CN114286413A - TSN network combined routing and stream distribution method and related equipment - Google Patents
TSN network combined routing and stream distribution method and related equipment Download PDFInfo
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Abstract
The application provides a TSN network combined routing and stream distribution method and related equipment, wherein the method comprises the following steps: building a system model of the TSN based on the software defined network, wherein the system model comprises a controller; establishing a Markov decision model of a communication flow distribution and routing problem in a TSN (traffic stream routing) network, and determining a state space, an action space and a reward function; taking the controller as an intelligent agent, and based on the Markov decision model, taking the minimum end-to-end average time delay of the communication flow under the condition of meeting the constraint as an optimization target, and obtaining a routing strategy of the communication flow by utilizing a DQN algorithm; and distributing a routing path for each communication flow according to the routing strategy. According to the technical scheme, the transmission of the low-priority flow can be completed within the maximum end-to-end time delay while the service quality of the high-priority flow transmission can be met.
Description
Technical Field
The present application relates to the field of communications network technologies, and in particular, to a TSN network joint routing and stream distribution method and related devices.
Background
A Time Sensitive Network (TSN), which is a mixed-flow system, has deterministic traffic and non-deterministic traffic. Each message generated in the TSN network is divided into Time Triggered (TT) traffic, Audio Video Bridging (AVB) traffic, and Best Effort (BE) traffic according to its communication demand.
The TSN network mainly relies on bounded delay and jitter to guarantee the service quality of the network, and in order to prevent best effort traffic from interfering with real-time traffic, different traffic in the TSN network needs to be scheduled and routed. At present, in order to solve the problems of simplification and abstraction complexity, a routing path set and a scheduling flow are assumed to be fixed and prior, so that the utilization rate of a network is low; in addition, these methods are no longer applicable when the link changes or burst traffic occurs, and the generalization capability is low, and traffic cannot be effectively scheduled.
Disclosure of Invention
In view of the above, an object of the present application is to provide a TSN network joint routing and flow distribution method and related device that solve the above problems.
Based on the above purpose, a first aspect of the present application provides a TSN network joint routing and flow distribution method, including:
building a system model of the TSN based on the software defined network, wherein the system model comprises a controller;
establishing a Markov decision model of a communication flow distribution and routing problem in a TSN (traffic stream routing) network, and determining a state space, an action space and a reward function;
taking the controller as an intelligent agent, and based on the Markov decision model, taking the minimum end-to-end average time delay of the communication flow under the condition of meeting the constraint as an optimization target, and obtaining a routing strategy of the communication flow by utilizing a DQN algorithm;
and distributing a routing path for each communication flow according to the routing strategy.
Further, the routing policy includes:
and the intelligent agent selects a next hop node for each communication flow in each current node queue according to the current network state until each communication flow completes path distribution or reaches a preset iteration number.
Further, the constraint is represented by the following formula:
wherein ,
representing the end-to-end delay of the traffic stream during its transmission from the source node to the destination node, t representing the time slot, fkRepresenting a communication flow, FTTRepresenting a set of time-triggered TT flows, τTTMaximum value, τ, representing end-to-end delay of TT trafficAVBMaximum value, F, representing the end-to-end delay of audio bridging AVB trafficAVBRepresenting the set of AVB traffic, T representing the communication period, FBERepresents the set of best-effort BE traffic,
indicating the link capacity, u, used by the traffic flow from node i to node jijRepresents the link capacity from node i to node j;
the optimization objective is represented by the following equation:
wherein ,ω1 and ω2Is a weight, represents an optimization tendency, and ω1+ω2T' denotes all time slots within a communication period,
represents the normalized average delay of TT traffic at time slot t,
the normalized average delay of the AVB traffic at time slot t is shown.
Further, the state space includes network states including states of node links, remaining capacity of node links, node queues, and the communication flow;
the action space comprises the steps of selecting a next hop node for each communication flow in the current node and forwarding the next hop node, so that the communication flow enters a corresponding priority queue;
the reward function rtRepresented by the formula:
wherein ,ρt,
ηtEach representing a control function, p, when said communication flows reach the destination node in time slot tt1, otherwise ρ t0; if the accumulated time delay of the current node exceeds the maximum allowable time delay,
otherwise
If the traffic does not reach the destination node and the maximum allowed delay is not exceeded, ηt1, otherwiset0; u are all constants greater than 0，ΦtRepresenting a function that is positively correlated to the current node queue length.
