073] When business demand increases, the current computing network resource configuration strategy may not be able to meet the requirements, or the computing network resources are reduced and cannot meet business demand due to reasons such as network coverage quality or available bandwidth, it is necessary to adjust the computing network resource strategy or the terminal-side business operation status, and the preset conditions can be set according to the actual conditions under which this situation occurs; by recalculating the computing network SLA and computing network resource configuration strategy corresponding to the new business demand indicators, and comparing this result with the predicted next stage computing network resource information; if the resources can match the demand, the edge side will send the new computing network resource configuration strategy to the terminal; if the resources cannot match the demand, the edge side will reversely calculate the business SLA indicators based on the predicted next stage of computing network resources, and adjust the terminal-side business operation status to a corresponding lower level based on the calculated business SLA indicators, and achieve business adaptation computing network through business layer optimization; ensure that the business quality is controllable.

[0074] Furthermore, the method further comprises:

[0075] Based on the predicted computing network resources for the next stage, before the edge side reversely calculates the service SLA indicators, the edge side initiates a computing network resource scheduling request to the cloud platform to apply for scheduling computing network resources;

[0076] Determine whether the computing network resource scheduling is successful;

[0077] If the scheduling is successful, the edge side will send the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal;

[0078] If the scheduling fails, the step of reversely calculating the service SLA indicators on the edge side is executed based on the predicted computing network resources for the next stage.

[0079] If the predicted computing network resources for the next stage do not match the computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information, the edge side can also initiate a computing network resource scheduling request to the cloud platform to apply for scheduling computing network resources. If the scheduling is successful, there is no need to reduce the business operation status on the terminal side. Only when scheduling fails, the business operation status on the terminal side will be adjusted according to the subsequently available computing network resources.

[0080] Furthermore, the fluctuation of the service status information or the predicted change trend of the computing network resources meets the preset conditions, including:

[0081] The computing network resources are reduced; or,

[0082] The business demand corresponding to the business status information becomes larger and will exceed the current indicator range, and the duration of the business demand change indicator exceeds the first time.

[0083] When it is detected that the business demand is increasing and will exceed the current indicator range, a time window can be set, such as the first time T1, which can be set according to the actual situation, such as 30 seconds, 1 minute, 5 minutes, etc. When it is determined that the duration of the business demand change indicator exceeds T1, it is considered that the business SLA exceeds the current indicator range, and the current computing network resource configuration strategy cannot meet the system requirements. At this time, no matter how the computing network resource trend changes, the computing network SLA and computing network resource configuration strategy corresponding to the new business demand indicator are recalculated to perform computing network resource configuration; if the business demand decreases or the business demand is stable, the computing network resource change trend can be determined first. If the computing network resources are stable or increase, the system can maintain the current operation strategy unchanged. If the computing network resources decrease, the computing network SLA corresponding to the new business demand indicator and the computing network resource configuration strategy corresponding to the computing network resource calculation are performed.

[0084] Furthermore, the method further comprises:

[0085] Monitoring system computing network status information;

[0086] Upload the monitored system's business status information and computing network status information, as well as the computing network SLA and computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information calculated by the edge side, and the business SLA indicators reversely calculated based on the predicted computing network resources in the next stage to the cloud platform to enrich the training set of the cloud prediction model and train the prediction model;

[0087] Update the prediction model regularly and send the updated prediction model to the edge side.

[0088] The business status information and computing network status information of the monitored system are uploaded to the cloud platform. When resources and demand can be matched, the edge side sends the new computing network configuration strategy to the terminal, or when the computing network resource scheduling is successful, the edge side sends the new computing network configuration strategy to the terminal, or when the terminal side business operation status is adjusted to a lower matching level, the calculation results and monitoring data are uploaded to the cloud platform, thereby enriching the cloud model training set. Better training of the prediction model makes the model's prediction more accurate.

