CN117640436A - Network quality monitoring method, system, device, equipment and medium - Google Patents
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Abstract
The disclosure provides a network quality monitoring method, a system, a device, equipment and a medium, and relates to the technical field of data communication. The method comprises the following steps: collecting service condition data of an uplink port queue and service condition data of a downlink port queue; determining the forwarding probability distribution of the flow of the uplink port on the downlink port according to the uplink port queue service condition data and the downlink port queue service condition data; and determining the quality of the network according to the forwarding probability distribution of the flow of the uplink port in the downlink port. According to the embodiment of the disclosure, the types of data to be acquired are fewer, and the data processing cost is lower.
Description
Technical Field
The disclosure relates to the technical field of data communication, and in particular relates to a network quality monitoring method, system, device, equipment and medium.
Background
The quality difference monitoring of the packet forwarding IP network always lacks a high-efficiency unified monitoring model, the traditional monitoring means are used for monitoring the change of the packet forwarding IP network to realize the perception of the network quality change based on the time delay, packet loss and jitter parameters of the packet forwarding, but the traditional monitoring means are required to realize the large difficulty and high cost based on the widely continuous real-time monitoring of the whole network flow.
It should be noted that the information disclosed in the above background section is only for enhancing understanding of the background of the present disclosure and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The disclosure provides a network quality monitoring method, a system, a device, equipment and a medium, which at least overcome the problems of high difficulty and high cost of network quality monitoring in the related technology to a certain extent.
Other features and advantages of the present disclosure will be apparent from the following detailed description, or may be learned in part by the practice of the disclosure.
According to one aspect of the present disclosure, there is provided a network quality monitoring method, including:
collecting service condition data of an uplink port queue and service condition data of a downlink port queue;
determining the forwarding probability distribution of the flow of the uplink port on the downlink port according to the uplink port queue service condition data and the downlink port queue service condition data;
and determining the quality of the network according to the forwarding probability distribution of the flow of the uplink port in the downlink port.
In one embodiment of the present disclosure, determining a forwarding probability distribution of traffic of an uplink port at a downlink port according to uplink port queue usage data and downlink port queue usage data includes:
calculating a downlink port probability distribution mathematical expectation E1 by using a uniform distribution model based on the collected downlink port queue service condition data;
calculating a downlink port probability distribution mathematical expectation E2 by using a normal distribution model based on the collected downlink port queue service condition data;
calculating a downlink port probability distribution mathematical expectation E3 by using a Lesi distribution model based on the collected downlink port queue service condition data;
calculating an uplink port probability distribution mathematical expectation E by using a Poisson distribution model based on the acquired uplink port queue service condition data;
and determining the forwarding probability distribution of the traffic of the uplink port on the downlink port based on the I E-E1I, the I E-E2I and the I E-E3I.
In one embodiment of the present disclosure, determining a forwarding probability distribution of traffic of an upstream port at a downstream port based on |E-E1|, |E-E2|, and |E-E3|, comprises:
under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E1I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is uniformly distributed;
under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E2I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is normal distribution;
and under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E3I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is a rice distribution.
In one embodiment of the present disclosure, determining the quality of a network based on a forwarding probability distribution of traffic at an upstream port at a downstream port includes:
when the flow of the uplink port presents rice distribution in the forwarding probability distribution of the downlink port, the network quality is abnormal.
In one embodiment of the present disclosure, determining a forwarding probability distribution of traffic of an uplink port at a downlink port according to uplink port queue usage data and downlink port queue usage data includes:
and determining the forwarding probability distribution of the flow of the uplink port on the downlink port according to the uplink port queue service condition data and the downlink port queue service condition data acquired in the preset acquisition period.
In one embodiment of the present disclosure, the method further comprises:
judging whether a preset acquisition period is reached;
and when the preset acquisition period is reached, carrying out standardized processing on the data format of the uplink port queue service condition data and the downlink port queue service condition data acquired in the last acquisition period.
