Traffic distribution method and device, electronic equipment and program product

Abstract

The invention discloses a flow distribution method. The method comprises the following steps: receiving network flow to be processed; determining a multi-level bucket dividing structure comprising a plurality of bucket dividing dimensions, wherein each level in the multi-level bucket dividing structure comprises one or more bucket dividing dimensions and / or a combination of the bucket dividing dimensions; carrying out bucket separation on the network flow according to the multi-level bucket separation structure; and distributing and processing the network flow according to a bucket dividing result. According to the traffic distribution scheme provided by the embodiment of the invention, a multi-level and multi-dimensional bucket distribution structure is introduced, bucket distribution can be carried out based on multiple dimensions, and more flexible and fine traffic division is realized, so that the requirements of a complex service scene are better met.

Description

Technical Field

[0001] The present disclosure relates to the field of computer application technology, and in particular to a flow distribution method, device, electronic equipment and program product. Background Art

[0002] In the field of Internet technology, traffic bucketing refers to a method for dividing network traffic into multiple buckets for applying different processing strategies to different traffic.

[0003] Traditional traffic bucketing technology usually divides traffic based on preset rules. However, with the diversification of online services, the characteristics and processing requirements of network traffic have become more complex.

[0004] Therefore, traditional traffic bucketing technology may face challenges in dealing with increasingly complex network environments and changing processing requirements, especially in scenarios that require more refined management.

[0005] The content of this background technology description is only for facilitating understanding of the relevant technology in this field and is not regarded as an admission of the prior art. Summary of the invention

[0006] The embodiments of the present disclosure provide a traffic allocation method, device, storage medium, and program product, aiming to improve the flexibility and accuracy of traffic bucketing.

[0007] In a first aspect, an embodiment of the present disclosure provides a flow distribution method, which may include:

[0008] Receive network traffic to be processed;

[0009] Determine a multi-level bucket structure including a plurality of bucket dimensions, wherein each level in the multi-level bucket structure includes one or more of the bucket dimensions and / or a combination of the bucket dimensions;

[0010] Bucketing the network traffic according to the multi-level bucketing structure; and

[0011] According to the bucketing result, the network traffic is allocated and processed.

[0012] Optionally, the multiple bucketing dimensions include at least one of the following: percentage traffic bucketing, daily sequential control volume bucketing, daily random control volume bucketing, user group bucketing, specified time bucketing and fixed bucketing.

[0013] Optionally, the classifying the network traffic according to the multi-level bucket structure includes:

[0014] Starting from the highest level of the multi-level bucket structure, determining in turn whether the network traffic meets the bucket conditions of each level;

[0015] When the bucketing conditions of a certain level are met, proceed to the next level to continue the judgment;

[0016] When the lowest level is reached or the preset termination condition is met, the classification result is determined.

[0017] Optionally, the determining of a multi-level bucket structure including multiple bucket dimensions includes:

[0018] Selecting a preset bucketing template, wherein the bucketing template sets multiple bucketing dimensions and a combination relationship and priority of each bucketing dimension;

[0019] For one or more bucketing units corresponding to each bucketing dimension set in the bucketing template, set a bucketing condition for each bucketing unit;

[0020] A multi-level bucketing structure is constructed based on the combination relationship and priority of each bucketing dimension and the bucketing conditions of each bucketing unit.

[0021] Optionally, the method of allocating and processing the network traffic according to the bucketing result further includes: monitoring the traffic allocation and processing of each bucket in real time to obtain the performance index of each bucket. Further optionally, the method further includes: dynamically adjusting the multi-level bucketing structure according to the performance index, wherein the dynamic adjustment includes at least one of the following: adjusting the parameters of the bucketing dimension; adding or deleting the bucketing dimension; adjusting the combination relationship of the bucketing dimension; updating the priority of the bucketing dimension.

[0022] Optionally, the allocating and processing the network traffic according to the bucketing results includes: setting a given test configuration for each bucket; processing the network traffic of each bucket based on the test configuration; obtaining feedback data generated by user interaction under the test configuration; and calculating the test index of each bucket based on the collected feedback data. Further optionally, the dynamically adjusting the multi-level bucketing structure according to the performance index includes: optimizing user interaction by adjusting the multi-level bucketing structure when the test index of the bucket does not meet the preset standard.

[0023] Optionally, the allocating and processing the network traffic according to the bucketing result includes: allocating a specified proportion of resources to each bucket, the resources including at least one of computing resources, storage resources and bandwidth resources; using the resources to process the network traffic allocated to each bucket; and monitoring the resource utilization and performance indicators of each bucket in real time. Further optionally, the dynamically adjusting the multi-level bucketing structure according to the performance indicators includes: when it is detected that the resource utilization of a bucket exceeds a preset threshold, adjusting the traffic allocation of the bucket by adjusting the multi-level bucketing structure.

[0024] In a second aspect, an embodiment of the present disclosure provides a flow distribution device, which may include:

[0025] An acquisition unit configured to acquire a traffic bucket configuration, wherein the traffic bucket configuration defines a plurality of bucket dimensions and a combination relationship between the plurality of bucket dimensions;

[0026] a receiving unit configured to receive network traffic to be processed;

[0027] a classification unit configured to classify the network traffic according to the traffic bucketing configuration, wherein the classification is performed based on a multi-level bucketing structure, each level of the multi-level bucketing structure includes one or more of the bucketing dimensions and / or a combination of the bucketing dimensions; and

[0028] The processing unit is configured to process the network traffic according to the classification result.

[0029] In a third aspect, an embodiment of the present disclosure provides a computer electronic device, which may include: a processor and a memory storing a computer program, wherein the processor is configured to implement the method described in the first aspect when running the computer program.

[0030] In a fourth aspect, an embodiment of the present disclosure provides a program product, including a computer program, wherein when the computer program is executed by a processor, the method as described in the first aspect is implemented.

[0031] The traffic distribution scheme of the embodiment of the present disclosure can receive the network traffic to be processed, determine a multi-level bucket structure including multiple bucket dimensions, bucket the network traffic based on this multi-level bucket structure, and distribute and process the network traffic according to the bucket results. Compared with the method of simply dividing traffic based on preset rules in the prior art, the traffic distribution scheme of the embodiment of the present disclosure can divide traffic based on multiple dimensions by introducing a multi-level and multi-dimensional bucket structure, thereby achieving more flexible and refined traffic division, thereby better meeting the needs of complex business scenarios. Compared with the single-dimensional bucket method, the multi-level bucket structure adopted by the traffic distribution scheme of the embodiment of the present disclosure allows traffic to be divided and managed at a more refined granularity, effectively overcoming the limitations of single-dimensional bucketing, improving the accuracy of the system's user behavior analysis and experimental control, and providing more precise technical support for Internet project optimization.

