Tunnel tuning method and management equipment

Abstract

The invention provides a tunnel tuning method and management equipment, and the method comprises the steps: obtaining a first network quality constant value of a sample service in a stable state, obtaining a second network quality sample value of a target service in a first period, and carrying out the re-calculation through the first network quality constant value and the second network quality sample value, and obtaining a List re-calculation value, obtaining a tunnel state rule according to the List re-calculation value, and carrying out tuning processing on the tunnel according to the state rule. Through the method, adjustment and optimization of the service tunnel can be realized.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a tunnel optimization method and management device. Background Art

[0002] TWAMP: Two-Way Active Measurement Protocol (TWAMP) is used to measure performance parameters such as two-way delay, delay jitter, and packet loss rate of messages between any two devices in the network, providing a basis for network quality analysis.

[0003] NQA (Network Quality Analysis) technology is a technology used to monitor, evaluate and optimize computer network performance. Its purpose is to ensure the reliability, availability and efficiency of network services and help network administrators identify and solve potential problems.

[0004] iFIT (In-band Flow Inspection Telemetry) is a network performance monitoring and fault diagnosis technology. iFIT technology embeds detection information in data packets, which can achieve real-time monitoring and analysis of network traffic without affecting normal data transmission.

[0005] Telemetry: Telemetry is a remote data collection technology that monitors the performance and failures of network devices. Telemetry can be implemented based on the high-performance open source software gRPC (Google Remote Procedure Call), which enables network devices to automatically read various data such as their interface traffic and report them to the collector in real time, providing a big data foundation for network quality analysis and optimization.

[0006] Artificial intelligence has been developing for decades. It is a closely related field to machine learning (ML), which is a method to achieve artificial intelligence. The goal of artificial intelligence is to create machines that can perform human intelligence tasks, and machine learning achieves this goal through the technology of automatically learning and improving from data. The concept of artificial intelligence can be traced back to the 1950s, when scholars began to explore how to make machines intelligent. Subsequently, with the development of computing power and data processing technology, machine learning has gradually become a research focus and has made significant progress in the 21st century, especially in the fields of image recognition, natural language processing, and predictive analysis. In short, the decisions made by machine learning do not rely on specific conditions set by humans, but are self-learned from a certain number of samples to obtain conditional parameters.

[0007] Among a group of chickens and rabbits in the same cage, if we want to distinguish whether the caught one is a chicken or a rabbit, and let the machine make a conclusion based on judgment, this condition is very easy to set, because there are many differences between the two and they are uniquely identifiable, such as chickens have two legs and rabbits have four legs; such artificial settings are very clear, and we can use it as a linear distinction condition, such as Figure 1 As shown in , y = mx + c, where m and c are conditional parameters, set manually. Figure 2 As shown in the figure, m and c are not set artificially, but are obtained by letting the machine learn a large number of samples, and finding the linear conditional curve that distinguishes the two major categories from the different characteristics of the samples, thereby reversely inferring the conditional parameters m and c.

[0008] In the field of data communications, the development from IP to MPLS and then to SRv6 has taken the path of achieving connection, fast connection and intelligent connection. The SDN architecture is also becoming increasingly popular. Through a unified controller, it is easier and more effective to detect equipment and schedule services. At present, this model is becoming increasingly popular in scenarios such as operators, government and enterprises, and finance. The core of SRv6 technology is to arrange and determine the path by setting a list path, and to achieve more programmable operations with the help of encapsulated message information to meet the increasingly intelligent scheduling needs. We also usually call the list path through SRv6 technology SRv6 Policy tunnel technology.

[0009] When the controller detects that the service traffic does not meet the preset conditions in terms of bandwidth, latency, and jitter, it triggers the controller to recalculate the list of new SRv6 Policy tunnels that carry the service.

[0010] However, in the existing tunnel technology tuning, adjustments can only be triggered based on the threshold triggered by real-time business traffic. This triggering is often sudden, and the triggering process is often when the traffic is already large. Unstable switching can easily cause other problems such as a large number of packet losses. Summary of the invention

[0011] In order to overcome the problems existing in the related art, this specification provides a tunnel tuning method and management device.

[0012] According to a first aspect of an embodiment of this specification, a method for tunnel tuning is provided, the method being applied in a controller, the method comprising:

[0013] Obtaining a first network quality constant value of the sample service in a stable state;

[0014] Obtain a second network quality sample value of the target service in the first period, and recalculate using the first network quality constant value and the second network quality sample value to obtain a List recalculated value;

[0015] Obtain tunnel status rules based on the List recalculation value and optimize the tunnel based on the status rules.

