Cloud private line quality monitoring method, device, equipment and computer storage medium

Abstract

The embodiment of the present application relates to the technical field of cloud private line, and discloses a cloud private line quality monitoring method, which is applied to a target cloud private line; the target cloud private line comprises a client, a cloud network fusion gateway and a user virtual cloud network connected in sequence; the method comprises the following steps: dialing the client and the user virtual cloud network respectively by a probe to obtain private line traffic data corresponding to the target cloud private line; wherein the probe is arranged on the cloud network fusion gateway, so that the service traffic between the client and the user virtual cloud network and the dialing traffic generated by the probe flow through the same physical channel; and analyzing and processing the private line traffic data to obtain a service quality monitoring result of the target cloud private line. Through the above method, the accuracy and efficiency of cloud private line quality monitoring are improved.

Description

Technical Field

[0001] This invention relates to the field of cloud private line technology, specifically to a cloud private line quality monitoring method, device, equipment, and computer storage medium. Background Technology

[0002] With the widespread application of cloud computing technology, customer systems are widely deployed on public clouds. Cloud private lines refer to dedicated line services that connect the customer on one end and access the cloud system on the other. Customers using cloud private line products generally have high requirements for service quality. Therefore, in order to improve the user experience and satisfaction of cloud private line users, it is necessary to effectively monitor the service quality of cloud private lines.

[0003] There are three main methods for monitoring the quality of cloud private lines: 1) Manual experience-based diagnosis, but this method is costly and inefficient; 2) Business monitoring and fault diagnosis based on network management system information, but this passive monitoring method cannot detect changes in the quality of customer services in advance, has low monitoring accuracy, and the update frequency of network management system information cannot achieve real-time monitoring of private line quality; 3) Fault location methods based on traditional network probes, which are not suitable for monitoring software-defined networks and virtualized computing resources in cloud private lines, and do not have the ability to configure multiple links and automatically at the tenant level under SDN networks, resulting in low monitoring efficiency.

[0004] Therefore, a more timely and accurate method for monitoring the quality of tenant-level cloud private lines is needed. Summary of the Invention

[0005] In view of the above problems, embodiments of the present invention provide a cloud dedicated line quality monitoring method to solve the problem that the efficiency and accuracy of cloud dedicated line quality monitoring in the prior art are both low.

[0006] According to one aspect of the present invention, a cloud private line quality monitoring method is provided, the method being applied to a target cloud private line; the target cloud private line includes a client, a cloud-network converged gateway, and a user virtual cloud network connected sequentially; the method includes:

[0007] The client and the user virtual network are probed to obtain the dedicated line traffic data corresponding to the target cloud dedicated line; wherein, the probe is deployed on the cloud network convergence gateway so that the service traffic between the client and the user virtual network and the probe-generated test traffic flow through the same physical channel.

[0008] The dedicated line traffic data is analyzed and processed to obtain the service quality monitoring results of the target cloud dedicated line.

[0009] In an alternative approach, the method further includes:

[0010] Obtain the dedicated line service attributes of the target cloud dedicated line;

[0011] Based on the leased line service attributes, the probe is configured with a dial-up test route and a dial-up test task, so that the probe connects to the cloud-network converged gateway and performs the configured on-the-path network dial-up test tasks on the cloud intranet segment and the cloud extranet segment respectively. The network segment between the cloud-network converged gateway and the user virtual network is the cloud intranet segment; the network segment between the cloud-network converged gateway and the client is the cloud extranet segment.

[0012] In an alternative approach, the method further includes:

[0013] Based on the leased line service attributes, determine the client attribute information of the client, the leased line monitoring requirement information of the client, the cloud network attribute information of the user virtual cloud network, and the gateway attribute information of the cloud-network converged gateway.

[0014] Configure the probe for the dial-up route based on the client attribute information, the cloud network attribute information, and the gateway attribute information;

[0015] Configure the probe for the in-line network testing task based on the dedicated line monitoring requirements information.

[0016] In an alternative approach, the method further includes:

[0017] Real-time detection of changes in the attributes of the dedicated line service;

[0018] The dial-up test route configuration and the associated network dial-up test task are updated based on the change information.

[0019] In an alternative approach, the method further includes:

[0020] When it is determined that the change information represents the activation of the leased line, a new probe is created, and the newly created probe is configured with the dial-up test route and the accompanying network dial-up test task according to the leased line service attributes.

[0021] When it is determined that the change information represents a change in the leased line, the client attribute change information of the client and the network attribute change information of the user's virtual cloud network are determined based on the change information.

[0022] Based on the client attribute change information and the network attribute change information, update the probe routing configuration and the accompanying network testing task on the target cloud private line.

[0023] When it is determined that the change information represents a cancellation of the dedicated line, the probes on the target cloud dedicated line are recycled.

[0024] In an alternative approach, the method further includes:

[0025] The dedicated line traffic data is parsed to obtain the link layer path change information, network layer transmission quality information, and application layer service quality information of the target cloud dedicated line.

[0026] The service quality monitoring results are obtained by aggregating and analyzing the link layer path change information, network layer transmission quality information, and application layer service quality information.

[0027] In an alternative approach, the method further includes:

[0028] Based on the comparison results of link layer path change information, network layer transmission quality information, and application layer service quality information with preset thresholds, the link layer alarm information, network layer alarm information, and application layer alarm information of the target cloud private line are determined respectively.

[0029] The service quality monitoring results are determined based on the co-occurrence of the link layer alarm information, network layer alarm information, and application layer alarm information.

[0030] According to another aspect of the present invention, a cloud private line quality monitoring device is provided and applied to a target cloud private line; the target cloud private line includes a client, a cloud-network converged gateway, and a user virtual cloud network connected in sequence; the device includes:

[0031] The detection module is used to perform dial-tests on the client and the user virtual network respectively using probes to obtain the dedicated line traffic data corresponding to the target cloud dedicated line; wherein, the probes are deployed on the cloud-network convergence gateway so that the service traffic between the client and the user virtual network and the dial-test traffic generated by the probes flow through the same physical channel;

[0032] The analysis module is used to analyze and process the dedicated line traffic data to obtain the service quality monitoring results of the target cloud dedicated line.

[0033] According to another aspect of the present invention, a cloud dedicated line quality monitoring device is provided, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus;

[0034] The memory is used to store at least one executable instruction that causes the processor to perform the operation of the cloud dedicated line quality monitoring method as described in any of the preceding claims.

[0035] According to another aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing at least one executable instruction that causes a cloud dedicated line quality monitoring device to perform the operation of the cloud dedicated line quality monitoring method as described in any of the preceding claims.