[0032] Other optional features and technical effects of the embodiments of the present disclosure are partially described below, and partially can be understood by reading this document. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. The elements shown are not limited by the proportions shown in the accompanying drawings. The same or similar reference numerals in the accompanying drawings represent the same or similar elements, wherein:

[0034] Figure 1 A first flow chart of a flow distribution method according to an embodiment of the present application is shown;

[0035] Figure 2 A second flow chart of the flow distribution method according to an embodiment of the present application is shown;

[0036] Figure 3 A third flow chart of the flow distribution method according to an embodiment of the present application is shown;

[0037] Figure 4 A fourth flow chart of the flow distribution method according to an embodiment of the present application is shown;

[0038] Figure 5 A fifth flow chart of the flow distribution method according to an embodiment of the present application is shown;

[0039] Figure 6 A fifth flow chart of the flow distribution method according to an embodiment of the present application is shown;

[0040] 7A to 7F An exemplary bucketing dimension for a traffic distribution method according to an embodiment of the present application is shown;

[0041] Figure 8 An exemplary multi-level bucket structure for a traffic distribution method according to an embodiment of the present application is shown;

[0042] Fig. 9 An exemplary structural diagram of a device according to an embodiment of the present disclosure is shown; and

[0043] Fig.10 An exemplary structural diagram of an electronic device capable of implementing the method according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present disclosure more clear, the present disclosure is further described in detail below in conjunction with specific implementation methods and drawings. Here, the illustrative implementation methods and descriptions of the present disclosure are used to explain the present disclosure, but are not intended to limit the present disclosure.

[0045] As used herein, the term "including" and its variations mean open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "based at least in part on". The terms "an example embodiment" and "an embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one some embodiments". The terms "first", "second", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0046] In the embodiments of the present disclosure, the traffic bucketing method involves allocating network traffic to different processing units (buckets), and the network traffic can be managed by classification through this method.

[0047] At present, as Internet business scenarios become increasingly complex, traditional simple traffic allocation methods can no longer meet the needs of refined management. Existing solutions usually use a single dimension such as a preset percentage or a fixed user group to divide traffic. They lack flexibility when dealing with diverse business needs, and it is difficult to achieve precise control of multi-dimensional factors, which cannot meet the requirements of modern Internet projects for refined traffic management.

[0048] In this regard, the embodiments of the present disclosure provide a traffic distribution method based on a multi-dimensional bucket structure, which can be used in a traffic control system, a load balancing system, or other computing systems with traffic management requirements. Figure 1 As shown, the flow distribution method provided by the embodiment of the present disclosure may at least include the following 110 to 140.

[0049] 110: Receiving network traffic to be processed.

[0050] In the embodiments of the present disclosure, the network traffic to be processed includes, but is not limited to, user access traffic, system service traffic, or business processing traffic in various online businesses.

[0051] 120: Determine a multi-level bucket structure including multiple bucket dimensions.

[0052] In the embodiment of the present disclosure, the bucketing dimension includes standards / strategies for classifying and dividing network traffic, wherein each bucketing dimension may represent a diversion standard / strategy.

[0053] In some embodiments of the present disclosure, the multiple bucketing dimensions may include but are not limited to one or more of the following bucketing dimensions: percentage traffic bucketing, daily sequential control bucketing, daily random control bucketing, user group bucketing, specified time bucketing and fixed bucketing. In this embodiment, the percentage traffic bucketing includes: a dimension of dividing network traffic according to a preset percentage value, and the traffic can be allocated to different bucket units by setting different percentage thresholds; the daily sequential control bucketing includes: a dimension of dividing network traffic based on the arrival order of network traffic, and the traffic can be controlled according to the order of arrival time of network traffic; the daily random control bucketing includes: a dimension of randomly sampling and dividing the daily traffic, and a specified number of network traffic is allocated to different bucket units through a random algorithm; the user group bucketing includes: a dimension of grouping and dividing network traffic according to user attributes or user tags of network traffic, and network traffic belonging to different users can be allocated to different bucket units; the specified time bucketing includes: a dimension of dividing network traffic according to time periods, and network traffic in different time periods can be allocated to different bucket units; the fixed bucketing includes: a dimension of allocating traffic to a preset bucket unit in a fixed manner.

[0054] The above-mentioned multiple bucket dimensions will be described below in conjunction with some specific embodiments of the present disclosure. Fig. 7A , which shows the percentage traffic bucketing: bucketing is performed based on the hash value of the network traffic modulo 100, 70% of the network traffic is allocated to processing unit A, and 30% of the network traffic is allocated to processing unit B. Figure 7B , which shows the daily sequential control volume bucketing: according to the order of the network traffic on that day, the first 1000 network traffic of each day are selected and allocated to processing unit A, and the remaining network traffic is allocated to processing unit B; refer to Figure 7C , which shows the random control volume bucketing of the day: 1000 network flows are randomly selected from the network flows of the day and allocated to processing unit A, and the remaining network flows are allocated to processing unit B. Fig.7D , which shows the user group bucketing: bucketing is performed according to predefined users / customer groups (groups), network traffic belonging to customer group A is divided into processing unit A, network traffic belonging to customer group B is divided into processing unit B, and network traffic not belonging to the predefined customer group is divided into processing unit C. Fig. 7E , which shows the specified time bucketing: bucketing is performed according to the preset time period 2024-12-18, the network traffic before 2024-12-18 is divided into processing unit A, the network traffic from 2024-12-18 to 2024-12-25 is divided into processing unit B, and the network traffic at the rest of the time is divided into processing unit C. Fig. 7E, wherein fixed bucketing is shown: all network traffic is fixedly allocated to processing unit A. The multiple bucketing dimensions provided by the embodiment of the present disclosure can adapt to different business scenarios and enhance the flexibility of network traffic bucketing.

[0055] In some embodiments of the present disclosure, multiple bucket dimensions can also be used in combination to build a multi-level bucket structure to achieve a more flexible network traffic distribution strategy. Figure 2 The step of determining a multi-level bucket structure including multiple bucket dimensions may at least include the following 121, 122 and 123.

