CN115134859B - Intelligent congestion control method for diversified transmission demands in high-dynamic network - Google Patents
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Abstract
The invention discloses an intelligent congestion control method and system facing diversified transmission demands in a high-dynamic network, wherein the development of the invention is mainly concentrated in an application layer and a transmission layer and comprises two modules: the invention relates to a channel capacity prediction module, which is used for predicting the capacity of a bottom network based on a long-short-term memory neural network to better adapt to a 5G network with high dynamic change of the channel capacity; the other is a reinforcement learning module, and the invention aims to design an intelligent congestion control algorithm based on reinforcement learning by combining specific bandwidth and time delay requirement values of application and preference of application among a plurality of requirement dimensions, thereby maximizing weighted requirement satisfaction rate and improving user experience.
Description
Technical Field
The invention relates to the field of network communication, in particular to an intelligent congestion control method and system for facing diversified transmission requirements in a high-dynamic network.
Background
In recent years, with the development of digital technology, mobile network architecture has been developed from 2G to 5G, i.e., fifth generation communication technology. Unlike 2G germination data, 3G induced growth data, 4G development data, 5G is a trans-time technology that proposes a "user-centric" concept aimed at providing a higher quality user experience for users. The computing power and transmission bandwidth of the 5G network promote the high-speed development of various novel applications such as Internet of vehicles, high-definition video and wireless medical treatment. Various applications put forward diversified transparent transmission demands in multiple dimensions such as throughput, time delay and the like, for example, automatic driving applications require time delay lower than 10ms and throughput higher than 1Gbps;4K/8K high definition video requires less than 10ms delay and higher than 100Mbps throughput, etc. In the data transmission process, the network needs to meet the clear transmission performance requirement values of diversified applications so as to ensure good user experience.
To support the transmission requirements of the application, 5G uses millimeter wave technology to provide greater channel capacity and lower latency. However, due to the use of higher frequencies, millimeter wave technology introduces serious propagation attenuation problems while achieving faster transmission rates and greater system capacity, resulting in transmission quality that is susceptible to device movement and obstructions, i.e., frequent switching between line-of-sight and non-line-of-sight transmissions (non-line-of-sight refers to propagation paths of radio frequencies that are partially obscured by obstructions, thereby making it difficult for wireless signals to pass). The channel capacity will vary greatly during line-of-sight transmission and non-line-of-sight transmission switching, causing serious impairment of the performance of the transmission protocol.
In order to fully exploit the 5G network capability to provide high quality transport services for applications, the congestion control algorithm of the transport layer needs to consider both the transparent transport requirements of diverse applications and the high dynamic underlying network environment. Because the sender cannot accurately sense the channel capacity and its variation, the congestion control algorithm coordinates the amount of data injected into the network, usually by adjusting the sending rate or the form of the congestion window. When the sending rate is too high or the congestion window is too large, the data volume injected into the network exceeds the channel capacity, so that the data packets are accumulated in the intermediate forwarding equipment, more data packet queuing situations occur, and the end-to-end time delay is increased. When the transmission rate is too low or the congestion window is too small, the throughput requirements of the application may not be met and the channel bandwidth capacity is not fully occupied. A good congestion control algorithm should achieve a good compromise between throughput and latency while fully exploiting the channel bandwidth and avoiding the latency from long queuing.
The existing congestion control algorithm is mainly divided into a traditional algorithm based on heuristic and an intelligent algorithm based on machine learning, and the algorithms are difficult to meet the clear transmission requirements of diversified applications under the millimeter wave network with high capacity and dynamic change. Conventional congestion control algorithms typically model the network in some simplified manner based on the experience of the designer, and cannot well reflect the actual situation of the network. The congestion control algorithm based on machine learning utilizes network state information with more dimensionalities to carry out congestion control decision, is more suitable for dynamic network environment compared with the traditional congestion control algorithm, can obtain the compromise effect of different weight preference between throughput and time delay by setting an objective function, and provides differentiated transmission service for different applications to a certain extent. However, in the design process, the two algorithms cannot sense the application-transparent multidimensional transmission requirement values and cannot measure the completion condition of each requirement index. In addition, the highly dynamic variation of channel capacity of 5G networks can impact the convergence of existing learning-based machine learning congestion control algorithms.
