A method and device for opening up computing power capabilities of a cloud-based private network

Abstract

The present invention discloses a method and device for opening up the computing power of a cloud-based private network. The method includes: daily updating and calculating the shortest paths between each pair of PE devices in a network hub node; hourly querying network quality data between each hop device based on the devices along the shortest path, and simultaneously querying the outbound port traffic data of each hop egress device; calculating the path of the traversed link based on the topological structure; saving the topological structure and port traffic data of the ports of the devices along each traversed path, calculating the network quality data of each traversed path and storing it in an ES, and setting a retention time; querying and filtering the data; and calculating a simulated price for each qualified path with the worst quality historical record H. The method and device utilize the underlying automatic path calculation function to calculate the price of each path based on a price rule library, provide a list of optional paths, and open it to the public.

Description

Technical Field

[0001] The present invention relates to the field of computing power networks, and in particular to a method and device for opening up computing power capabilities of a cloud-based private network. Background Art

[0002] The user has built a private network such as mobile cloud, and the network management system has also been equipped with basic network quality testing and traffic collection. The user hopes to calculate the simulated price of the path in the network based on the network quality and traffic data. Provide the optional path list capability and provide the ability to open to the outside world.

[0003] The computing power network is a new type of information infrastructure that allocates and flexibly schedules computing resources, storage resources, and network resources on demand between clouds, networks, and edges based on business needs; the hub is the hub node in the computing power network. Summary of the Invention

[0004] In order to solve the above-mentioned problems existing in the prior art, the present invention provides a method and device for opening up the computing power capabilities of a cloud private network, using the underlying automatic path calculation function to calculate the price of each path based on the price rule library, and providing the capability of listing optional paths, which is open to the outside world.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] In one embodiment of the present invention, a method for opening up computing power capabilities of a cloud-based private network is proposed, the method comprising:

[0007] S01. Update and calculate the shortest path between each pair of PE devices in the network hub node every day;

[0008] S02. Query the network quality data between each hop device according to the shortest path device every hour, and query the outbound port traffic data of each hop egress device at the same time;

[0009] S03. Calculate a path traversing the link according to the topology structure;

[0010] S04. Save the topology structure and port traffic data of the device ports along each traversal path, calculate the network quality data of each traversal path, store it in Elasticsearch (distributed full-text search engine, referred to as ES), and set a retention time;

[0011] Delay = sum (maximum delay of the path link), packet loss rate = max (packet loss rate of the path link);

[0012] S05. Query and filter the data;

[0013] S06. Calculate a simulated price for each qualified path with the worst quality historical record H.

[0014] Furthermore, the network quality data between each hop device includes: packet loss rate, average delay, maximum delay, and minimum delay.

[0015] Furthermore, the outbound port traffic data of each hop egress device includes: port bandwidth and flow rate.

[0016] Furthermore, the flow rate is calculated by taking the maximum value of the port inflow and outflow flow rates as the port flow rate.

[0017] Furthermore, the S05 includes:

[0018] S051. Filter PE devices that meet the user's screening conditions;

[0019] S052. Calculate the current shortest path between each pair of PE devices in S04, and calculate the path traversing the link;

[0020] S053. Find the worst quality historical record H in each traversal path from the data stored in S04.

[0021] Furthermore, the S053 includes:

[0022] S0531. Filter historical data by user-specified time range;

[0023] S0532. Group the data stored in S04 by traversal path. In each group, select the record with the largest delay as the historical record of the worst quality of each traversal path H.

[0024] S0533. Filter out H paths that meet the user input conditions, including: a delay less than the user input conditions, a packet loss rate less than the user input conditions, and calculate the final remaining bandwidth utilization based on the user input traffic, requiring the final remaining bandwidth utilization to be greater than or equal to 50%.

[0025] Furthermore, the remaining bandwidth utilization of the worst historical record H is calculated as follows:

[0026] For each link, bandwidth utilization = (maximum flow rate + user-entered flow rate) / bandwidth * 100%;

[0027] Remaining bandwidth utilization = 1-bandwidth utilization;

[0028] The minimum value of the remaining bandwidth utilization is taken as the final remaining bandwidth utilization of the H path.