[0056] 121: Select the preset bucket template.

[0057] In the embodiments of the present disclosure, the bucketing template can set multiple bucketing dimensions and the combination relationship and priority of each bucketing dimension. In some embodiments of the present disclosure, the multiple bucketing dimensions may include but are not limited to the multiple bucketing dimensions in the above embodiments of the present disclosure and other bucketing dimensions, which will not be repeated here. In this embodiment, the combination relationship of the bucketing dimensions includes but is not limited to the serial relationship and parallel relationship between the bucketing dimensions. In the embodiments of the present disclosure, by presetting the bucketing template, a standardized bucketing structure can be quickly constructed to improve the efficiency of bucketing configuration.

[0058] 122: For one or more bucketing units corresponding to each bucketing dimension set in the bucketing template, set a bucketing condition for each bucketing unit.

[0059] In the embodiment of the present disclosure, the one or more bucketing units may be applied with different bucketing dimensions. In other words, each bucketing dimension may correspond to one or more bucketing units. In this embodiment, bucketing conditions may be set for each bucketing unit according to the bucketing dimensions applied to the bucketing unit, and the network traffic that meets the bucketing conditions of a certain bucketing unit will be allocated to the bucketing unit, and may be further allocated to the processing unit later. It should be understood that bucketing units of different bucketing dimensions may correspond to different types of bucketing conditions, and the bucketing conditions include but are not limited to start and end time points, traffic distribution percentage examples, daily traffic volume, user group presets, etc.

[0060] In a specific example of the present disclosure, continue to refer to Fig. 7E , where the bucket dimension is percentage traffic bucketing, and two bucket units E1 and E2 are set. Bucket condition 1 is set for bucket unit E1: the network traffic timestamp belongs to before 2024-12-18, and bucket condition 2 is set for bucket unit E2: the network traffic timestamp belongs to the time period from 2024-12-18 to 2024-12-25.

[0061] 123: Build a multi-level bucketing structure based on the combination relationship and priority of each bucketing dimension and the bucketing conditions of each bucketing unit.

[0062] In some embodiments of the present disclosure, each level in the multi-level bucketing structure may include one or more of the bucketing dimensions and / or a combination of the bucketing dimensions.

[0063] In some embodiments of the present disclosure, a single level may include only one bucket dimension, such as using only time buckets. In another example, a single level may also include multiple parallel bucket dimensions, such as using percentage and user group buckets at the same time.

[0064] In some embodiments of the present disclosure, a serial relationship may be formed between different levels to achieve layer-by-layer screening of network traffic, and multiple dimensions within the same level may form a parallel relationship to achieve distribution of network traffic.

[0065] In some embodiments of the present disclosure, optionally, a priority relationship may exist between multiple parallel bucketing dimensions at each level in a multi-level bucketing structure. As an explanation but not limitation, the same network traffic may simultaneously meet the bucketing conditions of multiple parallel bucketing dimensions, and in this case, the network traffic allocation will be allocated to the bucketing dimension with a higher priority.

[0066] In the disclosed embodiment, bucket dimensions, bucket units, and bucket conditions form a hierarchical relationship structure, which enables the system to flexibly combine different bucket dimensions and accurately control the network traffic allocation of each bucket unit. The method of the disclosed embodiment also avoids potential network traffic allocation conflicts of multiple bucket dimensions by setting priorities for multiple parallel bucket dimensions at the same level, and can support the construction of a highly complex multi-level bucket structure.

[0067] The network traffic distribution method of the embodiment of the present disclosure will be further explained below in conjunction with a specific embodiment of the present disclosure. Figure 8 , showing a multi-level bucket structure constructed by combining multiple bucket dimensions in a specific embodiment of the present disclosure.

[0068] refer to Figure 8 The first level of the multi-level bucket structure includes 2 bucket dimensions (specified time bucket and fixed bucket), 3 bucket units (E3, E4 and E5 (not shown)), and the priority order of the 2 bucket dimensions is specified time bucket and fixed bucket from high to low.

[0069] In this first level, the specified time bucket dimension corresponds to bucket unit E3 and bucket unit E4, and the fixed bucket dimension corresponds to bucket unit E5 (not shown), wherein bucket unit E3 is set with bucket condition 1: the network traffic timestamp belongs to before 2024-12-18, and bucket unit E4 is set with bucket condition 2: the network traffic timestamp belongs to the time period from 2024-12-18 to 2024-12-25, and the fixed bucket dimension corresponds to bucket unit E5 (not shown) without setting any conditions.

[0070] Continue to refer Figure 8 The second level of the multi-level bucket structure includes 4 bucket dimensions (user group bucket, percentage traffic bucket, daily random control bucket and fixed bucket), 6 bucket units (E 3-1 , E 3-2 , E 3-3 (not shown), E 4-1 , E 4-2 and E 4-3 (Not shown)) The priority order of the two bucketing dimensions from high to low is user group bucketing, percentage traffic bucketing, daily random control bucketing and fixed bucketing.

[0071] In the second level, the user group bucket dimension is specified to correspond to the bucket unit E 3-1 and bucket unit E 3-2 , the percentage flow bucket corresponds to the bucket unit E 4-2 , the random control quantity bucket of the day corresponds to the bucket unit E 4-1 , fixed bucket corresponds to bucket unit E 3-3 and bucket unit E 4-3 (not shown). In this embodiment, the bucket unit E 3-1 Set bucket condition 3: the network traffic belongs to customer group A, bucket unit E 3-2 Bucketing condition 4 is set: the network traffic belongs to customer group B, and the fixed bucketing dimension corresponds to bucket unit E 3-3 (not shown) No conditions are set. In this embodiment, the bucket unit E 4-1 Bucket condition 3 is set: the network traffic belongs to the first 1000 of the day, bucket unit E 4-2 Set bucket condition 5: network traffic hash value mod 100 < 70, fixed bucket dimension corresponds to bucket unit E 4-3 (not shown) No conditions are set.

[0072] In some embodiments of the present disclosure, user demand information may also be obtained, and a bucketing template that meets the user's demand information may be generated based on the demand information.

[0073] In this embodiment, the step of determining a multi-level bucketing structure including multiple bucketing dimensions may at least include: generating a target bucketing template based on user demand information.