Further, the obtaining of the routing policy of the communication flow by using the DQN algorithm previously further includes:
acquiring a network topology map of the TSN network;
carrying out feature extraction on each node of the network topological graph by using a pre-trained graph convolution neural network to obtain a feature extraction result;
and updating the network state based on the feature extraction result.
Further, the number of layers of the graph convolution neural network is 2, and the propagation rule of the l-th hidden layer of the graph convolution neural network is represented by the following formula:
where σ (·) denotes an activation function,
denotes adding a self-loop for each node, an
J represents the connection relation between nodes, I is an identity matrix,
degree matrix, W, representing the number of links connected to a node(l)A weight matrix representing the ith layer of the graph convolutional neural network, σ (·) representing an activation function;
the graph convolution operator of the graph convolution neural network is represented by the following equation:
wherein ,
representing the characteristics of node i at the (l +1) th level,
representing the characteristics of node i at the l-th level,
a set of neighbor nodes representing a node i,
represents a normalization factor;
the graph convolution neural network forward propagation formula is as follows:
wherein ,
presentation pair
Standardization is carried out H(0)Representing a node feature matrix, W(0) and W(1)Weight matrices for the first and second layers of the graph convolutional neural network are represented, respectively.
Further, the system model comprises a topology management module, a traffic management module and a queue management module; wherein,
the topology management module is used for acquiring network topology information of the TSN and representing the network topology information by using a directed graph G (V, E), wherein V is { V ═ V }1,v2,…,vNDenotes a set of nodes of N switches in the network, E ═ EijL i, j belongs to N, i is not equal to j represents L physical link sets;
the traffic management module is used for acquiring a communication task in a TSN (TSN) network and abstracting the communication task into the communication flow, and the communication flow passes through the following tuple f'kRepresents:
wherein ,nsrc,k,ndst,kAnd e.g. V respectively represent the communication flow fkThe source and destination nodes of the network node,
represents the size, p, of the traffic flowk∈N*Indicating the period of the communication stream, tauk∈R+Representing the maximum allowable delay, delta, of the traffic flowkIndicates the priority of the communication flow, and
the queue management module is used for generating a node queue of the communication flow according to the priority of the communication flow, and the expression of the node queue is as follows:
qi≡{qi,1,qi,2,…,qi,p}
wherein ,qi,pRepresenting a node viThe p-th priority queue of (1).
Based on the same inventive concept, the second aspect of the present application provides a TSN network joint routing and stream distribution apparatus,
as can be seen from the above, the present application provides a method comprising:
a first building block: a system model configured to build a TSN network based on a software defined network, the system model including a controller;
a second building block: a Markov decision model configured to construct traffic flow allocation and routing problems in the TSN network, determine a state space, an action space, and a reward function;
a data processing module: the controller is used as an agent, based on the Markov decision model, the minimum end-to-end average time delay of the communication flow under the constraint condition is met as an optimization target, and a routing strategy of the communication flow is obtained by utilizing a DQN algorithm;
a policy enforcement module: is configured to assign a routing path to each of the communication flows in accordance with the routing policy.
Based on the same inventive concept, a third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of the first aspect when executing the program.
Based on the same inventive concept, a fourth aspect of the present application provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of the first aspect.
As can BE seen from the above, the TSN network joint routing and flow distribution method and the related device provided by the present application consider the mixed scheduling of the critical flow and the non-critical flow in the TSN network, are closer to the real network environment, increase the flexibility of flow scheduling, ensure the low delay and jitter of TT flow transmission, and also reduce the end-to-end delay of AVB flow, and the BE flow can also BE normally transmitted within the maximum end-to-end delay.
