Bandwidth adjustment method and device, electronic equipment and storage medium

Abstract

The invention provides a bandwidth adjustment method and device, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring historical network operation state data of a historical time period; wherein the network operation state data comprises network state data and service flow data of each service; processing the historical network operation state data through the prediction model to obtain a prediction result containing future network operation state data; inputting the current network operation state data and the prediction result into an adaptive updating decision model, and outputting a bandwidth adjustment decision for each service flow through the decision model; adjusting the service bandwidth according to the bandwidth adjustment decision; wherein the service bandwidth adjustment result is used for adaptively updating the decision model. According to the method, the future network state can be predicted through the historical data, and the bandwidth adjustment decision is output and implemented through the adaptive decision model in combination with the current data, so that dynamic bandwidth adjustment is realized, and the network resource utilization efficiency and the business service quality are improved.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a bandwidth adjustment method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the development of computer and network technologies, network traffic has become increasingly complex and diverse, necessitating adjustments to service bandwidth based on real-time changes in business demands and network conditions. The method of bandwidth adjustment directly impacts service performance.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] The purpose of this disclosure is to provide a bandwidth adjustment method, apparatus, electronic device, and storage medium.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0006] According to one aspect of this disclosure, a bandwidth adjustment method is provided, comprising: acquiring historical network operation status data for a historical period; wherein the network operation status data includes network status data and service flow data of each service; processing the historical network operation status data through a prediction model to obtain a prediction result containing future network operation status data; inputting the current network operation status data and the prediction result into an adaptively updated decision model, and outputting a bandwidth adjustment decision for each service flow through the decision model; and adjusting the service bandwidth according to the bandwidth adjustment decision; wherein the result of the service bandwidth adjustment is used to adaptively update the decision model.

[0007] In one embodiment of this disclosure, adaptively updating the decision model includes: determining an evaluation value under a preset dimension based on the result of service bandwidth adjustment; determining a feedback value based on the evaluation value under the preset dimension; and updating the parameters of the decision model based on the feedback value.

[0008] In one embodiment of this disclosure, the result of the service bandwidth adjustment includes at least one of the following: service latency change, service packet loss rate change, network bandwidth utilization, stability index of bandwidth adjustment range, and fairness index of bandwidth allocation among services; wherein, determining the evaluation value under a preset dimension based on the result of the service bandwidth adjustment includes at least one of the following: determining a latency evaluation value based on the service latency change and the priority weight of each service; determining a packet loss evaluation value based on the service packet loss rate change and the priority weight of each service; determining a utilization evaluation value based on the difference between the network bandwidth utilization rate and the target utilization rate; determining a stability evaluation value based on the stability index of the bandwidth adjustment range; and determining a fairness evaluation value based on the fairness index of bandwidth allocation among services.

[0009] In one embodiment of this disclosure, the bandwidth adjustment method further includes: determining feedback values ​​corresponding to multiple rounds of bandwidth adjustment decisions; and determining that the decision model has converged in response to the change amplitude of the feedback values ​​being less than a stability threshold.

[0010] In one embodiment of this disclosure, adjusting service bandwidth based on the bandwidth adjustment decision includes: responding to the bandwidth adjustment decision not meeting the service level agreement of a high-priority service, adjusting the bandwidth adjustment decision according to a preset bandwidth constraint strategy, and adjusting the service bandwidth based on the adjusted bandwidth adjustment decision; wherein the bandwidth constraint strategy includes at least one of the following: allocating the bandwidth adjustment amount of low-priority services to the high-priority services in ascending order of priority; ensuring that the low-priority services are configured with service bandwidth not lower than the available bandwidth threshold; and reallocating the remaining bandwidth to each service flow.

[0011] In one embodiment of this disclosure, the network status data includes at least one of the following: link bandwidth, bandwidth utilization, latency, and packet loss rate; the service flow data includes at least one of the following: service latency, service packet loss rate, service rate, allocated bandwidth, service type, and service priority.

[0012] In one embodiment of this disclosure, the service priority of each service is determined based on the service quality priority, service importance, and service real-time nature of the corresponding service.

[0013] In one embodiment of this disclosure, the bandwidth adjustment method further includes: in response to newly collected network operation status data reaching a data volume threshold or reaching an update cycle, using the newly collected network operation status data to train and update the prediction model.

[0014] According to another aspect of this disclosure, a bandwidth adjustment device is provided, comprising: an acquisition module for acquiring historical network operation status data for a historical period; wherein the network operation status data includes network status data and service flow data of each service; a prediction module for processing the historical network operation status data through a prediction model to obtain a prediction result containing future network operation status data; a decision output module for inputting the current network operation status data and the prediction result into an adaptively updated decision model, and outputting a bandwidth adjustment decision for each service flow through the decision model; and an adjustment module for adjusting the service bandwidth according to the bandwidth adjustment decision; wherein the result of the service bandwidth adjustment is used to adaptively update the decision model.

[0015] According to another aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the bandwidth adjustment method described above.

[0016] According to another aspect of this disclosure, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the bandwidth adjustment method described above by executing the executable instructions.