[0036] This invention utilizes probes deployed on a cloud-network converged gateway on the target cloud leased line to perform dial-up tests on the clients and user virtual networks on both sides of the gateway, obtaining leased line traffic data corresponding to the target cloud leased line. Since the probes are deployed on the cloud-network converged gateway, the service traffic between the clients and the user virtual networks flows through the same physical channel as the dial-up traffic generated by the probes. This enables in-path detection of both cloud-internal and cloud-external networks. Finally, the leased line traffic data is analyzed and processed to obtain the service quality monitoring results of the target cloud leased line. The analysis and aggregation of leased line traffic data can be performed from both cloud-internal and cloud-external directions, and from three dimensions: network layer, link layer, and application layer. This allows for pinpointing the cause of poor service quality to a specific level and location, thereby improving the efficiency and accuracy of cloud leased line service quality monitoring and enhancing the user's cloud leased line experience.

[0037] The above description is merely an overview of the technical solutions of the embodiments of the present invention. In order to better understand the technical means of the embodiments of the present invention and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0038] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0039] Figure 1 A flowchart illustrating the cloud dedicated line quality monitoring method provided in an embodiment of the present invention is shown.

[0040] Figure 2 A schematic diagram of a network quality matrix view in a cloud private line quality monitoring method provided in another embodiment of the present invention is shown;

[0041] Figure 3 This diagram illustrates the probe deployment location in the target cloud private line of a cloud private line quality monitoring method provided in another embodiment of the present invention.

[0042] Figure 4 This diagram illustrates the probe deployment process in the target cloud private line of the cloud private line quality monitoring method provided in another embodiment of the present invention.

[0043] Figure 5 This diagram illustrates the end-to-end network element configuration parameter planning process in a cloud private line quality monitoring method provided by another embodiment of the present invention.

[0044] Figure 6 This invention illustrates a schematic diagram of the cloud private line service orchestration module and probe service process in a cloud private line quality monitoring method provided by another embodiment of the present invention.

[0045] Figure 7 This invention illustrates a schematic diagram of the cloud network probe service orchestration process in a cloud dedicated line quality monitoring method provided by another embodiment of the present invention.

[0046] Figure 8 This diagram illustrates the cloud network end-to-end quality monitoring system in a cloud dedicated line quality monitoring method provided by another embodiment of the present invention.

[0047] Figure 9 A schematic diagram of the cloud dedicated line quality monitoring device provided in an embodiment of the present invention is shown;

[0048] Figure 10 A schematic diagram of the cloud dedicated line quality monitoring equipment provided in an embodiment of the present invention is shown. Detailed Implementation

[0049] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0050] Explanation of related terms: VGW: Virtual Gateway; VPC: Virtual Private Cloud; CE: Customer Equipment; PE: Private Cloud Equipment.

[0051] Before describing the embodiments of this invention, the existing technology and its problems are further explained: With the widespread application of cloud computing technology, customer systems are widely deployed on public clouds. Cloud private lines refer to dedicated line services that connect one end to the customer's side and the other end to the customer's cloud system. These are wired cloud access network products based on China Mobile's transmission network and cloud private network, providing one-stop and integrated activation services for cloud and on-premises network connections. The type of dedicated line involved in this article is the PTN dedicated line, which involves PTN access network, cloud private network, and cloud intranet. Because the service penetrates various domains and network elements, when faults or quality degradation occur, maintenance personnel need to spend a long time locating the problem, resulting in a decline in customer experience or direct economic losses from customers ceasing to use the dedicated line. Customers using cloud private line products have high service quality requirements. To improve the operator's business assurance capabilities, there needs to be a means to meet the following requirements: rapid end-to-end fault delimitation and location capabilities to achieve quality assurance on a case-by-case basis; proactive monitoring to provide customers and maintenance teams with quality monitoring methods that meet self-service maintenance needs.

[0052] There are three main methods for monitoring the quality of cloud private line services: Method 1: Manual experience-based diagnosis. After receiving service assurance requests, maintenance personnel query various systems for service information related to the fault. Then, based on their professional knowledge, they use various protocols and commands to perform service testing and diagnosis. This method is time-consuming, labor-intensive, and inefficient. Method 2: Service monitoring and fault diagnosis based on network management system information. First, network topology information for the three parts of the PTN cloud private line network is obtained through different network management systems, along with the service routes and network resource information involved in the corresponding network segments. Second, the basic information in the network management system is concatenated based on the service identifier, and customer-connected access devices are added to form a customer-level end-to-end service view, loading alarm data, performance data, and traffic data of the traversed network elements. Finally, when a fault occurs, the routing topology is loaded based on the customer service information, and each network element is checked using network protocols and test commands. The checks and judgments assist engineers in fault location. This solution fails to meet business requirements in two aspects. First, as a passive monitoring method, it cannot proactively detect changes in customer service quality. Second, relying on traditional equipment OMC network management information, the data time granularity is 15 minutes, and the update cycle is 30-40 minutes, making real-time monitoring impossible. Method 3: Fault location based on traditional network probes. Traditional probes are attached to the physical network equipment side and can actively initiate tests such as Ping and Trace route to cover quality testing from the customer side to the network end. Traditional network probes are suitable for monitoring physical networks and computing resources, but cannot delve into the monitoring of software-defined networks and virtualized computing resources. Services based on shared physical resources and logical isolation cannot provide automatic quality monitoring for each link. Testing each service individually requires initiating probes in each tenant network, requiring customer authorization and manual changes to monitoring configurations and probe routing policies. Currently, traditional probes lack tenant-level multi-link, automated configuration capabilities under SDN networks. Therefore, a more timely and accurate cloud private line service quality monitoring method is needed.

[0053] Figure 1 A flowchart of a cloud private line service quality method provided in an embodiment of the present invention is shown. This method is executed by a computer processing device. The computer processing device may include a mobile phone, a laptop computer, etc.

[0054] The method is applied to a target cloud leased line; the target cloud leased line includes a client, a cloud-network converged gateway, and a user virtual cloud network connected in sequence; wherein, the client includes the device corresponding to the user who has subscribed to the target cloud leased line service, and the cloud-network converged gateway is used to connect clients outside the cloud resource pool to the user virtual cloud network in the cloud resource pool, where there can be multiple tenants renting cloud computing resources. Optionally, a cloud leased line transmission device is also set up between the client and the cloud-network converged gateway. The cloud leased line transmission device includes transmission access devices, transmission aggregation devices, and transmission core devices in the metropolitan area network, etc. The cloud leased line transmission device is used to connect the client to the cloud-network converged gateway, so that the client can access cloud computing resources in the user virtual cloud network through the cloud-network converged gateway.

