CN110535770B - QoS-aware-based intelligent routing method for video stream in SDN environment - Google Patents
QoS-aware-based intelligent routing method for video stream in SDN environment Download PDFInfo
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- CN110535770B CN110535770B CN201910818813.4A CN201910818813A CN110535770B CN 110535770 B CN110535770 B CN 110535770B CN 201910818813 A CN201910818813 A CN 201910818813A CN 110535770 B CN110535770 B CN 110535770B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/302—Route determination based on requested QoS
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/302—Route determination based on requested QoS
- H04L45/306—Route determination based on the nature of the carried application
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/74—Address processing for routing
- H04L45/745—Address table lookup; Address filtering
Abstract
A video stream intelligent routing method based on QoS sensing in an SDN environment is characterized in that a service stream enters an SDN network, the SDN network judges whether the service stream table is successfully matched after receiving a data packet, if the service stream table is successfully matched, data is forwarded according to an Action field of the stream table, if the service stream table is not successfully matched, whether the service stream is a video stream service is judged, if not, transmission is carried out, and if yes, a QoS sensing module analyzes the QoS requirement of the video stream; the monitoring module detects link information of network underlying network equipment; the route selection module selects a path for the video stream by using a DQN algorithm according to the QoS requirement and the link information of the QoS sensing module, and issues the stream table to the underlying network equipment, and the underlying network equipment forwards data according to the Action field of the stream table. The invention optimizes the routing of the video stream service in the SDN environment, provides a special routing module for the video stream aiming at the characteristics of the video stream, and improves the transmission quality of the video stream.
Description
Technical Field
The invention relates to a routing method, in particular to a video stream intelligent routing method based on QoS (quality of service) perception in an SDN (software defined network) environment.
Background
With the development of society, the traffic of the current network is larger and larger, the traditional network is bloated and cannot be used, and great trouble is caused to the management of the network, in order to reform the traditional network, an Open Network Foundation (ONF) organization provides a Software Defined Network (SDN) architecture, which is a new network architecture, and the architecture of the SDN architecture is divided into an application plane, a control plane and a data plane from top to bottom. Compared with the traditional network, the method realizes the decoupling of a control plane and a data plane, and the control plane realizes the function of managing and controlling the network in a centralized way. In the SDN, a southbound interface realizes information exchange between a control plane and a data plane through an OpenFlow protocol, and a northbound interface does not have a unified network communication protocol at present. The north interface and the south interface provide programmable API for developers, so that the developers can manage the network in a programming mode according to the needs of the developers, and the possibility of intelligently managing the network flow is provided.
With the continuous development of the mobile internet, the number of internet users is continuously increased, and the video service is rapidly developed and becomes the main service of the internet. Cisco has indicated in a prediction report that internet video streaming will become the main traffic of the internet in the future. It is already a normal state for users to be able to use videos at any time, and the video experience also becomes a main factor when users select operators. This also presents a challenge to the processing of video stream services in the network, and in the face of large-scale video stream service requests in the network, if the processing is not good, the load of the network is increased, congestion is more likely to occur, and the user experience is also affected.
The core of the connectionless sequential streaming media technology adopted by the traditional streaming media transmission is hypertext Transfer Protocol (HTTP), and the technology is not suitable for live broadcast, is easy to cause loan waste, cannot flexibly control flow, cannot adjust video streaming service transmission according to the current network condition, and can affect the satisfaction degree of users. Later 3GPP proposed dynamic adaptive streaming over HTTP (DASH) as the mainstream streaming solution.
In this technique, a video stream is encoded into multiple quality levels, which differ in quality while a video file is sliced into fixed-size slices and a corresponding Media Presentation Description (MPD) file is generated.
In an SDN network, a routing algorithm used in most SDN controllers at present is a Dijstra algorithm, which is a greedy algorithm, the algorithm has simple steps, and a path with the minimum link cost is always selected for transmission, so that data packets are always concentrated on the same link, the network throughput is not ideal, congestion is easy to occur, QoS factors such as available bandwidth, time delay and packet loss rate of the link are not considered, and the routing calculation method is very likely to fail to obtain an optimal routing scheme.
Since video streaming traffic occupies a large amount of resources in the network, it is necessary to deploy a routing scheme that specially handles video streaming traffic in the network.