[0017] The bandwidth adjustment method provided in the embodiments of this disclosure can first predict the future network operating status based on historical network operating status data, and then make a decision by combining the prediction results corresponding to the historical data with real-time data through a decision model, outputting a bandwidth adjustment decision and adjusting accordingly, thereby achieving dynamic bandwidth optimization, which can improve network resource utilization efficiency and service quality; this method can also use the adjustment results to update the decision model, enhancing the system's adaptive capability.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0020] Figure 1 A schematic diagram of a network architecture to which an embodiment of the present disclosure can be applied is shown.

[0021] Figure 2 A flowchart of a bandwidth adjustment method according to an embodiment of the present disclosure is shown.

[0022] Figure 3A flowchart illustrating the updating of a decision model in a bandwidth adjustment method according to an embodiment of this disclosure is shown.

[0023] Figure 4 A flowchart of updating the decision model in a bandwidth adjustment method according to yet another embodiment of this disclosure is shown.

[0024] Figure 5 A block diagram of a bandwidth adjustment apparatus according to an embodiment of the present disclosure is shown.

[0025] Figure 6 A structural block diagram of a bandwidth adjustment computer device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0026] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0027] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0028] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this disclosure, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0029] In some embodiments of this disclosure, the acquisition of data and information, as well as the collection, updating, analysis, processing, use, transmission, and storage of related user personal information, may comply with the laws and regulations of the country where the location is situated.

[0030] In some embodiments of this disclosure, data, information, etc., may be obtained after obtaining the user's consent.

[0031] Figure 1A schematic diagram of a network architecture to which an embodiment of the present disclosure can be applied is shown.

[0032] like Figure 1 As shown, the system architecture may include a server 101, a network 102, and a client 103. The network 102 serves as the medium for providing a communication link between the client 103 and the server 101. The network 102 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0033] In an exemplary embodiment, the client 103 that transmits data with the server 101 may include, but is not limited to, electronic devices such as smartphones, desktop computers, tablets, laptops, smart speakers, digital assistants, AR (Augmented Reality) devices, VR (Virtual Reality) devices, and smart wearable devices. Optionally, the operating system running on the electronic device may include, but is not limited to, Android, iOS, Linux, and Windows systems.

[0034] Server 101 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. In some practical applications, server 101 can also be a server for a network platform, such as a trading platform, live streaming platform, social platform, or music platform, etc., which is not limited in this embodiment. The server can be a single server or a cluster of multiple servers, and the specific architecture of the server is not limited in this disclosure.

[0035] In an exemplary embodiment, the process by which server 101 implements the bandwidth adjustment method may be as follows: server 101 acquires historical network operation status data for a historical period; wherein, the network operation status data includes network status data and service flow data of each service; server 101 processes the historical network operation status data through a prediction model to obtain a prediction result containing future network operation status data; server 101 inputs the current network operation status data and the prediction result into an adaptively updated decision model, and outputs a bandwidth adjustment decision for each service flow through the decision model; server 101 performs service bandwidth adjustment according to the bandwidth adjustment decision; wherein, the result of the service bandwidth adjustment is used to adaptively update the decision model.

[0036] In addition, it should be noted that, Figure 1 The example shown is merely one application environment of the problem-solving method provided in this disclosure. Figure 1 The number of servers 101, networks 102, and clients 103 shown is merely illustrative; any number of clients, networks, and servers can be used as needed.

[0037] To enable those skilled in the art to better understand the technical solutions of this disclosure, the steps of the problem-solving method in the example embodiments of this disclosure will be described in more detail below with reference to the accompanying drawings and examples.

[0038] Figure 2 A flowchart of a bandwidth adjustment method according to an embodiment of this disclosure is shown. The method provided in this disclosure can be used by, for example... Figure 1 The server 101 or client 103 shown may execute this, but this disclosure is not limited thereto.

[0039] In the following example, server 101 is used as the execution subject for illustration.

[0040] like Figure 2 As shown, the bandwidth adjustment method provided in this embodiment may include the following steps.

[0041] Step S210: Obtain historical network operation status data for historical time periods; wherein, the network operation status data includes network status data and service flow data of each service.

[0042] In this embodiment of the disclosure, the network operation status data can be data from enterprise networks, data centers, or streaming media transmission networks. Historical network operation status data can cover network status data and service flow data of various services over a past period to reflect the historical operation of the network.

[0043] Network status data can describe the overall operational status of the network, and may include metrics such as network latency and packet loss rate. Service flow data can be data related to various services, reflecting the importance of different services, their traffic in the network, and their flow direction.

[0044] In this embodiment, network operation data within a specific historical time period can be collected to provide a basis for subsequent analysis and prediction. The specific historical time period may include a period of time up to the current time (such as 10 minutes or 20 minutes before the current time), or it may include relevant historical time windows in a historical cycle (such as 10 minutes or 20 minutes before the same time yesterday, or 10 minutes or 20 minutes after the same time yesterday).

[0045] Step S220: Process the historical network operation status data through a prediction model to obtain a prediction result that includes future network operation status data.

[0046] In this embodiment of the disclosure, a predictive model can be used to analyze historical data to infer the future operating state of the network. The predictive model can be a model used to predict the future operating state of the network based on historical data, inferring future situations by analyzing historical patterns. The predictive model can be a pre-trained neural network model. Neural network models can extract deep features from time-series data and can be used in network scenarios with large amounts of data and complex temporal relationships (such as long-distance dependencies and sudden anomalies). Neural network models can include, for example, Transformer models, Recurrent Neural Network (RNN) models, Convolutional Neural Network (CNN) models, Graph Neural Network (GNN) models, and generative models (such as LSTM-AE, VAE), etc.