[0055] like Figure 1 As shown, the method includes the following steps:

[0056] Step 10: Use probes to perform dial-up tests on the client and the user virtual network respectively to obtain the dedicated line traffic data corresponding to the target cloud dedicated line; wherein, the probes are deployed on the cloud-network convergence gateway so that the service traffic between the client and the user virtual network and the dial-up test traffic generated by the probes flow through the same physical channel.

[0057] Specifically, the probe can employ active network packet probing technology, and can be in hardware, software, or virtualized form. By deploying the probe on the cloud-network converged gateway, it can perform probe tests in two directions: clients outside the cloud network and user virtual networks within the cloud network. This logically divides the cloud private line service into two fault domains for isolated monitoring: network quality monitoring from the VGW to VPCs, customer cloud hosts, and cloud products within the cloud; and link quality monitoring from the VGW to the cloud private network, private line core, aggregation, and access devices outside the cloud. To achieve end-to-end performance quality monitoring of tenant-level cloud private line links, probe probe routing and task configuration can be based on the target cloud private line's private line service data. This embeds the corresponding service probe flow into the user's cloud private line service activation process, maintaining the same physical channel as the customer data flow, thus completing end-to-end performance quality monitoring of the tenant-level cloud private line link. The private line service data can include private line routing configuration data and private line service subscription data.

[0058] Specifically, routing information for clients, user virtual networks, and cloud-network converged gateways can be parsed from the dedicated line routing configuration data. First, the probe is configured based on the routing information of the cloud-network converged gateway to establish its connection. Then, the probe is configured based on the routing information of the clients and user virtual networks, enabling it to obtain dedicated line traffic data through the VGW's on-line testing capabilities to clients and VPCs on each dedicated cloud line. The routing information can include the client's address, VLAN, bandwidth, the probe's own IP address, and the IP address and VPC subnet address of the connected VGW. Simultaneously, to improve the targeting and personalization of dedicated line service quality monitoring, the user's needs for dedicated line quality monitoring can be determined based on dedicated line service subscription data, such as the type of dedicated line monitoring service subscribed to. Corresponding testing tasks can then be sent to the probe based on these needs.

[0059] Optionally, in order to ensure that the probes deployed on the leased line can be updated in a timely manner when changes occur, such as changes to the VPC subnet or the user-side subnet, thereby maintaining the ability to perform in-line detection on the changed leased line, the probes can also detect changes in the leased line service data of the target cloud leased line in real time, and update the probes' test routes and test task configurations based on these changes.

[0060] Specifically, leased line traffic data includes network probe traffic for different layers and tenant links of the target cloud leased line network, such as L2-L4 and L4-7, including ICMP, TraceRoute, TCPing, HTTP, RTSP, DNS, and Iperf. These different layers can include the link layer, network layer, and application layer. Link layer probing is primarily used to trace the cloud leased line path. Network layer probing can probe the quality status of the cloud leased line network layer based on ICMP / TCP protocols. For example, TCPing is mainly used to monitor application service port status and provide firewall penetration capabilities to monitor the quality status of different segments of the leased line network layer. Application layer probing is mainly used to monitor the availability and quality of experience of application services deployed by the customer (in the cloud or on the customer's intranet). Specifically, it can be done through simulated probing of various application layer protocols to output corresponding application service performance indicators, such as latency, packet loss, jitter, bandwidth performance, HTTP page load time, and page response time.

[0061] Therefore, in another embodiment of the invention, the following is included before step 10:

[0062] Step 101: Obtain the leased line service attributes of the target cloud leased line.

[0063] Specifically, the leased line service attributes are used to characterize the service subscription information and service configuration information of the target cloud leased line. The service subscription information may include the type of cloud leased line, cloud host type, and probe service subscription information subscribed by the user. Specifically, the probe service subscription information characterizes the user's subscription status for probe services used for cloud leased line monitoring, such as the type of probe subscribed and the subscription duration. The service configuration information is used to configure the target cloud leased line, enabling clients to access cloud host resources in the cloud resource pool. This information may include the target cloud leased line's customer CE address, VLAN, bandwidth, and VPC subnet address.

[0064] Step 102: Configure the probe with a dial-up test route and a dial-up test task according to the leased line service attributes, so that the probe can connect to the cloud-network converged gateway, and perform the configured on-the-way network dial-up test tasks for the cloud intranet network segment and the cloud extranet network segment respectively. The network segment between the cloud-network converged gateway and the user virtual network is the cloud intranet network segment; the network segment between the cloud-network converged gateway and the client is the cloud extranet network segment.

[0065] Specifically, the user's service monitoring needs are determined based on the aforementioned service subscription information, and probe testing tasks are issued according to these needs. The probe testing routing information is determined based on the service configuration information, so that after the cloud-network converged gateway is interconnected, the probe can perform the aforementioned issued testing tasks on both the intra-cloud network segment and the extra-cloud network segment through the cloud-network converged gateway.

[0066] Specifically, step 102 also includes:

[0067] Step 1021: Determine the client attribute information of the client, the client's dedicated line monitoring requirement information, the cloud network attribute information of the user's virtual cloud network, and the gateway attribute information of the cloud-network converged gateway according to the dedicated line service attributes.

[0068] Specifically, client attribute information includes the client's address, user subnet information, bandwidth, and VLAN information; cloud network attribute information includes VPC subnet address information; and gateway attribute information includes the VGW's IP address. Leased line testing requirements can be determined based on probe service subscription information within the leased line service attributes. Furthermore, the user's needs for leased line service quality monitoring for the target cloud leased line can be determined based on the probe type and subscription duration included in the probe service subscription information.

[0069] Step 1022: Configure the probe for the dial-up route based on the client attribute information, the cloud network attribute information, and the gateway attribute information.

[0070] Specifically, the probe is configured according to the gateway attribute information to achieve interconnection between the probe and the VGW. Then, the probe is configured according to the client attribute information and the cloud network attribute information to achieve the allocation of dedicated dial-up address segments between the VGW probe and the devices on the cloud private line.

[0071] Step 1023: Configure the probe for the network-as-you-go testing task according to the dedicated line monitoring requirement information.

[0072] Specifically, the corresponding in-line network testing task is determined based on the probe service order information in the leased line service attributes. For example, when the probe service order information is application service probe, the in-line network testing task may include simulating and testing multiple application layer protocols, outputting application service performance indicators, thereby monitoring the availability and experience quality of the application services deployed by the customer (in the cloud or on the customer's intranet).