[0016] The method for obtaining the first network quality constant value or the second network quality sample value includes:

[0017] The first network quality constant value or the second network quality sample value is obtained through the iFIT technology.

[0018] The first network quality constant value or the second network quality sample value includes:

[0019] Part or all of the bandwidth value, delay value, and jitter value.

[0020] The recalculating using the first network quality constant value and the second network quality sample value to obtain the List recalculated value includes:

[0021] The List recalculation value is obtained by the following formula:

[0022] S 重计算n＝ f1(C 带宽n ,A 带宽 )+f2(C 时延n ,A 时延 )+f3(C 抖动n ,A 抖动 )

[0023] Among them, C 带宽n、 C 时延n、 C 抖动n is the second network quality sample value;

[0024] A 带宽、 A 时延、 A 抖动 is the first network quality constant value.

[0025] The obtaining of the tunnel status rule according to the List recalculated value and performing optimization processing on the tunnel according to the status rule includes:

[0026] According to the recalculated values ​​of List, obtain the curve chart within the target period;

[0027] Obtain the tunnel status rules according to the curve chart, and optimize the tunnel according to the status rules;

[0028] The target period includes one first period or several first periods.

[0029] It can be seen from the above embodiments that by aggregating and analyzing the data samples of bandwidth, delay and jitter of the business traffic in the historical period, the regular periodic performance of the business can be identified and summarized. Through these analysis results, the controller can optimize the tunnel in advance before the emergency occurs to ensure that the business traffic is handled more smoothly and intelligently, thereby realizing the optimization of the business tunnel.

[0030] According to a second aspect of an embodiment of this specification, a management device is provided, the management device comprising:

[0031] An acquisition module, used to acquire a first network quality constant value of a sample service in a stable state;

[0032] The acquisition module is further used to acquire a second network quality sample value of the target service in the first period, and recalculate using the first network quality constant value and the second network quality sample value to obtain a List recalculation value;

[0033] The processing module is used to obtain the tunnel status rule according to the List recalculation value and perform tunnel optimization processing according to the status rule.

[0034] The acquisition module is specifically used to acquire the first network quality constant value or the second network quality sample value through the iFIT technology.

[0035] The first network quality constant value or the second network quality sample value includes:

[0036] Part or all of the bandwidth value, delay value, and jitter value.

[0037] The acquisition module is specifically used to obtain the List recalculation value through the following formula:

[0038] S recalculation n = f1 (C bandwidth n, A bandwidth) + f2 (C delay n, A delay) + f3 (C jitter n, A jitter)

[0039] Wherein, C bandwidth n, C delay n, and C jitter n are the second network quality sample values;

[0040] A bandwidth, A delay, and A jitter are first network quality constant values.

[0041] Wherein, the processing module is specifically used to obtain a curve graph within a target period according to the List recalculated value;

[0042] Obtain the tunnel status rules according to the curve chart, and optimize the tunnel according to the status rules;

[0043] The target period includes one first period or several first periods.

[0044] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the specification and, together with the description, serve to explain the principles of the specification.

[0046] Figure 1 It is a linear schematic diagram of manually set parameters according to an exemplary embodiment of this specification.

[0047] Figure 2 It is a linear schematic diagram of a machine learning sample parameter shown in this specification according to an exemplary embodiment.

[0048] Figure 3 This is a schematic diagram of a primary and standby list path switching according to an exemplary embodiment of the present specification.

[0049] Figure 4 It is a flowchart of a tunnel optimization method shown in this specification according to an exemplary embodiment.

[0050] Figure 5 It is a curve diagram shown in this specification according to an exemplary embodiment.

[0051] Figure 6 It is a curve diagram shown in this specification according to an exemplary embodiment. DETAILED DESCRIPTION

[0052] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this specification. Instead, they are merely examples of devices and methods consistent with some aspects of this specification as detailed in the appended claims.

[0053] The terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit this specification. The singular forms "a", "the" and "the" used in this specification and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0054] It should be understood that although the terms first, second, third, etc. may be used in this specification to describe various information, this information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of this specification, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0055] In the current implementation, for a certain type of service, a programmable and deterministic path forwarding is carried on the SRv6 tunnel. A main list path, a backup list path, and an escape path are often set if all list paths are not satisfied. Escape refers to traffic forwarding in SRv6 BE mode, that is, encapsulating the corresponding SRv6 message header according to the final destination address to complete the task of sending the message to the destination in a hop-by-hop manner according to the conventional route. This method does not have the ability to specify a path through a list, so it is often deployed as an escape plan for service forwarding.