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In order to more clearly illustrate the technical solutions in the present application or the related art, the drawings needed to be used in the description of the embodiments or the related art will be briefly introduced below, and it is obvious that the drawings in the following description are only embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a TSN network joint routing and flow distribution method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a system model of a TSN network according to an embodiment of the present application;
FIG. 3 is a flow chart of updating a network state using a graph convolution neural network according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a TSN network joint routing and flow distribution device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below with reference to the accompanying drawings in combination with specific embodiments.
It should be noted that technical terms or scientific terms used in the embodiments of the present application should have a general meaning as understood by those having ordinary skill in the art to which the present application belongs, unless otherwise defined. The use of "first," "second," and similar terms in the embodiments of the present application do not denote any order, quantity, or importance, but rather the terms are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items.
As described in the background section, the technical solution for scheduling traffic in a TSN network in the related art is also difficult to satisfy the requirement, in which only one of the traffic is scheduled by fixing one of the traffic, and the set of routing paths and the scheduled flow are fixed and a priori. The applicant finds that the technical scheme in the prior art has the following problems in the process of implementing the application: the fixed routing path set is used for transmitting the flow, so that the utilization rate of the network is low; more importantly, when a link between nodes in the network fails or traffic changes, the traffic cannot be reasonably scheduled.
In view of this, the present application provides a scheme of TSN network joint routing and stream allocation, which combines a Graph convolutional neural network (GCN) with a deep reinforcement learning algorithm to schedule and route traffic of the TSN network, thereby ensuring low delay and jitter of TT service transmission, reducing end-to-end delay of AVB service, and enabling normal transmission of BE traffic.
Hereinafter, the technical means of the present application will be described in detail by specific examples.
Referring to fig. 1, a method for jointly selecting a route and distributing a flow in a TSN network according to an embodiment of the present application specifically includes the following steps:
step S101, constructing a system model of the TSN based on a Software Defined Network (SDN), wherein the system model comprises a controller.
In this step, referring to fig. 2, the system model includes a data plane, a control plane, and an application plane.
Data plane end devices, switches, and full-duplex physical links between them. The terminal device is a network device that generates a communication task, and typically, the end device that generates a message is a talker, and then another end device to which a message transmitted through a physical link arrives is a receiver, and each end device is both a talker and a receiver. The switch acts as a bridge between the message passing processes, receiving and sending messages according to the schedule. The network topology can be represented as a directed graph G ═ (V, E), where V ═ V1,v2,…,vNDenotes a set of nodes of N switches in the network, E ═ EijAnd | i, j belongs to N, i ≠ j } represents L physical link sets.
The control plane includes controllers, which in turn include Centralized User Configuration (CUC), Centralized Network Controller (CNC), and SDN controllers. The controllers are connected through a gateway. It should be noted that the CUC is used to collect the communication requirement (frequency, delay/jitter requirement) and send it to the CNC; the CNC is used for calculating a routing path and a scheduling table according to a communication request provided by the CUC; dynamic network connectivity is provided in the physical system by the SDN controller due to the hop count limitation of the TSN network.
The control plane connects the data plane and the application plane, provides a global view of the data plane for the application plane, and collects network state information including communication flow states, node states, and the like. The application plane may provide different services including network monitoring, data storage, traffic scheduling and routing, etc.
Step S102, a Markov decision model of the problem of communication flow distribution and routing in the TSN is constructed, and a state space, an action space and a reward function are determined.
In this step, the process of allocating and routing traffic flows in the TSN network is a sequential decision problem, and therefore the process can be modeled as a markov decision process. In consideration of the dynamic change of the environment for solving the problem, the large solving space and the high complexity, a reinforcement learning algorithm can be adopted for solving.
Step S103, taking the controller as an agent, based on the Markov decision model, and taking the minimum end-to-end average time delay of the communication flow under the condition of meeting the constraint as an optimization target, and obtaining the routing strategy of the communication flow by using a DQN (Deep Q-Learning) algorithm.