[0073] When the probe service subscription information is link-layer probing, the in-line network probing task may include TraceRoute probing to obtain link-layer routing information, thereby determining whether there is a possibility of network quality degradation due to changes in the link layer. Furthermore, considering that changes to leased line services may cause the probe's probing traffic to no longer flow through the same physical channel as the service traffic between the client and the user's virtual network, meaning the probe no longer has the ability to perform in-line probing of the cloud network, therefore, after step 102, the following is also included:

[0074] Step 110: Real-time detection of changes in the attributes of the leased line service.

[0075] Specifically, the information regarding changes in leased line service attributes includes the time of change, the type of change, and the service attribute information of the leased line service after the change. The type of change can be one of the following: leased line activation, leased line modification, or leased line cancellation.

[0076] Step 111: Update the dial-up test route configuration and the associated network dial-up test task according to the change information.

[0077] To ensure that probes can perform separate on-path testing of the target cloud leased line within the cloud and on the cloud network, the probe routing configuration and on-path network testing task configuration are updated according to the current leased line service attributes when the leased line is activated or changed. Conversely, when the leased line is unsubscribed, the probes on the target cloud leased line are recycled to conserve probe resources.

[0078] Specifically, step 111 also includes:

[0079] Step 1111: When it is determined that the change information represents the activation of the leased line, a new probe is created, and the newly created probe is configured with the dial-up test route and the associated network dial-up test task according to the leased line service attributes.

[0080] Specifically, the process of configuring the dial-up test route and the associated network dial-up test task for the newly created probe according to the leased line service attributes can refer to the aforementioned step 102, and will not be repeated here.

[0081] Step 1112: When it is determined that the change information represents a change in the leased line, the client attribute change information of the client and the network attribute change information of the user virtual cloud network are determined according to the change information.

[0082] Specifically, client attribute change information includes the changed client address and changed bandwidth. Network attribute change information includes the changed VPC subnet address.

[0083] Step 1113: Update the probe routing configuration and the accompanying network testing task on the target cloud private line according to the client attribute change information and the network attribute change information.

[0084] Specifically, the probe's dial-up route is configured with the changed client address and the changed VPC subnet address, so that the updated probe can perform on-the-fly probes of the external network segment and the internal network segment of the changed target cloud private line.

[0085] Step 1114: When it is determined that the change information represents a cancellation of the dedicated line, the probes on the target cloud dedicated line are retrieved.

[0086] By recycling probes from cancelled dedicated lines, probe resources can be saved and the accuracy of probe management can be improved.

[0087] Step 20: Analyze and process the dedicated line traffic data to obtain the service quality monitoring results of the target cloud dedicated line.

[0088] In one embodiment of the present invention, considering that a user's poor leased line experience may be caused by one or more levels of influencing factors, among which there are derivative and cross-influence relationships between the influencing factors, such as the application layer, network layer and link layer, the poor quality of the network layer will directly lead to a decrease in the service quality of the upper application layer, or the change of the leased line link path of the lower link layer (such as equipment switching, route convergence, etc.) will directly affect the quality of the network layer and application layer. Therefore, it is necessary to conduct a comprehensive analysis of the leased line traffic data from multiple levels to obtain the problems in the user's leased line quality and the corresponding fault causes. For example, when a cloud leased line user complains about poor leased line quality, it can be determined whether it is caused by the transmission quality of the leased line network (specifically down to the segment) or by the customer's own application service.

[0089] Specifically, when performing link-layer analysis on leased line traffic data, relevant information about the leased line path can be extracted to determine if the path has changed. When performing network-layer analysis on leased line traffic data, transmission latency information can be extracted to determine whether network-layer transmission quality affects user experience. When performing application-layer analysis on leased line traffic data, the performance of various application services can be extracted to obtain user perception data at the application layer. Application service performance data can include aspects such as playback latency, throughput, packet loss, domain name resolution success rate, and domain name resolution latency.

[0090] Therefore, in another embodiment of the present invention, step 20 further includes:

[0091] Step 201: Parse the dedicated line traffic data to obtain the link layer path change information, network layer transmission quality information, and application layer service quality information of the target cloud dedicated line.

[0092] Specifically, considering that changes in the transmission path of cloud private line services directly affect the service quality, real-time monitoring of these changes can be performed based on private line traffic data. This monitoring can be conducted using the TraceRoute method, extracting values ​​from the acquired data according to preset path change metrics to obtain link-layer path change information. These path change metrics include one or more of the following: number of route changes, route hop count, hop latency in the path, and hop packet loss rate in the path.

[0093] For the network layer, it is possible to establish, for example Figure 2 The network / transport layer full mesh quality matrix view shown monitors the quality status of different segments of the leased line network layer. (Reference) Figure 2The network layer quality monitoring matrix allows for a direct view of degraded segments in the cloud private line network quality through the monitoring links formed between monitoring points (e.g., A, B, C, D). Specific monitoring methods include cloud private line network layer quality status detection capabilities based on the ICMP / TCP protocol. The acquired data is processed to extract network layer transmission quality information based on preset network quality metrics. These metrics include one or more of the following: packet loss rate to the target network, latency to the target network, and jitter value to the target network.

[0094] For the application layer, an end-to-end quality view can be established to monitor the availability and quality of experience of application services deployed by the customer (in the cloud or on the customer's intranet). Through simulation testing of various application layer protocols, the performance values ​​of leased line traffic data under preset application service performance indicators are used as application layer service quality information. Specifically, the application service performance indicators for DNS protocol applications include domain name resolution success rate and domain name resolution latency. The application service performance indicators for HTTP protocol applications include: connection establishment latency, connection establishment success rate, first byte latency, first screen loading latency, HTTP return codes, and page element download latency. The application service performance indicators for RTSP video applications include: RTSP error codes, playback latency, throughput, packet loss, and latency.

[0095] Step 202: Perform aggregation analysis based on the link layer path change information, network layer transmission quality information, and application layer service quality information to obtain the service quality monitoring results.

[0096] First, the link layer path change information, network layer transmission quality information, and application layer service quality information are compared with corresponding thresholds to determine whether there are alarm messages indicating poor service quality in any one or more of the link layer, network layer, and application layer of the target cloud private line. Then, the alarm causes are comprehensively analyzed and corresponding measures are taken based on the correlation between the corresponding layers where alarms exist. Thus, based on the data indicators reflected by different monitoring views, a comprehensive judgment and analysis can be performed, which can intuitively locate the level, aspect, and fault location that causes the quality degradation, thereby improving the user experience of the target cloud private line more efficiently and accurately.