[0056] like Figure 3 As shown, the switch between the primary and backup lists is generally performed by deploying BFD detection on the device. If the BFD detection link is unavailable, it will trigger the switch between the primary and backup lists. If the backup list is also unavailable, it will switch to the final escape path. There is also a second type of list switching trigger and recalculation, which is triggered by one or a combination of factors that conventionally judge link quality, such as bandwidth, latency, and jitter. In addition, the second type of list is no longer a primary-backup switch, but a new list is recalculated. The overall detection, perception, and recalculation process is as follows:

[0057] 1. The controller deploys detection technologies such as TWAMP or NQA on the device to detect the bandwidth, delay, and jitter of the link.

[0058] 2. When the set bandwidth, delay, jitter and other conditions do not meet the main list forwarding, the controller will recalculate the new policy path that meets the business and form a new main and backup list path.

[0059] 3. The controller sends a new Policy path to the device, replacing the original Policy path, so that the service follows the main path under the new Policy.

[0060] In actual business use, the second type of list recalculation is relatively more common, and the problem that links are prone to is congestion rather than interruption. Currently, the controller judges and triggers the recalculation based on the real-time status to trigger the list recalculation. This is a normal method to solve the problem, but it is not the best method. The business use of many enterprises often shows periodicity or regular fluctuations with the change of time period. For example, the office business of an enterprise is a peak period from 9 to 11 in the morning from Monday to Friday, because this period is often the busiest for everyone to handle office business. According to the normal thinking, if the peak period of office business emerges after 9 o'clock and the traffic exceeds the set monitoring threshold, the calculation and adjustment of the new active and standby lists will be triggered.

[0061] However, in the existing tunnel technology tuning, adjustments can only be triggered based on the threshold triggered by real-time business traffic. This triggering is often sudden, and the triggering process is often when the traffic is already large. Unstable switching can easily cause other problems such as a large number of packet losses.

[0062] To solve the above technical problems, the present disclosure provides a method for tunnel optimization, which is applied in a controller. Figure 4 As shown, the method includes:

[0063] S401 obtains a first network quality constant value of a sample service in a stable state;

[0064] S402 obtains a second network quality sample value of the target service in the first period, and recalculates using the first network quality constant value and the second network quality sample value to obtain a List recalculated value;

[0065] S403 obtains the tunnel status rule according to the List recalculated value, and performs tuning processing on the tunnel according to the status rule.

[0066] In this embodiment, the first network quality constant value and the second network quality sample value may be part or all of a bandwidth value, a delay value, and a jitter value.

[0067] Among them, the first network quality constant value is a constant value of the sample service in a stable state. Generally speaking, the sample service can be selected from various services, and the selected service can be defined as a sample service. After determining the sample service, it can be determined whether the sample service is working in a stable state, for example, a smooth video state, a smooth voice state, etc. When it is determined that the sample service is working smoothly, the first network quality constant value of the sample service is obtained.

[0068] In this embodiment, the first network quality constant value of the sample service obtained can be expressed as A 带宽 , A 时延 , A 抖动The first network quality constant value obtained can be used to evaluate the deviation from the actual value in machine learning.

[0069] In step S402, when the tunnel is tuned for the service message, the second network quality sample value of the first period can be obtained. For example, the first period is 7*24 hours. Periodic real-time bandwidth, delay and jitter detection is performed based on the main list, and these data are transmitted to the controller through Telemetry technology. For example, the second network quality sample value is C 带宽n , C 时延n , C 抖动n ,As shown in Table 1, some sample values ​​of the second network quality are shown.

[0070] Business A <![CDATA[C 带宽n ]]> <![CDATA[C 时延n ]]> <![CDATA[C 抖动 ]]> 10:00 13.2Mbps 21ms 65us 10:05 13.4Mbps 24ms 69us 10:10 15.1Mbps 30ms 110us 10:15 13.2Mbps 25ms 72us 10:20 12.5Mbps 20ms 61us …

[0071] Table 1

[0072] In step S402, after obtaining the second network quality sample value, the first network quality constant value and the second network quality sample value may be used to perform recalculation to obtain a List recalculation value, which is specifically performed by the following formula:

[0073] S 重计算n＝ f1(C 带宽n ,A 带宽 )+f2(C 时延n ,A 时延 )+f3(C 抖动n ,A 抖动 ).

[0074] An overall calculation target is set: List recalculation value S recalculation; it represents whether the main list of this Policy tunnel needs to be pushed down and the controller needs to recalculate a new list for forwarding.

