CN104902515A - Load aware-based multi-layer satellite network routing method - Google Patents

Abstract

The invention discloses a multi-layer satellite network routing method based on load perception, which uses a multi-layer satellite network layered framework to periodically sense the network load, and dynamically adjusts the service at the MEO layer according to the collected load information. The proportion of offloading to the LEO layer achieves network load balancing. By matching the QoS requirements of the business with the structural characteristics of the network, when the network business load is heavy, the business with the lowest delay requirement is first offloaded, so that it can be distributed from the MEO layer. transmission, while delay-sensitive services are always transmitted from the LEO layer. The invention alleviates the local congestion caused by the uneven distribution of services, improves the throughput of the network, avoids the frequent information interaction caused by the calculation route of the satellite in the LEO layer, reduces the overhead of the system, and reduces the cost of information exchange. Delay enables the network to recover more quickly from node failure or link interruption, improving the adaptability and robustness of the network.

Description

A Routing Method for Multi-Layer Satellite Networks Based on Load Sensing

technical field

The invention belongs to the technical field of satellite communication, in particular to a multi-layer satellite network routing method based on load perception.

Background technique

As an outstanding representative of the next-generation satellite communication architecture, Multi-Layer Satellite Network (MLSN) has received extensive attention and research in recent years. Different from the traditional single-layer satellite network, the multi-layer satellite network is composed of various satellite constellations such as low-orbit satellites (LEO), medium-orbit satellites (MEO) and geosynchronous orbit satellites (GEO), so it has coverage Wide, short transmission delay, large network capacity and other advantages. In addition, this hierarchical network architecture facilitates efficient management of the network, thereby reducing the overhead of information exchange, making the network more robust, and able to quickly recover from node failures and link interruptions. In order to be able to efficiently transport traffic in multi-layer satellite networks, routing strategies are essential. However, due to the satellites periodically orbiting the earth, the topology of the satellite network changes rapidly, and link interruptions and handovers occur frequently, which pose a great challenge to the effectiveness of routing protocols. In addition, affected by factors such as climate, economy, and terrain, the population distribution on the earth's surface is extremely uneven, resulting in large differences in service requirements. As a result, some satellites are already very congested and begin to lose a lot of packets, while some satellites are not. fully utilized situation. Therefore, it is necessary to propose an effective routing protocol to capture and utilize network topology changes, and achieve network load balancing to improve overall throughput performance.

At present, in order to improve the throughput of LEO/MEO multi-layer satellite network, researchers have proposed many routing algorithms. According to the degree of utilization of MEO layer satellites, these algorithms can be roughly divided into two categories: one is to use MEO layer satellites to realize network management, and the other is to use MEO layer satellites to realize long-distance service transmission. Specifically, in the first type of routing algorithm, the MEO layer is mainly used to periodically collect the state information of the inter-satellite link in the LEO layer, such as link interruption, propagation delay, queuing delay, etc., and it uses such information for The LEO layer satellite establishes a routing information table and distributes it to the LEO layer satellite, while the LEO layer satellite is responsible for the transmission of services; the core of the second type of routing algorithm is to use the MEO layer satellite to transmit long-distance services, and the LEO layer is used to transmit short-distance services, so that MEO layer satellites and LEO layer satellites are utilized to varying degrees, alleviating the congestion of the LEO layer. All in all, the above two types of algorithms use different functions of the MEO layer to make routing decisions, thereby improving the throughput of the multi-layer satellite network.

However, the two types of algorithms proposed above have not well integrated the characteristics of both network management and long-distance service transmission in the MEO layer of the multi-layer satellite network. Specifically, the first type of algorithm does not make full use of the ability of the MEO layer to transmit services, so when the LEO layer is heavily loaded and packet loss occurs, the MEO layer satellites are still not used to relieve the transmission pressure of the LEO layer, thus failing to reach the network The second type of algorithm classifies the business according to the distance from the source to the destination node, rather than distinguishing based on the specific quality of service (QoS) requirements (such as delay, throughput, etc.) of the business, which is to a certain extent This limits the ability of the network to guarantee different QoS services. On the other hand, this type of algorithm does not take advantage of the advantages of MEO layer satellites to facilitate network management. The overhead of network interaction information is relatively large, occupying a part of transmission resources, and limiting the throughput of the network. In addition, the above two types of algorithms do not make routing decisions based on network load changes to dynamically adjust the distribution ratio of services at the MEO layer and LEO layer, and match the QoS requirements of services with the structural characteristics of the network, so that different types of The business can be transmitted through the most suitable layer, so as to further guarantee the QoS of different services on the basis of load balancing.