[0097] Therefore, in another embodiment of the present invention, step 202 further includes:

[0098] Step 2021: Based on the comparison results of the link layer path change information, network layer transmission quality information, and application layer service quality information with preset thresholds, determine the link layer alarm information, network layer alarm information, and application layer alarm information of the target cloud private line respectively.

[0099] Specifically, link-layer path change information is compared with the corresponding threshold values ​​of path change measurement indicators to obtain link-layer alarm information. Network-layer transmission quality information is compared with the corresponding threshold values ​​of network-layer transmission quality measurement indicators to obtain network-layer alarm information.

[0100] The application layer service quality information is compared with the corresponding threshold values ​​of the application layer service quality measurement indicators to obtain application layer alarm information.

[0101] Specifically, at the underlying transmission link layer, since leased lines use fixed transmission routes, any route change along the leased line path will cause a degrade in network quality. Therefore, the threshold for the route change indicator in leased line path tracking and monitoring needs to be set to 1.

[0102] For the middle network / transport layer, segmented matrix-style quality monitoring capabilities are provided, primarily for transmission and processing latency. Because there are fewer forwarding and processing devices in leased lines, the transmission distances between the two ends are relatively close. Therefore, latency is mainly reflected in transmission distance. For latency threshold settings, the baseline latency measured during leased line commissioning and acceptance can be used as a reference; or the threshold index can be set based on the distance between the user / segment network. The following can be used as a reference: "Inter-provincial / inter-city latency: 0.8ms / 100km (single-way latency); within the local network, less than or equal to 10ms."

[0103] For the upper application layer, the application experience of cloud private lines is mainly reflected in the user perception level. Different application types and users will have different application experience perceptions. Taking HTTP applications as an example, the overall page opening latency is less than 2 seconds, and the overall experience perception is quite satisfactory. The threshold for the first screen opening latency is set to 2-4 seconds.

[0104] For example, when the link layer path change information includes the number of route changes, the number of route hops, the latency of hops in the path, and the packet loss rate of hops in the path, the number of route changes, the number of route hops, the latency of hops in the path, and the packet loss rate of hops in the path are compared with the corresponding thresholds for the number of route changes, the number of route hops, the latency of hops in the path, and the packet loss rate of hops in the path.

[0105] Step 2022: Determine the service quality monitoring result based on the co-occurrence of the link layer alarm information, network layer alarm information, and application layer alarm information.

[0106] Specifically, co-occurrence patterns are used to characterize the simultaneous occurrence of alarm information at the link layer, network layer, and application layer. Based on co-occurrence patterns, a multi-dimensional view of alarm information can be constructed. Cross-analysis of these multi-dimensional views allows for rapid problem identification. Once a quality degradation or fault is detected in any monitoring view, a comprehensive analysis can be performed based on the data indicators reflected by different monitoring views, allowing for a direct identification of the level, aspect, and location of the fault causing the quality degradation. The judgment principles are as follows: poor upper-layer application quality does not affect the network layer; poor network layer quality directly leads to application layer quality degradation; changes in the leased line link path (equipment switching, route convergence, etc.) directly affect both network and application layer quality; and a comprehensive analysis is performed using the application layer view and the network layer full mesh quality monitoring matrix. When a cloud leased line user complains about poor leased line quality, it can be determined whether the problem is caused by the transmission quality of the leased line network (specifically down to the segment level) or by issues with the customer's own application services.

[0107] In yet another embodiment of the invention, such as Figure 3 As shown, the target cloud leased line may include sequentially connected clients, cloud network transmission equipment, cloud network converged gateway, and user virtual cloud network. Probes A, B, C, and D are deployed on the client, cloud network transmission equipment, cloud network converged gateway, and user virtual cloud network, respectively. Network probe traffic (such as ICMP, TraceRoute, TCPing, HTTP, RTSP, DNS, Iperf, etc.) is generated by probing multiple probes to different network layers (L2-L4, L4-7) and different tenant links. Through probe traffic between probes or between probes and peer service reference points, quality detection and analysis at the network layer and service layer are achieved. Figure 3 In the cloud dedicated line quality monitoring architecture, probe C, which is attached to the VGW at the cloud egress, is an aggregation node that can perform probing tests on all services. Even without deploying physical or software probing points A, B, and D, it can complete the link monitoring of all cloud dedicated lines. Only customer authorization is required for probing, so probes A, B, and D can be deployed for seamless probing.

[0108] Among them, reference Figure 4 It can use automated orchestration technology to monitor the quality of cloud network probe devices connected to the VGW (which is managed by the SDN virtual network), supporting multi-tenant and multi-cloud leased links. The specific process includes the following:

[0109] The first step is to complete the fiber optic connection between probe C and VGW to ensure physical link reachability.

[0110] The second step is to complete the end-to-end service activation of the cloud private line to ensure that the customer’s CE side can reach the VPC in the cloud, i.e. step 1 in Figure 4.

[0111] The third step involves automatically planning the configuration parameters for the VGW and Probe C connection using the cloud private line service orchestration module. This includes sending the interconnection IP address between the Probe C and VGW, VLANs, and routing information from the client-side and cloud-internal VPCs to the Probe C to the Probe management platform. (See...) Figure 4 Step 2;

[0112] Finally, the service orchestration module and the probe management platform complete the configuration for vGW, cloud PE, and probe C, respectively. After completion, the probe management platform distributes the probing tasks and collects, analyzes, and presents the monitoring data. See Figure 4 Steps 3, 4, and 5.

[0113] Furthermore, considering that the probe service will automatically adapt to changes in cloud leased line tenants' services and cancellations, it is essential to ensure that probe point C can always maintain on-the-path monitoring with the customer's network. The probe management platform enables polling and concurrent testing of probe tasks, ensuring real-time, on-demand monitoring of all cloud leased line tenant services. The platform also integrates the results of cloud network probe monitoring tasks with resource and configuration data from key connection points of the cloud leased line tenants, thereby achieving tenant-level link-level service quality presentation and SLA (service level agreement) assurance.

[0114] For information on automated orchestration technology, please refer to [link / reference]. Figure 5 :

[0115] like Figure 5 As shown, a dedicated address range for dialing between probes VGW and cloud network probes is allocated in the cloud private line service orchestration module, enabling probes to dial through VGW to clients and VPCs on each cloud private line. The basic parameters include client CE address, VLAN, bandwidth, interconnection IP address (its own and the connected VGW), and VPC subnet address.

[0116] The cloud dedicated line service orchestration module connects to the cloud network probe service management platform via a RESTful interface, exchanging parameters bidirectionally. This facilitates the transmission of probe planning parameters between the service orchestration module and message exchange between the two modules after the execution of scheduling instructions for the controlled resources. This ensures the smooth operation of probe services throughout the activation, modification, and cancellation process of the cloud dedicated line. For detailed service interaction procedures, please refer to [reference needed]. Figure 6 .

