Method and device for monitoring network congestion, electronic device and storage medium
By classifying and performing bitwise operations on the network quality parameters of boundary devices, the problem of incomplete network lag monitoring is solved, enabling more accurate network lag detection and efficient storage management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA MOBILEHANGZHOUINFORMATION TECH CO LTD
- Filing Date
- 2023-12-04
- Publication Date
- 2026-06-16
AI Technical Summary
Existing technologies rely on user experience quality to determine network lag, resulting in incomplete network lag monitoring.
By receiving network data from multiple edge devices, the network quality parameters are categorized into binary data bit classification queues according to preset classification rules. Bitwise operations are then performed and the probability of network lag is queried, enabling comprehensive monitoring of network lag.
It improves the accuracy of network lag monitoring, reduces storage space usage, and increases storage speed.
Smart Images
Figure CN118828650B_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of data processing technology, and in particular relates to a method, device, electronic device and storage medium for monitoring network lag. Background Technology
[0002] In recent years, mobile internet technology has developed rapidly, with a proliferation of applications on terminals, including instant messaging, video services, and games. This increase in applications puts pressure on mobile networks and terminal applications, potentially reducing application quality. Quality of experience (QoE) is used to evaluate user satisfaction with terminal device applications. When user experience quality is poor, operators can analyze potential problems at each network node to identify the root causes of perceived quality issues within that node.
[0003] In related technologies, network lag is judged by whether the user experience quality of the corresponding edge device is poor, which makes the monitoring of network lag incomplete. Summary of the Invention
[0004] This application provides a method, apparatus, electronic device, and storage medium for monitoring network lag, in order to solve the technical problem that judging network lag based on user experience quality leads to incomplete monitoring of network lag.
[0005] In a first aspect, embodiments of this application provide a method for monitoring network lag, the method comprising:
[0006] It receives network data sent by multiple border devices, and the network data includes at least one network quality identifier and network quality parameters corresponding to the network quality identifier.
[0007] The classification queue corresponding to the network quality parameters is determined according to the preset classification rules corresponding to the network quality identifier;
[0008] The classification queue includes binary data bits that correspond one-to-one with multiple edge devices. In the classification queue corresponding to the network quality parameters, the values of the data bits corresponding to the edge devices are updated until all network quality parameters are processed, resulting in a classification queue corresponding to the sampling period.
[0009] Perform bitwise operations on the classification queues corresponding to multiple consecutive sampling periods to obtain the bitwise operation results corresponding to the boundary device;
[0010] Based on the preset mapping relationship, the stuttering probability corresponding to the bit operation result is obtained, and the stuttering probability of the network where each boundary device is located is obtained.
[0011] In one embodiment, the preset classification rules include n classification ranges, each corresponding to one of the n classification queues, where n is a natural number greater than 2; determining the classification queue corresponding to the network quality parameters based on the preset classification rules corresponding to the network quality identifier includes:
[0012] Based on the network quality identifier corresponding to the first network quality parameter, a preset classification rule corresponding to the first network quality parameter is determined, and the multiple network quality parameters include the first network quality parameter;
[0013] Determine the first classification range in which the first network quality parameter is located in the corresponding preset classification rule, and the n classification ranges include the first classification range;
[0014] Determine the correspondence between the first network quality parameter and the classification queue corresponding to the first classification range.
[0015] In one embodiment, the network quality identifier is of type p, and m consecutive sampling periods correspond to q classification queues, where q is m*(n1+n2+...np);
[0016] Where n1 is the number of classification ranges included in the preset classification rule corresponding to the first network quality identifier, n2 is the number of classification ranges included in the preset classification rule corresponding to the second network quality identifier, np is the number of classification ranges included in the preset classification rule corresponding to the p-th network quality identifier, and p and m are natural numbers greater than 2.