[0029] Furthermore, the S06 includes:

[0030] S061. The maximum delay weighted score is recorded as S1. If the user does not enter a delay value, the weighted score S1 = 1. If the user enters a delay value T', S1 = (T'-average delay of H) / T', rounded to two decimal places.

[0031] S062. The weighted score of the maximum packet loss rate is recorded as S2. If the user does not enter the packet loss rate, the weighted score S2 = 1. If the user enters the packet loss rate Pkg', S2 = (Pkg'-H's packet loss rate) / Pkg', rounded to two decimal places.

[0032] S063, the weighted score of the remaining bandwidth utilization is recorded as S3. The remaining bandwidth ratio and weighted score are pre-configured by the user;

[0033] S064. Calculate the simulated price, where the simulated price weight = (maximum delay weighted score S1*40% + average packet loss rate weighted score S2*20% + remaining bandwidth utilization weighted score S3*40%)*100.

[0034] In one embodiment of the present invention, a device for opening up computing power capabilities of a cloud-based private network is also proposed, the device comprising:

[0035] The shortest path update module updates and calculates the shortest path between each pair of PE devices in the network hub node every day;

[0036] The network data query module queries the network quality data between each hop device according to the shortest path device every hour, and queries the outbound port traffic data of each hop exit device at the same time;

[0037] The traversal link module calculates the path of the traversal link according to the topological structure;

[0038] The Elasticsearch storage module saves the topology structure and port traffic data of the device ports along each traversal path, calculates the network quality data of each traversal path, stores it in Elasticsearch, and sets a retention time.

[0039] Data screening module, query and filter data;

[0040] The simulation price calculation module calculates the simulation price for each qualified worst quality historical record H path.

[0041] Furthermore, the network quality data between each hop device in the network data query module includes: packet loss rate, average delay, maximum delay, and minimum delay.

[0042] Furthermore, the outbound port flow data of each hop of the egress device in the network data query module includes: port bandwidth and flow rate.

[0043] Furthermore, the flow rate is calculated by taking the maximum value of the port inflow and outflow flow rates as the port flow rate.

[0044] Furthermore, the data screening module includes:

[0045] PE device screening module, according to user screening conditions, screen out qualified PE devices;

[0046] The path calculation module calculates the current shortest path between each pair of PE devices in the Elasticsearch storage module and calculates the path traversing the link;

[0047] The worst quality path module finds the worst quality historical record H in each traversal path from the data stored in the Elasticsearch storage module.

[0048] Furthermore, the worst quality path module includes:

[0049] Historical data screening module, filtering historical data according to the user-specified time range;

[0050] The maximum latency selection module groups the data stored in the Elasticsearch storage module by traversal path. In each group, the record with the largest latency is selected as the historical record of the worst quality of each traversal path.

[0051] The remaining bandwidth utilization calculation module selects H paths that meet the user-input conditions, including: the delay is less than the user-input conditions, the packet loss rate is less than the user-input conditions, and calculates the final remaining bandwidth utilization based on the user-input traffic, requiring the final remaining bandwidth utilization to be greater than or equal to 50%.

[0052] Furthermore, the remaining bandwidth utilization of the worst historical record H is calculated as follows:

[0053] For each link, bandwidth utilization = (maximum flow rate + user-entered flow rate) / bandwidth * 100%;

[0054] Remaining bandwidth utilization = 1-bandwidth utilization;

[0055] The minimum value of the remaining bandwidth utilization is taken as the final remaining bandwidth utilization of the H path.

[0056] Furthermore, the simulated price calculation module includes:

[0057] The maximum delay weighted score module and the maximum delay weighted score are recorded as S1. If the user does not enter a delay value, the weighted score S1 = 1. If the user enters a delay value T', S1 = (T'-average delay of H) / T', rounded to two decimal places.

[0058] The maximum packet loss rate weighted score module and the maximum packet loss rate weighted score are recorded as S2. If the user does not enter the packet loss rate, the weighted score S2 = 1 directly; if the user enters the packet loss rate Pkg', S2 = (Pkg'-H's packet loss rate) / Pkg', rounded to 2 decimal places;

[0059] The remaining bandwidth utilization weighted score module and the remaining bandwidth utilization weighted score are recorded as S3. The remaining bandwidth ratio and weighted score are pre-configured by the user;

[0060] Simulation price calculation module, simulation price weight = (maximum delay weighted score S1*40%+average packet loss rate weighted score S2*20%+remaining bandwidth utilization weighted score S3*40%)*100.