Therefore, in order to fully utilize the bottom network resources to bring better user experience for the upper layer application, the invention aims to design an intelligent congestion control algorithm for diversified transparent transmission requirements under the high-dynamic network, and the 5G network with high dynamic capacity for the bottom network channel is designed, and meets the transparent requirement values of the upper layer application for transmission performance in multiple dimensions such as throughput, time delay and the like through intelligent congestion control, thereby providing satisfactory transmission service for users.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides an intelligent congestion control method and a control system facing diversified transmission requirements in a high-dynamic network.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
An intelligent congestion control method facing diversified transmission demands in a high dynamic network comprises the following steps:
S1, sampling millimeter wave channel capacity, and pre-training a channel capacity prediction model by using sampled channel capacity information;
S2, predefining an API interface, and setting a requirement value and a requirement weight through the predefined API interface;
s3, counting network state information and demand completion conditions at set time intervals, and delivering the counted information to a congestion control algorithm. The channel capacity prediction module calculates a channel capacity prediction value through historical channel capacity information;
S4, the reinforcement learning module performs reinforcement learning according to the channel capacity predicted value, the counted network state information and the demand completion condition, outputs a congestion window adjustment strategy and adjusts the strategy adjustment, and calculates a transmission interval according to the window value/average RTT form;
S5, repeating the steps S3-S4 until the transmission is completed.
Further, the requirement value and the requirement weight in S2 are specifically:
The throughput requirement in Mbps and the delay requirement value in ms are the delay requirement weight ω d and the throughput requirement weight ω b, and ω b+ωd =1.
Further, the calculation mode of the requirement completion condition in S3 is as follows:
wherein b 0 is throughput demand, d 0 is delay demand, I is single-dimensional demand satisfaction and
Further, the step S3 specifically includes the following steps:
S31, updating the counted RTT, throughput, packet loss number and estimated channel capacity when each ACK data packet is received;
S32, when the timer exceeds the time interval, calculating average throughput, average RTT, minimum RTT, time delay gradient, packet loss rate, demand completion rate, maximum network bandwidth capacity and rewarding value in the current time interval;
S33, delivering the data calculated in the step S32 to a congestion control algorithm. The channel capacity module calculates a channel capacity prediction value.
Further, the calculation method of the channel capacity prediction value in S33 is as follows:
S331, adding channel capacity information counted by a current time interval into a historical channel capacity queue, wherein the historical channel capacity queue maintains a section of historical channel capacity information with a fixed length all the time;
s332, inputting the information in the historical channel capacity queue into a trained network capacity prediction model, and outputting the channel capacity of the next time interval.
Further, the channel capacity prediction module and the reinforcement learning module are arranged on a transmission layer, wherein,
The channel capacity prediction module takes historical channel capacity as input, takes predicted future channel capacity as output, inputs the predicted future channel capacity into a state space of the reinforcement learning module, and assists the reinforcement learning module in making decisions;
The reinforcement learning module takes future channel capacity information, network state information at the current moment and transmission demand completion degree as input of a state space of the reinforcement learning module, and in each decision time period, the learner fits a congestion control strategy according to DDPG algorithm, uses the demand completion degree as a reward function, and outputs a congestion window adjustment strategy.
The invention has the following beneficial effects:
1. Congestion control is performed according to the clear transmission performance requirement values and the performance preference weights of the diversified applications, the weighted requirement satisfaction rate can be maximized, the user experience is improved, and the performance is finally improved.
2. Congestion control is performed by combining current network state information and predicted future channel capacity, so that the method can be better suitable for the 5G network with high dynamic change of the channel capacity of the bottom network.
Drawings
Fig. 1 is a schematic diagram of an intelligent congestion control system framework for diversified transmission requirements in a high dynamic network.