[0017] In one embodiment, bitwise operations are performed on classification queues corresponding to multiple consecutive sampling periods to obtain bitwise operation results corresponding to the boundary device, including:
[0018] Determine the first data bit of the first boundary device in the classification queue, where the first boundary device is included among multiple boundary devices;
[0019] Perform bitwise operations on the first data bits of the q classification queues to obtain the first bit operation result corresponding to the first boundary device;
[0020] Based on the preset mapping relationship, the stuttering probability corresponding to the bitwise operation result is obtained, and the stuttering probability of the network where each boundary device is located is obtained, including:
[0021] The first stutter probability corresponding to the first operation result is obtained by querying the preset mapping relationship, and the first stutter probability of the network where the first boundary device is located is obtained. The preset mapping relationship includes the mapping relationship between n1 classification ranges, n2 classification ranges, ... np classification ranges and stutter probabilities.
[0022] In one embodiment, the method for monitoring network lag further includes:
[0023] Calculate the average value of the first stutter probability of the first boundary device within the first time period, and determine the stutter status of the first boundary device based on the average value. The first time period includes r sampling periods, where r > m.
[0024] In one embodiment, updating the value of the data bit corresponding to the edge device in the classification queue corresponding to the network quality parameters includes:
[0025] In the classification queue corresponding to the network quality parameters, update the value of the data bit corresponding to the edge device to 1.
[0026] In one embodiment, the network quality identifier includes optical attenuation value, downlink traffic, and TCP retransmission rate;
[0027] The multiple classification queues include multiple optical attenuation queues corresponding to optical attenuation values, multiple downlink traffic queues corresponding to downlink traffic, and multiple TCP retransmission queues corresponding to TCP retransmission rates.
[0028] Secondly, embodiments of this application provide a network lag monitoring device, comprising:
[0029] The receiving module is used to receive network data sent by multiple border devices respectively. The network data includes at least one network quality identifier and network quality parameters corresponding to the network quality identifier.
[0030] The determination module is used to determine the classification queue corresponding to the network quality parameters according to the preset classification rules corresponding to the network quality identifier;
[0031] The update module is used to classify queues, which include binary data bits that correspond one-to-one with multiple edge devices. In the classification queues corresponding to network quality parameters, the value of the data bits corresponding to the edge devices is updated until all network quality parameters are processed, and a classification queue corresponding to the sampling period is obtained.
[0032] The calculation module is used to perform bit operations on the classification queues corresponding to multiple consecutive sampling periods to obtain the bit operation results corresponding to the boundary device.
[0033] The probability query module is used to query the stuttering probability corresponding to the bit operation result based on the preset mapping relationship, and to obtain the stuttering probability of the network where each boundary device is located.
[0034] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the network lag monitoring method described above.
[0035] Fourthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the network lag monitoring method described above.
[0036] The network lag monitoring method, apparatus, electronic device, and storage medium provided in this application classify network quality parameters corresponding to network quality identifiers, update binary data bits in the classification queue according to the classification results, perform bit operations on the classification queues corresponding to multiple consecutive sampling periods, and query the lag probability of the network where the boundary device is located based on the bit operation results. This makes network lag monitoring comprehensive and improves the accuracy of network lag monitoring. By classifying network quality parameters into classification queues that include binary data bits for storage, the storage space is small and the storage speed is fast. Attached Figure Description
[0037] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0038] Figure 1 This is a flowchart illustrating a network lag monitoring method provided in an embodiment of this application;
[0039] Figure 2 This is a flowchart illustrating a network lag monitoring method provided in an embodiment of this application;
[0040] Figure 3 This is a schematic diagram of the network lag monitoring device provided in one embodiment of this application;
[0041] Figure 4 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation
[0042] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.
[0043] It should be noted that, in this document, relational terms such as "second" and "third" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.
[0044] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The embodiments will now be described in detail with reference to the accompanying drawings.
[0045] Currently, when users write on electronic devices, they often unnecessarily touch the screen, causing unnecessary content to be displayed, thus reducing writing efficiency and the quality of the written content.
[0046] To address the problems of existing technologies, embodiments of this application provide a method, apparatus, electronic device, and storage medium for monitoring network lag. The method for monitoring network lag provided in this application embodiment will be described first below.