Disclosure of Invention
The invention aims to provide a video stream intelligent routing method based on QoS (quality of service) perception in an SDN (software defined network) environment.
In order to achieve the purpose, the invention adopts the following technical scheme:
a video stream intelligent routing method based on QoS perception in an SDN environment comprises the following steps:
s1: the service flow enters the SDN network, the SDN network judges whether the service flow table is successfully matched after receiving the data packet, if the service flow table is successfully matched, the data is forwarded according to the Action field of the flow table, if the service flow table is not successfully matched, whether the service flow is a video flow service is judged, if not, the transmission is carried out, and if so, the step S2 is carried out;
s2: an optimization module is arranged in the controller, and comprises a monitoring module, a QoS sensing module and a routing module; a QoS sensing module in the optimization module analyzes the QoS requirement of the video stream and transmits the QoS requirement to a routing module;
the monitoring module detects link information of current network underlying network equipment and transmits the detected link information to the routing module;
s3: and the routing module in the video flow routing optimization module selects a path for the video flow by using a DQN algorithm according to the QoS requirement and the link information received by the QoS sensing module, and issues the flow table to the underlying network equipment, and the underlying network equipment forwards data according to the flow table Action domain.
In a further improvement of the present invention, in step S1, it is determined whether the data packet is a video streaming service according to the ToS field of the data packet.
A further development of the invention is that in step S1 the Dijstra algorithm is used for the transmission.
A further improvement of the present invention is that, in step S3, the specific process of selecting a path for the video stream by using the DQN algorithm is as follows:
taking the current video stream fragment state as a state space, and selecting an action in an action space in a deep learning model on the premise of the state space to obtain a reward R of a t gaptAnd awarding the t gap to RtStoring the data in a memory pool as training data, and repeating the process continuously until all paths in the network are traversed, and rewarding RtThe path with the largest value is the appropriate path.
A further improvement of the invention is that the state space is as follows:
Sti＝{IDi,Sizei,QoSi}
wherein S istiIndicating the status of the ith video stream segment at time t, where IDi，Sizei，QoSiRespectively, the ID, length, QoS requirements of the video stream clip.
The invention is further improved in that the motion space is as follows:
at＝{link1,link2,link3......}
wherein, atThe link1, link2, and link3 represent link information of the network.
The invention is further improved in that the reward R of the t-gaptThe following were used:
Rt＝R{St,at,St+1}＝qt-αLoss-βDelay-γ[max(0,Bthr-Bthr(t+1))]
wherein q istIndicates the reward obtained under the action selected, Loss indicates the packet Loss rate, Delay indicates the time Delay, BthrIndicating the bandwidth of the link, Bthr(t+1)If the bandwidth of the link at the moment of t +1 exceeds a threshold value, a negative reward value is obtained; the weight of α being LossAnd the weight beta is the weight of Delay, and the weight gamma is the weight of the maximum bandwidth of the link.
Compared with the prior art, the invention has the following beneficial effects: as the network is now becoming larger and larger, and the variety and demand of the video stream service by the users is becoming higher and higher along with the growth of the end users, the present invention adopts a network architecture, Software Defined Network (SDN), and deploys a module for specially processing the video stream service, i.e. a video stream route optimization module (VSRO), in the controller of the SDN. The video stream routing optimization module comprises three sub-modules, a monitoring module, a QoS sensing module and a routing module. In the routing module, a classic algorithm-DQN (deep Q network) of a deep reinforcement learning technology is used, the algorithm solves the problem that the state and the action space are small in the traditional reinforcement learning, and is suitable for the condition of large network scale. The deep reinforcement learning technology is introduced into the video stream routing in the SDN, and the intelligence of the network is enhanced. The detection module monitors the state change of the network link, can dynamically adjust the path for transmitting the video stream service, adapts to the change of the network, and can ensure the transmission quality even if the network state changes. The invention optimizes the routing of the video stream service in the SDN environment, provides a special routing module for the video stream aiming at the characteristics of the video stream, and improves the transmission quality of the video stream.
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FIG. 1 is a flow chart of the present invention.