[0117] The probe service model is designed based on changes in cloud private line services, involving three scenarios: initial deployment of tenant probe tasks, changes in customer network configuration, and unsubscription (see Table 1). The corresponding orchestration scheme design can be referenced here. Figure 6 .

[0118]

[0119]

[0120] Table 1. Network element configuration change relationships under three probe service scenarios.

[0121] Specifically, such as Figure 7 As shown, the operation process of the cloud dedicated line service orchestration module includes:

[0122] Step 1: The cloud dedicated line service orchestration module and the probe service management platform's service information synchronization module synchronize dedicated line activation, modification, and cancellation information in real time, including basic service information such as the dedicated line user's user-side subnet, VPC name, and VPC subnet, completing process 1;

[0123] Step 2: When a user initiates a probe service subscription, the probe service subscription module queries the service information synchronization module for synchronization information and sends an initialization request to the probe service configuration requirement module. It then initiates new, change, and unsubscribe requests to the cloud dedicated line service orchestration module through the RESTful interface and accepts the unified configuration plan of the service orchestrator. The module executes the routing configuration data and completes the information synchronization process of the configuration information, thus completing process 2, 2.1-2.4.

[0124] Step 3: The monitoring task module obtains the latest status of the probe and leased line based on the query and information synchronization interface, starts the test task for the cloud and the customer side, and completes the standardized dialing test command to probe C through the test module.

[0125] In addition, the detection and analysis module is synchronized with probe C in real time to analyze and display the test results.

[0126] Finally, regarding the cloud network end-to-end quality monitoring system:

[0127] The cloud network probe is deployed at the VGW, a key network connection point of the cloud private line product, and is adapted and connected to the VGW. The cloud private line service is logically divided into two fault domains for isolation and monitoring. Network quality monitoring is performed from the VGW to the VPC and customer cloud hosts and cloud products in the cloud; link quality monitoring is performed from the VGW to the cloud private network, private line core, aggregation and access equipment outside the cloud.

[0128] Based on the aforementioned VGW cloud network probe deployment method and detection capabilities, combined with Figure 3 Probes A and D in the system enable proactive fault discovery and end-to-end quality monitoring, as well as rapid fault delimitation, location, and root cause analysis both within and outside the cloud. They support scenarios such as service activation performance verification and 24 / 7 long-term service monitoring, providing efficient analysis tools and monitoring methods for cloud dedicated line operation and maintenance. For specific monitoring processes, please refer to [reference needed]. Figure 8 .

[0129] Among them, a multi-dimensional monitoring view is constructed by probes deployed on the dedicated line link, which consists of monitoring views of three dimensions: application layer, network layer and link layer. Once any monitoring view shows a quality degradation or failure, a comprehensive judgment and analysis can be carried out based on the data indicators reflected by different monitoring views. The level, aspect and location of the failure caused by the quality degradation can be intuitively located.

[0130] Step 1: Establish a cloud private line path tracing status view to monitor changes in the transmission path of cloud private line services (which directly affect the quality of cloud private line services) in real time. The monitoring method is based on TraceRoute, and the metrics are the number of route changes, the number of route hops, the latency of hops in the path, and the packet loss rate of hops in the path.

[0131] Step 2: Establish a network / transport layer full mesh quality matrix view to monitor the quality status of different segments of the leased line network layer. The monitoring method is based on the cloud leased line network layer quality status detection capability using ICMP / TCP (using TCPing primarily for monitoring application service port status and providing firewall penetration capabilities). The metrics are packet loss rate to the target network, latency to the target network, and jitter value to the target network. (The above...) Figure 2 The network layer quality monitoring matrix, through the monitoring links formed between the monitoring points in the matrix, allows for a direct view of the segmented areas where cloud private line network quality has deteriorated.

[0132] Step 3: Establish an end-to-end quality view at the application layer, primarily monitoring the availability and quality of experience of application services deployed by the customer (in the cloud or on the customer's intranet). Through simulation testing of various application layer protocols, output application service performance metrics:

[0133] 1) DNS protocol applications: domain name resolution success rate, domain name resolution latency, etc.

[0134] 2) HTTP protocol applications: connection establishment latency, connection establishment success rate, first byte latency, first screen loading latency, HTTP return code, page element download latency, etc.

[0135] RTSP video applications: RTSP error codes, playback latency, throughput, packet loss, latency, etc.

[0136] Step 4, the recommended settings for the metrics in the multi-dimensional view are as follows:

[0137] At the bottom transmission link layer, because the leased line uses a fixed transmission route, any route change along the leased line path will cause a degrade in network quality. Therefore, the threshold for the route change indicator in the leased line path tracking and monitoring should be set to 1. At the middle network / transmission layer, segmented matrix-style quality monitoring capabilities are provided, mainly for transmission and processing latency. Because there are fewer forwarding and processing devices in the leased line link, the transmission distance between the two ends is relatively close. Therefore, latency is mainly reflected in the transmission distance. Thus, the latency threshold setting can be based on the baseline latency measured during the leased line commissioning and acceptance; or the threshold indicator can be set using the distance between the user / segment network. The following can be used as a reference: "Inter-provincial / inter-city latency: 0.8ms / 100km (single-way latency); within the local network, less than or equal to 10ms". At the upper application layer, the cloud leased line application experience is mainly reflected in the user's perception. Different application types and users will have different application experience perceptions. Taking HTTP applications as an example, if the overall page opening latency is less than 2 seconds, the overall experience perception is quite satisfactory. It is recommended to set the first screen opening latency threshold to 2-4 seconds.

[0138] Step 5: Multi-dimensional cross-analysis enables rapid problem identification. Once a quality degradation or malfunction is detected in any monitoring view, a comprehensive analysis can be conducted based on the data indicators from different monitoring views. This allows for a direct identification of the level, aspect, and location of the malfunction causing the quality degradation. The judgment principles are as follows:

[0139] Poor quality in upper-layer applications does not affect the network layer, but poor network layer quality directly leads to a decline in application layer quality. Changes in leased link paths (equipment failover, route convergence, etc.) directly affect the quality of both the network and application layers. A comprehensive analysis is performed using the application layer view and the network layer full mesh quality monitoring matrix. When a cloud leased line user complains about poor leased line quality, it can be determined whether the problem stems from the transmission quality of the leased line network (down to the segment level) or from issues with the customer's own application services.