[0047] Figure 1 A flowchart illustrating a network lag monitoring method according to an embodiment of this application is shown. The method includes the following steps:
[0048] S110, receiving network data sent by multiple border devices respectively, the network data including at least one network quality identifier and network quality parameters corresponding to the network quality identifier;
[0049] S120, determine the classification queue corresponding to the network quality parameters according to the preset classification rules corresponding to the network quality identifier;
[0050] S130, the classification queue includes binary data bits that correspond one-to-one with multiple edge devices. In the classification queue corresponding to the network quality parameters, the values of the data bits corresponding to the edge devices are updated until each network quality parameter is processed, and a classification queue corresponding to the sampling period is obtained.
[0051] S140, perform bitwise operations on the classification queues corresponding to multiple consecutive sampling periods to obtain the bitwise operation results corresponding to the boundary device;
[0052] S150: Based on the preset mapping relationship, query the stutter probability corresponding to the bit operation result to obtain the stutter probability of the network where each boundary device is located.
[0053] An edge device is a physical device that uses data link layer and network layer information to transmit data packets between different networks. Edge devices can be gateways, routers, etc. The following discussion uses gateways as an example.
[0054] The detection setup can manage multiple edge devices simultaneously, meaning it can receive network data from multiple edge devices and analyze the data to determine if there is any network congestion in the network where each edge device is located. Each edge device is equipped with a probe, which sends network data to the detection device every sampling period, allowing the detection device to receive network data from multiple edge devices independently. Those skilled in the art can set the sampling period duration as needed, for example: 1 minute, 5 minutes, 10 minutes, etc. Optionally, the sampling period is 10 minutes.
[0055] Network data refers to data related to network transmission sent by border devices. Network quality identifiers are the names of data related to network transmission, and network quality parameters are the values corresponding to each network quality identifier. Network quality identifiers may include uplink traffic, downlink traffic, bandwidth, latency, etc., which can be set by those skilled in the art as needed.
[0056] For each network quality identifier, classification rules are pre-established to categorize network quality parameters into corresponding classification queues. Optionally, network quality identifiers include optical attenuation value, downlink traffic, and TCP retransmission rate. Multiple classification queues include multiple optical attenuation queues corresponding to optical attenuation values, multiple downlink traffic queues corresponding to downlink traffic, and multiple TCP retransmission queues corresponding to TCP (Transmission Control Protocol) retransmission rates. Classification rules are set for optical attenuation value, downlink traffic, and TCP retransmission rate respectively. Based on these rules, network quality parameters can be categorized into any one of the multiple optical attenuation queues, any one of the multiple downlink traffic queues, and any one of the multiple TCP retransmission queues.
[0057] The classification queue can be a pre-set blank BitMap queue based on the number of monitored boundary devices. The BitMap queue contains x data bits, with y boundary devices corresponding one-to-one with the y data bits in the BitMap queue, where x is greater than or equal to y. When x > y, the BitMap queue can allocate data bits from unassigned boundary devices to newly added boundary devices as needed. Each data bit in the blank BitMap queue is a binary data bit, meaning each data bit is either 0 or 1. It can be pre-defined that all data bits in the blank BitMap queue are either 0 or 1.
[0058] Each border device has an identification identifier, which can be used to determine the corresponding data bits for the network quality parameters uploaded together. The identification identifier can be a MAC address. The position of each data bit in the Bitmap queue can be set as an offset, and the value of the offset corresponding to each MAC address ranges from 1 to x. For example, if gateway A's MAC address is E8910F528E30, and its offset is 1, then it corresponds to the first bit in the Bitmap queue. If gateway B's MAC address is B030556D2929, and its offset is y, then it corresponds to the y-th bit in the Bitmap queue.
[0059] When a network quality parameter is determined to belong to any classification queue according to preset classification rules, the value of the corresponding data bit in that classification queue is modified. For example, if the optical attenuation value of gateway A is -10, and -10 is classified into the normal queue among multiple optical attenuation queues, and all data bits in the blank normal queue are 0, then the first data bit corresponding to gateway A in the normal queue is modified to 1. After processing the network data sent by multiple border devices, multiple classification queues corresponding to that time period are obtained. The binary data in the multiple classification queues can reflect the network status of the border devices within that time period.