Fig. 2 is a schematic diagram of intelligent congestion control based on capacity prediction and reinforcement learning according to an embodiment of the present invention.
Fig. 3 is a flow chart of an intelligent congestion control method facing diversified transmission demands in a high dynamic network according to an embodiment of the present invention.
FIG. 4 is a schematic diagram of an embodiment of the present invention for increasing the demand satisfaction rate compared to a conventional algorithm.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
An intelligent congestion control system facing diversified transmission demands in a high dynamic network, as shown in fig. 1, comprises an application layer and a transmission layer, wherein the transmission layer comprises a channel capacity prediction module and a reinforcement learning module, and in particular, as shown in fig. 2,
The channel capacity prediction module takes historical channel capacity as input, takes predicted future channel capacity as output, inputs the predicted future channel capacity into a state space of the reinforcement learning module, and assists reinforcement learning to make decisions; in this embodiment, the capacity of the bottom network is predicted based on the long-short-term memory neural network, so as to better adapt to the 5G network with high dynamic change of the channel capacity.
The reinforcement learning module takes future channel capacity information, network state information at the current moment and transmission demand completion degree as input of a state space of the reinforcement learning module, and in each decision time period, the learner fits a congestion policy according to DDPG algorithm, uses the demand completion degree as a reward function, and outputs a congestion window adjustment policy.
The specific definition is as follows: the custom throughput requirement b 0, the latency requirement d 0, and the throughput and latency preference weights ω b and ω d are applied, and ω b+ωd＝1.bt and d t represent the throughput and latency at time t. The demand completion of the application at time t can be defined as follows:
Wherein the method comprises the steps of Representing the satisfaction of the single-dimensional requirement, when the value is 1, representing that the requirement value of the dimension is satisfied, otherwise, representing the satisfaction proportion of the performance requirement when the value is not 1. The reward function is shown in equation (2).
An intelligent congestion control method facing diversified transmission demands in a high dynamic network, as shown in fig. 3, comprises the following steps:
S1, sampling millimeter wave channel capacity, and pre-training a channel capacity prediction model by using sampled channel capacity information;
Before the algorithm is used, millimeter wave channel capacity is sampled, and a channel capacity prediction model (such as a long-term and short-term memory neural network) is pre-trained by using the channel capacity information in a period of time.
S2, predefining an API interface, and setting a requirement value and a requirement weight through the predefined API interface;
In this embodiment, the application sets specific requirement values and requirement weights through a predefined API interface. Specifically including the throughput requirement in Mbps and the latency requirement value in ms, the latency requirement preference ω d and the throughput requirement preference ω b, and ω b+ωd =1. If the algorithm is not specified, a default value is used.
S3, counting network state information and demand completion conditions at set time intervals, and delivering the counted information to a congestion control algorithm.
In this embodiment, the counted network status information is delivered to a congestion control algorithm, which includes a channel capacity prediction module and a reinforcement learning module. The statistical channel capacity information is used for predicting the channel capacity, and the rest information is input into a reinforcement learning module for reinforcement learning, specifically, the method comprises the following steps:
S31, updating the counted RTT, throughput, packet loss number and estimated channel capacity when each ACK data packet is received;
And S32, when the timer exceeds the time interval, calculating average throughput, average RTT, minimum RTT, time delay gradient, packet loss rate, demand completion degree, maximum network bandwidth capacity and rewarding value in the current time interval, wherein the calculation of the rewarding value is shown in a formula (2).
S33, delivering the data calculated in the step S32 to a congestion control algorithm. The channel capacity prediction module calculates a channel capacity prediction value, and the specific prediction method comprises the following steps:
S331, adding channel capacity information counted by a current time interval into a historical channel capacity queue, wherein the historical channel capacity queue maintains a section of historical channel capacity information with a fixed length all the time;
s332, inputting the information in the historical channel capacity queue into a trained network capacity prediction model, and outputting the channel capacity of the next time interval.