According to another aspect of the present disclosure, there is provided a network quality monitoring system comprising:
the device index generation module is used for collecting the service condition data of the uplink port queue and the service condition data of the downlink port queue of the network device in real time;
the index acquisition module is used for acquiring the uplink port queue use condition data and the downlink port queue use condition data of the network equipment acquired by the equipment index generation module, and sending the uplink port queue use condition data and the downlink port queue use condition data to the index analysis module;
the index analysis module is used for determining the forwarding probability distribution of the flow of the uplink port at the downlink port according to the uplink port queue use condition data and the downlink port queue use condition data, and determining the quality of the network according to the forwarding probability distribution of the flow of the uplink port at the downlink port.
In one embodiment of the present disclosure, the device index generation module is built into the network device.
In one embodiment of the present disclosure, the index collection module is a platform deployment.
According to another aspect of the present disclosure, there is provided a network quality monitoring apparatus, comprising:
the data acquisition module is used for acquiring service condition data of the uplink port queue and service condition data of the downlink port queue;
the data analysis module is used for determining the forwarding probability distribution of the flow of the uplink port at the downlink port according to the uplink port queue service condition data and the downlink port queue service condition data;
and the network quality judging module is used for determining the quality of the network according to the forwarding probability distribution of the flow of the uplink port in the downlink port.
According to still another aspect of the present disclosure, there is provided an electronic apparatus including: a memory for storing instructions; and the processor is used for calling the instructions stored in the memory to realize the network quality monitoring method.
According to yet another aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the network quality monitoring method described above.
According to yet another aspect of the present disclosure, there is provided a computer program product storing instructions that, when executed by a computer, cause the computer to implement the network quality monitoring method described above.
According to yet another aspect of the present disclosure, there is provided a chip comprising at least one processor and an interface;
an interface for providing program instructions or data to at least one processor;
the at least one processor is configured to execute the program instructions to implement the network quality monitoring method described above.
According to the network quality monitoring method, system, device, equipment and medium provided by the embodiment of the disclosure, the forwarding probability distribution of the flow of the uplink port at the downlink port is determined according to the collected uplink port queue service condition data and the downlink port queue service condition data, and then the quality of the network is determined according to the forwarding probability distribution of the flow of the uplink port at the downlink port. The data types needing to be collected are fewer, and the data processing cost is lower.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
It will be apparent to those of ordinary skill in the art that the drawings in the following description are merely examples of the disclosure and that other drawings may be derived from them without undue effort.
FIG. 1 illustrates a schematic diagram of a network quality monitoring system in an embodiment of the present disclosure;
FIG. 2 is a diagram showing data changes in network monitoring in the related art;
FIG. 3 is a schematic diagram illustrating correspondence between devices and queues in an embodiment of the present disclosure;
FIG. 4 illustrates a device port queue schematic diagram in an embodiment of the present disclosure;
FIG. 5 illustrates a flow chart of a network quality monitoring method in an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of a network quality monitoring process in an embodiment of the present disclosure;
FIG. 7 illustrates another network quality monitoring method flow diagram in an embodiment of the present disclosure;
FIG. 8 is a schematic diagram of a network quality monitoring device in an embodiment of the disclosure;
fig. 9 shows a block diagram of an electronic device in an embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully hereinafter with reference to the accompanying drawings.
It should be noted that the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein.
Fig. 1 illustrates a network quality monitoring system according to an embodiment of the present disclosure, and as illustrated in fig. 1, a network quality monitoring system 100 provided in an embodiment of the present disclosure includes a device index generating module 101, an index collecting module 102, and an index analyzing module 103.
The device index generation module 101 is configured to collect, in real time, uplink port queue usage data and downlink port queue usage data of the network device.
As an example, the device index generation module 101 is built in the network device, and collects the queue usage condition of each port of the device in real time.