[0060] Since the classification queues are stored in binary form, bitwise operations can be used to directly calculate the binary bits of integers for multiple consecutive sampling periods. The preset mapping relationship is based on the n classification queues corresponding to the network quality identifier and the probability of buffering. For example, if the mapping relationship specifies three consecutive periods of extremely poor optical attenuation, three consecutive periods of extremely low downlink traffic, and three consecutive periods of normal TCP retransmission rate, the corresponding probability of buffering is 81%. Performing bitwise operations on the classification queues corresponding to the three consecutive sampling periods yields the following result for gateway A: three consecutive periods of extremely poor optical attenuation, three consecutive periods of extremely low downlink traffic, and three consecutive periods of normal TCP retransmission rate. Therefore, the corresponding probability of buffering is also 81%.
[0061] In this application, network quality parameters corresponding to multiple network quality identifiers are classified, and the classification queue is updated based on the classification results. Bitwise operations are performed on the classification queues corresponding to multiple consecutive sampling periods to obtain the stuttering probability corresponding to the bitwise operation results. This makes network stuttering monitoring more comprehensive and improves the accuracy of network stuttering monitoring. By accepting network quality parameters corresponding to multiple network quality identifiers and classifying them into classification queues that include binary data bits for storage, the storage space usage is small and the storage speed is fast. Using the method provided in this application to store network quality parameters of 10 million gateway devices can reduce storage space by 96% compared to using ordinary databases (such as MySQL, MongoDB, etc.).
[0062] In some embodiments, the preset classification rules include n classification ranges, each of which corresponds one-to-one with a classification queue, where n is a natural number greater than 2; S120 includes:
[0063] S210, Based on the network quality identifier corresponding to the first network quality parameter, determine the preset classification rule corresponding to the first network quality parameter, wherein the multiple network quality parameters include the first network quality parameter;
[0064] S220, determine the first classification range of the first network quality parameter in the corresponding preset classification rule, and the n classification ranges include the first classification range;
[0065] S230, determine the correspondence between the first network quality parameters and the classification queue corresponding to the first classification range.
[0066] The preset classification rules include n classification ranges. Network quality parameters are categorized into their corresponding ranges based on their numerical values, thus determining the corresponding classification queue for each parameter. For example, the preset classification rules for optical attenuation values include four ranges: (-∞, -70), [-70, -40), [-40, -28), and [-28, 0]. Therefore, when gateway A sends an optical attenuation value of -10, this value is classified into the range [-28, 0] and placed in the corresponding classification queue.
[0067] For example, the preset classification rules corresponding to the light attenuation value are as follows:
[0068] The range of the serious lumminous aecay is (-∞, -70).
[0069] The fair luminous aecay interval is [-70, -40].
[0070] The average luminous aecay interval is [-40, -28].
[0071] The normal queue (good Luminous Aecay) ranges from [-28, 0].
[0072] The preset classification rules corresponding to downlink traffic are as follows:
[0073] Very Low Downward Flow (WHW) range: [0, 100Kb).
[0074] Low Downward Flow (LDFlow) range: [100Kb, 1M).
[0075] Normal flow queue (medium downward flow) range: [1M, 10M);
[0076] High Downward Flow (HDW) range: [10M, 100M).
[0077] Very High Downward Flow (VHD) range: [100M, ∞).
[0078] The preset classification rules corresponding to the TCP retransmission rate are as follows:
[0079] The range of the most serious TCP reconnect rate is [1%, ∞).
[0080] The high congestion rate (fair TCP reconnect rate) ranges from 0.02% to 1%.
[0081] The average TCP reconnect rate ranges from 0.01% to 0.02%.
[0082] Normal queue (good TCP Reconnect Rate) range [0~0.01%].