S4, the reinforcement learning module performs reinforcement learning according to the channel capacity predicted value, the counted network state information and the demand completion condition, outputs a congestion window adjustment strategy and adjusts a congestion window, and calculates a transmission interval according to the window value/average RTT;
S5, repeating the steps S3-S4 until the transmission is completed.
Based on the above scheme, the algorithm (QoSCC) provided by the present invention improves the demand satisfaction rate compared with the conventional algorithm as shown in fig. 4. It can be seen that under different transmission requirement setting conditions, the actually achieved transmission performance of the invention is different, but the application requirements are met in two dimensions of time delay and throughput. However, other algorithms can only achieve a fixed transmission effect, and under the condition of different transmission requirement settings, the situation that the time delay or throughput dimension does not meet the application requirement occurs.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principles and embodiments of the present invention have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.
Claims (4)
1. An intelligent congestion control method facing diversified transmission demands in a high dynamic network is characterized by comprising the following steps:
S1, sampling millimeter wave channel capacity, and pre-training a channel capacity prediction model by using sampled channel capacity information;
S2, predefining an API interface, and setting a requirement value and a requirement weight through the predefined API interface;
S3, counting the network state information and the demand completion condition at set time intervals,
The calculation mode of the requirement completion condition is as follows:
wherein, The degree of completion of the demand at time t,In order to meet the throughput requirements of the users,In order for the time delay requirement to be met,AndIndicating the throughput and the time delay at time t,AndFor throughput and delay preference weight values, I is one-dimensional demand satisfaction and；
Delivering the counted information to a congestion control algorithm, wherein the channel capacity prediction module calculates a channel capacity prediction value through historical channel capacity information, and specifically comprises the following steps of:
S31, updating the counted RTT, throughput, packet loss number and estimated channel capacity when each ACK data packet is received;
S32, when the timer exceeds the time interval, calculating average throughput, average RTT, minimum RTT, time delay gradient, packet loss rate, demand completion degree, maximum network capacity and rewarding value in the current time interval;
S33, delivering the data obtained by calculation in the step S32 to a congestion control algorithm, and calculating a channel capacity predicted value by a channel capacity prediction module;
S4, the reinforcement learning module performs reinforcement learning according to the channel capacity predicted value, the counted network state information and the demand completion condition, outputs a congestion window adjustment strategy and adjusts the congestion window, and simultaneously, according to the channel capacity predicted value, the counted network state information and the demand completion condition In the form of (a) calculates a transmission interval;
S5, repeating the steps S3-S4 until the transmission is completed.
2. The intelligent congestion control method for diversified transmission requirements in a high dynamic network according to claim 1, wherein the requirement value and the requirement weight in S2 are specifically:
throughput requirement in Mbps and latency requirement value in ms and latency requirement weight Sum throughput demand weightAnd (2) and。
3. The intelligent congestion control method for diversified transmission requirements in a high dynamic network according to claim 1, wherein the calculating manner of the channel capacity predicted value in S33 is as follows:
S331, adding channel capacity information counted by a current time interval into a historical channel capacity queue, wherein the historical channel capacity queue maintains a section of historical channel capacity information with a fixed length all the time;
s332, inputting the information in the historical channel capacity queue into a trained network capacity prediction model, and outputting the channel capacity of the next time interval.
4. The method for intelligent congestion control for diverse transmission demands in a highly dynamic network according to any one of claims 1-3, wherein the channel capacity prediction module and the reinforcement learning module are disposed in a transmission layer,
The channel capacity prediction module takes historical channel capacity as input, takes predicted future channel capacity as output, inputs the predicted future channel capacity into a state space of the reinforcement learning module, and assists the reinforcement learning module in making decisions;
The reinforcement learning module takes future channel capacity information, network state information at the current moment and transmission demand completion degree as input of a state space of the reinforcement learning module, and in each decision time period, the learner fits a congestion control strategy according to DDPG algorithm, uses the demand completion degree as a reward function, and outputs a congestion window adjustment strategy.
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