[0074] In some embodiments of the present disclosure, the target bucketing template generated based on user demand information may include at least A1 to A3.

[0075] A1: Obtain the user's demand text.

[0076] In some embodiments of the present disclosure, the demand text may be in natural language form. In this embodiment, the demand text includes but is not limited to information such as bucket dimensions, description of the combination relationship between bucket dimensions, and network traffic flow process, wherein the bucket dimensions can refer to the description in the above embodiment 120 of the present disclosure, and will not be repeated here; the combination relationship description may include: for explaining the hierarchical relationship and combination method between different bucket dimensions. In a specific example of the present disclosure, the combination relationship description may include but is not limited to "first perform user group diversion, then distribute according to percentage", "simultaneously use fixed buckets and random control buckets" and other description information; the network traffic flow process includes: for describing the specific distribution rules and flow paths of network traffic between each bucket unit. In a specific example of the present disclosure, the traffic flow process may include but is not limited to "allocate 60% of the traffic to processing unit A and 40% of the traffic to processing unit B".

[0077] A2: Parse the requirement text and match the first preset bucket template with the highest similarity.

[0078] In some embodiments of the present disclosure, the demand text may be parsed by natural language processing technology to extract key fields, including but not limited to bucket dimension fields, bucket relationship fields, etc.

[0079] In some embodiments of the present disclosure, each preset bucketing template may include a corresponding bucketing feature set, and the bucketing feature set may include bucketing dimension fields, bucketing hierarchical relationship fields, and bucketing dimension execution order fields included in the corresponding bucketing template. In a specific example of the present disclosure, a bucketing feature set corresponding to a two-layer bucketing structure is: {"bucketing dimension fields": ["user component buckets", "percentage buckets"], "bucketing hierarchical relationship fields": {"first layer": "user component buckets", "second layer": "percentage buckets"}, "bucketing dimension execution order fields": {"priority 1": "user component buckets", "priority 2": "percentage buckets"}}.

[0080] In some embodiments of the present disclosure, the template with the highest similarity can be found in the preset bucket templates based on the key fields. In a specific example of the present disclosure, it can be identified that the requirements contain three bucket dimension fields, namely user group buckets, fixed buckets and percentage flow buckets, and that user group buckets and fixed buckets are executed in series, thereby determining the template with the highest similarity.

[0081] A3: Adjust the first preset bucketing template according to the requirement text to obtain the target bucketing template.

[0082] In the embodiment of the present disclosure, the difference items between the first preset bucketing template and the requirement text can be identified, and the difference items may include but are not limited to: missing bucketing dimensions, inconsistent hierarchical relationships, inconsistent execution order, etc. In the embodiment of the present disclosure, the adjustment may include but is not limited to: adding missing bucketing dimensions, deleting redundant bucketing dimensions, adjusting the hierarchical relationship of bucketing dimensions, modifying the execution order of bucketing dimensions, etc.

[0083] In a specific example of the present disclosure, for the requirement text description "first divide by user group, then divide by time period, and finally distribute by percentage", the first preset bucketing template only includes user group bucketing and specified time bucketing, where the user group bucketing and specified time bucketing are executed in series, and compared with the requirement text description, the percentage bucketing and the execution relationship of the percentage traffic bucketing are missing. In this embodiment, in view of the above situation where the percentage traffic bucketing dimension is missing, the percentage traffic bucketing dimension can be added to the first preset bucketing template, and it can be set to be executed in series with the specified time bucketing dimension, and executed after the time bucketing dimension.

[0084] The disclosed embodiment improves the configuration efficiency of the bucket structure and reduces the error rate of manual configuration by introducing natural language demand analysis and intelligent template matching mechanism. At the same time, the disclosed embodiment can also automatically adjust the bucket template to ensure that the final generated bucket structure matches the business requirements.

[0085] 130: Bucketize network traffic based on a multi-level bucketing structure.

[0086] In some embodiments of the present disclosure, Figure 3 As shown, the bucketing of the network traffic according to the multi-level bucketing structure may at least include the following 131, 132 and 133.

[0087] 131: Starting from the highest level of the multi-level bucket structure, determine whether the network traffic meets the bucket conditions of each level in turn.

[0088] 132: When the bucketing conditions of a certain level are met, proceed to the next level to continue the judgment.

[0089] In the embodiment of the present disclosure, the highest level of the multi-level bucket structure is the source of the network traffic to be allocated. 7A to 7F , where the highest level of the multi-level bucket structure is the traffic entrance in the figure. In this embodiment, the traffic entrance may include but is not limited to: mobile application entrance, web page entrance, server interface call entrance, third-party traffic forwarding entrance, etc., and this disclosure does not limit this.

[0090] In some embodiments of the present disclosure, the network traffic to be allocated may first start from the highest level of the multi-level bucket structure, and then judge the first level, the second level, etc. in sequence. Fig. 7A , it can be determined whether the hash value mod 100 of the network traffic passing through the traffic inlet is less than 70. In a specific embodiment of the present disclosure, refer to Figure 8 First, we can determine whether the network traffic passing through the traffic inlet meets the bucketing condition of bucketing unit E3 in the first level. The traffic with a traffic timestamp before 2024-12-18 is allocated to bucketing unit E3 for subsequent allocation to the processing unit. Then, we can determine whether the network traffic passing through bucketing unit E3 meets the bucketing condition of bucketing unit E3 in the second level. 3-1 The bucketing condition is that the network traffic belonging to customer group A is allocated to processing unit A for business processing.

[0091] 133: When the lowest level is reached or a preset termination condition is met, the classification result is determined.

[0092] In some embodiments of the present disclosure, the preset termination condition may include that the traffic is allocated to the bucket unit corresponding to the fixed bucket. As an explanation but not limitation, the bucket unit corresponding to the fixed bucket does not have a next-level bucket unit, and when the network traffic enters these bucket units, it will be directly allocated to the corresponding processing unit.

[0093] In a specific example of the present disclosure, continue to refer to Figure 8 The traffic at the traffic entrance first matches the bucket unit E3 and the bucket unit E4, where the traffic before 2024-12-18 is allocated to the bucket unit E3 to be allocated to the processing unit, and the traffic between 2024-12-18 and 2024-12-25 is allocated to the bucket unit E4 to be allocated to the processing unit, and other traffic is directly allocated to the bucket unit corresponding to the fixed bucket, and is allocated to the processing unit F for business processing.