In this step, the reinforcement learning algorithm is an algorithm that acquires information by interacting with the environment without environment prior knowledge, and the reinforcement learning process is as follows: at time step t, the state s received by the agenttWhen the element belongs to S, the strategy is pi (a)t|st) Selecting an action atThe mapping from the state space to the action space when the strategy is pi is shown as pi by belonging to A: s → P (A), when the action a is executedtLater receive a timely reward rtThen at transition probability P(s)t+1|st,at) Transition to the next state st+1And continuously iterating until the turn is finished or a termination condition is met. The goal of the agent is to maximize the long-term cumulative revenue in the end state, which can be expressed as a cumulative reward
Wherein, gamma belongs to (0, 1)]Representing a discount factor.
Specifically, the reinforcement learning algorithm uses a state-value function (state-value function) or an action-value function (action value) to evaluate the performance of the agent in a certain state or the performance of a certain action of the agent in a given state. State s of state value under strategy pitCan be decomposed into the following bellman expectation equation representation:
wherein ,V(st) Indicating the state s at time slot ttFunction of the state value of V(s)t+1) Represents the state s at time slot t +1t+1A state value function of. The Bellman expectation equation can find the optimal strategy, wherein the optimal action value function is defined as:
optimal strategy after state value convergence*Can be calculated by the following formula:
it should be noted that, because a priori knowledge of a scene cannot be obtained, a model-free reinforcement learning algorithm is adopted, and secondly, because an action space and a state space of a traffic flow distribution and routing process in a TSN network are large, enumerating all states and actions in a value-iterative reinforcement learning algorithm (Q-learning) will increase much time and memory cost, so that a DQN algorithm is adopted, and a DQN algorithm introduces deep learning on the basis of Q-learning.
DQN networks consist of two networks of the same structure but with different parameters: the target network parameter updating method comprises a target network and a Q network, wherein the parameter of the target network is updated once by adopting the parameter of the Q network after each C-time iteration. DQN employs a deep network (Q network) to approximate a value function Q(s)t,at) The approximation function obtained by the Q network can be expressed as Q(s)t,at；θi)，θiAnd representing a parameter of the Q network at the ith iteration, namely the connection weight of the neural network. The goal of each iteration of the Q network optimization is generated by the target network and can be expressed as:
wherein ,at+1Represents the next moment of action, st+1The status at the next moment in time is shown,
representing parameters of the target network.
During the Q-network training, the parameters are updated by minimizing the following loss function:
L(θi)＝E[(yt-Q(st,at|θi))2]
the partial derivative of the loss function can be obtained:
another improvement of the DQN algorithm is the introduction of an empirical playback mechanism. At time step t, the agent will experience et＝(st,at,rt,st+1) The current state, the action, the reward and the next state are stored in an experience pool D, small batch samples are randomly sampled from D each time to update the network parameters, and the expression is as follows:
and step S104, distributing a routing path for each communication flow according to the routing strategy.
Therefore, the technical scheme of the embodiment considers the mixed scheduling of the key traffic and the non-key traffic in the TSN network, is closer to the real network environment, increases the flexibility of traffic scheduling, ensures the low delay and jitter of TT traffic transmission, reduces the end-to-end delay of AVB traffic, ensures the normal transmission of BE traffic, and provides favorable conditions for the implementation of reinforcement learning and improves the calculation efficiency of the DQN algorithm.
In some embodiments, the routing policy comprises:
and the intelligent agent selects a next hop node for each communication flow in each current node queue according to the current network state until each communication flow completes path distribution or reaches a preset iteration number.
In the embodiment, the non-critical flow and the critical flow are subjected to mixed scheduling by combining the time delay characteristics of different types of communication flows, so that the method is closer to a real network environment, and the scheduling flexibility is improved.
In some embodiments, the constraint is represented by the following expression:
wherein ,
representing the end-to-end delay of the traffic stream during its transmission from the source node to the destination node, t representing the time slot, fkRepresenting a communication flow, FTTRepresenting a set of time-triggered TT flows, τTTMaximum value, τ, representing end-to-end delay of TT trafficAVBMaximum value, F, representing the end-to-end delay of audio bridging AVB trafficAVBRepresenting the set of AVB traffic, T representing the communication period, FBERepresents the set of best-effort BE traffic,
indicating the link capacity, u, used by the traffic flow from node i to node jijRepresenting the link capacity from node i to node j.