[0089] The disclosed embodiment first uses the cloud platform to profile the industry's business needs, describes them as business SLA indicators, and obtains the computing network resource configuration plan through the computing network SLA indicator translation algorithm and policy mapping method, and sends this policy configuration to the terminal, so that the system runs normally. After that, the computing network status and business status monitoring are realized through the key node indicator collection, the computing network and business indicator information is visualized, and the computing network resources and business needs are analyzed and predicted in real time. When changes in computing network resources and business needs are monitored, the edge side is calculated according to the pre-configured computing network SLA indicator translation algorithm and policy mapping method, and the new system policy is sent to the terminal according to the calculation results. At the same time, the calculation results and monitoring information are uploaded to the cloud platform, and the analysis and prediction model is trained in the cloud. The present disclosure can realize two-way adjustment of policies on the business side and the computing network side. On the one hand, according to business perception and demand changes, agile tuning of the computing network is realized to ensure business experience and adapt the computing network to the business. On the other hand, based on computing network information such as network coverage quality or available bandwidth prediction, the computing network capabilities are opened to upper-layer businesses, the business layer can call the computing network capabilities and optimize the business layer, and the business can adapt to the computing network, so as to achieve the effect of network-industry collaboration and ensure controllable business quality.

[0090] The second embodiment of the present disclosure also provides a cloud-edge collaborative computing network and service SLA bidirectional adaptive method, such as Figure 2 As shown, the method includes:

[0091] S1. The cloud platform generates demand profiles for the input industry business scenarios and describes the industry business needs as specific business SLA indicators, such as transmission cycle, application data volume, etc.

[0092] S2. The cloud platform maps business SLA indicators to computing network SLA indicators, such as user rate, latency, computing power, etc., based on the preset business SLA to computing network SLA indicator mapping rules.

[0093] S3. The cloud platform configures a computing network resource policy set for the system based on the mapped computing network SLA indicators and the preset computing network resource configuration rules. The policy information includes: network policy and computing power policy. The business SLA indicators and computing network SLA indicators applicable to this policy set are all within a certain range, not a specific value.

[0094] S4. The cloud platform sends the computing network resource strategy to each terminal in the system, and the system operates normally.

[0095] S5. Collect key system node indicators in real time through the system monitoring device, and upload the computing network status information and business status information to the edge side and cloud platform.

[0096] S6. The edge side uses the preset prediction model to analyze and predict the changing trends of business needs and computing network resources in real time, and compares them with the business SLA indicators and computing network SLA indicators in the current computing network strategy. The comparison and response strategies can be divided into the following situations:

[0097] (1) When it is detected that the business demand is increasing and will exceed the current indicator range, a time window can be set here, such as T1. When it is determined that the duration of the business demand change indicator exceeds T1;

[0098] The edge side recalculates the computing network SLA and computing network resource configuration strategy corresponding to the new business demand indicators, and compares this result with the predicted computing network resource information for the next stage:

[0099] If the resources match the demand, the edge side will send the new computing network configuration strategy to the terminal, and upload the computing results and monitoring data to the cloud platform to enrich the cloud model training set;

[0100] If the resources do not match the demand, the edge side will initiate a request to the cloud platform to schedule computing network resources;

[0101] If the scheduling is successful, the edge side will send the new computing network configuration strategy to the terminal, and upload the computing results and monitoring data to the cloud platform to enrich the cloud model training set.

[0102] If scheduling fails, the business SLA indicators are reversely calculated on the edge side based on the computing network resources, and this application policy is sent to the system control module to adjust the terminal-side business operation status to a matching lower level. At the same time, the calculation results and monitoring data are uploaded to the cloud platform to enrich the cloud model training set.

[0103] (2) When it is detected that the business demand is decreasing and will exceed the current indicator range, a time window can be set here, such as T2. When the duration of the business demand change indicator exceeds T2, the change trend of the computing network resources is also determined:

[0104] If the computing network resources are stable or increase, the system can maintain the current operation strategy unchanged.