[0083] Please see Figure 2If the binary data bit corresponding to gateway A is located at the first position in the queue, and the network data sent by gateway A includes: optical attenuation value of -27, downlink traffic of 1.2M, TCP retransmission rate of 0.01%, and the current time is 20220412135612 (13:56:12 on April 12, 2022), then the optical attenuation value of -27 is classified into the normal queue (good Luminous Aecay), and the data value at the first position in this queue is modified to "1". The downlink traffic of 1.2M is classified into the normal queue (medium downward flow), and the data value at the first position in this queue is modified to "1". The TCP retransmission rate of 0.01% is classified into the normal queue (good Tcp Reconnect Rate), and the data value at the first position in this queue is modified to "1".
[0084] By pre-establishing multiple preset classification rules, it is easier to classify and store network quality parameters, thereby reducing the amount of stored data and subsequent computation.
[0085] In some embodiments, the network quality identifier is of type p, and m consecutive sampling periods correspond to q classification queues, where q is m*(n1+n2+...np);
[0086] Where n1 is the number of classification ranges included in the preset classification rule corresponding to the first network quality identifier, n2 is the number of classification ranges included in the preset classification rule corresponding to the second network quality identifier, np is the number of classification ranges included in the preset classification rule corresponding to the p-th network quality identifier, and p and m are natural numbers greater than 2.
[0087] For example, if network quality indicators include optical attenuation value, downlink traffic, and TCP retransmission rate, then p is 3.
[0088] Each sampling period corresponds to (n1+n2+...np) classification queues. Therefore, m consecutive sampling periods correspond to q classification queues, including m*(n1+n2+...np) classification queues. For example, if the number of optical attenuation queues is 4, the number of downlink traffic queues is 5, and the number of TCP retransmission queues is 4, then q is m*(4+5+4).
[0089] By performing bit operations on the classification queues corresponding to m consecutive sampling periods, the bit operation results can reflect the stuttering probability of multiple sampling periods, thereby reducing the impact of abnormal network quality parameters on the stuttering probability.
[0090] In some embodiments, S140 includes:
[0091] S410, determine the first data bit of the first boundary device in the classification queue, the multiple boundary devices include the first boundary device;
[0092] S420, perform bitwise operations on the first data bits of the q classification queues to obtain the first bit operation result corresponding to the first boundary device;
[0093] S150 includes:
[0094] S430: Based on the preset mapping relationship, query the first stutter probability corresponding to the first calculation result, and obtain the first stutter probability of the network where the first boundary device is located. The preset mapping relationship includes the mapping relationship between n1 classification ranges, n2 classification ranges, ... np classification ranges and stutter probabilities.
[0095] For example, if the network data sent by gateway A includes: an optical attenuation value of -27 and a time point of 20220412134612 (13:46:12 on April 12, 2022), and an optical attenuation value of -27 and a time point of 20220412135612 (13:56:12 on April 12, 2022), the optical attenuation value of -27 is classified into the normal queue (good Luminous Aecay).
[0096] Based on the category queues corresponding to 20220412134612 and 20220412135612, bitwise operations are performed to obtain the first data bit corresponding to gateway A as follows:
[0097]
[0098] Performing bitwise operations on the first data bit of gateway A yields 0001, which can then be used to query the first probability of lag. Therefore, calculating the lag probability of each boundary device using bitwise operations involves less computation and is more efficient.
[0099] The stuttering probability in the preset mapping relationship is calculated in advance by those skilled in the art through summarizing multiple samples. For example, the preset mapping relationship is set as follows:
[0100]
[0101]
[0102]
[0103] In some embodiments, S140 can also perform bit operations on the classification queues corresponding to multiple consecutive sampling periods according to preset conditions to obtain the stuttering probability corresponding to the bit operation result.