[0094] In this embodiment, for the traffic from bucket unit E3, bucket unit E is first matched. 3-1 and bucket unit E 3-2 , where the traffic belonging to customer group A is allocated to bucket unit E3-1 After being allocated to the processing unit, the traffic belonging to customer group B is allocated to bucket unit E. 3-2 To be allocated to the processing unit, other traffic is directly sent to the bucket unit corresponding to the fixed bucket and allocated to processing unit C for business processing.

[0095] In this embodiment, for the traffic from bucket unit E4, bucket unit E is first matched. 4-2 : Traffic hash value mod 100<70 traffic is allocated to bucket unit E 4-2 To be allocated to the processing unit, the flow mod 100 ≥ 70 is then allocated to the bucket unit E 4-1 Match, the traffic belonging to the first 1000 of the day is allocated to bucket unit E 4-1 To be allocated to the processing unit, other traffic is directly allocated to the bucket unit corresponding to the fixed bucket, and is allocated to the processing unit E for business processing.

[0096] 140: Allocate and process network traffic based on the bucketing results.

[0097] In the embodiment of the present disclosure, network traffic can be allocated and processed in corresponding processing units according to the bucketing results. In this embodiment, different bucketing results can correspond to different subsequent processing units for business processing, wherein the processing units include but are not limited to computing models, server resources, load balancing equipment, etc., and the business processing includes but is not limited to: business diversion, grayscale release, and load balancing, etc. It should be understood that according to different business scenarios, those skilled in the art can set different subsequent processing units according to needs, and the present disclosure does not limit this.

[0098] The traffic distribution scheme of the embodiment of the present disclosure can receive the network traffic to be processed, determine a multi-level bucket structure including multiple bucket dimensions, bucket the network traffic based on this multi-level bucket structure, and distribute and process the network traffic according to the bucket results. Compared with the method of simply dividing traffic based on preset rules in the prior art, the traffic distribution scheme of the embodiment of the present disclosure can divide traffic based on multiple dimensions by introducing a multi-level and multi-dimensional bucket structure, thereby achieving more flexible and refined traffic division, thereby better meeting the needs of complex business scenarios. Compared with the single-dimensional bucket method, the multi-level bucket structure adopted by the traffic distribution scheme of the embodiment of the present disclosure allows traffic to be divided and managed at a more refined granularity, effectively overcoming the limitations of single-dimensional bucketing, and improving the system's accuracy in user behavior analysis and network traffic, such as grayscale release, business diversion, load balancing, version control, etc., providing more accurate technical support for Internet project optimization.

[0099] In some embodiments of the present disclosure, a further flow monitoring and feedback adjustment mechanism may be combined on the basis of the multi-level bucket structure constructed in the embodiments of the present disclosure. Figure 4 As shown, the allocation and processing of network traffic according to the bucketing result may at least include the following 141 and 142.

[0100] 141: Monitor the traffic distribution and processing of each bucket in real time to obtain the performance indicators of each bucket.

[0101] In the embodiments of the present disclosure, the performance indicators of the buckets may vary according to the specific business scenarios. In one example, the method of the embodiments of the present disclosure is used in a traffic load balancing scenario, in which case the performance indicators may include server response time, request success rate, resource utilization, etc. In another example, the method of the embodiments of the present disclosure is used in an online business testing scenario, in which case the performance indicators may include user conversion rate, average access time, business completion rate, etc.

[0102] 142: Dynamically adjust the multi-level bucket structure according to the performance indicator.

[0103] In the embodiment of the present disclosure, the dynamic adjustment may include but is not limited to: adjusting the parameters of the bucket dimensions, adding / deleting the bucket dimensions, adjusting the combination relationship of the bucket dimensions, and updating the priority of the bucket dimensions.

[0104] In a specific embodiment of the present disclosure, when a financial product is initially launched, percentage traffic bucketing and specified time bucketing are used as bucketing dimensions. If it is found that the performance of a processing unit in a certain period of time fluctuates greatly, the bucketing condition parameters of the bucketing unit corresponding to the specified time bucketing dimension can be adjusted to process peak trading periods and trough periods separately to optimize system performance.

[0105] In another specific embodiment of the present disclosure, when testing a new version of a wealth management product, originally only daily random control bucketing was used for traffic distribution, but it was found that the number of test users in each time period could not be accurately controlled. In this case, the daily sequential control bucketing dimension could be added to ensure a stable number of users for testing in each time period.

[0106] In another specific embodiment of the present disclosure, when monitoring discovers that a bottleneck occurs in the processing capacity of a processing unit of a specific user group in the payment system, the combination relationship between the user group bucket and the fixed bucket can be adjusted, and the original "user group bucket-fixed bucket" can be adjusted to "fixed bucket-user group bucket", and load balancing is achieved preferentially through fixed buckets, and then refined processing of the user group is performed.

[0107] The method of the disclosed embodiment uses this dynamic adjustment mechanism to continuously optimize the multi-level bucket structure according to the actual operating conditions of the current network traffic distribution, so that the bucket structure of the network traffic distribution can flexibly adapt to various business scenarios.

[0108] In some embodiments of the present disclosure, the network traffic distribution method of the present disclosure can also be combined with test indicators to dynamically optimize the user's interactive experience. Figure 5 As shown, the steps of allocating and processing the network traffic according to the bucketing result may at least include the following 1411 to 1414.

[0109] 1411: Set the given test configuration for each bucket.

[0110] As mentioned above, a preset bucketing template may be provided in the embodiments of the present disclosure. Accordingly, in some embodiments of the present disclosure, one or more preset bucketing templates may be provided for the scenarios where business testing is required, and one or more bucketing units may be constructed based on the one or more preset bucketing templates, thereby constructing a bucketing structure. In this embodiment, the bucketing structure is, for example, the multi-level bucketing structure described in the above embodiments of the present disclosure.

[0111] In some embodiments of the present disclosure, the bucketing unit may be configured with a preset test configuration, and the test configuration may include but is not limited to parameter configuration in the bucketing conditions of the bucketing unit.

[0112] 1412: Process the network traffic of each bucket based on the test configuration.

[0113] In the disclosed embodiment, the bucketing conditions of each bucketing unit may be configured based on the test configuration, and the network traffic may be processed by the corresponding processing unit according to the bucketing results of each bucketing unit.

[0114] 1413: Obtain feedback data generated by user interaction under the test configuration.