Fig. 2 is an architecture diagram of a video stream optimization module (VSRO) in an SDN.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
For the routing optimization problem of Video streams in the SDN network, the present invention deploys a Video Stream Routing Optimizer (VSRO) module in the SDN controller. The method aims to meet the QoS requirements of users while ensuring the transmission quality of video services and improving the bandwidth utilization rate of a network. The deployment position of the video stream route optimization module in the SDN is shown in figure 1.
Referring to fig. 2, the optimization module (VSRO) comprises three modules, a monitoring module, a QoS aware module, and a routing module. Bottom network equipment and video stream optimization module
The monitoring module is used for monitoring the change of the link state in the network and providing the link condition.
The QoS sensing module is used for sensing the QoS requirement of the video stream service and providing the requirement for the routing module, and the requirement of the video stream service for the bandwidth is higher, so that the bandwidth requirement of the video stream service is mainly sensed, and the transmission quality of the video stream is ensured.
The route selection module is used for selecting routes for the current video stream service.
(1) The monitoring module is a basic function of the SDN, acquires the topology and the type of bottom equipment of the network, combines the characteristics of the video stream, selects link information such as packet loss rate, time delay, residual bandwidth and the like to monitor, and provides the link information to the routing module.
(2) The QoS sensing module obtains the QoS requirement of the user and the required minimum bandwidth by analyzing the MPD file.
(3) The route selection module dynamically selects a proper transmission path for the video stream service according to the link condition information provided by the monitoring module, so that the transmission quality of the video stream service is ensured.
Referring to fig. 1 and 2, the present invention includes the steps of:
s1: and the service flow enters the SDN network, the SDN network judges whether the service flow table is successfully matched after receiving the data packet, if the service flow table is successfully matched, the data is forwarded according to the Action field of the flow table, if the service flow table is not successfully matched, whether the service flow table is a video flow service is judged according to the ToS field of the data packet, if not, the Dijstra algorithm is used for transmission, and if so, the step S2 is carried out.
S2: the controller is internally provided with an optimization module (VSRO), wherein the optimization module comprises a monitoring module, a QoS perception module and a routing module; a QoS aware module in a video stream route optimization module (VSRO) parses the QoS requirements of the video stream and transmits the QoS requirements to a route selection module.
The monitoring module detects link information of current network underlying network equipment and transmits the detected link information to the routing module;
s3: and the routing module in the video flow routing optimization module selects a path for the video flow by using a DQN algorithm according to the QoS requirement and the link information received by the QoS sensing module, and issues the flow table to the underlying network equipment, and the underlying network equipment forwards data according to the flow table Action domain.
Specifically, the algorithm adopted in the invention is a DQN algorithm, and DQN is a classic algorithm of deep reinforcement learning. The invention solves the video flow routing selection problem by using a deep reinforcement learning model.
The video stream segments with different transmission quality grades are transmission tasks to be completed, when a new transmission task exists, the monitoring module monitors the current network state to obtain the quality of a link, and at the moment, for the controller, the number, the length and the QoS (quality of service) requirements of the video stream segments are obtained through QoS (quality of service) perception and are known, so the states of the video stream segments are also determined.
The specific process of selecting a path for a video stream using the DQN algorithm is as follows:
taking the current video stream fragment state as a state space, and selecting an action in an action space in a deep learning model on the premise of the state space to obtain a reward R of a t gaptAnd awarding the t gap to RtStoring the data in a memory pool as training data, and repeating the process continuously until all paths in the network are traversed, and rewarding RtThe path with the largest value is the appropriate path.
The deep reinforcement learning model is the intelligence that an Agent of an Agent executes a certain Action decision Action in the current State, and the decision is continuously optimized according to the Reward of the system, so that the utilization rate of the whole resources is improved.
When a DRL is introduced into the SDN, a controller of the SDN is used as an Agent to interact with a State, make a decision and execute an Action.
The state space is as follows:
Sti＝{IDi,Sizei,QoSi}
wherein S istiIndicating the shape of the ith video stream segment at time tState wherein IDi，Sizei，QoSiRespectively, the ID, length, QoS requirements of the video stream clip.
The motion space is as follows:
the decision Action of the agent refers to the control Action executed by the agent on the system, and is the response of the agent to the observed state, and uses atIndicating that the agent is in accordance with the current system state StAnd the last moment feedback Rt determines the decision a to be executed currentlyt。
at＝{link1,link2,link3......}
Wherein, atThe link1, link2, and link3 represent link information of the network.