Contents of the invention

The purpose of the present invention is to provide a multi-layer satellite network routing method based on load perception, aiming to solve the problem that the throughput of the LEO/MEO multi-layer satellite network existing in the existing satellite communication is low, and the load balance of the network cannot be achieved. The problem of high overhead of network interaction information.

The present invention is achieved in this way, a multi-layer satellite network routing method based on load perception, the multi-layer satellite network routing method based on load perception utilizes a multi-layer satellite network hierarchical framework to periodically sense the load of the network , and according to the collected load information, dynamically adjust the distribution ratio of the business at the MEO layer and the LEO layer to achieve network load balancing. By matching the QoS requirements of the business with the structural characteristics of the network, the most The traffic with the lowest delay requirement is transmitted from the MEO layer first, and the delay-sensitive services are always transmitted from the LEO layer.

Further, the multi-layer satellite network routing method based on load perception specifically includes the following steps:

Step 1, each MEO node M _u constructs a cluster C _u (t) with itself as the cluster head node according to the network topology Γ(t) stored in advance on the star;

Step 2: information exchange is performed in each cluster, the cluster head MEO node M _u collects the link state information λ _ui (t) and Q _ij (t) of the LEO nodes in the cluster C _u (t), and constructs the corresponding routing subgraph RG _Mu (t);

Step 3, build a global routing graph RG(t)={V _t , E _t ,w(i,j)};

Step 4, each MEO node M _u calculates the optimal shunt factor χ _u (t) for the LEO node L _ui in the cluster of C _u (t),

Step five, each MEO node M _u uses the Dijkstra algorithm or Bellman-Ford algorithm to obtain the shortest path of each node in the C _u (t) cluster according to the global routing graph RG(t), and builds a routing table accordingly For cluster-head M _u nodes, the final routing table And for LEO node L _ui in the cluster, if χ _u (t)=0, the final routing table If χ _u (t)>0, then the next hop node of this part of the business is the cluster head node M _u ;

Step 6, the current LEO node L _ui calculates the final distribution ratios χ _uA (t), χ _uB (t), and χ _uC (t) for different types of services according to the routing table RT _ui (t) and selects the corresponding route, the current MEO The node M _u transmits the data to the next hop node according to the routing table RT _u (t).

Step 7: After a topology update cycle ΔT, each node in the network starts to re-execute the above steps 1 to 6 to ensure periodic update of link cost information, and then dynamically adjust routing selection to achieve network load balance And guarantee the QoS of different services.

Further, said step one specifically includes:

In the first step, based on the predictability and periodicity of the satellite trajectory, all MEO nodes can calculate in advance and store the overall topology Γ(t) of the network in the t-th topology update period on the satellite;

The second step is to obtain the overall topology Γ(t) of the current t-th topology update period when the topology update period arrives;

In the third step, each MEO node M _u constructs a cluster C _u (t) according to Γ(t), where the cluster head is the M _u node, and the nodes in the C _u (t) cluster consist of a series of LEO nodes L that meet the following conditions _ui , that is, the L _ui node is within the coverage of the M _u node and the node M _u is the nearest neighbor MEO node. The set of clustering elements is expressed as:

C _u (t)＝{M _u ,L _u1 ,L _u2 ,…,L _u|Cu| },

Where |C _u | is the number of LEO nodes in the C _u (t) cluster, and the L _ui node represents the i-th LEO node in the C _u (t) cluster.

Further, said step two specifically includes:

In the first step, each cluster-head MEO node M _u broadcasts a HELLO packet containing the node's IP, geographic location information and packet sequence number to the LEO node L _ui in the C _u (t) cluster;

In the second step, the LEO node L _ui in the cluster receives the HELLO packet from the C _u (t) cluster head M _u node and obtains the location information of the cluster head node. If it is found that the cluster head node has changed, the new cluster head Node M _u replaces the original cluster head node;

In the third step, each LEO node L _ui monitors the newly arrived external service flow λ _ui (t) and the average queuing delay Q _ij (t) information during the topology update cycle, and sends the information to the cluster head MEO node C _u (t) M _u sends a packet containing this information;

In the fourth step, the M _u node collects the data packets of all LEO nodes L _ui in the C _u (t) cluster and constructs a routing _subgraph RG _Mu (t)={V(M _u ),E(M _u ),w(i,j)}.