[0115] In the embodiments of the present disclosure, the feedback data includes but is not limited to the user's interaction data under different test configurations, including but not limited to browsing time, click path, transaction completion time, whether the operation is interrupted midway, product ratings and complaint suggestions, etc. It should be understood that the information, data and signals involved in the present disclosure are all authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data comply with the relevant laws, regulations and standards of relevant countries and regions.

[0116] 1414: Based on the collected feedback data, calculate the test indicator of each bucket.

[0117] As mentioned above, in the embodiments of the present disclosure, bucket performance indicators may be provided. Accordingly, in some embodiments of the present disclosure, the test indicators of each bucket unit may be calculated for the scenario requiring service testing.

[0118] In the embodiment of the present disclosure, the test indicators can be converted according to the user's feedback data, and the test indicators can be used to reflect the customer satisfaction. In this embodiment, the test indicators may include but are not limited to the average time for the processing unit to process user transactions under the test configuration, the completion rate of the user operation process, the user satisfaction score, etc.

[0119] In this embodiment, continue to refer to Figure 5 The step of dynamically adjusting the multi-level bucket structure according to the performance indicator may include:

[0120] 1421: When the bucket test indicators do not meet the preset standards, optimize user interaction by adjusting the multi-level bucket structure.

[0121] In the disclosed embodiment, the adjustment of the multi-level bucket structure includes but is not limited to: adjusting the parameters of the bucket conditions, adding / deleting bucket dimensions, adjusting the combination relationship of bucket dimensions, and updating the priority of bucket dimensions.

[0122] In some embodiments of the present disclosure, when one or more test indicators of a bucket unit do not meet the preset standards, the user's business test requirements can be obtained, the priority of the test indicators can be determined according to the user's business test requirements, and for the test indicators with higher priority, feasible bucket adjustment suggestions can be recommended based on the preset bucket template. In a specific example of the present disclosure, the bucket unit of a certain business test is configured with three test indicators of "transaction success rate", "response time" and "user satisfaction", among which the transaction success rate and response time do not meet the preset standards. In this regard, combined with the business test requirement of "ensuring transaction stability during the event" proposed by the user, the transaction success rate can be determined as the most important test indicator at present, and thus based on the preset bucket template, it is recommended to add the "server load bucket" dimension to the existing bucket structure to reasonably distribute peak transaction requests to servers with different processing capabilities.

[0123] The method of the disclosed embodiment provides a preset test configuration for scenarios with business testing requirements based on a multi-level bucket structure, and can flexibly adjust the test configuration based on user feedback, and can dynamically optimize the test configuration, thereby effectively improving the efficiency of business testing.

[0124] In some embodiments of the present disclosure, the network traffic distribution method of the present disclosure can also dynamically adjust the server load and reasonably allocate resources in combination with the resource usage data of the server. Figure 6As shown, the step of allocating and processing the network traffic according to the bucketing result may also include the following 1415 to 1417.

[0125] 1415: Allocate a specified proportion of resources to each bucket.

[0126] In some embodiments of the present disclosure, the resources allocated to the buckets (processing units) may include but are not limited to computing resources, storage resources, bandwidth resources, etc. In one example, the computing resources may include but are not limited to the number of CPU cores, the number of processing threads, etc.; the storage resources may include but are not limited to memory capacity, cache space, the number of database connections, etc.; the bandwidth resources may include but are not limited to network bandwidth, the number of concurrent connections, etc.

[0127] In this embodiment, different resource allocation strategies can be set for different buckets (processing units). In one example, for the processing unit of the financial transaction business, a higher proportion of computing resources is provided during allocation to process complex transaction logic. In another example, for the processing unit of the large transfer business, a higher proportion of storage resources is provided during allocation to ensure the integrity of the transaction records. In another example, for the processing unit of the payment business, a higher proportion of bandwidth resources is provided during allocation to ensure fast response.

[0128] 1416: Utilize resources to process the network traffic allocated to each bucket.

[0129] In the disclosed embodiment, the allocated resources can be used to support the bucket (processing unit) to perform specific business logic processing on the allocated network traffic. In this embodiment, the business logic processing includes but is not limited to using the allocated CPU resources to perform transaction calculations, using the allocated storage resources to save transaction data, and using the allocated bandwidth resources to transmit request responses.

[0130] 1417: Monitor the resource utilization and performance indicators of each bucket in real time.

[0131] In the embodiments of the present disclosure, the resource utilization and performance indicators of each bucket (processing unit) can be continuously monitored, wherein the performance indicators include but are not limited to: computing resource usage (CPU usage, number of processing threads, etc.), storage resource usage (memory occupancy, database connection status, etc.) and bandwidth resource usage (network throughput, number of concurrent connections, etc.) of each processing unit.

[0132] In this embodiment, continue to refer to Figure 6 The dynamically adjusting the multi-level bucket structure according to the performance index may also include:

[0133] 1422: When it is detected that the resource utilization of a bucket exceeds a preset threshold, the traffic distribution of the bucket is adjusted by adjusting the multi-level bucket structure.

[0134] In an embodiment of the present disclosure, a preset threshold value may be set for each bucket (processing unit) for preset resource utilization. In this embodiment, the preset threshold value may include but is not limited to a CPU usage threshold value, a memory occupancy threshold value, and a bandwidth usage threshold value. In some embodiments, the preset threshold value may also include multiple levels of threshold values. In a specific example, for the CPU usage threshold value: 80% is set as a warning value, and 90% is set as a severe alarm value; for the memory occupancy threshold value: 75% is set as a warning value, and 85% is set as a severe alarm value; for the bandwidth usage threshold value: 70% is set as a warning value, and 80% is set as a severe alarm value.

[0135] In some embodiments of the present disclosure, when it is detected that the resource utilization of a bucket (processing unit) exceeds a preset threshold, the bucket unit can be determined as a bucket unit to be adjusted, and the traffic distribution of the bucket can be adjusted by adjusting the multi-level bucket structure.

[0136] In some embodiments of the present disclosure, an adjustment strategy may be selectively executed to adjust a multi-level bucket structure. In this embodiment, the adjustment strategy includes but is not limited to a level balancing strategy and a dimension adjustment strategy, wherein the level balancing strategy may be used to adjust the bucket condition parameters of the bucket unit to be adjusted and the bucket units of the same level and bucket dimension as the bucket unit to be adjusted, and the dimension adjustment strategy may be used to adjust the priority of the bucket dimension in the level where the bucket unit to be adjusted is located.