[0061] In one embodiment of the present invention, a computer device is also proposed, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the aforementioned cloud private network computing power capability is opened.

[0062] In one embodiment of the present invention, a computer-readable storage medium is also proposed, which stores a computer program for executing the opening of cloud private network computing power capabilities.

[0063] Beneficial effects:

[0064] 1. The present invention relies on the existing network quality test history data of the network management system and the shortest path algorithm between PE devices to calculate the traversal path of the link between each PE in the hub node, save the device port and flow data of each traversal path, and calculate the network quality;

[0065] 2. The present invention can filter data according to user conditions, dynamically calculate the simulated price of each PE link in real time according to the price rule base pre-configured by the user, and provide a path list that meets the user's requirements;

[0066] 3. Compared with calculating network quality by device, this invention has smaller granularity and higher data accuracy, meeting the user's needs for dynamically adjusting resources in the computing network. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 This is a flow chart of the method for opening up the computing power of a cloud-based private network according to the present invention;

[0068] Figure 2 This is a schematic diagram of the principle of the method for opening up the computing power capabilities of the cloud private network of the present invention;

[0069] Figure 3 is the shortest path topology diagram in an embodiment of the present invention;

[0070] Figure 4This is a schematic diagram of the device structure for opening up the computing power of the cloud private network of the present invention;

[0071] Figure 5 It is a schematic diagram of the computer device structure of the present invention. DETAILED DESCRIPTION

[0072] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make this disclosure more thorough and complete, and to fully convey the scope of the present disclosure to those skilled in the art.

[0073] Those skilled in the art will appreciate that embodiments of the present invention may be implemented as a device, apparatus, node, method, or computer program product. Therefore, the present disclosure may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0074] The implementation mode of the present invention proposes a method and device for opening up the computing power of a cloud-based private network, using the underlying automatic path calculation function to calculate the price of each path based on a price rule library, providing an optional path list capability, and opening it to the outside world.

[0075] The principles and spirit of the present invention are explained in detail below with reference to several representative embodiments of the present invention.

[0076] Figure 1 This is a flow chart of the method for opening up the computing power of the cloud private network of the present invention. Figure 1 、 Figure 2 As shown, the method includes:

[0077] S01. Update and calculate the shortest path between each pair of PE devices in the network hub node every day;

[0078] S02. Query the network quality data between each hop device according to the shortest path device every hour, and query the outbound port traffic data of each hop egress device at the same time;

[0079] Furthermore, the network quality data between each hop device includes: packet loss rate, average delay, maximum delay, and minimum delay.

[0080] Furthermore, the outbound port traffic data of each hop egress device includes: port bandwidth and flow rate.

[0081] Furthermore, the flow rate is calculated by taking the maximum value of the port inflow and outflow flow rates as the port flow rate.

[0082] S03. Calculate a path traversing the link according to the topology structure;

[0083] S04. Save the topology structure and port traffic data of the device ports along each traversal path, calculate the network quality data of each traversal path, store it in Elasticsearch (distributed full-text search engine, referred to as ES), and set a retention time;

[0084] Delay = sum (maximum delay of the path link), packet loss rate = max (packet loss rate of the path link);

[0085] S05. Query and filter the data;

[0086] Furthermore, the S05 includes:

[0087] S051. Filter PE devices that meet the user's screening conditions;

[0088] S052. Calculate the current shortest path between each pair of PE devices in S04, and calculate the path traversing the link;

[0089] S053. Find the worst quality historical record H in each traversal path from the data stored in S04.

[0090] Furthermore, the S053 includes:

[0091] S0531. Filter historical data by user-specified time range;

[0092] S0532. Group the data stored in S04 by traversal path. In each group, select the record with the largest delay as the historical record of the worst quality of each traversal path H.

[0093] S0533. Filter out H paths that meet the user input conditions, including: a delay less than the user input conditions, a packet loss rate less than the user input conditions, and calculate the final remaining bandwidth utilization based on the user input traffic, requiring the final remaining bandwidth utilization to be greater than or equal to 50%.