[0104] For example: if the preset conditions are three consecutive instances of weak light, one instance of extremely low traffic out of three consecutive instances, and three consecutive instances of TCP retransmission rate congestion, then:
[0105] seriousLuminousAecay:01&
[0106] seriousLuminousAecay:02&
[0107] seriousLuminousAecay:03&
[0108] averageTcpReconnectRate:01&
[0109] averageTcpReconnectRate:02&
[0110] averageTcpReconnectRate:03& [ (
[0113] veryLowDownwardFlow:01&
[0114] ~veryLowDownwardFlow:02&
[0115] ~veryLowDownwardFlow:03 )
[0117] | (
[0119] ~veryLowDownwardFlow:01&
[0120] ~veryLowDownwardFlow:02&
[0121] veryLowDownwardFlow:03 )
[0123] | (
[0125] ~veryLowDownwardFlow:01&
[0126] veryLowDownwardFlow:02&
[0127] ~ veryLowDownwardFlow:03 ) ]
[0130] By setting preset conditions, a more suitable probability of lag can be obtained, thereby further improving the accuracy of the lag probability.
[0131] In some embodiments, the method for monitoring network lag further includes:
[0132] S510, calculate the average value of the first stutter probability of the first boundary device within the first time period, and determine the stuttering status of the first boundary device based on the average value. The first time period includes r sampling periods, where r > m.
[0133] The first time period can be a day, a week, or a duration longer than the sampling period. By calculating the average of multiple lag probabilities, this average can reflect the lag situation over a longer time period.
[0134] In some embodiments, updating the value of the data bit corresponding to the edge device in the classification queue corresponding to the network quality parameters includes:
[0135] In the classification queue corresponding to the network quality parameters, update the value of the data bit corresponding to the edge device to 1.
[0136] The value of each data bit in the blank classification queue is 0. When network quality parameters are allocated to this classification queue, the value of the data bit corresponding to the edge device will be updated to 1, so as to store the network data of multiple edge devices through binary data storage.
[0137] Based on the network lag monitoring method provided in the above embodiments, this application also provides a specific implementation of the network lag monitoring device. Please refer to the following embodiments.
[0138] First see Figure 3 The network lag monitoring device 200 provided in this application embodiment includes:
[0139] The receiving module 10 is used to receive network data sent by multiple border devices respectively. The network data includes at least one network quality identifier and network quality parameters corresponding to the network quality identifier.
[0140] The determination module 20 is used to determine the classification queue corresponding to the network quality parameters according to the preset classification rules corresponding to the network quality identifier;
[0141] The update module 30 is used to classify queues including binary data bits that correspond one-to-one with multiple edge devices. In the classification queue corresponding to the network quality parameters, the value of the data bits corresponding to the edge devices is updated until each network quality parameter is processed, and a classification queue corresponding to the sampling period is obtained.
[0142] The calculation module 40 is used to perform bit operations on the classification queues corresponding to multiple consecutive sampling periods to obtain the bit operation results corresponding to the boundary device;
[0143] The probability query module 50 is used to query the stuttering probability corresponding to the bit operation result according to the preset mapping relationship, and obtain the stuttering probability of the network where each boundary device is located.
[0144] As one implementation of this application, the preset classification rules include n classification ranges, each of which corresponds one-to-one with a classification queue, where n is a natural number greater than 2; the aforementioned determining module 20 is also used for:
[0145] Based on the network quality identifier corresponding to the first network quality parameter, a preset classification rule corresponding to the first network quality parameter is determined, and the multiple network quality parameters include the first network quality parameter;
[0146] Determine the first classification range in which the first network quality parameter is located in the corresponding preset classification rule, and the n classification ranges include the first classification range;
[0147] Determine the correspondence between the first network quality parameter and the classification queue corresponding to the first classification range.
[0148] As one implementation of this application, the network quality identifier has p types, m consecutive sampling periods correspond to q classification queues, and q is m*(n1+n2+...np);
[0149] Where n1 is the number of classification ranges included in the preset classification rule corresponding to the first network quality identifier, n2 is the number of classification ranges included in the preset classification rule corresponding to the second network quality identifier, np is the number of classification ranges included in the preset classification rule corresponding to the p-th network quality identifier, and p and m are natural numbers greater than 2.