The index collection module 102 is configured to obtain the uplink port queue usage data and the downlink port queue usage data of the network device collected by the device index generation module, and send the uplink port queue usage data and the downlink port queue usage data to the index analysis module.
As one example, the index collection module 102 is configured for a platform deployment for collecting queue usage of monitoring devices in real time.
The index analysis module 103 is configured to determine a forwarding probability distribution of the traffic of the uplink port at the downlink port according to the uplink port queue usage data and the downlink port queue usage data, and determine the quality of the network according to the forwarding probability distribution of the traffic of the uplink port at the downlink port.
As one example, the index analysis module 103 completes the analysis of the quality differences by the network quality monitoring method provided by the present disclosure. The network quality monitoring method is described in the following embodiments.
It should be noted that, the index analysis module 103 in the embodiment of the disclosure may be used as a core module for monitoring the quality of service; the device index generation module 101 and/or the index collection module 102 may be used as technical standards of a device manufacturer.
The term "and/or" in this disclosure is merely one association relationship describing the associated object, and indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
As shown in fig. 2, the conventional network quality detection focuses on the change of the message forwarding time sequence, which is shown as monitoring of time delay, packet loss and jitter.
The inventor finds that from the perspective of equipment, time delay, packet loss and jitter can be equivalent to the utilization rate of the equipment port queue.
As shown in fig. 3 and fig. 4, from the perspective of the device, the time delay, packet loss and jitter can be equivalent to the usage rate of the device port queue, and the length of the queue reflects the statistics of the time delay; the queue length change reflects the statistics of jitter; and counting out the overrun response packet loss of the queue.
In some embodiments, the index analysis module 103 determines the forwarding probability distribution of the traffic of the uplink port on the downlink port according to the uplink port queue usage data and the downlink port queue usage data, which may be a mathematical expected value comparing different fitting distributions of the uplink port and the downlink port, and selects the closest forwarding probability distribution as the forwarding probability distribution in the downlink port monitoring period.
In one example, determining the forwarding probability distribution of traffic of the upstream port on the downstream port may include the steps of:
calculating a downlink port probability distribution mathematical expectation E1 by using a uniform distribution model based on the collected downlink port queue service condition data;
calculating a downlink port probability distribution mathematical expectation E2 by using a normal distribution model based on the collected downlink port queue service condition data;
calculating a downlink port probability distribution mathematical expectation E3 by using a Lesi distribution model based on the collected downlink port queue service condition data;
calculating an uplink port probability distribution mathematical expectation E by using a Poisson distribution model based on the acquired uplink port queue service condition data;
and determining the forwarding probability distribution of the traffic of the uplink port on the downlink port based on the I E-E1I, the I E-E2I and the I E-E3I.
As one example, determining a forwarding probability distribution of traffic of an upstream port at a downstream port based on |e-e1|, |e-e2|, and |e-e3|, includes:
under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E1I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is uniformly distributed;
under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E2I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is normal distribution;
and under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E3I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is a rice distribution.
In the above embodiment, the index analysis module 103 determines the quality of the network according to the forwarding probability distribution of the traffic of the uplink port at the downlink port, which may be that the network quality is abnormal when the forwarding probability distribution of the traffic of the uplink port at the downlink port presents rice distribution.
Correspondingly, when the forwarding probability distribution of the flow of the uplink port on the downlink port is uniformly distributed, the network quality is ideal; when the forwarding probability distribution of the traffic of the uplink port in the downlink port is normal distribution, the network quality is normal.
In the embodiment of the disclosure, the acquisition period may be preset.
In one example, the preset acquisition period may be an acquisition period of the index acquisition module 102. The index acquisition module 102 determines the forwarding probability distribution of the flow of the uplink port at the downlink port according to the uplink port queue service condition data and the downlink port queue service condition data acquired in a preset acquisition period. At this time, the device index generation module 101 may continuously collect data. The index analysis module 103 analyzes that the data used is data in one acquisition cycle.