[0047] In an exemplary embodiment, the prediction model may also be a statistical learning model, a machine learning model, or the like.

[0048] In this step, the prediction results are data about the future network operating status obtained by the prediction model after processing historical data. This data may include future network status data for future time periods and future service flow data for each service.

[0049] Step S230: Input the current network operating status data and the prediction results into the adaptively updated decision model, and output the bandwidth adjustment decision for each service flow through the decision model.

[0050] In this embodiment of the disclosure, the current network operating status data can be data that reflects the current network operating status in real time, or it can be the current network status data and the service flow data of each service.

[0051] By combining the current network conditions with future predictions, a decision model can be used to determine bandwidth adjustment schemes for each service flow. The adaptively updated decision model can automatically optimize and adjust its own parameters based on input data and service bandwidth adjustment results to output a more reasonable bandwidth adjustment decision. The decision model can be a reinforcement learning model.

[0052] The bandwidth adjustment decision can be a scheme output by the decision model regarding how to adjust the bandwidth of each service flow, and can include the amount of bandwidth adjustment for each service. For example, the bandwidth adjustment decision could be: increase the bandwidth of service A by 90Mbps, decrease the bandwidth of service B by 20Mbps, decrease the bandwidth of service C by 60Mbps, decrease the bandwidth of service D by 10Mbps, and so on.

[0053] Step S240: Adjust the service bandwidth according to the bandwidth adjustment decision; wherein the result of the service bandwidth adjustment is used to adaptively update the decision model.

[0054] In this embodiment of the disclosure, adjusting service bandwidth can be an operation that actually adjusts the bandwidth of each service flow in the network based on a decision-making scheme.

[0055] Furthermore, the decision-making model can be optimized based on the actual effects of bandwidth adjustments, enabling it to better adapt to network changes.

[0056] As can be seen from the above steps, the bandwidth adjustment method provided in this disclosure can first predict the future network operating status based on historical network operating status data. Then, a decision model can be used to make a decision by combining the prediction results corresponding to the historical data with real-time data, outputting a bandwidth adjustment decision and adjusting accordingly. This achieves dynamic bandwidth optimization, which can improve network resource utilization efficiency and service quality. Furthermore, the adjustment results can be used to update the decision model and enhance the system's adaptive capabilities.

[0057] In some embodiments, the network status data includes at least one of the following: link bandwidth, bandwidth utilization, latency, and packet loss rate; the service flow data includes at least one of the following: service latency, service packet loss rate, service rate, allocated bandwidth, service type, and service priority.

[0058] In this embodiment of the disclosure, these network status data are important bases for evaluating the overall performance and health of the network. By collecting and analyzing data such as link bandwidth, bandwidth utilization, latency, and packet loss rate, we can understand the network congestion, transmission capacity, and stability, providing basic information for subsequent bandwidth adjustment decisions and helping the decision-making model determine whether network bandwidth needs to be reallocated to optimize network performance.

[0059] Business flow data can be used to understand the characteristics and requirements of various services. By collecting and analyzing data such as service latency, packet loss rate, service speed, allocated bandwidth, service type, and service priority, we can gain a deeper understanding of the operational status and resource requirements of each service. This provides a basis for decision-making models to formulate personalized bandwidth adjustment strategies, ensuring that different services can obtain appropriate resource allocation in the network, thereby improving the overall operational quality of services and user experience.

[0060] In an exemplary embodiment, network state data and service flow data may exist in the form of data features.

[0061] First, we can obtain the network link set E = {e1, e2, ..., e}. m};Link bandwidth capacity C e,e∈E; network service set S={s1,s2,...,s n}; Business path

[0062] Based on this, the continuously collected network state data N is obtained. t N t This can include link bandwidth b, bandwidth utilization u, latency d, and packet loss l; that is, network state data can be represented as:

[0063] And it can collect business data collection S t S t This can include latency d, packet loss l, service rate f, service allocated bandwidth b, QoS priority, service type (such as real-time conferencing, data backup, online transactions, email, etc.), and service priority p. i In other words, business flow data can be represented as: Wherein, the service rate f can be information configured for service specification, and the service priority p i Priorities can be determined in advance for businesses based on factors such as their importance and impact on user experience.

[0064] Afterwards, the collected data features can be normalized to obtain normalized network state data. and normalized business flow data

[0065] This disclosure clarifies the specific content of network status data and service flow data, providing a comprehensive and accurate data foundation for determining historical and current network operating status, predicting future status, and making bandwidth adjustment decisions, thereby improving the rationality of decision-making.

[0066] In some embodiments, the service priority of each service is determined based on the service quality priority, service importance, and service real-time nature of the corresponding service.

[0067] In this embodiment of the disclosure, after considering service quality priority, business importance and business real-time performance, a certain method or model can be used to comprehensively calculate and process these three factors to obtain the priority of each business.

[0068] Service Quality (QoS) priority can be a priority set based on the specific requirements of a service for network QoS. Different services have different requirements for network QoS metrics (such as bandwidth, latency, jitter, packet loss rate, etc.). For example, real-time voice services are very sensitive to latency and jitter, requiring low latency and jitter to ensure clear and smooth voice communication, while file transfer services have high bandwidth requirements to ensure fast file transfer. Based on these different requirements, corresponding QoS priorities can be set for each service.