It should be noted that constraint (a) indicates that the end-to-end delay of TT traffic is less than or equal to the maximum allowed end-to-end delay of TT traffic; constraint (b) indicates that the end-to-end delay of the AVB traffic is less than or equal to the maximum allowed end-to-end delay of the AVB traffic; the constraint (c) indicates that the transmission of the BE flow is completed in a preset communication period so as to ensure that the BE flow can BE transmitted in time; constraint (d) indicates that the link utilization for the current time slot cannot exceed the link capacity.
In particular, the end-to-end delay in the transmission of a traffic stream from a source node to a destination node
Can be represented by the following formula:
wherein ,dprRepresents a processing delay, the size of which depends on the switch design; dtrThe representation of the transmission delay is determined by the frame size and the link transmission rate; dpgRepresenting the propagation delay of the link, the size of which is determined by the propagation medium and the length of the cable; dqIndicating queuing delay.
The above delays are deterministic and bounded, however the queuing delay occurs when several traffic streams attempt to transmit at an egress port of the switch, the value of the queuing delay is indeterminate, depending on the current queue length. Thus, the end-to-end delay is mainly determined by the queuing delay, and the traffic flow can be isolated in space and time to reduce the queuing delay.
The optimization objective is represented by the following equation:
wherein ,ω1 and ω2Is a weight, represents an optimization tendency, and ω1+ω2T' denotes all time slots within a communication period,
represents the normalized average delay of TT traffic at time slot t,
the normalized average delay of the AVB traffic at time slot t is shown.
Specifically, the normalized average delay of TT traffic at time slot t is represented by the following formula:
wherein ,|FTTI denotes the number of TT flows, τTTThe maximum allowed end-to-end delay, which represents TT traffic, is a constant.
The normalized average delay of AVB traffic at time slot t is represented by:
wherein ,|FAVBI denotes the number of AVB traffic, τAVBThe maximum allowed end-to-end delay, which represents AVB traffic, is a constant.
In some embodiments, the state space comprises network states comprising remaining capacity g of node linksiNode queue qiSaid communication flow fkState γkAnd information about the status of the network,
nsrc,k,ndst,krepresenting source node, destination node, n, respectively, of a communication flowpos,kIndicating the node where the traffic is currently located, rkRepresents the size, p, of the traffic flowkIndicating the period of the communication flow, ζkRepresenting the cumulative delay, δ, of the traffic flow to the current nodekIndicating the priority of the communication flow. The related information of the network state comprises the number K of communication tasks, the number N of network nodes and the priority P of the queue.
In particular, the state S of the time slot t in the state space StRepresented by the formula:
st＝{g1(t),g2(t),…,gN(t),q1(t),q2(t),…,qN(t),Υ1(t),Υ2(t),…,ΥK(t)}
wherein ,gi(t)＝[gi1(t),gi2(t),…,giN(t)]Indicating the remaining link capacity of the link connected to the node i; q. q.si(t)＝[qi,1(t),qi,2(t),…,qn,p(t)]Y, the queue representing node ik＝
Representing the communication flow state of the node.
The action space comprises the steps of selecting a next hop node for each communication flow in the current node and forwarding, so that the communication flow enters a corresponding priority queue.
It should be noted that, when routing each communication flow (the communication flows are inseparable and only transmit on one path at the same time), the agent needs to allocate a path from the source node to the destination node for each flow, and since it is difficult to directly select one path for the communication flow in a large-scale network, shared links existing between different paths may cause collisions and interferences between different flows. In the scheme, the agent selects the next hop for each communication flow in the nodes, sends the communication flow to the next node and enters the corresponding priority queue. Compared with the direct path selection, the method for selecting the next hop improves the generalization capability of the algorithm, and finally completes the path selection process of the communication flow through continuous iteration.
Specifically, A represents an action space, and action a is performed in t time slottε A is expressed as:
wherein ,
indicates that communication flow k is selected as a candidate node for the next hop
Indicating that node n is selected as the next hop for communication flow k, and vice versa,
indicating that node n has not been selected.