In another example, the preset acquisition period may be an acquisition period of the device index generation module 101. When the preset collection period is reached, the device index generation module 101 collects data, and sends the collected data to the index collection module 102. The index analysis module 103 analyzes that the data used is data in one acquisition cycle.
In some embodiments, the index collection module 102 or the index analysis module 103 is further configured to, when a preset collection period is reached, perform a normalization process on the data format of the uplink port queue usage data and the downlink port queue usage data collected in the previous collection period.
The network quality monitoring system provided by the embodiment of the disclosure adopts a statistical method to process monitoring data, and has the advantage of low cost; focusing the monitor values on the device queue reduces the requirement for global synchronization.
In addition, the inventor finds that the quality difference occurs in two scenes that a plurality of ports send traffic to one port or a port with a large bandwidth sends traffic to a port with a small bandwidth, so that whether the quality difference trend exists can be judged by monitoring whether the two situations occur. The disclosed embodiments are based on a basic model of congestion occurrence: the large and small and multiple and few models simplify the system implementation; and single index is focused, so that the technical difficulty of multi-index synchronization is avoided.
Fig. 5 shows a flowchart of a network quality monitoring method according to an embodiment of the present disclosure, and as shown in fig. 5, the network quality monitoring method provided in the embodiment of the present disclosure includes the following steps:
s502, collecting uplink port queue use condition data and downlink port queue use condition data;
s504, determining the forwarding probability distribution of the flow of the uplink port on the downlink port according to the uplink port queue service condition data and the downlink port queue service condition data;
s506, determining the quality of the network according to the forwarding probability distribution of the flow of the uplink port in the downlink port.
The network quality monitoring method in the embodiment of the disclosure can be applied to a network quality monitoring system. As an example, the main implementation of the network quality monitoring method may be the index analysis module 103 in the embodiment of fig. 1, which is used to monitor the network quality difference.
The inventor finds that the quality difference occurs in two scenes that a plurality of ports send traffic to one port or a port with large bandwidth sends traffic to a port with small bandwidth, so that whether the quality difference trend exists can be judged by monitoring whether the two situations occur.
It should be noted that the embodiments of the present disclosure may be used for real-time monitoring of network congestion in a data communication network, and for assistance in quality difference judgment for different applications.
The network quality monitoring method provided by the embodiment of the present disclosure is described in detail below with reference to fig. 6.
As shown in fig. 6, for the uplink port, the state between the null state and the busy state in the message receiving process is a fuzzy low band for judging whether the quality is poor, the poisson process can be set as a message arrival model, and the mathematical expectation can be regarded as the rate of queue growth.
For the downlink ports, the messages sent from the uplink ports are uniformly distributed to the ports in the optimal state, the common forwarding state is that the traffic is sent to the downlink ports in normal distribution (the traffic of some ports is relatively large), if the traffic shows rice distribution, the traffic of the uplink ports is highly concentrated to 1 and 2 downlink ports, the forwarding of the ports inevitably has a degradation trend, and the quality difference early warning can be performed in advance.
In some embodiments, S504 may be a calculation of the downlink port probability distribution mathematical expectation E1 according to a uniform distribution model based on the collected downlink port queue usage data; calculating a downlink port probability distribution mathematical expectation E2 by using a normal distribution model based on the collected downlink port queue service condition data; calculating a downlink port probability distribution mathematical expectation E3 by using a Lesi distribution model based on the collected downlink port queue service condition data; calculating an uplink port probability distribution mathematical expectation E by using a Poisson distribution model based on the acquired uplink port queue service condition data; and determining the forwarding probability distribution of the traffic of the uplink port on the downlink port based on the I E-E1I, the I E-E2I and the I E-E3I.
As an example, in the above embodiment, in the case where the minimum value among |e-e1|, |e-e2|, and |e-e3| is |e-e1|, it is determined that the forwarding probability distribution of the traffic of the upstream port at the downstream port is uniformly distributed; under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E2I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is normal distribution; and under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E3I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is a rice distribution.