[0069] Business importance can correspond to the degree of criticality and value of a business in the company's operations, user experience, or the overall functionality of the business system. Business importance can be pre-defined based on this.

[0070] Business real-time performance refers to the sensitivity of a business to time-sensitive responses; that is, how long a business needs to complete processing or receive a response. Businesses with high real-time requirements need to complete data transmission and processing within a very short time to ensure timeliness and smoothness. Business real-time performance can be pre-set based on the level of real-time response needs of each business.

[0071] In an exemplary embodiment, the service priority p i The determination method can be:

[0072] p i =min{p i_QoS +1,p i_QoS *(1+α*I i +β*R i )}.

[0073] Where, p i_QoS For QoS priority; I i Represents business importance, and its value can range from [0,1]; R i Represents the real-time nature of the business, and its value range can be [0,1]. α and β are preset weight parameters.

[0074] Through the embodiments of this disclosure, service priorities can be determined based on the quality of service priority, importance, and real-time nature of services, providing a priority basis for bandwidth adjustment decisions and ensuring that high-priority services are given priority in network resource allocation.

[0075] Figure 3 A flowchart illustrating the updating of a decision model in a bandwidth adjustment method according to an embodiment of this disclosure is shown.

[0076] like Figure 3 As shown, in some embodiments, adaptively updating the decision model may include the following steps.

[0077] Step S310: Determine the evaluation value under the preset dimension based on the result of the service bandwidth adjustment.

[0078] In this embodiment of the disclosure, the result of service bandwidth adjustment can be the new state and performance of the network and services after the actual adjustment operation of the bandwidth of each service flow, such as changes in the transmission rate of each service flow and changes in the overall network congestion.

[0079] Predefined dimensions can be multiple aspects or angles used to evaluate the effectiveness of service bandwidth adjustments. These dimensions can be flexibly set according to network characteristics, service needs, and key concerns, such as network latency, bandwidth utilization, and packet loss rate. Each dimension can reflect the impact of bandwidth adjustments on the network and services from different perspectives. Evaluation values ​​can be numerical values ​​obtained by quantitatively analyzing the results of service bandwidth adjustments under the preset dimensions. These evaluation values ​​can intuitively reflect the extent or impact of bandwidth adjustments in different aspects.

[0080] Step S320: Determine the feedback value based on the evaluation value under the preset dimension.

[0081] In this embodiment of the disclosure, the feedback value can serve as a comprehensive measure of the overall effect of service bandwidth adjustment. The feedback value can be obtained by weighted summation, averaging, or other mathematical operations on evaluation values ​​from different dimensions using specific algorithms or rules.

[0082] Feedback values ​​can serve as an important basis for updating decision-making models, guiding them to adjust in a better direction.

[0083] Step S330: Update the parameters of the decision model based on the feedback value.

[0084] In this embodiment of the disclosure, a model optimization algorithm corresponding to the decision model can be used for model updating. For example, if the decision model is a reinforcement learning model, the parameters of this reinforcement learning model can be optimized based on the PPO (Proximal Policy Optimization) algorithm.

[0085] In this embodiment, after each bandwidth adjustment based on the bandwidth adjustment decision output by the decision model, a feedback value is determined, and then the decision model is updated. This allows the decision model to continuously adapt to changes in the network environment and service requirements, improving the accuracy and rationality of subsequent bandwidth adjustment decisions.

[0086] Through the embodiments of this disclosure, a preset dimension evaluation value can be determined based on the service bandwidth adjustment result, and then a feedback value can be determined to update the decision model parameters, so that the decision model can be continuously optimized according to the actual adjustment effect, thereby improving the accuracy of bandwidth adjustment decisions.

[0087] In an exemplary embodiment, during the process of outputting bandwidth adjustment decisions by the decision model, the following constraints can also be used for decision generation: the total bandwidth of each service flow after adjustment does not exceed the total bandwidth capacity of the link; the bandwidth of each service flow after adjustment is not less than zero.

[0088] In some embodiments, the result of the service bandwidth adjustment includes at least one of the following: service latency change, service packet loss rate change, network bandwidth utilization, stability index of bandwidth adjustment range, and fairness index of bandwidth allocation among services.

[0089] In this embodiment of the disclosure, the result of the service bandwidth adjustment can include multiple aspects. The indicators of these aspects can reflect the changes in the state of the network and services after the service bandwidth adjustment from different perspectives, providing a rich data foundation for subsequent comprehensive evaluation of the adjustment effect.

[0090] The change in service latency refers to the difference in transmission latency before and after bandwidth adjustment. Latency is an important indicator for measuring service transmission efficiency, and the change in latency can directly reflect the impact of bandwidth adjustment on service transmission speed. For example, a decrease in latency indicates that the bandwidth adjustment has a positive effect.

[0091] The change in packet loss rate can be the difference between the packet loss rate before and after a bandwidth adjustment. Packet loss rate reflects the reliability of service transmission, and the change in packet loss rate can indicate the impact of bandwidth adjustment on service transmission quality. For example, a decrease in packet loss rate can indicate that the bandwidth adjustment has a positive effect.