[0094] Furthermore, the remaining bandwidth utilization of the worst historical record H is calculated as follows:

[0095] For each link, bandwidth utilization = (maximum flow rate + user-entered flow rate) / bandwidth * 100%;

[0096] Remaining bandwidth utilization = 1-bandwidth utilization;

[0097] The minimum value of the remaining bandwidth utilization is taken as the final remaining bandwidth utilization of the H path.

[0098] S06. Calculate a simulated price for each qualified worst quality historical record path H;

[0099] Furthermore, the S06 includes:

[0100] S061. The maximum delay weighted score is recorded as S1. If the user does not enter a delay value, the weighted score S1 = 1. If the user enters a delay value T', S1 = (T'-average delay of H) / T', rounded to two decimal places.

[0101] S062. The weighted score of the maximum packet loss rate is recorded as S2. If the user does not enter the packet loss rate, the weighted score S2 = 1. If the user enters the packet loss rate Pkg', S2 = (Pkg'-H's packet loss rate) / Pkg', rounded to two decimal places.

[0102] S063, the weighted score of the remaining bandwidth utilization is recorded as S3. The remaining bandwidth ratio and weighted score are pre-configured by the user;

[0103] As shown in Table 1 below:

[0104] Remaining bandwidth ratio range S3 weighted score >=50% and <=60% 1 <＝70％ 0.8 <＝80％ 0.6 <＝90％ 0.4 >90％ 0

[0105] Table 1

[0106] S064. Calculate the simulated price, where the simulated price weight = (maximum delay weighted score S1*40% + average packet loss rate weighted score S2*20% + remaining bandwidth utilization weighted score S3*40%)*100.

[0107] It should be noted that although the operations of the method of the present invention are described in a specific order in the above embodiments and drawings, this does not require or imply that these operations must be performed in this specific order, or that all illustrated operations must be performed to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0108] In order to more clearly explain the above-mentioned method of opening up the computing power capabilities of the cloud private network, a specific embodiment is used for illustration below. However, it is worth noting that this embodiment is only for the purpose of better illustrating the present invention and does not constitute an improper limitation on the present invention.

[0109] Example:

[0110] For example, the shortest path topology of PE devices: Beijing CPE1 to Shanghai CPE1 is as follows: Figure 3 shown.

[0111] There are 4 traversal paths, as shown in Table 2 below:

[0112]

[0113] Table 2

[0114] The data collected and counted by the network management system is recorded every 15 minutes and stored in Elasticsearch (distributed full-text search engine, referred to as ES).

[0115] The following table only provides data at two time points as an example, as shown in Table 3

[0116]

[0117] Table 3

[0118] 1. If the user needs to select a suitable path between Beijing CPE1 and Shanghai CPE1, the worst quality historical record H of each traversed path is first found based on the worst latency from the above statistical data, as shown in Table 4 below:

[0119]

[0120] Table 4

[0121] 2. Assume that the user enters the delay conditions of less than 35ms, packet loss rate less than 1%, and flow rate 1M, and performs filtering on each worst history, as shown in Table 5 below:

[0122]

[0123] Table 5

[0124] For each link, the bandwidth utilization after adding traffic = (traffic rate + user-entered traffic rate) / bandwidth * 100%;

[0125] Remaining bandwidth utilization = 1 - bandwidth utilization

[0126] The minimum value of the remaining bandwidth utilization is taken as the final remaining bandwidth utilization of the H path.

[0127] 3. After screening, there are 2 paths remaining. Calculate the weighted scores, as shown in Table 6 below:

[0128]

[0129] Table 6

[0130] Maximum delay weighted S1:

[0131] S1 = (average delay of 35-H) / 35, rounded to 2 decimal places;

[0132] Maximum packet loss rate weighted score S2:

[0133] S2 = (1-H packet loss rate) / 1, rounded to 2 decimal places;

[0134] Remaining bandwidth utilization weighted score S3: The remaining bandwidth percentage and weighted score are pre-configured by the user, as shown in Table 7 below:

[0135] Remaining bandwidth ratio range S3 weighted score >=50% and <=60% 1 <＝70％ 0.8 <＝80％ 0.6 <＝90％ 0.4 >90％ 0

[0136] Table 7

[0137] Calculate simulated prices:

[0138] Simulation price weight = (maximum delay weighted score S1*40% + average packet loss rate weighted score S2*20% + remaining bandwidth utilization weighted score S3*40%)*100

[0139] The two paths presented to the user are as shown in Table 8 below.