The reward function rtRepresented by the formula:
wherein ,ρt,
ηtEach representing a control function, p, when said communication flows reach the destination node in time slot tt1, otherwise ρ t0; if the accumulated time delay of the current node exceeds the maximum allowable time delay,
otherwise
If the traffic does not reach the destination node and the maximum allowed delay is not exceeded, ηt1, otherwiset0; u are all constants greater than 0, phitRepresenting a function that is positively correlated to the current node queue length.
In some embodiments, referring to fig. 3, the obtaining of the routing policy of the communication flow by using the DQN algorithm further includes the following steps:
step S301, a network topology map of the TSN network is obtained.
In this step, the network topology map of the TSN network may be obtained by the topology management module in the control plane.
Step S302, feature extraction is carried out on each node of the network topological graph by utilizing a pre-trained graph convolution neural network so as to obtain a feature extraction result.
In the step, feature extraction is carried out on each node through the graph convolution neural network, and the processed node features not only comprise the features of the current node but also comprise the features of the neighbor nodes.
Step S303, updating the network status based on the feature extraction result.
In this step, when the network topology changes, the current network node characteristics are updated in time to ensure the validity of the routing strategy.
In some embodiments, the number of layers of the convolutional neural network is 2, and the propagation rule of the ith hidden layer of the convolutional neural network is represented by the following formula:
where σ (·) denotes an activation function,
denotes adding a self-loop for each node, an
J represents the connection relation between nodes, I is an identity matrix,
degree matrix, W, representing the number of links connected to a node(l)A weight matrix, σ (·) representing the l-th layer of the graph convolutional neural network) Representing an activation function.
The graph convolution operator of the graph convolution neural network is represented by the following equation:
wherein ,
representing the characteristics of node i at the (l +1) th level,
representing the characteristics of node i at the l-th level,
a set of neighbor nodes representing a node i,
representing a normalization factor.
The graph convolution neural network forward propagation formula is as follows:
wherein ,
presentation pair
Standardization is carried out H(0)Representing a node feature matrix, W(0) and W(1)Weight matrices for the first and second layers of the graph convolutional neural network are represented, respectively.
In some embodiments, in conjunction with fig. 2, the system model includes a topology management module (TPM), a Traffic Management Module (TMM), and a Queue Management Module (QMM). Wherein,
the topology management module is used for acquiring the TSN networkAnd is represented by a directed graph G ═ (V, E), where V ═ { V ═ V-1,v2,…,vNDenotes a set of nodes of N switches in the network, E ═ EijAnd | i, j belongs to N, i ≠ j } represents L physical link sets.
The traffic management module is used for acquiring a communication task in a TSN (TSN) network and abstracting the communication task into the communication flow, and the communication flow passes through the following tuple f'kRepresents:
wherein ,nsrc,k,ndst,kAnd e.g. V respectively represent the communication flow fkThe source and destination nodes of the network node,
represents the size, p, of the traffic flowk∈N*Indicating the period of the communication stream, tauk∈R+Representing the maximum allowable delay, delta, of the traffic flowkIndicates the priority of the communication flow, and
the queue management module is used for generating a node queue of the communication flow according to the priority of the communication flow, and the expression of the node queue is as follows:
qi≡{qi,1,qi,2,…,qi,p}
wherein ,qi,pRepresenting a node viThe p-th priority queue of (1).
It should be noted that the method of the embodiment of the present application may be executed by a single device, such as a computer or a server. The method of the embodiment can also be applied to a distributed scene and completed by the mutual cooperation of a plurality of devices. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiment, and the multiple devices interact with each other to complete the method.
It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Based on the same inventive concept, corresponding to the method of any embodiment, the application also provides a TSN network joint routing and stream distribution device.
Referring to fig. 4, the TSN network joint routing and flow distribution apparatus includes:
a first building module 401 configured to build a system model of the TSN network based on the software defined network, the system model including a controller;
a second construction module 402 configured to construct a markov decision model of traffic flow allocation and routing problems in the TSN network, determine a state space, an action space, and a reward function;
a data processing module 403, configured to use the controller as an agent, and based on the markov decision model, obtain a routing policy of the communication flow by using a DQN algorithm with a minimum end-to-end average delay of the communication flow satisfying a constraint condition as an optimization target;
a policy enforcement module 404 configured to allocate a routing path for each of the communication flows according to the routing policy.