In one example, network quality is abnormal when the forwarding probability distribution of traffic at an upstream port is rice distribution at a downstream port.
In another example, network quality is ideal when the traffic on the upstream port is uniformly distributed on the forwarding probability distribution of the downstream port.
In yet another example, network quality is normal when traffic at an upstream port is normally distributed in the forwarding probability distribution of a downstream port.
In some embodiments, the acquisition period may be preset.
As an example, S504 may be determining a forwarding probability distribution of the traffic of the uplink port at the downlink port according to the uplink port queue usage data and the downlink port queue usage data acquired in the preset acquisition period.
In one example, the network quality monitoring method provided by the embodiment of the present disclosure may further include the following steps:
judging whether a preset acquisition period is reached;
and when the preset acquisition period is reached, carrying out standardized processing on the data format of the uplink port queue service condition data and the downlink port queue service condition data acquired in the last acquisition period.
In one embodiment, the queue statistics of the uplink port may be set to Eu (x), the queue statistics of the downlink port may be Ed (x) (x=1, 2 … …), and the statistics may be an average value of a monitoring period, where the monitoring period is T.
And respectively fitting distribution functions of uniform distribution, normal distribution and rice distribution through monitoring values Ed (x) of the downlink port queues, and solving mathematical expectations of the distribution functions.
Poisson distribution is fitted through an uplink port monitoring value Eu (x), and mathematical expectations are obtained.
And comparing mathematical expected values of different fitting distributions of the upper and lower ports, and selecting the closest forwarding probability distribution as the forwarding probability distribution in the monitoring period of the lower port.
Furthermore, although the steps of the methods in the present disclosure are depicted in a particular order in the drawings, this does not require or imply that the steps must be performed in that particular order or that all illustrated steps be performed in order to achieve desirable results.
In some embodiments, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform, etc.
Fig. 7 shows a flowchart of a network quality monitoring method according to an embodiment of the present disclosure, and as shown in fig. 7, the network quality monitoring method provided in the embodiment of the present disclosure includes the following steps:
s701, collecting the number of bytes used by the uplink and downlink port queues.
S702, judging whether one acquisition period is satisfied.
When the acquisition time does not satisfy one acquisition cycle, S701 is continued to be executed.
And when the acquisition time reaches one acquisition period, performing S703 data format standardization and reporting.
S704, calculating the downlink port probability distribution mathematical expectation E1 by using a uniform distribution model.
S705, calculating the downlink port probability distribution mathematical expectation E2 by using a normal distribution model.
S706, calculating the downlink port probability distribution mathematical expectation E3 by a Lees distribution model.
S707, calculating the mathematical expectation E of the probability distribution of the uplink port by using a Poisson distribution model.
S708, calculating the minimum value of the I E-E1I, the I E-E2I and the I E-E3I.
When the minimum value is |E-E1|, the network quality is ideal; when the minimum value is |E-E2|, the network quality is normal; at a minimum of |E-E3|, the network quality is abnormal.
The embodiment of the disclosure only counts the forwarding performance of the equipment, and can realize low-cost monitoring. Meanwhile, the method relates to modeling of index statistical information, can improve monitoring precision by adopting a big data method, and can play a certain prediction role.
Based on the same inventive concept, a network quality monitoring device is also provided in the embodiments of the present disclosure, as described in the following embodiments. Since the principle of solving the problem of the embodiment of the device is similar to that of the embodiment of the method, the implementation of the embodiment of the device can be referred to the implementation of the embodiment of the method, and the repetition is omitted.
Fig. 8 shows a schematic diagram of a network quality monitoring device according to an embodiment of the disclosure, as shown in fig. 8, the network quality monitoring device 800 includes:
the data acquisition module 802 is configured to acquire uplink port queue usage data and downlink port queue usage data;
the data analysis module 804 is configured to determine a forwarding probability distribution of the traffic of the uplink port at the downlink port according to the uplink port queue usage data and the downlink port queue usage data;
the network quality judging module 806 is configured to determine the quality of the network according to the forwarding probability distribution of the traffic of the uplink port on the downlink port.