[0140]

[0141]

[0142] Table 8

[0143] Based on the same inventive concept, the present invention also proposes a device for opening up the computing power capabilities of a cloud-based private network. The implementation of this device can refer to the implementation of the above-mentioned method, and the repeated parts will not be repeated here. The term "module" used below can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0144] Figure 4 This is a schematic diagram of the device structure for opening up the computing power of the cloud private network of the present invention. Figure 4 As shown, the device includes:

[0145] The shortest path update module 110 updates and calculates the shortest paths between each pair of PE devices in the network hub node every day;

[0146] The network data query module 120 queries the network quality data between each hop device according to the shortest path device every hour, and queries the outbound port flow data of each hop egress device at the same time;

[0147] The link traversal module 130 calculates the path of the traversal link according to the topological structure;

[0148] The Elasticsearch storage module 140 stores the topology structure and port traffic data of the device ports along each traversal path, calculates the network quality data of each traversal path, stores it in Elasticsearch, and sets a retention time;

[0149] Data screening module 150, querying and screening data;

[0150] The simulated price calculation module 160 calculates a simulated price for each qualified path with the worst quality historical record H.

[0151] The network quality data between each hop device in the network data query module 120 includes: packet loss rate, average delay, maximum delay, and minimum delay.

[0152] The outbound port flow data of each hop of the egress device in the network data query module 120 includes: port bandwidth and flow rate.

[0153] The flow rate is calculated by taking the maximum value of the port inflow and outflow flow rates as the port flow rate.

[0154] The data screening module 150 includes:

[0155] The PE device screening module 151 screens PE devices that meet the user screening conditions;

[0156] The path calculation module 152 calculates the current shortest path between each pair of PE devices in the Elasticsearch storage module 140 and calculates the path traversing the link;

[0157] The worst quality path module 153 finds the worst quality historical record H in each traversal path from the data stored in the Elasticsearch storage module 140 .

[0158] The worst quality path module 153 includes:

[0159] The historical data screening module 1531 filters historical data according to a user-specified time range;

[0160] The maximum delay selection module 1532 groups the data stored in the Elasticsearch storage module 140 according to the traversal path, and selects the record with the largest delay in each group as the historical record of the worst quality of each traversal path H.

[0161] The remaining bandwidth utilization calculation module 1533 selects H paths that meet the user input conditions, including: the delay is less than the user input conditions, the packet loss rate is less than the user input conditions, and calculates the final remaining bandwidth utilization based on the user input traffic, requiring the final remaining bandwidth utilization to be greater than or equal to 50%.

[0162] The calculation method of the remaining bandwidth utilization of the worst historical record H is:

[0163] For each link, bandwidth utilization = (maximum flow rate + user-entered flow rate) / bandwidth * 100%;

[0164] Remaining bandwidth utilization = 1-bandwidth utilization;

[0165] The minimum value of the remaining bandwidth utilization is taken as the final remaining bandwidth utilization of the H path.

[0166] The simulated price calculation module 160 includes:

[0167] Maximum delay weighted score module 161, the maximum delay weighted score is recorded as S1, the user does not enter the delay value, directly record the weighted score S1 = 1; the user enters the delay value T', S1 = (T'-H average delay) / T', rounded to 2 decimal places;

[0168] The maximum packet loss rate weighted score module 162 records the maximum packet loss rate weighted score as S2. If the user does not enter the packet loss rate, the weighted score S2 = 1. If the user enters the packet loss rate Pkg', S2 = (Pkg'-H's packet loss rate) / Pkg', with two decimal places.

[0169] The remaining bandwidth utilization weighted score module 163 and the remaining bandwidth utilization weighted score are recorded as S3. The remaining bandwidth ratio and weighted score are pre-configured by the user.

[0170] The simulation price calculation module 164 , the simulation price weight=(maximum delay weighted score S1*40%+average packet loss rate weighted score S2*20%+remaining bandwidth utilization weighted score S3*40%)*100.