[0137] In some embodiments of the present disclosure, when it is detected that the resource utilization of a bucket exceeds a preset threshold, the traffic distribution of the bucket is adjusted by adjusting the multi-level bucket structure, which may also include the following B1 to B2.

[0138] B1: When it is determined that the resource utilization rates of other bucket units at the same level and bucket dimension as the bucket unit to be adjusted are lower than the preset threshold, perform level balancing adjustment.

[0139] In some embodiments of the present disclosure, when it is determined that the resources of the bucket unit to be adjusted are tight, while the resources of other bucket units at the same level and bucket dimension are sufficient, dynamic balance of resource utilization can be achieved at the same level and bucket dimension by adjusting the traffic allocation parameters of other bucket units.

[0140] In a specific example of the present disclosure, when the bandwidth utilization rate of the bucket (processing unit) corresponding to the bucket unit E6 is 90%> the preset threshold value of 80%, the percentage flow bucket parameter of the bucket unit E6 in the bucket (structure) 2 can be reduced from 60% to 50%, and at the same time, the percentage flow bucket parameter of the bucket unit E7 at the same level as the bucket unit E6 can be increased from 40% to 50%, thereby balancing the resource utilization of the processing units, improving the processing efficiency of network traffic, and avoiding downtime due to resource exhaustion, affecting the normal operation of the business.

[0141] This adjustment method of the embodiment of the present disclosure can quickly respond and alleviate local resource pressure while keeping the existing bucket structure unchanged overall, and alleviate the load pressure of a single bucket unit through reasonable traffic redistribution.

[0142] B2: When the resource indicators of multiple bucket units at the same level and in the same bucket dimension exceed the preset threshold, the dimension adjustment strategy is executed.

[0143] In some embodiments of the present disclosure, when multiple bucket units at the same level and the same bucket dimension experience resource shortage at the same time, it indicates that the hierarchical balancing adjustment can no longer solve the resource pressure problem. At this time, a higher level of resource optimization can be achieved by adjusting the priority of the bucket dimensions at the same level.

[0144] In a specific example of the present disclosure, when the bandwidth usage of bucket units E6 and E7 is 85% and 82% respectively, and both are greater than the preset threshold of 80%, simply adjusting the traffic allocation ratio between E6 and E7 can no longer solve the resource shortage problem. At this time, the priority of the percentage traffic bucket dimension to which E6 and E7 belong can be adjusted from the first priority to the second priority, and at the same time, the priority of the daily sequential control bucket dimension can be increased from the second priority to the first priority, so that traffic allocation is prioritized according to the daily sequential control, and the time series characteristics are used to balance the resource pressure, thereby alleviating the resource shortage under the original percentage traffic bucket dimension.

[0145] This adjustment method of the embodiment of the present disclosure achieves a more fundamental resource allocation optimization by changing the diversion strategy of the bucket structure. Compared with a simple traffic ratio adjustment, the dimension adjustment strategy can achieve load balancing over a larger range by adjusting the priority of the diversion dimension when overall resources are tight, thereby improving the overall processing capability of the system.

[0146] The method of the disclosed embodiment achieves efficient use of system resources and improves the overall performance and stability of the system by real-time monitoring of resource usage and dynamically adjusting the bucket structure. The method of the disclosed embodiment can respond to resource pressure in a timely manner, flexibly adjust the flow distribution strategy, effectively prevent system overload, and ensure service continuity and reliability.

[0147] In some embodiments of the present disclosure, Fig. 9 As shown, a flow distribution device 900 is also provided, and the flow distribution device 900 may include an acquisition unit 910, a receiving unit 920, a classification unit 930, and a processing unit 940. Among them:

[0148] The acquisition unit 910 is configured to acquire a traffic bucketing configuration, wherein the traffic bucketing configuration defines a plurality of bucketing dimensions and a combination relationship between the plurality of bucketing dimensions.

[0149] The receiving unit 920 is configured to receive network traffic to be processed.

[0150] The classification unit 930 is configured to classify the network traffic according to the traffic bucketing configuration, wherein the classification is performed based on a multi-level bucketing structure, and each level of the multi-level bucketing structure contains one or more of the bucketing dimensions and / or a combination of the bucketing dimensions.

[0151] The processing unit 940 is configured to process the network traffic according to the classification result.

[0152] In the embodiments of the present disclosure, method features may be combined into apparatus to obtain new embodiments, and vice versa.

[0153] In some embodiments of the present disclosure, Fig.10 As shown, a schematic diagram of an electronic device 1000 that can implement the method of the disclosed embodiment is provided, and in some embodiments, more or fewer electronic devices than shown may be included. In some embodiments, it can be implemented using a single or multiple electronic devices. In some embodiments, it can be implemented using cloud or distributed electronic devices.

[0154] like Fig.10 As shown, the electronic device 1000 includes a processor 1001, which can perform various appropriate operations and processes according to the programs and / or data stored in the read-only memory (ROM) 1002 or the programs and / or data loaded from the storage part 1008 to the random access memory (RAM) 1003. CPU 1001 can be a multi-core processor, or it can include multiple processors. In some embodiments, the processor 1001 can include a general main processor and one or more special coprocessors, such as a graphics processing unit (GPU), a neural network processor (NPU), a digital signal processor (DSP), etc. In RAM 1003, various programs and data required for the operation of the electronic device 1000 are also stored. CPU 1001, ROM 1002 and RAM 1003 are connected to each other via bus 1004. Input / output (I / O) interface 1005 is also connected to bus 1004.

[0155] The processor and the memory are used together to execute the program stored in the memory. When the program is executed by the computer, the steps or functions of the methods described in the above embodiments can be implemented.

[0156] The following components are connected to the I / O interface 1005: an input section 1006 including a keyboard, a mouse, a touch screen, etc.; an output section 1007 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, a modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the I / O interface 1005 as needed. A removable medium 1011, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 1010 as needed, so that a computer program read therefrom is installed into the storage section 1008 as needed. Fig.10 Only some components are schematically shown in the figure, which does not mean that the computer system 1000 only includes Fig.10 Components shown.

[0157] In some embodiments, the electronic device 1000 refers to a mobile terminal or a computer, including a mobile phone, a car terminal, a smart TV, etc. Taking a mobile phone as an example, the electronic device 1000 also includes a display screen with a touch function, an external speaker, a gyroscope, a camera, a 4G / 5G antenna and other device modules.