In some embodiments, the data analysis module 804 includes:
the first calculation unit is used for calculating a downlink port probability distribution mathematical expectation E1 according to the acquired downlink port queue service condition data and a uniform distribution model;
the second calculation unit is used for calculating a downlink port probability distribution mathematical expectation E2 according to a normal distribution model based on the collected downlink port queue service condition data;
the third calculation unit is used for calculating a downlink port probability distribution mathematical expectation E3 according to the acquired downlink port queue service condition data and a Lesi distribution model;
the fourth calculation unit is used for calculating the mathematical expectation E of the uplink port probability distribution by using a Poisson distribution model based on the acquired service condition data of the uplink port queue;
and the data processing unit is used for determining the forwarding probability distribution of the traffic of the uplink port on the downlink port based on the I E-E1I, the I E-E2I and the I E-E3I.
The "calculation units" such as the first calculation unit, the second calculation unit, the third calculation unit, and the fourth calculation unit may be the same calculation unit or may be different calculation units, and are not limited herein.
In some embodiments, the data processing unit may be specifically configured to:
under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E1I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is uniformly distributed;
under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E2I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is normal distribution;
and under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E3I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is a rice distribution.
In some embodiments, the network quality determination module 806 is specifically configured to:
and when the forwarding probability distribution of the traffic of the uplink port in the downlink port presents rice distribution, determining that the network quality is abnormal.
In one example, the network quality judging module 806 may be further configured to determine that the network quality is ideal when the traffic of the uplink port is uniformly distributed in the forwarding probability distribution of the downlink port; or alternatively, the first and second heat exchangers may be,
when the forwarding probability distribution of the traffic of the uplink port on the downlink port is normal distribution, the network quality is determined to be normal.
In some embodiments, the data analysis module 804 is configured to determine a forwarding probability distribution of the traffic of the uplink port at the downlink port according to the uplink port queue usage data and the downlink port queue usage data acquired in the preset acquisition period.
In some embodiments, the network quality monitoring apparatus 800 may further include:
the time judging module is used for judging whether a preset acquisition period is reached;
and the standardized processing module is used for carrying out standardized processing on the data formats of the uplink port queue service condition data and the downlink port queue service condition data acquired in the last acquisition period when the preset acquisition period is reached.
The terms "first," "second," and the like in this disclosure are used solely to distinguish one from another device, module, or unit, and are not intended to limit the order or interdependence of functions performed by such devices, modules, or units.
The specific manner in which the respective modules perform the operations in the network quality monitoring apparatus of the above embodiments has been described in detail in the embodiments related to the network quality monitoring method, and will not be described in detail herein.
In summary, in the network quality monitoring device provided in the embodiment of the present application, the forwarding probability distribution of the traffic of the uplink port at the downlink port is determined according to the collected uplink port queue usage data and the downlink port queue usage data, and then the quality of the network is determined according to the forwarding probability distribution of the traffic of the uplink port at the downlink port, so that the number of data types to be collected is fewer, and the data processing cost is lower.
It should be noted that although in the above detailed description several modules or units of a device for action execution are mentioned, such a division is not mandatory.
Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.
An electronic device provided by an embodiment of the present disclosure is described below with reference to fig. 9. The electronic device 900 shown in fig. 9 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.
Fig. 9 shows a schematic architecture diagram of an electronic device 900 according to the present disclosure. As shown in fig. 9, the electronic device 900 includes, but is not limited to: at least one processor 910, at least one memory 920.
Memory 920 for storing instructions.
In some embodiments, memory 920 may include readable media in the form of volatile memory units, such as Random Access Memory (RAM) 9201 and/or cache memory 9202, and may further include Read Only Memory (ROM) 9203.