Further, the specific implementation of the fourth step includes:

Step 1, Mu node obtains the complete topology Γ(t) in the current topology update cycle, takes _Mu node as the root node to obtain all its neighbor node set V(M _{u )} and the chain connecting _Mu node and its neighbor nodes road set E(M _u );

Step 2, calculate the cost w(i,j)=p(i,j)+q(i,j) of all links (i,j) in the link set E(M _u ), where p(i,j ) is the propagation delay of the link, q(i,j) is the queuing delay of the link, p(i,j) is calculated in advance according to the topology Γ(t) of the network, and q(i,j)=Q _ij (t);

Step 3, synthesize the final routing subgraph RG _Mu (t)={V(M _u ),E(M _u ),w(i,j)}.

Further, said step three specifically includes:

In the first step, in the initialization phase, each MEO node M _u broadcasts a data packet containing RG _Mu (t) and Q _uk (t) information to all neighbor MEO nodes _{M k} , and receives a packet containing RG _Mk (t ) and Q _ku (t) packets of information;

In the second step, when the MEO node _{Mu receives the data packet from M k ,} check whether the data packet is contained in the cache, if so, delete the packet, and add _Mu to the set corresponding to the packet sequence number n In Π _n , if not, then save the data packet and send it to the neighbor MEO nodes outside the set Π _n ;

In the third step, when each MEO node obtains the cost w(i,j) of all links (i,j)∈E _t , the exchange process is terminated, and the final routing graph RG(t)={V _t ,E _t ,w(i,j)}.

Further, said step four specifically includes:

The first step is to calculate the total amount of newly arrived services of LEO nodes in the cluster λ _u (t) = ∑λ _ui (t);

The second step is to define the LEO/MEO multi-layer network characteristic parameters φ _M , φ _L , and perform the following calculations:

${\mathrm{<span\; class="notranslate">\; \&phi;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 8</span>}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 4</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>$

${\mathrm{<span\; class="notranslate">\; \&phi;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 8</span>}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 4</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ;</span>$

The third step is to calculate the average congestion degree Θ _u (t) of the nodes in the cluster:

${\mathrm{<span\; class="notranslate">\; \&Theta;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$

Among them, λ _M (t), C _M , and d _M are the average arrival rate of services actually arriving at MEO layer links, the transmission capacity and propagation delay of MEO layer inter-satellite links, and λ _L (t), C _L , d _L are the average arrival rate of services actually arriving at the LEO layer link, the transmission capacity of the MEO layer inter-satellite link and the propagation delay. Note that the above λ _M (t) and λ _L (t) include the newly arrived services of external users and are The business relayed by neighboring nodes can be calculated by the following formula:

${\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&chi;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&phi;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; ,</span>$

${\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&chi;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&phi;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; ;</span>$

In the fourth step, the optimal shunt factor χ _u (t) can be obtained by solving the following equation:

$\frac{<span\; class="notranslate">\; \&part;</span>\mathrm{<span\; class="notranslate">\; \&Theta;</span>}}{<span\; class="notranslate">\; \&part;</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; \&part;</span>\mathrm{<span\; class="notranslate">\; \&Theta;</span>}}{<span\; class="notranslate">\; \&part;</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; .</span>$

Further, in the step 6, the business is divided into three categories according to the requirements for time delay: Class A business is a time delay sensitive business, which has the highest requirement for time delay and the highest priority, and a typical service representative is a real-time voice service; The service is a delay-tolerant service, which has certain requirements on the delay, and the priority is the second. A typical service representative is the video streaming service; the C-type service is to ensure the service as best as possible, has no requirement on the delay, and has the lowest priority. It represents file transfer and web page browsing services in the Internet.

Compared with the prior art, the present invention has the following advantages:

1) The present invention makes full use of the hierarchical architecture of the LEO/MEO multi-layer satellite network to periodically collect the load traffic information of the network, and make routing decisions accordingly, so that the LEO layer satellites can dynamically monitor the traffic flow according to the load changes of the network. Adjust the shunt factor to transmit the optimal proportion of business from the MEO layer, thus greatly relieving the local congestion caused by uneven business distribution, achieving load balancing of the entire network, and improving the throughput of the network.