[0158] The systems, devices, modules or units described in the above embodiments may be implemented by a computer or its associated components. The computer may be, for example, a mobile terminal, a smart phone, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a personal digital assistant, a media player, a navigation device, a game console, a tablet computer, a wearable device, a smart TV, an Internet of Things system, a smart home, an industrial computer, a server or a combination thereof.

[0159] Although not shown, in an embodiment of the present disclosure, a program product is provided, including a computer program, and when the computer program is executed by a processor, the method of any one of the embodiments of the present disclosure is implemented.

[0160] Although not shown, in an embodiment of the present disclosure, a storage medium is provided, the storage medium storing a computer program, the computer program being configured to implement any method of the embodiments of the present disclosure when executed.

[0161] The storage media in the embodiments of the present disclosure include permanent and non-permanent, removable and non-removable items that can be implemented by any method or technology to store information. Examples of storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0162] The methods, programs, systems, devices, etc. of the embodiments of the present disclosure may be executed or implemented in a single or multiple networked computers, or may be practiced in a distributed computing environment. In the embodiments of the present specification, in these distributed computing environments, tasks may be performed by remote processing devices connected via a communication network.

[0163] Those skilled in the art should understand that the embodiments of the present specification may be provided as methods, systems or computer program products. Therefore, those skilled in the art may imagine that the functional modules / units or controllers and related method steps described in the above embodiments may be implemented in software, hardware or a combination of software / hardware.

[0164] Unless explicitly stated, the actions or steps of the methods, programs, and embodiments of the present disclosure do not have to be performed in a specific order and can still achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0165] In this article, multiple embodiments of the present disclosure are described, but for the sake of brevity, the description of each embodiment is not exhaustive, and the same or similar features or parts between the embodiments may be omitted. In this article, "one embodiment", "some embodiments", "example", "specific example", or "some examples" are meant to be applicable to at least one embodiment or example according to the present disclosure, rather than all embodiments. The above terms do not necessarily mean to refer to the same embodiment or example. In the absence of contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0166] The exemplary systems and methods of the present disclosure have been specifically shown and described with reference to the above embodiments, which are merely examples of the best modes for implementing the present systems and methods. It will be appreciated by those skilled in the art that various changes may be made to the embodiments of the systems and methods described herein when implementing the present systems and / or methods without departing from the spirit and scope of the present disclosure as defined in the appended claims.

Claims

1. A flow distribution method, characterized in that: include: Receive network traffic to be processed; Determine a multi-level bucket structure including a plurality of bucket dimensions, wherein each level in the multi-level bucket structure includes one or more of the bucket dimensions and / or a combination of the bucket dimensions; Bucketing the network traffic according to the multi-level bucketing structure; and According to the bucketing result, the network traffic is allocated and processed.

2. The flow distribution method according to claim 1, characterized in that: The multiple bucketing dimensions include at least one of the following: percentage traffic bucketing, daily sequential control volume bucketing, daily random control volume bucketing, user group bucketing, specified time bucketing and fixed bucketing.

3. The flow distribution method according to claim 1, characterized in that: Bucketing the network traffic according to the multi-level bucketing structure includes: Starting from the highest level of the multi-level bucket structure, determining in turn whether the network traffic meets the bucket conditions of each level; When the bucketing conditions of a certain level are met, proceed to the next level to continue the judgment; When the lowest level is reached or the preset termination condition is met, the classification result is determined.

4. The flow distribution method according to any one of claims 1 to 3, characterized in that: The determining of a multi-level bucket structure including multiple bucket dimensions includes: Selecting a preset bucketing template, wherein the bucketing template sets multiple bucketing dimensions and a combination relationship and priority of each bucketing dimension; For one or more bucketing units corresponding to each bucketing dimension set in the bucketing template, set a bucketing condition for each bucketing unit; A multi-level bucketing structure is constructed based on the combination relationship and priority of each bucketing dimension and the bucketing conditions of each bucketing unit.

5. The flow distribution method according to any one of claims 1 to 3, characterized in that: The allocating and processing the network traffic according to the bucketing result includes: Monitor the traffic distribution and processing of each bucket in real time to obtain the performance indicators of each bucket; According to the performance indicator, the multi-level bucket structure is dynamically adjusted, wherein the dynamic adjustment includes at least one of the following: adjusting the parameters of the bucket dimension; adding or deleting the bucket dimension; adjusting the combination relationship of the bucket dimension; updating the priority of the bucket dimension.

6. The flow distribution method according to claim 5, characterized in that: The allocating and processing the network traffic according to the bucketing result includes: Setting a given test configuration for each bucket; processing network traffic of each bucket based on the test configuration; obtaining feedback data generated by user interaction under the test configuration; calculating the test index of each bucket based on the collected feedback data; The dynamically adjusting the multi-level bucket structure according to the performance indicator includes: when the test indicator of the bucket does not meet the preset standard, optimizing the user interaction by adjusting the multi-level bucket structure.

7. The flow distribution method according to claim 5, characterized in that: The allocating and processing the network traffic according to the bucketing result includes: allocating a specified proportion of resources to each bucket, the resources including at least one of computing resources, storage resources and bandwidth resources; using the resources to process the network traffic allocated to each bucket; and monitoring the resource utilization and performance indicators of each bucket in real time; The dynamically adjusting the multi-level bucket structure according to the performance indicator includes: when it is detected that the resource utilization of a bucket exceeds a preset threshold, adjusting the traffic distribution of the bucket by adjusting the multi-level bucket structure.

8. A flow distribution device, characterized in that: include: An acquisition unit configured to acquire a traffic bucket configuration, wherein the traffic bucket configuration defines a plurality of bucket dimensions and a combination relationship between the plurality of bucket dimensions; a receiving unit configured to receive network traffic to be processed; a classification unit configured to classify the network traffic according to the traffic bucketing configuration, wherein the classification is performed based on a multi-level bucketing structure, each level of the multi-level bucketing structure comprising one or more of the bucketing dimensions and / or a combination of the bucketing dimensions; and The processing unit is configured to process the network traffic according to the classification result.

9. A computer electronic device, characterized in that: include: A processor and a memory storing a computer program, wherein the processor is configured to implement the method according to any one of claims 1 to 7 when running the computer program.

10. A computer program product, characterized in that The method comprises a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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