[0140] Specific monitoring view analysis and positioning system matrix:

[0141]

[0142] Table 2 Monitoring View Analysis and Positioning System Matrix

[0143] This invention utilizes probes deployed on a cloud-network converged gateway on the target cloud leased line to perform dial-up tests on the clients and user virtual networks on both sides of the gateway, obtaining leased line traffic data corresponding to the target cloud leased line. Since the probes are deployed on the cloud-network converged gateway, the service traffic between the clients and the user virtual networks flows through the same physical channel as the dial-up traffic generated by the probes. This enables in-path detection of both cloud-internal and cloud-external networks. Finally, the leased line traffic data is analyzed and processed to obtain the service quality monitoring results of the target cloud leased line. The analysis and aggregation of leased line traffic data can be performed from both cloud-internal and cloud-external directions, and from three dimensions: network layer, link layer, and application layer. This allows for pinpointing the cause of poor service quality to a specific level and location, thereby improving the efficiency and accuracy of cloud leased line service quality monitoring and enhancing the user's cloud leased line experience.

[0144] Figure 9 This diagram illustrates the structure of a cloud private line quality monitoring device provided in an embodiment of the present invention. The device is applied to a target cloud private line; the target cloud private line includes a client, a cloud-network converged gateway, and a user virtual cloud network connected sequentially; as shown... Figure 9 As shown, the device 30 includes a detection module 301 and an analysis module 302.

[0145] The detection module 301 is used to perform dial-tests on the client and the user virtual network respectively using probes to obtain the dedicated line traffic data corresponding to the target cloud dedicated line; wherein, the probe is deployed on the cloud-network convergence gateway so that the service traffic between the client and the user virtual network and the dial-test traffic generated by the probe flow through the same physical channel.

[0146] Analysis module 302 is used to analyze and process the dedicated line traffic data to obtain the service quality monitoring results of the target cloud dedicated line.

[0147] The operation process of the cloud dedicated line quality monitoring device provided in this embodiment of the invention is largely the same as that of the aforementioned method embodiment, and will not be repeated here.

[0148] The cloud dedicated line quality monitoring device provided in this embodiment of the invention uses probes deployed on the cloud-network converged gateway of the target cloud dedicated line to perform dial-up tests on the clients and the user virtual network on both sides of the cloud-network converged gateway, respectively, to obtain dedicated line traffic data corresponding to the target cloud dedicated line. Since the probes are deployed on the cloud-network converged gateway, the service traffic between the clients and the user virtual network and the dial-up traffic generated by the probes flow through the same physical channel, enabling in-path detection of both cloud-internal and cloud-external networks. Finally, the dedicated line traffic data is analyzed and processed to obtain the service quality monitoring results of the target cloud dedicated line. Specifically, the dedicated line traffic data can be analyzed and aggregated from both cloud-internal and cloud-external directions, and from three dimensions: network layer, link layer, and application layer. This allows for pinpointing the cause of poor service quality to a specific level and location, thereby improving the efficiency and accuracy of cloud dedicated line service quality monitoring and enhancing the user's cloud dedicated line experience.

[0149] Figure 10 The diagram shows a schematic of the structure of a cloud dedicated line quality monitoring device provided in an embodiment of the present invention. The specific implementation of the cloud dedicated line quality monitoring device is not limited by the specific embodiments of the present invention.

[0150] like Figure 10 As shown, the cloud dedicated line quality monitoring device may include: processor 402, communication interface 404, memory 406, and communication bus 408.

[0151] The processor 402, communication interface 404, and memory 406 communicate with each other via communication bus 408. Communication interface 404 is used to communicate with other network elements, such as clients or other servers. The processor 402 executes program 410, specifically performing the relevant steps described in the embodiment of the cloud dedicated line quality monitoring method.

[0152] Specifically, program 410 may include program code, which includes computer-executable instructions.

[0153] Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The cloud dedicated line quality monitoring equipment includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0154] Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0155] Specifically, program 410 can be called by processor 402 to cause the cloud dedicated line quality monitoring device to perform the following operations:

[0156] The client and the user virtual network are probed to obtain the dedicated line traffic data corresponding to the target cloud dedicated line; wherein, the probe is deployed on the cloud network convergence gateway so that the service traffic between the client and the user virtual network and the probe-generated test traffic flow through the same physical channel.

[0157] The dedicated line traffic data is analyzed and processed to obtain the service quality monitoring results of the target cloud dedicated line.

[0158] The operation process of the cloud dedicated line quality monitoring equipment provided in this embodiment of the invention is largely the same as that of the aforementioned method embodiment, and will not be repeated here.

[0159] The cloud dedicated line quality monitoring device provided in this embodiment of the invention uses probes deployed on the cloud-network converged gateway of the target cloud dedicated line to perform dial-up tests on the clients and the user virtual network on both sides of the cloud-network converged gateway, respectively, to obtain dedicated line traffic data corresponding to the target cloud dedicated line. Since the probes are deployed on the cloud-network converged gateway, the service traffic between the clients and the user virtual network and the dial-up traffic generated by the probes flow through the same physical channel, enabling in-path detection of both cloud-internal and cloud-external networks. Finally, the dedicated line traffic data is analyzed and processed to obtain the service quality monitoring results of the target cloud dedicated line. Specifically, the dedicated line traffic data can be analyzed and aggregated from both cloud-internal and cloud-external directions, and from three dimensions: network layer, link layer, and application layer. This allows for pinpointing the cause of poor service quality to a specific level and location, thereby improving the efficiency and accuracy of cloud dedicated line service quality monitoring and enhancing the user's cloud dedicated line experience.

[0160] This invention provides a computer-readable storage medium storing at least one executable instruction. When the executable instruction is executed on a cloud dedicated line quality monitoring device, the cloud dedicated line quality monitoring device performs the cloud dedicated line quality monitoring method in any of the above method embodiments.

[0161] Specifically, the executable instructions can be used to cause the cloud private line quality monitoring equipment to perform the following operations:

[0162] The client and the user virtual network are probed to obtain the dedicated line traffic data corresponding to the target cloud dedicated line; wherein, the probe is deployed on the cloud network convergence gateway so that the service traffic between the client and the user virtual network and the probe-generated test traffic flow through the same physical channel.

[0163] The dedicated line traffic data is analyzed and processed to obtain the service quality monitoring results of the target cloud dedicated line.

[0164] The operation process of storing executable instructions on the computer storage medium provided in this embodiment of the invention is largely the same as that in the aforementioned method embodiments, and will not be described again.