For convenience of description, the above devices are described as being divided into various modules by functions, and are described separately. Of course, the functionality of the various modules may be implemented in the same one or more software and/or hardware implementations as the present application.
The apparatus in the foregoing embodiment is used to implement the corresponding TSN network joint routing and stream distribution method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to the method of any embodiment described above, the present application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the TSN network joint routing and stream distribution method described in any embodiment above is implemented.
Fig. 5 is a schematic diagram illustrating a more specific hardware structure of an electronic device according to this embodiment, where the electronic device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein the processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 are communicatively coupled to each other within the device via bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 1020 may store an operating system and other application programs, and when the technical solution provided by the embodiments of the present specification is implemented by software or firmware, the relevant program codes are stored in the memory 1020 and called to be executed by the processor 1010.
The input/output interface 1030 is used for connecting an input/output module to input and output information. The i/o module may be configured as a component in a device (not shown) or may be external to the device to provide a corresponding function. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 1040 is used for connecting a communication module (not shown in the drawings) to implement communication interaction between the present apparatus and other apparatuses. The communication module can realize communication in a wired mode (such as USB, network cable and the like) and also can realize communication in a wireless mode (such as mobile network, WIFI, Bluetooth and the like).
It should be noted that although the above-mentioned device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The electronic device in the foregoing embodiment is configured to implement the corresponding TSN network joint routing and stream distribution method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again.
Based on the same inventive concept, corresponding to any of the above-described embodiment methods, the present application further provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the TSN network joint routing and stream distribution method according to any of the above embodiments.
Computer-readable media of the present embodiments, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
The computer instructions stored in the storage medium of the foregoing embodiment are used to enable the computer to execute the TSN network joint routing and stream distribution method according to any of the foregoing embodiments, and have the beneficial effects of corresponding method embodiments, and are not described herein again.
Those of ordinary skill in the art will understand that: the discussion of any embodiment above is meant to be exemplary only, and is not intended to intimate that the scope of the disclosure, including the claims, is limited to these examples; within the context of the present application, features from the above embodiments or from different embodiments may also be combined, steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of brevity.
In addition, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown in the provided figures for simplicity of illustration and discussion, and so as not to obscure the embodiments of the application. Furthermore, devices may be shown in block diagram form in order to avoid obscuring embodiments of the application, and this also takes into account the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the application are to be implemented (i.e., specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that the embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative instead of restrictive.
While the present application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic ram (dram)) may use the discussed embodiments.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, substitutions, improvements, and the like that may be made without departing from the spirit and principles of the embodiments of the present application are intended to be included within the scope of the present application.
Claims (10)
1. A TSN network joint routing and flow distribution method is characterized by comprising the following steps:
building a system model of the TSN based on the software defined network, wherein the system model comprises a controller;
establishing a Markov decision model of a communication flow distribution and routing problem in a TSN (traffic stream routing) network, and determining a state space, an action space and a reward function;
taking the controller as an intelligent agent, and based on the Markov decision model, taking the minimum end-to-end average time delay of the communication flow under the condition of meeting the constraint as an optimization target, and obtaining a routing strategy of the communication flow by utilizing a DQN algorithm;
and distributing a routing path for each communication flow according to the routing strategy.
2. The method of claim 1, wherein the routing policy comprises:
and the intelligent agent selects a next hop node for each communication flow in each current node queue according to the current network state until each communication flow completes path distribution or reaches a preset iteration number.
3. The method of claim 1, wherein the constraint is represented by:
wherein ,
representing the end-to-end delay of the traffic stream during its transmission from the source node to the destination node, t representing the time slot, fkRepresenting a communication flow, FTTRepresenting a set of time-triggered TT flows, τTTMaximum value, τ, representing end-to-end delay of TT trafficAVBMaximum value, F, representing the end-to-end delay of audio bridging AVB trafficAVBRepresenting the set of AVB traffic, T representing the communication period, FBERepresents the set of best-effort BE traffic,
indicating the link capacity, u, used by the traffic flow from node i to node jijRepresents the link capacity from node i to node j;
the optimization objective is represented by the following equation:
wherein ,ω1 and ω2Is a weight, represents an optimization tendency, and ω1+ω2T' denotes all time slots within a communication period,
represents the normalized average delay of TT traffic at time slot t,
the normalized average delay of the AVB traffic at time slot t is shown.