The reward function may evaluate the reward that is received when the agent selects an action in a certain state. To express the desired reward for the state. (to evaluate the performance of the action).
Reward R for t gaptThe following were used:
Rt＝R{St,at,St+1}＝qt-αLoss-βDelay-γ[max(0,Bthr-Bthr(t+1))]
wherein q istIndicates the reward obtained under the action selected, Loss indicates the packet Loss rate, Delay indicates the time Delay, BthrIndicating the bandwidth of the link, Bthr(t+1)For the bandwidth of the link at the time t +1, the penalty term is set because the bandwidth requirement of the video stream service is high, and if the bandwidth requirement exceeds the threshold value, a negative reward value is obtained. Alpha is the weight of Loss, beta is the weight of Delay, gamma is the weight of the maximum bandwidth of the link, and different parameters are set for different QoS parameters simultaneously.
The DQN is improved compared with Q-Learning by using a deep convolutional network (CNN) to approximate a value function and using an empirical replay training to strengthen the Learning process of Learning, namely, the DQN puts states, actions and reward values into a memory pool, and randomly selects data from the memory pool to train when training, so that the relevance among the data is broken.
The invention utilizes the characteristic that the SDN network can obtain the global view, and the monitoring module monitors the change of the network link, so that the controller can dynamically allocate paths to the video stream according to the change of the network bandwidth, and effectively prevent network congestion when facing large-scale video stream requests. The QoS sensing module can sense the QoS requirement of the current video stream service and carry out path selection aiming at the QoS requirement of the video stream. The routing module can select a route by using a DQN algorithm according to the information provided by the first two modules.
Claims (4)
1. A video stream intelligent routing method based on QoS perception in an SDN environment is characterized by comprising the following steps:
s1: the service flow enters the SDN network, the SDN network judges whether the service flow table is successfully matched after receiving the data packet, if the service flow table is successfully matched, the data is forwarded according to the Action field of the flow table, if the service flow table is not successfully matched, whether the service flow is a video flow service is judged, if not, the transmission is carried out, and if so, the step S2 is carried out;
s2: an optimization module is arranged in the controller, and comprises a monitoring module, a QoS sensing module and a routing module; a QoS sensing module in the optimization module analyzes the QoS requirement of the video stream and transmits the QoS requirement to a routing module;
the monitoring module detects link information of current network underlying network equipment and transmits the detected link information to the routing module;
s3: a routing module in the video stream routing optimization module selects a path for the video stream by using a DQN algorithm according to the QoS requirement and the link information received by the QoS sensing module, and issues the stream table to the underlying network equipment, and the underlying network equipment forwards data according to the stream table Action domain; the specific process of selecting a path for a video stream by using the DQN algorithm is as follows:
taking the current video stream fragment state as a state space, and selecting an action in an action space in a deep learning model on the premise of the state space to obtain a reward R of a t gaptAnd awarding the t gap to RtStoring in memory pool as training data, and repeating the process continuouslyRewarding R until all paths in the network are traversedtThe path with the maximum value is a proper path;
reward R for t gaptThe following were used:
Rt＝R{St,at,St+1}＝qt-αLoss-βDelay-γ[max(0,Bthr-Bthr(t+1))]
wherein q istIndicating the reward obtained under the action of selection, Loss indicating the packet Loss rate, Delay indicating the time Delay, BthrIndicating the bandwidth of the link, Bthr(t+1)If the bandwidth of the link at the moment of t +1 exceeds a threshold value, a negative reward value is obtained; alpha is the weight of Loss, beta is the weight of Delay, and gamma is the weight of the maximum bandwidth of the link;
the state space is as follows:
Sti＝{IDi,Sizei,QoSi}
wherein S istiIndicating the status of the ith video stream segment at time t, where IDi，Sizei，QoSiRespectively, the ID, length, QoS requirements of the video stream clip.
2. The method as claimed in claim 1, wherein in step S1, it is determined whether the video stream service is based on the ToS field of the packet.
3. The method according to claim 1, wherein in step S1, the Dijstra algorithm is used for transmission.
4. The method of claim 1, wherein the action space is as follows:
at＝{link1,link2,link3......}
wherein, atThe link1, link2, and link3 represent link information of the network.
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