2) The present invention classifies services according to the delay requirements, and matches the service quality requirements of different types of services with the characteristics of the LEO/MEO network structure, so that different types of services can be transmitted on the most suitable path, and can be transmitted on the network When congestion is reached, differentiated distribution is performed, that is, class C services are first distributed to ensure best-effort services, and class A delay-sensitive services are distributed last, and the QoS requirements of different services are guaranteed as much as possible under the condition of load balance.

3) The present invention utilizes the MEO node to periodically collect the link state information of the LEO node, which avoids the frequent information interaction brought by the satellite in the LEO layer due to the calculation of the route, reduces the overhead of the system, and reduces the time delay of information exchange. It enables the network to recover more quickly from node failure or link interruption, and improves the adaptability and robustness of the network.

Description of drawings

FIG. 1 is a schematic diagram of an applicable LEO/MEO multi-layer satellite network scenario provided by an embodiment of the present invention;

Fig. 2 is a flow chart of a multi-layer satellite network routing method based on load perception provided by an embodiment of the present invention.

detailed description

In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

The present invention periodically perceives the load of the network by using the hierarchical structure of the multi-layer satellite network, and dynamically adjusts the distribution ratio of the business at the MEO layer and the LEO layer according to the collected load information, so as to achieve the load balance of the network. In addition, by matching the QoS requirements of services with the structural characteristics of the network, when the network service load is heavy, the services with the lowest delay requirements are first offloaded and transmitted from the MEO layer, while delay-sensitive services are always transmitted from the LEO layer, ensuring QoS for different types of services.

The application principle of the present invention will be described in detail below in conjunction with the accompanying drawings.

With reference to Fig. 1, the LEO/MEO multilayer satellite network that the present invention uses is made of LEO layer and LEO layer, and LEO layer network comprises all LEO satellite nodes, and it comprises _PL orbit, and each orbit has _SL satellite; MEO The layer network includes all MEO nodes, it includes P _M orbits, each orbit has S _M satellites, in addition, both the LEO layer and the MEO layer can provide global coverage, and the MEO layer satellite orbit height is higher than the LEO layer satellite orbit, Correspondingly, the number of satellites in the MEO layer is less than that in the LEO layer.

With reference to Fig. 2, the concrete realization steps of the present invention are as follows:

Step 1. Each MEO node M _u constructs a cluster C _u (t) with itself as the cluster head node according to the network topology Γ(t) stored in advance on the star;

(1a) Based on the predictability and periodicity of the satellite trajectory, all MEO nodes can calculate in advance and store the overall topology Γ(t) of the network in the t-th topology update period on the satellite. Simulation software STK simulation implementation;

(1b) When the topology update period arrives, obtain the overall topology Γ(t) of the current t-th topology update period;

(1c) Each MEO node M _u constructs a cluster C _u (t) according to Γ(t), where the cluster head is the M _u node, and the nodes in the C _u (t) cluster consist of a series of LEO nodes L _ui that meet the following conditions Composition, that is, the L _ui node is within the coverage of the M _u node and the node M _u is the nearest neighbor MEO node, the set of clustering elements can be expressed as:

C _u (t)＝{M _u ,L _u1 ,L _u2 ,…,L _u|Cu| },

Where |C _u | is the number of LEO nodes in the C _u (t) cluster, and the L _ui node represents the i-th LEO node in the C _u (t) cluster.

Step 2, exchange information in each cluster, the cluster head MEO node M _u collects the link state information λ _ui (t) and Q _ij (t) of the LEO nodes in the cluster C _u (t), and constructs the corresponding routing subgraph RG _Mu (t);

(2a) Each cluster head MEO node M _u broadcasts a HELLO packet containing the node's IP, geographic location information and packet sequence number to the LEO node L _ui in the C _u (t) cluster;

(2b) The LEO node L _ui in the cluster receives the HELLO packet from the C _u (t) cluster head M _u node and obtains the location information of the cluster head node. If it is found that the cluster head node has changed, the new cluster head node _Mu replaces the original cluster head node;

(2c) Each LEO node L _ui monitors the newly arrived external service flow λ _ui (t) and the average queuing delay Q _ij (t) information during the topology update period, and sends the cluster head MEO node M to C _u (t) _u sends a packet containing this information;