4. The method of claim 3, wherein the state space comprises network states and wherein the network states comprise node links, remaining capacity of node links, node queues, and states of the communication flow;
the action space comprises the steps of selecting a next hop node for each communication flow in the current node and forwarding the next hop node, so that the communication flow enters a corresponding priority queue;
the reward function rtRepresented by the formula:
wherein ,
each representing a control function, p, when said communication flows reach the destination node in time slot tt1, otherwise ρt0; if the accumulated time delay of the current node exceeds the maximum allowable time delay,
otherwise
If the communication flow does not reach the destination nodeAnd does not exceed the maximum allowable delay, ηt1, otherwiset0; u are all constants greater than 0, phitRepresenting a function that is positively correlated to the current node queue length.
5. The method of claim 4, wherein said deriving a routing policy for said communication flow using DQN algorithm further comprises:
acquiring a network topology map of the TSN network;
carrying out feature extraction on each node of the network topological graph by using a pre-trained graph convolution neural network to obtain a feature extraction result;
and updating the network state based on the feature extraction result.
6. The method of claim 1, wherein the number of layers of the graph convolution neural network is 2, and the propagation rule of the I layer hidden layer of the graph convolution neural network is represented by the following formula:
where σ (·) denotes an activation function,
denotes adding a self-loop for each node, an
J represents the connection relation between nodes, I is an identity matrix,
degree matrix, W, representing the number of links connected to a node(l)A weight matrix representing the ith layer of the graph convolutional neural network, σ (·) representing an activation function;
the graph convolution operator of the graph convolution neural network is represented by the following equation:
wherein ,
representing the characteristics of node i at the (l +1) th level,
representing the characteristics of node i at the l-th level,
a set of neighbor nodes representing a node i,
represents a normalization factor;
the graph convolution neural network forward propagation formula is as follows:
wherein ,
presentation pair
Standardization is carried out H(0)Representing a node feature matrix, W(0) and W(1)Weight matrices for the first and second layers of the graph convolutional neural network are represented, respectively.
7. The method of claim 1, wherein the system model comprises a topology management module, a traffic management module, and a queue management module; wherein,
the topology management module is used for acquiring network topology information of the TSN and representing the network topology information by using a directed graph G (V, E), wherein V is { V ═ V }1，v2，…，vNDenotes a set of nodes of N switches in the network, E ═ EijL i, j belongs to N, i is not equal to j represents L physical link sets;
the traffic management module is used for acquiring a communication task in a TSN (TSN) network and abstracting the communication task into the communication flow, and the communication flow passes through the following tuple f'kRepresents:
wherein ,nsrc，k，ndst，kAnd e.g. V respectively represent the communication flow fkThe source and destination nodes of the network node,
indicating the size of the communication flow, Pk∈N*Indicating the period of the communication stream, tauk∈R+Representing the maximum allowable delay, delta, of the traffic flowkIndicates the priority of the communication flow, and
the queue management module is used for generating a node queue of the communication flow according to the priority of the communication flow, and the expression of the node queue is as follows:
qi≡{qi，1，qi，2，…，qi，p}
wherein ,qi，pRepresenting a node viThe p-th priority queue of (1).
8. A TSN network joint routing and flow distribution apparatus, comprising:
a first building block: a system model configured to build a TSN network based on a software defined network, the system model including a controller;
a second building block: a Markov decision model configured to construct traffic flow allocation and routing problems in the TSN network, determine a state space, an action space, and a reward function;
a data processing module: the controller is used as an agent, based on the Markov decision model, the minimum end-to-end average time delay of the communication flow under the constraint condition is met as an optimization target, and a routing strategy of the communication flow is obtained by utilizing a DQN algorithm;
a policy enforcement module: is configured to assign a routing path to each of the communication flows in accordance with the routing policy.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 7.
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