[0105] If the computing network resources are reduced, the edge-side indicator mapping module recalculates the computing network SLA and computing network resource configuration strategy corresponding to the new business demand indicators, and compares this result with the predicted computing network resource information for the next stage;

[0106] If the resources match the demand, the edge side will send the new computing network configuration strategy to the terminal, and upload the computing results and monitoring data to the cloud platform to enrich the cloud model training set;

[0107] If the resources do not match the demand, the edge side will initiate a request to the cloud platform to schedule computing network resources:

[0108] If the scheduling is successful, the edge side will send the new computing network configuration strategy to the terminal, and upload the computing results and monitoring data to the cloud platform to enrich the cloud model training set.

[0109] If scheduling fails, the business SLA indicators are reversely calculated on the edge side based on the computing network resources, and this application policy is sent to the system control module to adjust the terminal-side business operation status to a matching lower level. At the same time, the calculation results and monitoring data are uploaded to the cloud platform to enrich the cloud model training set.

[0110] (3) When the business demand is stable and the trend of computing network resources is determined (the response is the same as (2)):

[0111] If the computing network resources are stable or increase, the system can maintain the current operation strategy unchanged.

[0112] If the computing network resources are reduced, the edge-side indicator mapping module recalculates the computing network SLA and computing network resource configuration strategy corresponding to the new business demand indicators, and compares this result with the predicted computing network resource information for the next stage;

[0113] If the resources match the demand, the edge side will send the new computing network configuration strategy to the terminal, and upload the computing results and monitoring data to the cloud platform to enrich the cloud model training set;

[0114] If the resources do not match the demand, the edge side will initiate a request to the cloud platform to schedule computing network resources:

[0115] If the scheduling is successful, the edge side will send the new computing network configuration strategy to the terminal, and upload the computing results and monitoring data to the cloud platform to enrich the cloud model training set.

[0116] If scheduling fails, the business SLA indicators are reversely calculated on the edge side based on the computing network resources, and this application policy is sent to the system control module to adjust the terminal-side business operation status to a matching lower level. At the same time, the calculation results and monitoring data are uploaded to the cloud platform to enrich the cloud model training set.

[0117] S7. The cloud platform regularly updates the training model to the edge side.

[0118] Figure 3 The architecture diagram of a cloud-edge collaborative computing network and business SLA bidirectional adaptive system provided in the third embodiment of the present disclosure is as follows: Figure 3 As shown, the system comprises:

[0119] A portrait module 11, which is configured to perform demand portrait for business scenarios and describe business demands as business SLA indicators;

[0120] A mapping and configuration module 12, which is configured to map the business SLA indicators to the computing network SLA indicators according to a preset translation algorithm of the business SLA indicators to the computing network SLA indicators, and configure the computing network resource strategy for the system according to the strategy mapping method of the computing network SLA indicators to the computing network resource configuration;

[0121] A sending module 13 is configured to send the computing network resource strategy to each terminal in the system so that the system can operate normally;

[0122] A monitoring module 14, which is configured to monitor the service status information of the system;

[0123] A prediction module 15, which is configured to predict the change trend of computing network resources through a preset prediction model at the edge side;

[0124] The adjustment module 16 is configured to adjust the computing network resource strategy or adjust the terminal side business operation status according to the monitored business status information and the change trend of the computing network resources.

[0125] Furthermore, the adjustment module 16 is specifically configured as follows:

[0126] When the monitoring module 14 detects fluctuations in the service status information or the change trend of the computing network resources predicted by the prediction module 15 meets the preset conditions, the computing network SLA and computing network resource configuration strategy corresponding to the service SLA indicator of the current service status information are recalculated on the edge side, and the calculation result is compared with the predicted computing network resources for the next stage;

[0127] If the predicted computing network resources for the next stage can match the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information, the sending module 13 sends the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal from the edge side;

[0128] If the predicted computing network resources for the next stage do not match the computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information, the business SLA indicators will be reversely calculated on the edge side based on the predicted computing network resources for the next stage, and the business operation status on the terminal side will be adjusted to a corresponding lower level based on the calculated business SLA indicators.