In some embodiments, memory 920 may also include a program/utility 9204 having a set (at least one) of program modules 9205, such program modules 9205 include, but are not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
In some embodiments, memory 920 may store an operating system. The operating system may be a real-time operating system (Real Time eXecutive, RTX), LINUX, UNIX, WINDOWS or OS X like operating systems.
In some embodiments, memory 920 may also have data stored therein.
As one example, processor 910 may read data stored in memory 920, which may be stored at the same memory address as the instructions, or which may be stored at a different memory address than the instructions.
A processor 910 for invoking instructions stored in memory 920 to perform steps according to various exemplary embodiments of the present disclosure described in the above "exemplary methods" section of the present specification. For example, the processor 910 may perform the following steps of the method embodiments described above:
collecting service condition data of an uplink port queue and service condition data of a downlink port queue;
determining the forwarding probability distribution of the flow of the uplink port on the downlink port according to the uplink port queue service condition data and the downlink port queue service condition data;
and determining the quality of the network according to the forwarding probability distribution of the flow of the uplink port in the downlink port.
It should be noted that the processor 910 may be a general-purpose processor or a special-purpose processor. Processor 910 may include one or more processing cores, and processor 910 performs various functional applications and data processing by executing instructions.
In some embodiments, the processor 910 may include a central processing unit (central processing unit, CPU) and/or a baseband processor.
In some embodiments, processor 910 may determine an instruction based on a priority identification and/or functional class information carried in each control instruction.
In the present disclosure, the processor 910 and the memory 920 may be separately provided or may be integrated.
As one example, processor 910 and memory 920 may be integrated on a single board or System On Chip (SOC).
As shown in fig. 9, the electronic device 900 is embodied in the form of a general purpose computing device. The electronic device 900 may also include a bus 930.
The bus 930 may be any one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.
The electronic device 900 may also communicate with one or more external devices 940 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 900, and/or any devices (e.g., routers, modems, etc.) that enable the electronic device 900 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 950.
Also, electronic device 900 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 960.
As shown in fig. 9, the network adapter 960 communicates with other modules of the electronic device 900 over the bus 930.
It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 900, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
It is to be understood that the illustrated structure of the presently disclosed embodiments does not constitute a particular limitation of the electronic device 900. In other embodiments of the present disclosure, electronic device 900 may include more or fewer components than shown in FIG. 9, or may combine certain components, or split certain components, or a different arrangement of components. The components shown in fig. 9 may be implemented in hardware, software, or a combination of software and hardware.
The present disclosure also provides a computer readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the network quality monitoring method described in the above method embodiments.
A computer-readable storage medium in an embodiment of the present disclosure is a computer instruction that can be transmitted, propagated, or transmitted for use by or in connection with an instruction execution system, apparatus, or device.
As one example, the computer-readable storage medium is a non-volatile storage medium.
In some embodiments, more specific examples of the computer readable storage medium in the present disclosure may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, a U disk, a removable hard disk, or any suitable combination of the foregoing.
In an embodiment of the present disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with computer instructions (readable program code) carried therein.
Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing.
Any readable medium other than a readable storage medium, the readable medium
In some examples, the computing instructions contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
The disclosed embodiments also provide a computer program product storing instructions that, when executed by a computer, cause the computer to implement the network quality monitoring method described in the above method embodiments.
The instructions may be program code. In particular implementations, the program code can be written in any combination of one or more programming languages.
The programming languages include object oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as the "C" language or similar programming languages.
The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server.
In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The embodiment of the disclosure also provides a chip comprising at least one processor and an interface;
an interface for providing program instructions or data to at least one processor;
the at least one processor is configured to execute the program instructions to implement the network quality monitoring method described in the method embodiments above.
In some embodiments, the chip may also include a memory for holding program instructions and data, the memory being located either within the processor or external to the processor.
Those of ordinary skill in the art will appreciate that all or a portion of the steps implementing the above embodiments may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein.