(2d) The M _u node collects the data packets of all LEO nodes L _ui in the C _u (t) cluster and builds a routing _subgraph RG _Mu (t)＝{V(M _u ),E(M _u ),w(i,j)}, the specific implementation steps include:

(2d1) M _u node obtains the complete topology Γ(t) in the current topology update period, takes M _u node as the root node to obtain all its neighbor node set V(M _u ) and the chain connecting M _u node and its neighbor nodes road set E(M _u );

(2d2) Calculate the cost w(i,j)=p(i,j)+q(i,j) of all links (i,j) in the link set E(M _u ), where p(i,j ) is the propagation delay of the link, q(i,j) is the queuing delay of the link, p(i,j) can be calculated in advance according to the topology Γ(t) of the network, and q(i,j)=Q _ij (t);

(2d3) Synthesize the final routing subgraph RG _Mu (t)={V(M _u ),E(M _u ),w(i,j)}.

Step 3, build a global routing graph RG(t)={V _t , E _t ,w(i,j)};

(3a) In the initialization phase, each MEO node Mu broadcasts a data packet containing RG _Mu (t) and Q _uk (t) information to all neighboring MEO nodes M _k , and receives a packet containing RG _Mk (t) from _{M k nodes} and Q _ku (t) packets of information;

(3b) When the MEO node _{Mu receives the data packet from M k ,} check whether the data packet is contained in the cache, if so, delete the packet, and add _Mu to the set Π corresponding to the packet sequence number n In _n , if not, then save the data packet, and send it to the neighbor MEO node outside the set _Πn ;

(3c) After each MEO node obtains the cost w(i,j) of all links (i,j)∈E _t , the exchange process is terminated, and the final routing graph RG(t)={V _t , E _t ,w(i,j)}.

Step 4, each MEO node M _u calculates the optimal shunt factor χ _u (t) for the LEO node L _ui in the cluster of C _u (t),

(4a) Calculate the total amount of newly arrived services of LEO nodes in the cluster λ _u (t) = Σλ _ui (t);

(4b) Define the LEO/MEO multi-layer network characteristic parameters φ _M , φ _L , and perform the following calculations:

${\mathrm{<span\; class="notranslate">\; \&phi;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 8</span>}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 4</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>$

${\mathrm{<span\; class="notranslate">\; \&phi;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 8</span>}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; S</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}<span\; class="notranslate">\; (</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 4</span>}}\underset{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; /</span>\mathrm{<span\; class="notranslate">\; 2</span>}}{<span\; class="notranslate">\; \&Sigma;</span>}}{<span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 3</span>}}<span\; class="notranslate">\; )</span>}^{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; +</span>\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ;</span>$

(4c) Calculate the average congestion degree Θ _u (t) of the nodes in the cluster:

${\mathrm{<span\; class="notranslate">\; \&Theta;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; h</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}{{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; d</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span><span\; class="notranslate">\; ;</span>$

Among them, λ _M (t), C _M , and d _M are the average arrival rate of services actually arriving at MEO layer links, the transmission capacity and propagation delay of MEO layer inter-satellite links, and λ _L (t), C _L , d _L are the average arrival rate of services actually arriving at the LEO layer link, the transmission capacity of the MEO layer inter-satellite link and the propagation delay. Note that the above λ _M (t) and λ _L (t) include the newly arrived services of external users and are The business relayed by neighboring nodes can be calculated by the following formula:

${\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; \&chi;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&phi;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; ,</span>$

${\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\frac{{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&chi;</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>{\mathrm{<span\; class="notranslate">\; \&phi;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}}{<span\; class="notranslate">\; |</span>{\mathrm{<span\; class="notranslate">\; C</span>}}_{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; |</span>}<span\; class="notranslate">\; ;</span>$

(4d) The optimal shunt factor χ _u (t) can be obtained by solving the following equation:

$\frac{<span\; class="notranslate">\; \&part;</span>\mathrm{<span\; class="notranslate">\; \&Theta;</span>}}{<span\; class="notranslate">\; \&part;</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; m</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; =</span>\frac{<span\; class="notranslate">\; \&part;</span>\mathrm{<span\; class="notranslate">\; \&Theta;</span>}}{<span\; class="notranslate">\; \&part;</span>{\mathrm{<span\; class="notranslate">\; \&lambda;</span>}}_{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; )</span>}<span\; class="notranslate">\; .</span>$

Step 5, each MEO node M _u uses the Dijkstra algorithm or Bellman-Ford algorithm to obtain the shortest path of each node in the C _u (t) cluster according to the global routing graph RG(t), and builds the routing table accordingly For cluster-head M _u nodes, the final routing table And for LEO node L _ui in the cluster, if χ _u (t)=0, the final routing table If χ _u (t)>0, then the next hop node of this part of the service is the cluster head node M _u .