[0129] Furthermore, the system also includes an application module 17;

[0130] The application module 17 is configured to initiate a network resource scheduling request to the cloud platform at the edge side to apply for scheduling network resources before the adjustment module reversely calculates the service SLA indicators at the edge side based on the predicted network resources of the next stage; and

[0131] Determine whether the computing network resource scheduling is successful;

[0132] If the scheduling is successful, the sending module 13 sends the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal from the edge side;

[0133] If the scheduling fails, the adjustment module 16 executes the step of reversely calculating the service SLA index on the edge side according to the predicted computing network resources for the next stage.

[0134] Furthermore, the fluctuation of the service status information or the predicted change trend of the computing network resources meets the preset conditions, including:

[0135] The computing network resources are reduced; or,

[0136] The business demand corresponding to the business status information becomes larger and will exceed the current indicator range, and the duration of the business demand change indicator exceeds the first time.

[0137] Furthermore, the system also includes a model updating module 18;

[0138] The monitoring module 14 is also configured to monitor the network status information of the monitoring system; and

[0139] Upload the monitored system's business status information and computing network status information to the cloud platform

[0140] The adjustment module 16 is further configured to upload the computing network SLA and computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information calculated by the edge side, as well as the business SLA indicators reversely calculated according to the predicted computing network resources in the next stage, to the cloud platform to enrich the training set of the cloud prediction model and train the prediction model;

[0141] The updating module 18 is configured to regularly update the prediction model and send the updated prediction model to the edge side.

[0142] The cloud-edge collaborative computing network and business SLA bidirectional adaptive system of the embodiment of the present disclosure is used to implement the cloud-edge collaborative computing network and business SLA bidirectional adaptive method in method embodiment 1 and embodiment 2, so the description is relatively simple. For details, please refer to the relevant description in the previous method embodiment 1 and embodiment 2, which will not be repeated here.

[0143] In addition, if Figure 4 As shown, the fourth embodiment of the present disclosure further provides an electronic device, including a memory 100 and a processor 200, wherein the memory 100 stores a computer program, and when the processor 200 runs the computer program stored in the memory 100, the processor 200 executes the above-mentioned various possible methods.

[0144] The memory 100 is connected to the processor 200. The memory 100 may be a flash memory, a read-only memory or other memory, and the processor 200 may be a central processing unit or a single-chip microcomputer.

[0145] In addition, an embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is executed by a processor to perform the above-mentioned various possible methods.

[0146] The computer-readable storage medium includes volatile or non-volatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), Digital Video Disc (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0147] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present disclosure, but the present disclosure is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and substance of the present disclosure, and these modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A cloud-edge collaborative computing network and business service level agreement (SLA) bidirectional adaptive method, characterized in that: The method comprises: Profiling business scenarios and describing business requirements as business SLA indicators; According to the preset translation algorithm of the business SLA indicator to the computing network SLA indicator, the business SLA indicator is mapped to the computing network SLA indicator, and according to the strategy mapping method of the computing network SLA indicator to the computing network resource configuration, the computing network resource strategy is configured for the system; Distribute the computing network resource strategy to each terminal in the system to ensure the normal operation of the system; Monitoring system business status information; Predict the changing trend of computing network resources through the preset prediction model on the edge side; Adjust the computing network resource strategy or the terminal-side business operation status based on the monitored business status information and the changing trend of computing network resources.

2. The method according to claim 1, characterized in that The adjusting of the computing network resource strategy or the terminal side business operation status according to the monitored business status information and the change trend of the computing network resources includes: When the fluctuation of business status information is monitored or the change trend of the predicted computing network resources meets the preset conditions, the computing network SLA and computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information are recalculated on the edge side, and the calculation result is compared with the predicted computing network resources for the next stage; If the predicted computing network resources for the next stage match the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information, the edge side sends the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal; If the predicted computing network resources for the next stage do not match the computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information, the business SLA indicators will be reversely calculated on the edge side based on the predicted computing network resources for the next stage, and the business operation status on the terminal side will be adjusted to a corresponding lower level based on the calculated business SLA indicators.