[0165] The executable instructions stored on the computer storage medium provided in this embodiment of the invention are probed by probes deployed on the cloud-network converged gateway of the target cloud leased line to the clients and the user virtual network on both sides of the cloud-network converged gateway, respectively, to obtain leased line traffic data corresponding to the target cloud leased line. Since the probes are deployed on the cloud-network converged gateway, the service traffic between the clients and the user virtual network and the probe-generated test traffic flow through the same physical channel, enabling in-path detection of both cloud-internal and cloud-external networks. Finally, the leased line traffic data is analyzed and processed to obtain the service quality monitoring results of the target cloud leased line. The leased line traffic data can be analyzed and aggregated from both cloud-internal and cloud-external directions, and from three dimensions: network layer, link layer, and application layer, thereby pinpointing the cause of poor service quality to a specific level and location. This improves the efficiency and accuracy of cloud leased line service quality monitoring and enhances the user's cloud leased line experience.

[0166] This invention provides a cloud dedicated line quality monitoring device for performing the above-described cloud dedicated line quality monitoring method.

[0167] This invention provides a computer program that can be called by a processor to cause a cloud dedicated line quality monitoring device to execute the cloud dedicated line quality monitoring method in any of the above method embodiments.

[0168] This invention provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, which, when executed on a computer, cause the computer to perform the cloud dedicated line quality monitoring method in any of the above method embodiments.

[0169] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of the present invention are not directed to any particular programming language. It should be understood that the content of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0170] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0171] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of the invention. However, this disclosure should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim.

[0172] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0173] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for monitoring the quality of cloud dedicated lines, characterized in that, The method is applied to a target cloud private line; the target cloud private line includes a client, a cloud-network converged gateway, and a user virtual network connected sequentially; the method includes: The client and the user virtual network are probed to obtain the dedicated line traffic data corresponding to the target cloud dedicated line; wherein, the probe is deployed on the cloud network convergence gateway so that the service traffic between the client and the user virtual network and the probe-generated test traffic flow through the same physical channel. The dedicated line traffic data is analyzed and processed to obtain the service quality monitoring results of the target cloud dedicated line; Prior to obtaining the dedicated line traffic data of the target cloud dedicated line by performing probe tests on the client and the user virtual network respectively, the process includes: Obtain the dedicated line service attributes of the target cloud dedicated line; Based on the leased line service attributes, the probe is configured with a dial-up test route and a dial-up test task to connect to the cloud-network converged gateway. The probe then performs the configured on-the-path network dial-up test tasks on both the cloud-internal network segment and the cloud-external network segment. The network segment between the cloud-network converged gateway and the user virtual network is the cloud-internal network segment; the network segment between the cloud-network converged gateway and the client is the cloud-external network segment. The process of analyzing and processing the dedicated line traffic data to obtain the service quality monitoring results of the target cloud dedicated line includes: The dedicated line traffic data is parsed to obtain the link layer path change information, network layer transmission quality information, and application layer service quality information of the target cloud dedicated line. The service quality monitoring results are obtained by aggregating and analyzing the link layer path change information, network layer transmission quality information, and application layer service quality information.

2. The method according to claim 1, characterized in that, The step of configuring the probe's dial-up routing and dial-up task according to the leased line service attributes includes: Based on the leased line service attributes, determine the client attribute information of the client, the leased line monitoring requirement information of the client, the cloud network attribute information of the user virtual network, and the gateway attribute information of the cloud-network converged gateway. Configure the probe for the dial-up route based on the client attribute information, the cloud network attribute information, and the gateway attribute information; Configure the probe for the in-line network testing task based on the dedicated line monitoring requirements information.

3. The method according to claim 1, characterized in that, After configuring the probe with test routes and test tasks according to the leased line service attributes, the process includes: Real-time detection of changes in the attributes of the dedicated line service; The dial-up test route configuration and the associated network dial-up test task are updated based on the change information.

4. The method according to claim 3, characterized in that, The step of updating the dial-up route configuration and the accompanying network dial-up task based on the change information includes: When it is determined that the change information represents the activation of the leased line, a new probe is created, and the newly created probe is configured with the dial-up test route and the accompanying network dial-up test task according to the leased line service attributes. When it is determined that the change information represents a change in the leased line, the client attribute change information of the client and the network attribute change information of the user virtual network are determined based on the change information. Based on the client attribute change information and the network attribute change information, update the probe routing configuration and the accompanying network testing task on the target cloud private line. When it is determined that the change information represents a cancellation of the dedicated line, the probes on the target cloud dedicated line are recycled.

5. The method according to claim 1, characterized in that, The process of aggregating and analyzing the link layer path change information, network layer transmission quality information, and application layer service quality information to obtain the service quality monitoring results includes: Based on the comparison results of link layer path change information, network layer transmission quality information, and application layer service quality information with preset thresholds, the link layer alarm information, network layer alarm information, and application layer alarm information of the target cloud private line are determined respectively. The service quality monitoring results are determined based on the co-occurrence of the link layer alarm information, network layer alarm information, and application layer alarm information.

6. A cloud dedicated line quality monitoring device, characterized in that, The device is applied to a target cloud private line; the target cloud private line includes a client, a cloud-network converged gateway, and a user virtual network connected in sequence; the device includes: The detection module is used to perform dial-tests on the client and the user virtual network respectively using probes to obtain the dedicated line traffic data corresponding to the target cloud dedicated line; wherein, the probes are deployed on the cloud-network convergence gateway so that the service traffic between the client and the user virtual network and the dial-test traffic generated by the probes flow through the same physical channel; The analysis module is used to analyze and process the dedicated line traffic data to obtain the service quality monitoring results of the target cloud dedicated line. Specifically, the dedicated line traffic data is parsed to obtain the link layer path change information, network layer transmission quality information, and application layer service quality information of the target cloud dedicated line. The service quality monitoring results are obtained by aggregating and analyzing the link layer path change information, network layer transmission quality information, and application layer service quality information. The device is also used to: acquire the leased line service attributes of the target cloud leased line; Based on the leased line service attributes, the probe is configured with a dial-up test route and a dial-up test task, so that the probe connects to the cloud-network converged gateway and performs the configured on-the-path network dial-up test tasks on the cloud intranet segment and the cloud extranet segment respectively. The network segment between the cloud-network converged gateway and the user virtual network is the cloud intranet segment; the network segment between the cloud-network converged gateway and the client is the cloud extranet segment.

7. A cloud dedicated line quality monitoring device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction, which causes the processor to perform the operation of the cloud dedicated line quality monitoring method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The storage medium stores at least one executable instruction, which, when executed on the cloud dedicated line quality monitoring device, causes the cloud dedicated line quality monitoring device to perform the operation of the cloud dedicated line quality monitoring method as described in any one of claims 1-5.

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