[0171] It should be noted that while the detailed description above mentions several modules of the device for exposing cloud-based computing capabilities, this division is merely exemplary and not mandatory. In practice, depending on the embodiments of the present invention, the features and functions of two or more modules described above may be embodied in a single module. Conversely, the features and functions of a single module described above may be further divided and embodied by multiple modules.

[0172] Based on the above invention concept, Figure 5 As shown, the present invention also proposes a computer device 200, including a memory 210, a processor 220 and a computer program 230 stored in the memory 210 and executable on the processor 220. When the processor 220 executes the computer program 230, the aforementioned method of opening up the computing power of a private cloud network is implemented.

[0173] Based on the aforementioned inventive concept, the present invention also proposes a computer-readable storage medium, which stores a computer program for executing the aforementioned cloud private network computing power capability opening.

[0174] The method and device for opening up the computing power capabilities of a cloud-based private network proposed in the present invention use the network quality test history data already available in the network management system and the shortest path algorithm between PE devices to calculate the traversal path of the links between each PE in the hub node, save the device ports and traffic data of each traversal path, and calculate the network quality. The present invention can filter data according to user conditions, and dynamically calculate the simulated price of the links between each PE in real time according to the price rule library pre-configured by the user, providing a path list that meets the user's requirements. Compared with calculating network quality by device, the present invention has smaller granularity and higher data accuracy, meeting the user's needs for dynamically adjusting resources in the computing power network.

[0175] Although the spirit and principles of the present invention have been described with reference to several specific embodiments, it should be understood that the present invention is not limited to the specific embodiments disclosed, and the division into various aspects does not mean that the features of these aspects cannot be combined to benefit. Such division is only for the convenience of expression. The present invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

[0176] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0177] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0178] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0179] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0180] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0181] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0182] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0183] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

[0184] Regarding the limitation of the protection scope of the present invention, those skilled in the art should understand that, based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative work are still within the protection scope of the present invention.

Claims

1. A method for opening up computing power capabilities of a cloud-based private network, characterized in that: The method includes: S01. Update and calculate the shortest path between each pair of PE devices in the network hub node every day; S02. Query the network quality data between each hop device according to the shortest path device every hour, and query the outbound port traffic data of each hop egress device at the same time; S03. Calculate a path traversing the link according to the topology structure; S04. Save the topology structure and port traffic data of the device ports along each traversal path, calculate the network quality data of each traversal path, store it in Elasticsearch, and set a retention time; S05. Query and filter the data, including: S051. Filter PE devices that meet the user's screening conditions; S052. Calculate the current shortest path between each pair of PE devices in S04, and calculate the path traversing the link; S053. Find the worst quality historical record H in each traversal path from the data stored in S04. S06. For each qualified path with the worst quality historical record H, calculate the simulated price, including: S061. The maximum delay weighted score is recorded as S1. If the user does not enter a delay value, the weighted score S1 = 1. If the user enters a delay value T', S1 = (T'-average delay of H) / T', rounded to two decimal places. S062. The weighted score of the maximum packet loss rate is recorded as S2. If the user does not enter the packet loss rate, the weighted score S2 = 1. If the user enters the packet loss rate Pkg', S2 = (Pkg'-H's packet loss rate) / Pkg', rounded to two decimal places. S063, the weighted score of the remaining bandwidth utilization is recorded as S3. The remaining bandwidth ratio and weighted score are pre-configured by the user; S064. Calculate the simulated price, where the simulated price weight = (maximum delay weighted score S1*40% + average packet loss rate weighted score S2*20% + remaining bandwidth utilization weighted score S3*40%)*100.

2. The method for opening up computing power of a cloud-based private network according to claim 1, characterized in that: The network quality data between each hop device in S02 includes: packet loss rate, average delay, maximum delay, and minimum delay.

3. The method for opening up computing power of a cloud-based private network according to claim 1, characterized in that: The outbound port flow data of each hop egress device in S02 includes: port bandwidth and flow rate.

4. The method for opening up computing power of a cloud-based private network according to claim 3, characterized in that: The flow rate is calculated by taking the maximum value of the port inflow and outflow flow rates as the port flow rate.