[0092] Network bandwidth utilization rate represents the ratio of actual bandwidth used in a network to the total network bandwidth, reflecting the degree of utilization of network resources. If the network bandwidth utilization rate is within a preset ideal range, it indicates that bandwidth adjustments have had a positive effect.

[0093] The stability index of bandwidth adjustment range can be used to measure the fluctuation of the adjustment range during the bandwidth adjustment process. The better the stability index, the more stable the bandwidth adjustment is and the smaller the impact on the network.

[0094] Fairness metrics for bandwidth allocation among services can be used to assess whether the bandwidth allocated to different services in the network is reasonable and fair, ensuring that each service can obtain appropriate network resources.

[0095] Figure 4 A flowchart of updating the decision model in a bandwidth adjustment method according to yet another embodiment of this disclosure is shown.

[0096] like Figure 4 As shown, in some embodiments, adaptively updating the decision model may include the following steps.

[0097] Step S410: Determine the latency assessment value based on the change in service latency and the priority weight of each service.

[0098] In this embodiment, different services have varying sensitivities to latency, with higher-priority services having stricter latency requirements. By combining the actual changes in service latency with their priority weights, the impact of bandwidth adjustments on service latency can be assessed more reasonably. The priority weights can be determined by the service priority.

[0099] In an exemplary embodiment, reduced service latency can be positively rewarded, and this reward can be related to service priority, with higher-priority services contributing more. For service s i Its delay reward can be expressed as The total delay reward (i.e., the assessed value) can be: Where ω i For business s i The weight can be determined based on business priority using the following method: ω i =p i / p max .

[0100] Step S420: Determine the packet loss assessment value based on the change in the packet loss rate of the service and the priority weight of each service.

[0101] In this embodiment of the disclosure, packet loss rate can directly affect service quality, and high-priority services typically cannot tolerate high packet loss rates. By considering changes in packet loss rate and service priority, the impact of bandwidth adjustments on service packet loss can be accurately assessed.

[0102] In an exemplary embodiment, a decrease in the packet loss rate of a service can be positively rewarded. This reward can be related to service priority, with higher-priority services contributing more. For service s i Its packet loss reward can be expressed as The total packet loss reward (i.e., the assessed value) is:

[0103] Step S430: Determine the utilization evaluation value based on the difference between the network bandwidth utilization rate and the target utilization rate.

[0104] In this embodiment of the disclosure, network bandwidth utilization can reflect the degree of utilization of network resources. The difference between the network bandwidth utilization rate and the target utilization rate can reflect whether the bandwidth adjustment has made reasonable use of network resources, thereby evaluating the adjustment effect.

[0105] In an exemplary embodiment, both excessively high and excessively low bandwidth utilization can affect network performance. A target optimal bandwidth utilization rate (e.g., 80%, 85%) and a maximum bandwidth utilization rate (e.g., 95%, 97%) can be set. Network bandwidth utilization can be expressed as: Each link's bandwidth utilization satisfies u e ≤95%. Then the bandwidth utilization bonus (i.e., the evaluated value) can be expressed as: Where u tar To achieve the optimal bandwidth utilization, u max This represents the maximum bandwidth utilization.

[0106] Step S440: Determine the stability assessment value based on the stability index of the bandwidth adjustment range.

[0107] In this embodiment of the disclosure, stable bandwidth adjustment contributes to the smooth operation of the network, and the stability index can be used to directly assess the stability of the bandwidth adjustment process.

[0108] In an exemplary embodiment, network stability can be enhanced by avoiding over-tuning. The stability reward (i.e., the evaluation value) can be represented as... Where η is the penalty coefficient.

[0109] Step S450: Determine the fairness assessment value based on the fairness index of bandwidth allocation between services.

[0110] In this embodiment of the disclosure, bandwidth should be reasonably allocated to different services in the network. Fairness indicators can measure whether the bandwidth allocation is reasonable, and thus assess the impact of bandwidth adjustment on fairness among services.

[0111] In an exemplary embodiment, while ensuring high-priority services, the fairness of the overall allocation can be maintained as much as possible through a fairness assessment value, preventing low-priority services from being unable to receive service for extended periods. The fairness reward (i.e., the assessment value) can be calculated based on the Jain's Fairness Index, a quantitative indicator used to measure the fairness of resource allocation and to assess whether the allocation of a limited resource among multiple individuals / nodes is fair.

[0112] Step S460: Determine the feedback value based on the evaluation value.

[0113] In this embodiment of the disclosure, the preset dimensions are the aforementioned latency dimension, packet loss dimension, network bandwidth utilization dimension, stability dimension, and fairness dimension.

[0114] The feedback value can be obtained by weighted summation based on one or more of the following: latency assessment value, packet loss assessment value, network bandwidth utilization assessment value, stability assessment value, and fairness assessment value.

[0115] Step S470: Update the parameters of the decision model based on the feedback value.

[0116] Figure 4 Other aspects of the embodiments can be found in the other embodiments described above.

[0117] This disclosure clarifies the indicators for service bandwidth adjustment results and provides methods for determining the evaluation values ​​of each preset dimension. It can comprehensively evaluate the bandwidth adjustment effect from multiple aspects, provide a comprehensive basis for updating decision model parameters, and ensure that bandwidth adjustment is scientific and reasonable.

[0118] In some embodiments, the bandwidth adjustment method further includes: determining feedback values ​​corresponding to multiple rounds of bandwidth adjustment decisions; and determining that the decision model has converged in response to the change magnitude of the feedback values ​​being less than a stability threshold.