3. The method according to claim 2, characterized in that The method further comprises: Based on the predicted computing network resources for the next stage, before the edge side reversely calculates the service SLA indicators, the edge side initiates a computing network resource scheduling request to the cloud platform to apply for scheduling computing network resources; Determine whether the computing network resource scheduling is successful; If the scheduling is successful, the edge side will send the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal; If the scheduling fails, the step of reversely calculating the service SLA indicators on the edge side is executed based on the predicted computing network resources for the next stage.

4. The method according to claim 2 or 3, characterized in that: The fluctuation of the service status information or the predicted change trend of the computing network resources meets the preset conditions including: The computing network resources are reduced; or, The business demand corresponding to the business status information becomes larger and will exceed the current indicator range, and the duration of the business demand change indicator exceeds the first time.

5. The method according to any one of claims 1 to 3, characterized in that: The method further comprises: Monitoring system computing network status information; Upload the monitored system's business status information and computing network status information, as well as the computing network SLA and computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information calculated by the edge side, and the business SLA indicators reversely calculated based on the predicted computing network resources in the next stage to the cloud platform to enrich the training set of the cloud prediction model and train the prediction model; Update the prediction model regularly and send the updated prediction model to the edge side.

6. A cloud-edge collaborative computing network and business service level agreement (SLA) bidirectional adaptive system, characterized by: The system comprises: The portrait module is configured to perform demand portraits on business scenarios and describe business requirements as business SLA indicators; A mapping and configuration module, which is configured to map the business SLA indicators to the computing network SLA indicators according to a preset translation algorithm from the business SLA indicators to the computing network SLA indicators, and configure the computing network resource strategy for the system according to a strategy mapping method from the computing network SLA indicators to the computing network resource configuration; The distribution module is configured to distribute the computing network resource strategy to each terminal in the system to enable the system to operate normally; A monitoring module configured to monitor the service status information of the system; A prediction module, which is configured to predict the change trend of computing network resources through a preset prediction model on the edge side; The adjustment module is configured to adjust the computing network resource strategy or the terminal side business operation status according to the monitored business status information and the change trend of the computing network resources.

7. The system according to claim 6, characterized in that The adjustment module is specifically configured as follows: When the monitoring module detects fluctuations in the business status information or the change trend of the computing network resources predicted by the prediction module meets the preset conditions, the computing network SLA and computing network resource configuration strategy corresponding to the business SLA indicator of the current business status information are recalculated on the edge side, and the calculation result is compared with the predicted computing network resources for the next stage; If the predicted computing network resources for the next stage can match the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information, the sending module sends the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal from the edge side; If the predicted computing network resources for the next stage do not match the computing network resource configuration strategy corresponding to the business SLA indicators of the current business status information, the business SLA indicators will be reversely calculated on the edge side based on the predicted computing network resources for the next stage, and the business operation status on the terminal side will be adjusted to a corresponding lower level based on the calculated business SLA indicators.

8. The system according to claim 7, characterized in that The system also includes an application module; The application module is configured to initiate a network resource scheduling request to the cloud platform at the edge side to apply for scheduling network resources before the adjustment module reversely calculates the service SLA indicators at the edge side based on the predicted network resources of the next stage; and Determine whether the computing network resource scheduling is successful; If the scheduling is successful, the sending module sends the computing network resource configuration policy corresponding to the service SLA indicator of the current service status information to the terminal from the edge side; If the scheduling fails, the adjustment module executes the step of reversely calculating the service SLA indicators on the edge side according to the predicted computing network resources for the next stage.

9. An electronic device, characterized in that: It includes a memory and a processor, the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the cloud-edge collaborative computing network and business SLA bidirectional adaptive method as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the cloud-edge collaborative computing network and business SLA bidirectional adaptive method as described in any one of claims 1-5.

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