This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (12)
1. A method for monitoring network quality, comprising:
collecting service condition data of an uplink port queue and service condition data of a downlink port queue;
determining the forwarding probability distribution of the flow of the uplink port on the downlink port according to the uplink port queue service condition data and the downlink port queue service condition data;
and determining the quality of the network according to the forwarding probability distribution of the flow of the uplink port in the downlink port.
2. The method of claim 1, wherein determining a forwarding probability distribution of traffic of the upstream port at the downstream port based on the upstream port queue usage data and the downstream port queue usage data comprises:
calculating a downlink port probability distribution mathematical expectation E1 by using a uniform distribution model based on the collected downlink port queue service condition data;
calculating a downlink port probability distribution mathematical expectation E2 by using a normal distribution model based on the collected downlink port queue service condition data;
calculating a downlink port probability distribution mathematical expectation E3 by using a Lesi distribution model based on the collected downlink port queue service condition data;
calculating an uplink port probability distribution mathematical expectation E by using a Poisson distribution model based on the acquired uplink port queue service condition data;
and determining the forwarding probability distribution of the traffic of the uplink port on the downlink port based on the I E-E1I, the I E-E2I and the I E-E3I.
3. The method of claim 2, wherein determining the forwarding probability distribution of traffic for the upstream port at the downstream port based on 1E-E1, 1E-E2, and |e-E3|, comprises:
under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E1I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is uniformly distributed;
under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E2I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is normal distribution;
and under the condition that the minimum value of the I E-E1I, the I E-E2I and the I E-E3I is the I E-E3I, determining that the forwarding probability distribution of the traffic of the uplink port on the downlink port is a rice distribution.
4. A method according to claim 3, wherein determining the quality of the network based on the forwarding probability distribution of traffic of the upstream port over the downstream port comprises:
when the flow of the uplink port presents rice distribution in the forwarding probability distribution of the downlink port, the network quality is abnormal.
5. The method according to any one of claims 1-4, wherein determining a forwarding probability distribution of traffic of an uplink port at a downlink port according to the uplink port queue usage data and the downlink port queue usage data comprises:
and determining the forwarding probability distribution of the flow of the uplink port at the downlink port according to the uplink port queue service condition data and the downlink port queue service condition data acquired in the preset acquisition period.
6. The method of claim 5, wherein the method further comprises:
judging whether a preset acquisition period is reached;
and when the preset acquisition period is reached, carrying out standardized processing on the data format of the uplink port queue service condition data and the downlink port queue service condition data acquired in the last acquisition period.
7. A network quality monitoring system, comprising:
the device index generation module is used for collecting the service condition data of the uplink port queue and the service condition data of the downlink port queue of the network device in real time;
the index acquisition module is used for acquiring the uplink port queue use condition data and the downlink port queue use condition data of the network equipment acquired by the equipment index generation module, and sending the uplink port queue use condition data and the downlink port queue use condition data to the index analysis module;
the index analysis module is used for determining the forwarding probability distribution of the flow of the uplink port at the downlink port according to the uplink port queue service condition data and the downlink port queue service condition data, and determining the quality of the network according to the forwarding probability distribution of the flow of the uplink port at the downlink port.
8. The system of claim 7, wherein the device index generation module is built into a network device.
9. The system of claim 7, wherein the index collection module is a platform deployment.
10. A network quality monitoring device, comprising:
the data acquisition module is used for acquiring service condition data of the uplink port queue and service condition data of the downlink port queue;
the data analysis module is used for determining the forwarding probability distribution of the flow of the uplink port at the downlink port according to the uplink port queue service condition data and the downlink port queue service condition data;
and the network quality judging module is used for determining the quality of the network according to the forwarding probability distribution of the flow of the uplink port in the downlink port.
11. An electronic device, comprising:
a memory for storing instructions;
a processor for invoking instructions stored in said memory to implement the network quality monitoring method of any of claims 1-6.
12. A computer readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the network quality monitoring method of any of claims 1-6.
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