[0119] In this embodiment of the disclosure, multiple rounds of decision-making, implementation, and effect feedback can be conducted during the application of this solution. After each round of bandwidth adjustment decision execution, feedback values ​​can be calculated based on the results of the service bandwidth adjustment. These feedback values ​​can reflect the actual impact of each round of decisions on the network and services.

[0120] By utilizing the collected feedback values ​​from multiple rounds, the parameters of the decision-making model can be continuously adjusted, enabling the model to better adapt to changes in the network environment and business needs in subsequent decisions. This iterative update is a gradual optimization process that allows the decision-making model to continuously learn and improve, thereby enhancing the accuracy and effectiveness of bandwidth adjustment decisions.

[0121] During the iterative update process, the changes in the feedback value can be continuously monitored. When the feedback value gradually stabilizes, i.e., the change is less than a pre-set stability threshold, the decision model can be considered to have converged. This indicates that after multiple rounds of adjustments, the effect of the bandwidth adjustment decision in the output of the decision model has reached a relatively stable state, and further adjustments to the parameter weights have little effect on improving the feedback value.

[0122] When the change in the feedback value is less than the stability threshold, it can be determined that the parameter weights of the decision model have been adjusted to a more appropriate state, and the update process of the decision model is complete. At this point, the decision model can provide more stable, reliable, and effective decision support.

[0123] In an exemplary embodiment, when the feedback value tends to stabilize (e.g., the rate of change of the feedback value after N iterations is less than a preset threshold), the algorithm can be considered to have converged, and the iteration process can be terminated.

[0124] Through the embodiments of this disclosure, the parameters of the decision model can be iteratively updated based on multiple rounds of feedback values, and the convergence of the decision model can be determined when the change amplitude of the feedback value is less than the stability threshold. This can ensure that the decision model is fully optimized and can stably output high-quality bandwidth adjustment decisions.

[0125] In some embodiments, adjusting service bandwidth based on the bandwidth adjustment decision includes: in response to the bandwidth adjustment decision not meeting the service level agreement of a high-priority service, adjusting the bandwidth adjustment decision according to a preset bandwidth constraint strategy, and adjusting the service bandwidth based on the adjusted bandwidth adjustment decision; wherein the bandwidth constraint strategy includes at least one of the following: allocating the bandwidth adjustment amount of low-priority services to the high-priority services in ascending order of priority; ensuring that the low-priority services are configured with service bandwidth not lower than the available bandwidth threshold; and reallocating the remaining bandwidth to each service flow.

[0126] In this embodiment of the disclosure, network services can be classified into higher priority categories based on factors such as the importance of the service, its impact on user experience, and revenue. For example, services requiring network stability, low latency, and high reliability, such as real-time video communication and online financial transactions, can be classified as high-priority services. High-priority services are usually directly related to the core user experience and critical operations of the service.

[0127] The Service Level Agreement (SLA) specifies the quality of service standards that high-priority services should meet, such as specific bandwidth ranges, latency limits, and packet loss rate requirements. The system can first evaluate the bandwidth adjustment decision generated based on the decision model to determine whether the high-priority service can meet the various indicators set in its SLA after the decision is implemented. If it does, the service bandwidth can be adjusted directly according to the original bandwidth adjustment decision; if it does not, subsequent adjustment procedures are triggered.

[0128] When it is determined that the original bandwidth adjustment decision does not meet the SLA of high-priority services, the decision can be modified according to the preset bandwidth constraint strategy. The bandwidth constraint strategy provides a variety of adjustment methods, and one or more methods can be selected and combined to adjust the decision according to the actual situation, so as to ensure that the adjusted decision can not only meet the SLA of high-priority services, but also take into account the reasonable needs of other services.

[0129] One approach is to allocate bandwidth adjustments from low-priority services to high-priority services, prioritizing them from lowest to highest priority. This bandwidth adjustment method allows for the reassessment of low-priority services in ascending order of priority when their bandwidth demands cannot be met. A portion of the bandwidth allocated to low-priority services is then distributed to high-priority services. This sequential allocation ensures that while meeting the needs of high-priority services, the impact on overall service performance is minimized, prioritizing bandwidth adjustments for less important services to reduce system impact.

[0130] Ensuring that low-priority services are allocated bandwidth no less than the available bandwidth threshold means that bandwidth for low-priority services cannot be reduced indefinitely during bandwidth allocation. The available bandwidth threshold can be a lower limit determined based on factors such as the minimum operating requirements of low-priority services and user experience, such as 15% or 12%. When allocating bandwidth adjustments from low-priority services to high-priority services, the system ensures that the allocated bandwidth for low-priority services remains no less than this available bandwidth threshold, thereby guaranteeing that low-priority services can maintain basic operation and service quality.

[0131] The remaining bandwidth can also be redistributed to various service flows. After ensuring the bandwidth requirements of high-priority services and making reasonable allocations to low-priority services, if there is still some unallocated bandwidth in the network, it can be redistributed to various service flows according to certain rules (such as according to service priority ratio, according to service traffic demand ratio, or even distribution) to further improve the utilization rate of network resources and optimize the overall service operation effect.