[0075] in,

[0076] f1(C 带宽n ,A 带宽 ) represents the value obtained by multiplying the deviation between the real-time bandwidth and the steady-state bandwidth by the weighted coefficient, which represents the influence of the bandwidth on the recalculated value. If the real-time bandwidth is less than or equal to the steady-state bandwidth, it is considered to have no influence and the value of this item is 0.

[0077] f2(C 时延n ,A 时延 ) represents the value obtained by multiplying the deviation between the real-time delay and the steady-state delay by the weighted coefficient, which represents the influence of the delay on the recalculation. If the real-time delay is less than or equal to the steady-state delay, it is considered to have no influence and the value of this item is 0.

[0078] f3(C 抖动n ,A抖动 ) represents the value obtained by multiplying the deviation between real-time jitter and steady-state jitter by the weighted coefficient, which represents the influence of jitter on the weight calculation. If the real-time jitter is less than or equal to the steady-state jitter, it is considered to have no influence and the value of this item is 0.

[0079] In this embodiment, the weighting coefficients in the three functions can be adjusted dynamically according to the situation. The overall evaluation bandwidth has a relatively high impact on the service traffic, and the weight parameter can be higher than the delay and jitter.

[0080] At the same time, the weights in the machine algorithm can be dynamically adjusted according to the type of business. For example, in voice and video conferencing, the jitter impact will have a greater impact on user perception. The weight parameters can be increased, and the bandwidth and delay weight coefficients can be reduced.

[0081] In step S403, a curve graph within the target period may be obtained according to the List recalculated value;

[0082] The tunnel status rules are obtained according to the curve graph, and the tunnel is optimized according to the status rules.

[0083] In this embodiment, the first cycle may include several small cycles, with 24 hours as a large statistical cycle, and a curve chart of S recalculation n within the 24 hours is drawn. The time period can be dynamically adjusted, and 5 minutes or 10 minutes can be set as a small cycle. A curve chart can be drawn according to the List recalculation value, such as Figure 5 shown.

[0084] Among them, 1. The smaller the statistical time period, the more continuous the curve.

[0085] 2. The peak indicates that the S recalculation value is the highest. At this time, the bandwidth deviation weight value + delay deviation weight value + jitter deviation weight value are the largest, which also means that the distance to trigger recalculation of a new List is closer.

[0086] 3. The trough indicates the lowest S recalculation value. At this time, the bandwidth deviation weight value + delay deviation weight value + jitter deviation weight value are the smallest, which also means that the distance to trigger recalculation of the new list is the farthest, that is, it is least likely to trigger recalculation of the new list.

[0087] In another embodiment, 7 days is used as a large statistical cycle, 7 daily S weight calculations are aggregated together, and the average value of the 7 points is calculated at each time point.

[0088] That is S 重计算ave＝ (S 重计算1 +S 重计算2 +S 重计算3 +S 重计算4 +S 重计算5 +S 重计算6 +S重计算7 ) / 7.

[0089] In this embodiment, a one-week curve is drawn again with a new average value, and the trigger threshold M is calculated based on the highest and lowest points of the threshold, as shown in FIG. Figure 6 shown.

[0090] M=f(the highest point of the weekly curve is X1, the lowest point of the weekly curve is X2)

[0091] M is calculated from the highest point X1 and the lowest point X2, and can be calculated according to different requirements, such as setting 90% of the highest point X1 as the threshold, or setting the value of the deviation between X1 and X2 at a 10% ratio of the deviation from the highest point X1, etc. This value is the final decision value for the machine learning module to reversely guide the controller for tuning.

[0092] After the sample analysis is completed, the M value calculated for the next week based on the data of one week can be used to decide whether to trigger the calculation of a new list in advance.

[0093] The learning and training of samples can be carried out in a set periodicity, and the trigger threshold obtained by the sample in the machine learning algorithm can also be set with a refresh cycle to continuously guide the dynamic refresh of the tunnel list.

[0094] It can be seen from the above format examples that the technical solution provided by this embodiment can identify and summarize the regular periodic performance rules of the business by summarizing and analyzing the data samples of the three dimensions of bandwidth, delay and jitter of the business traffic in the historical period. Through these analysis results, before the emergency occurs, the controller can optimize the tunnel in advance to ensure that the business traffic is processed more smoothly and intelligently, thereby achieving the optimization goal of the business tunnel.

[0095] Based on the above method embodiments, the present disclosure further provides a management device, the management device comprising:

[0096] An acquisition module, used to acquire a first network quality constant value of a sample service in a stable state;

[0097] The acquisition module is further used to acquire a second network quality sample value of the target service in the first period, and recalculate using the first network quality constant value and the second network quality sample value to obtain a List recalculation value;

[0098] The processing module is used to obtain the tunnel status rule according to the List recalculation value and perform tunnel optimization processing according to the status rule.