[0150] As one implementation of this application, the computing module 40 is also used for:
[0151] Determine the first data bit of the first boundary device in the classification queue, where the first boundary device is included among multiple boundary devices;
[0152] Perform bitwise operations on the first data bits of the q classification queues to obtain the first bit operation result corresponding to the first boundary device;
[0153] The probability query module 50 is also used for:
[0154] The first stutter probability corresponding to the first operation result is obtained by querying the preset mapping relationship, and the first stutter probability of the network where the first boundary device is located is obtained. The preset mapping relationship includes the mapping relationship between n1 classification ranges, n2 classification ranges, ... np classification ranges and stutter probabilities.
[0155] As one implementation of this application, the network lag monitoring device also includes:
[0156] The average calculation module is used to calculate the average value of the first stutter probability of the first boundary device within the first time period, and to determine the stuttering status of the first boundary device based on the average value. The first time period includes r sampling cycles, where r > m.
[0157] As one implementation of this application, the update module 30 is also used for:
[0158] In the classification queue corresponding to the network quality parameters, update the value of the data bit corresponding to the edge device to 1.
[0159] As one implementation of this application, the network quality identifier includes optical attenuation value, downlink traffic, and TCP retransmission rate;
[0160] The multiple classification queues include multiple optical attenuation queues corresponding to optical attenuation values, multiple downlink traffic queues corresponding to downlink traffic, and multiple TCP retransmission queues corresponding to TCP retransmission rates.
[0161] The network lag monitoring device provided in this embodiment of the invention can implement the steps in the above method embodiments, and will not be repeated here to avoid repetition.
[0162] Figure 4 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.
[0163] The electronic device may include a processor 1001 and a memory 1002 storing computer program instructions.
[0164] Specifically, the processor 1001 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.
[0165] Memory 1002 may include mass storage for data or instructions. For example, and not limitingly, memory 1002 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1002 may include removable or non-removable (or fixed) media. Where appropriate, memory 1002 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 1002 is non-volatile solid-state memory.
[0166] Memory may include read-only memory (ROM), random access memory (RAM), disk storage media devices, optical storage media devices, flash memory devices, and electrical, optical, or other physical / tangible memory storage devices. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methods according to one aspect of this disclosure.
[0167] The processor 1001 reads and executes computer program instructions stored in the memory 1002 to implement any of the network lag monitoring methods in the above embodiments.
[0168] In one example, the electronic device may also include a communication interface 1003 and a bus 1010. The processor 1001, memory 1002, and communication interface 1003 are connected via the bus 1010 and communicate with each other.
[0169] The communication interface 1003 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.
[0170] Bus 1010 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 1010 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.
[0171] The electronic device can be based on the above embodiments to realize the above-described method and apparatus for monitoring network lag.
[0172] Furthermore, in conjunction with the network lag monitoring methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the network lag monitoring methods in the above embodiments and achieve the same technical effect. To avoid repetition, further details are omitted here. The aforementioned computer-readable storage medium may include non-transitory computer-readable storage media, such as read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, etc., and is not limited thereto.
[0173] In addition, this application also provides a computer program product, including computer program instructions, which, when executed by a processor, can implement the steps and corresponding content of the aforementioned method embodiments.
[0174] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.
[0175] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.
[0176] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.
[0177] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.
[0178] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.
Claims
1. A method for monitoring network lag, characterized in that, The method includes: The system receives network data sent by multiple border devices, the network data including at least one network quality identifier and network quality parameters corresponding to the network quality identifier. The classification queue corresponding to the network quality parameter is determined according to the preset classification rule corresponding to the network quality identifier; The classification queue includes binary data bits that correspond one-to-one with the plurality of boundary devices. In the classification queue corresponding to the network quality parameters, the values of the data bits corresponding to the boundary devices are updated until each of the network quality parameters is processed, resulting in a classification queue corresponding to the sampling period. Bitwise operations are performed on the classification queues corresponding to multiple consecutive sampling periods to obtain the bitwise operation results corresponding to the boundary device; Based on the preset mapping relationship, the stuttering probability corresponding to the bit operation result is obtained, and the stuttering probability of the network where each of the boundary devices is located is obtained.