[0132] Through the embodiments of this disclosure, when the bandwidth adjustment decision does not meet the service level agreement of high-priority services, the bandwidth of low-priority services can be reasonably allocated to ensure the needs of high-priority services, while ensuring that the bandwidth of low-priority services is not lower than the available threshold, thus balancing service needs.

[0133] In some embodiments, the bandwidth adjustment method further includes: in response to newly collected network operation status data reaching a data volume threshold or reaching an update cycle, using the newly collected network operation status data to train and update the prediction model.

[0134] In this embodiment of the disclosure, the amount of newly collected network operation status data can be continuously monitored. When the accumulated amount of this newly collected data reaches a preset data volume threshold, the training and update operation of the prediction model can be triggered.

[0135] Setting a data volume threshold ensures that there is enough new data to reflect the changing trends in network operation status, enabling the model to learn more accurate and representative patterns. For example, if the data volume threshold is set to 1000 network operation status records, the trigger condition can be met when the newly collected data reaches this number.

[0136] It can also check whether the preset update cycle has been reached. The update cycle can be a fixed time interval, such as hourly, daily, or weekly. Therefore, regardless of whether the amount of newly collected data reaches the threshold, as long as this preset time point is reached, training and updating the prediction model can begin. Regularly updating the model ensures that it always adapts to the latest network conditions.

[0137] In an exemplary embodiment, the process of training and updating the prediction model may include: inputting new data into the model, adjusting the model's parameters based on the difference (i.e., error) between the prediction model's output data and the actual network operating state, so that the prediction model can more accurately predict the future operating state of the network based on the new data. For example, if the prediction model was originally used to predict network bandwidth usage, through training and updating with new data, the prediction model can better adapt to changes in network traffic, improve prediction accuracy, and thus provide a more accurate data basis for generating bandwidth adjustment decisions.

[0138] Through the embodiments of this disclosure, the prediction model can be updated regularly to adapt to changes in network conditions and the evolution of business models, maintaining prediction accuracy and thus ensuring the effectiveness of bandwidth adjustment strategies over the long term.

[0139] It should be noted that the above figures are merely illustrative representations of the processes included in methods according to some embodiments of this disclosure, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0140] The following are embodiments of the apparatus disclosed herein, which can be used to execute embodiments of the method disclosed herein. For details not disclosed in the apparatus embodiments of this disclosure, please refer to the embodiments of the method disclosed herein.

[0141] Figure 5 A block diagram of a bandwidth adjustment apparatus 500 according to an embodiment of the present disclosure is shown. (Refer to...) Figure 5 The device includes: an acquisition module 501, a prediction module 502, a decision output module 503, an adjustment module 504, and an update module 505.

[0142] The acquisition module 501 is used to acquire historical network operation status data for a historical period; wherein, the network operation status data includes network status data and service flow data of each service; the prediction module 502 is used to process the historical network operation status data through a prediction model to obtain a prediction result containing future network operation status data; the decision output module 503 is used to input the current network operation status data and the prediction result into an adaptively updated decision model, and output a bandwidth adjustment decision for each service flow through the decision model; the adjustment module 504 is used to adjust the service bandwidth according to the bandwidth adjustment decision; wherein, the result of the service bandwidth adjustment is used to adaptively update the decision model.

[0143] In some embodiments, the update module 505 adaptively updates the decision model, including: determining an evaluation value under a preset dimension based on the result of service bandwidth adjustment; determining a feedback value based on the evaluation value under the preset dimension; and updating the parameters of the decision model based on the feedback value.

[0144] In some embodiments, the result of the service bandwidth adjustment includes at least one of the following: service latency change, service packet loss rate change, network bandwidth utilization, stability index of bandwidth adjustment magnitude, and fairness index of bandwidth allocation among services; wherein, the update module 505 determines the evaluation value under a preset dimension based on the result of the service bandwidth adjustment, including at least one of the following: determining a latency evaluation value based on the service latency change and the priority weight of each service; determining a packet loss evaluation value based on the service packet loss rate change and the priority weight of each service; determining a utilization evaluation value based on the difference between the network bandwidth utilization and the target utilization; determining a stability evaluation value based on the stability index of the bandwidth adjustment magnitude; and determining a fairness evaluation value based on the fairness index of bandwidth allocation among services.

[0145] In some embodiments, the update module 505 is further configured to: determine the feedback value corresponding to the multi-round bandwidth adjustment decision; and determine that the decision model has converged in response to the change amplitude of the feedback value being less than a stability threshold.

[0146] In some embodiments, the adjustment module 504 adjusts the service bandwidth according to the bandwidth adjustment decision, including: in response to the bandwidth adjustment decision not meeting the service level agreement of a high-priority service, adjusting the bandwidth adjustment decision according to a preset bandwidth constraint strategy, and adjusting the service bandwidth according to the adjusted bandwidth adjustment decision; wherein the bandwidth constraint strategy includes at least one of the following: allocating the bandwidth adjustment amount of low-priority services to the high-priority services in ascending order of priority; ensuring that the low-priority services are configured with service bandwidth not lower than the available bandwidth threshold; and reallocating the remaining bandwidth to each service flow.

[0147] In some embodiments, the network status data includes at least one of the following: link bandwidth, bandwidth utilization, latency, and packet loss rate; the service flow data includes at least one of the following: service latency, service packet loss rate, service rate, allocated bandwidth, service type, and service priority.