[0099] The acquisition module is specifically used to acquire the first network quality constant value or the second network quality sample value through the iFIT technology.

[0100] The first network quality constant value or the second network quality sample value includes:

[0101] Part or all of the bandwidth value, delay value, and jitter value.

[0102] The acquisition module is specifically used to obtain the List recalculation value through the following formula:

[0103] S recalculation n = f1 (C bandwidth n, A bandwidth) + f2 (C delay n, A delay) + f3 (C jitter n, A jitter)

[0104] Wherein, C bandwidth n, C delay n, and C jitter n are the second network quality sample values;

[0105] A bandwidth, A delay, and A jitter are first network quality constant values.

[0106] Wherein, the processing module is specifically used to obtain a curve graph within a target period according to the List recalculated value;

[0107] Obtain the tunnel status rules according to the curve chart, and optimize the tunnel according to the status rules;

[0108] The target period includes one first period or several first periods.

[0109] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this specification. A person of ordinary skill in the art can understand and implement it without paying creative labor.

[0110] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0111] Those skilled in the art will readily appreciate other embodiments of the specification after considering the specification and practicing the invention claimed herein. The specification is intended to cover any variations, uses or adaptations of the specification that follow the general principles of the specification and include common knowledge or customary techniques in the art that are not claimed in the specification. The specification and examples are to be considered exemplary only, and the true scope and spirit of the specification are indicated by the following claims.

[0112] It should be understood that the present description is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present description is limited only by the appended claims.

[0113] The above description is only a preferred embodiment of this specification and is not intended to limit this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this specification should be included in the scope of protection of this specification.

Claims

1. A tunnel tuning method, characterized in that: The method is applied in a controller, and the method comprises: Obtaining a first network quality constant value of the sample service in a stable state; Obtain a second network quality sample value of the target service in the first period, and recalculate using the first network quality constant value and the second network quality sample value to obtain a List recalculated value; Obtain tunnel status rules based on the List recalculation value and optimize the tunnel based on the status rules.

2. The method according to claim 1, characterized in that The method for obtaining the first network quality constant value or the second network quality sample value includes: The first network quality constant value or the second network quality sample value is obtained through the iFIT technology.

3. The method according to claim 1, characterized in that The first network quality constant value or the second network quality sample value includes: Part or all of the bandwidth value, delay value, and jitter value.

4. The method according to claim 1, characterized in that The recalculating using the first network quality constant value and the second network quality sample value to obtain the List recalculated value includes: The List recalculation value is obtained by the following formula: S 重计算n＝ f1(C 带宽n ,A 带宽 )+f2(C 时延n ,A 时延 )+f3(C 抖动n ,A 抖动 ) Among them, C 带宽n、 C 时延n、 C 抖动n is the second network quality sample value; A 带宽、 A 时延、 A 抖动 is the first network quality constant value.

5. The method according to claim 1, characterized in that The obtaining of the tunnel status rule according to the List recalculated value and performing tunnel optimization processing according to the status rule include: According to the recalculated values ​​of List, obtain the curve chart within the target period; Obtain the tunnel status rules according to the curve chart, and optimize the tunnel according to the status rules; The target period includes one first period or several first periods.

6. A management device, characterized in that: The management device comprises: An acquisition module, used to acquire a first network quality constant value of a sample service in a stable state; The acquisition module is further used to acquire a second network quality sample value of the target service in the first period, and recalculate using the first network quality constant value and the second network quality sample value to obtain a List recalculation value; The processing module is used to obtain the tunnel status rule according to the List recalculation value and perform tunnel optimization processing according to the status rule.

7. The management device according to claim 6, characterized in that: The acquisition module is specifically used to acquire the first network quality constant value or the second network quality sample value through the iFIT technology.

8. The management device according to claim 6, characterized in that: The first network quality constant value or the second network quality sample value includes: Part or all of the bandwidth value, delay value, and jitter value.

9. The management device according to claim 6, characterized in that: The acquisition module is specifically used to obtain the List recalculation value through the following formula: S recalculation n = f1 (C bandwidth n, A bandwidth) + f2 (C delay n, A delay) + f3 (C jitter n, A jitter) where, C bandwidth n, C delay n, C jitter n are the second network quality sample values; A bandwidth, A delay, and A jitter are first network quality constant values.

10. The management device according to claim 6, characterized in that: The processing module is specifically used to obtain a curve graph within a target period according to the List recalculated value; Obtain the tunnel status rules according to the curve chart, and optimize the tunnel according to the status rules; The target period includes one first period or several first periods.

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