2. The method for monitoring network lag according to claim 1, characterized in that, The preset classification rules include n classification ranges, and each of the n classification ranges corresponds one-to-one with a classification queue, where n is a natural number greater than 2. The step of determining the classification queue corresponding to the network quality parameter according to the preset classification rule corresponding to the network quality identifier includes: Based on the network quality identifier corresponding to the first network quality parameter, a preset classification rule corresponding to the first network quality parameter is determined, wherein the network quality parameter includes the first network quality parameter; Determine the first classification range in which the first network quality parameter is located in the corresponding preset classification rule, wherein the n classification ranges include the first classification range; The first network quality parameter is determined to correspond to the classification queue corresponding to the first classification range.
3. The method for monitoring network lag according to claim 2, characterized in that, The network quality identifier is of type p, and m consecutive sampling periods correspond to q classification queues, where q is m*(n1+n2+...np); Where n1 is the number of classification ranges included in the preset classification rule corresponding to the first network quality identifier, n2 is the number of classification ranges included in the preset classification rule corresponding to the second network quality identifier, np is the number of classification ranges included in the preset classification rule corresponding to the p-th network quality identifier, and p and m are natural numbers greater than 2.
4. The method for monitoring network lag according to claim 3, characterized in that, The step of performing bitwise operations on the classification queues corresponding to multiple consecutive sampling periods to obtain the bitwise operation result corresponding to the boundary device includes: Determine a first data bit of the first boundary device in the classification queue, wherein the plurality of boundary devices includes the first boundary device; Perform bitwise operations on the first data bits of the q classification queues to obtain the first bit operation result corresponding to the first boundary device; The step of querying the stutter probability corresponding to the bit operation result according to the preset mapping relationship to obtain the stutter probability of the network where each of the boundary devices is located includes: The first stutter probability corresponding to the first bit operation result is obtained by querying the preset mapping relationship, and the first stutter probability of the network where the first boundary device is located is obtained. The preset mapping relationship includes the mapping relationship between n1 classification ranges, n2 classification ranges, ... np classification ranges and stutter probabilities.
5. The method for monitoring network lag according to claim 4, characterized in that, The network lag monitoring method also includes: Calculate the average value of the first stutter probability of the first boundary device within the first time period, and determine the stutter status of the first boundary device based on the average value. The first time period includes r sampling periods, where r > m.
6. The method for monitoring network lag according to claim 1, characterized in that, Updating the value of the data bit corresponding to the boundary device in the classification queue corresponding to the network quality parameter includes: In the classification queue corresponding to the network quality parameter, update the value of the data bit corresponding to the boundary device to 1.
7. The method for monitoring network lag according to claim 1, characterized in that, The network quality identifiers include optical attenuation value, downlink traffic, and TCP retransmission rate; The multiple classification queues include multiple optical attenuation queues corresponding to the optical attenuation value, multiple downlink traffic queues corresponding to the downlink traffic, and multiple TCP retransmission queues corresponding to the TCP retransmission rate.
8. A network lag monitoring device, characterized in that, include: The receiving module is used to receive network data sent by multiple border devices, wherein the network data includes at least one network quality identifier and network quality parameters corresponding to the network quality identifier; The determination module is used to determine the classification queue corresponding to the network quality parameter according to the preset classification rule corresponding to the network quality identifier; The update module is used to update the value of the data bit corresponding to the boundary device in the classification queue, which includes binary data bits that correspond one-to-one with the plurality of boundary devices, until all the network quality parameters have been processed, and to obtain a classification queue corresponding to the sampling period. The calculation module is used to perform bit operations on the classification queues corresponding to multiple consecutive sampling periods to obtain the bit operation results corresponding to the boundary device; The probability query module is used to query the stutter probability corresponding to the bit operation result according to the preset mapping relationship, and to obtain the stutter probability of the network where each of the boundary devices is located.
9. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the network lag monitoring method as described in any one of claims 1-7.
10. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed by a processor, implement the network lag monitoring method as described in any one of claims 1-7.