[0148] In some embodiments, the service priority of each service is determined based on the service quality priority, service importance, and service real-time nature of the corresponding service.

[0149] In some embodiments, the update module 505 is further configured to: in response to the newly collected network operating status data reaching a data volume threshold or reaching an update cycle, use the newly collected network operating status data to train and update the prediction model.

[0150] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0151] Figure 6 This diagram illustrates a structural block diagram of a bandwidth adjustment computer device according to an embodiment of the present disclosure. It should be noted that the illustrated electronic device is merely an example and should not be construed as limiting the functionality or scope of the embodiments of the present invention.

[0152] The following reference Figure 6 To describe an electronic device 600 according to this embodiment of the present invention. Figure 6 The electronic device 600 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.

[0153] like Figure 6 As shown, the electronic device 600 is manifested in the form of a general-purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, and a bus 630 connecting different system components (including storage unit 620 and processing unit 610).

[0154] The storage unit stores program code that can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 2 The method shown.

[0155] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0156] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0157] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0158] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. As shown, network adapter 660 communicates with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0159] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible embodiments, various aspects of the invention may also be implemented as a program product comprising program code that, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of the invention described in the "Exemplary Methods" section of this specification.

[0160] According to embodiments of the present invention, a program product for implementing the above-described method may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0161] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0162] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0163] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0164] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0165] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0166] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0167] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0168] According to one aspect of this disclosure, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in various optional implementations of the above embodiments.

[0169] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A bandwidth adjustment method, characterized in that, include: Obtain historical network operation status data for historical time periods; the network operation status data includes network status data and service flow data for each service. The historical network operation status data is processed by a predictive model to obtain a prediction result that includes future network operation status data; The current network operating status data and the prediction results are input into the adaptively updated decision model, and the decision model outputs bandwidth adjustment decisions for each service flow. The service bandwidth is adjusted according to the bandwidth adjustment decision; wherein the result of the service bandwidth adjustment is used to adaptively update the decision model.

2. The method according to claim 1, characterized in that, Adaptive updating of the decision model includes: The evaluation value under the preset dimensions is determined based on the results of the service bandwidth adjustment. The feedback value is determined based on the evaluation value under the preset dimension; The parameters of the decision model are updated based on the feedback value.

3. The method according to claim 2, characterized in that, The results of the service bandwidth adjustment include at least one of the following: service latency change, service packet loss rate change, network bandwidth utilization, stability index of bandwidth adjustment range, and fairness index of bandwidth allocation among services. The evaluation value determined based on the results of service bandwidth adjustment under a preset dimension includes at least one of the following: The latency assessment value is determined based on the change in latency of the aforementioned services and the priority weight of each service. The packet loss assessment value is determined based on the change in the packet loss rate of the aforementioned services and the priority weight of each service. The utilization rate assessment value is determined based on the difference between the network bandwidth utilization rate and the target utilization rate. The stability assessment value is determined based on the stability index of the bandwidth adjustment range; The fairness assessment value is determined based on the fairness index of bandwidth allocation among the services.

4. The method according to claim 2, characterized in that, The method further includes: Determine the feedback values ​​corresponding to multiple rounds of bandwidth adjustment decisions; The decision model is considered to have converged when the change in the feedback value is less than the stability threshold.

5. The method according to claim 1, characterized in that, Adjusting service bandwidth based on the bandwidth adjustment decision includes: In response to the bandwidth adjustment decision not meeting the service level agreement of high-priority services, the bandwidth adjustment decision is adjusted according to a preset bandwidth constraint strategy, and the service bandwidth is adjusted according to the adjusted bandwidth adjustment decision. The bandwidth constraint strategy includes at least one of the following: The bandwidth adjustment amount of low-priority services is allocated to high-priority services in order of priority from low to high. Ensure that low-priority services are configured with bandwidth no less than the available bandwidth threshold; The remaining bandwidth will be redistributed to the various service flows.

6. The method according to claim 1, characterized in that, The network status data includes at least one of the following: link bandwidth, bandwidth utilization, latency, and packet loss rate; The service flow data includes at least one of the following: service latency, service packet loss rate, service rate, allocated bandwidth, service type, and service priority.

7. The method according to claim 6, characterized in that, The service priority of each service is determined based on the service quality priority, service importance, and real-time nature of the corresponding service.

8. The method according to claim 1, characterized in that, The method further includes: In response to the newly collected network operation status data reaching a data volume threshold or reaching the update cycle, the prediction model is trained and updated using the newly collected network operation status data.

9. A bandwidth adjustment device, characterized in that, include: The acquisition module is used to acquire historical network operation status data for historical time periods; the network operation status data includes network status data and service flow data of each service. The prediction module is used to process the historical network operation status data through a prediction model to obtain prediction results that include future network operation status data. The decision output module is used to input the current network operating status data and the prediction results into the adaptively updated decision model, and output the bandwidth adjustment decision for each service flow through the decision model; An adjustment module is used to adjust service bandwidth based on the bandwidth adjustment decision; wherein the result of the service bandwidth adjustment is used to adaptively update the decision model.

10. A computer-readable storage medium having a computer program stored thereon, the program, when executed by a processor, implementing the bandwidth adjustment method as described in any one of claims 1-8.

11. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the bandwidth adjustment method as described in any one of claims 1-8.

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