5. The method for opening up computing power of a cloud-based private network according to claim 1, characterized in that: The S053 includes: S0531. Filter historical data by user-specified time range; S0532. Group the data stored in S04 by traversal path. In each group, select the record with the largest delay as the historical record of the worst quality of each traversal path H. S0533. Filter out H paths that meet the user input conditions, including: a delay less than the user input conditions, a packet loss rate less than the user input conditions, and calculate the final remaining bandwidth utilization based on the user input traffic, requiring the final remaining bandwidth utilization to be greater than or equal to 50%.

6. The method for opening up computing power of a cloud-based private network according to claim 5, characterized in that: The calculation method of the remaining bandwidth utilization of the worst historical record H is: For each link, bandwidth utilization = (maximum flow rate + user-entered flow rate) / bandwidth * 100%; Remaining bandwidth utilization = 1-bandwidth utilization; The minimum value of the remaining bandwidth utilization is taken as the final remaining bandwidth utilization of the H path.

7. A device for opening up computing power of a cloud-based private network, characterized in that: The device includes: The shortest path update module updates and calculates the shortest path between each pair of PE devices in the network hub node every day; The network data query module queries the network quality data between each hop device according to the shortest path device every hour, and queries the outbound port traffic data of each hop exit device at the same time; The traversal link module calculates the path of the traversal link according to the topological structure; The Elasticsearch storage module saves the topology structure and port traffic data of the device ports along each traversal path, calculates the network quality data of each traversal path, stores it in Elasticsearch, and sets a retention time. Data screening module, query and filter data, including: PE device screening module, according to user screening conditions, screen out qualified PE devices; The path calculation module calculates the current shortest path between each pair of PE devices in the Elasticsearch storage module and calculates the path traversing the link; The worst quality path module finds the worst quality historical record H in each traversal path from the data stored in the Elasticsearch storage module; The simulation price calculation module calculates the simulation price for each qualified worst quality historical record path H, including: The maximum delay weighted score module and the maximum delay weighted score are recorded as S1. If the user does not enter a delay value, the weighted score S1 = 1. If the user enters a delay value T', S1 = (T'-average delay of H) / T', rounded to two decimal places. The maximum packet loss rate weighted score module and the maximum packet loss rate weighted score are recorded as S2. If the user does not enter the packet loss rate, the weighted score S2 = 1 directly; if the user enters the packet loss rate Pkg', S2 = (Pkg'-H's packet loss rate) / Pkg', rounded to 2 decimal places; The remaining bandwidth utilization weighted score module and the remaining bandwidth utilization weighted score are recorded as S3. The remaining bandwidth ratio and weighted score are pre-configured by the user; Simulation price calculation module, simulation price weight = (maximum delay weighted score S1*40%+average packet loss rate weighted score S2*20%+remaining bandwidth utilization weighted score S3*40%)*100.

8. The device for opening up computing power of a cloud-based private network according to claim 7, characterized in that: The network quality data between each hop device in the network data query module includes: packet loss rate, average delay, maximum delay, and minimum delay.

9. The device for opening up computing power of a cloud-based private network according to claim 7, characterized in that: The outbound port flow data of each hop exit device in the network data query module includes: port bandwidth and flow rate.

10. The device for opening up computing power of a cloud-based private network according to claim 9, characterized in that: The flow rate is calculated by taking the maximum value of the port inflow and outflow flow rates as the port flow rate.

11. The device for opening up computing power of a cloud-based private network according to claim 7, characterized in that: The worst quality path module includes: Historical data screening module, filtering historical data according to the user-specified time range; The maximum latency selection module groups the data stored in the Elasticsearch storage module by traversal path. In each group, the record with the maximum latency is selected as the historical record H of the worst quality for each traversal path. The remaining bandwidth utilization calculation module selects H paths that meet the user-input conditions, including: the delay is less than the user-input conditions, the packet loss rate is less than the user-input conditions, and calculates the final remaining bandwidth utilization based on the user-input traffic, requiring the final remaining bandwidth utilization to be greater than or equal to 50%.

12. The device for opening up computing power of a cloud-based private network according to claim 11, characterized in that: The calculation method of the remaining bandwidth utilization of the worst historical record H is: For each link, bandwidth utilization = (maximum flow rate + user-entered flow rate) / bandwidth * 100%; Remaining bandwidth utilization = 1-bandwidth utilization; The minimum value of the remaining bandwidth utilization is taken as the final remaining bandwidth utilization of the H path.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

14. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program for executing the method according to any one of claims 1 to 6.

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