Step 6, the current LEO node L _ui calculates the final distribution ratio χ _uA (t), χ _uB (t), χ _uC (t) for different types of services according to the routing table RT _ui (t) and selects the corresponding route, the current MEO The node M _u transmits the data to the next hop node according to the routing table RT _u (t).

Referring to Table 1, the services are divided into three categories according to the delay requirements: Class A services are delay-sensitive services, which have the highest requirements for delay and the highest priority. Typical service representatives are real-time voice services; Class B services are time-sensitive services. Delay tolerant services have certain requirements on delay, and the priority is next. The typical service representative is video streaming service; Class C service is to ensure the service as best as possible, has no requirement on delay, and has the lowest priority. The typical representative is the Internet. services such as file transfer and web browsing;

Table 1

When the offload factor χ _u (t)>0, the class C service is first offloaded and transmitted from the MEO layer. If the offloaded traffic of the C class service is less than the traffic volume that needs to be offloaded to the MEO layer, part of the B service will be offloaded. In order to ensure the delay requirement of class A service, it will not be offloaded. The specific process is: when χ _u (t)λ _u (t)<λ _uC (t), only χ(t) λ _u (t)/λ _C (t) ratio of Class C business is shunted to the MEO layer for transmission, and other services keep the original route unchanged; when χ _u (t)λ _u (t)＞λ _uC (t) and When χ _u (t)λ _u (t)<λ _uB (t)+λ _uC (t), Class C services are all offloaded to the MEO layer for transmission, (χ _u (t)λ _u (t)-λ _uC ( t))/λ _uB (t) ratio of Class B business is also shunted, while Class A business keeps the original route unchanged; when χ _u (t)λ _u (t)＞λ _uB (t)+λ _uC ( At time t), all Class B and Class C services are diverted to the MEO layer for transmission, while Class A services still maintain the original route.

Step 7. In order to adapt to the real-time dynamic changes of the network load, after a topology update period ΔT, each node in the network starts to re-execute the above steps 1 to 6 to ensure that the link cost information is updated periodically. Then dynamically adjust the routing selection to achieve network load balancing and guarantee the QoS of different services.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention should be included in the protection of the present invention. within range.

Claims (9)

1. the multilayer satellite network method for routing based on Load-aware, it is characterized in that, the described multilayer satellite network method for routing based on Load-aware utilizes multilayer satellite network Layered architecture to carry out periodically perception to the load of network, and dynamically adjust the shunt ratio of business at MEO layer and LEO layer according to the load information collected, reach the load balancing of network, by the QoS demand of business and the architectural characteristic of network are matched, shunt the minimum business of delay requirement at first when network traffic load is heavier, transmit from MEO layer, and delay sensitive business is transmitted from LEO layer all the time.

2., as claimed in claim 1 based on the multilayer satellite network method for routing of Load-aware, it is characterized in that, the described multilayer satellite network method for routing based on Load-aware specifically comprises the following steps:

Step one, each MEO node M _uthe sub-clustering C of cluster head node is configured to according to the network topology Γ (t) that star stores in advance _u(t);

Step 2, carries out information exchange in each bunch, cluster head MEO node M _ucollect C _uthe link-state information λ of LEO node in (t) bunch _ui(t) and Q _ij(t), and according to building corresponding route subgraph

Step 3, builds overall routing diagram RG (t)={ V _t, E _t, w (i, j) };

Step 4, each MEO node M _ufor C _ulEO node L in (t) bunch _uicalculate optimum shunting factor χ _u(t);

Step 5, each MEO node M _udijkstra's algorithm or bellman-ford algorithm is used to obtain C according to overall routing diagram RG (t) _uthe shortest path of each node in (t) bunch, and build routing table for cluster head M _unode, final routing table and for bunch in LEO node L _uiif, χ _u(t)=0, then final route table if χ _ut () > 0, then the next-hop node of partial service is cluster head node M _u;

Step 6, current LEO node L _uiaccording to routing table RT _uit () is the final shunt ratio χ of dissimilar service computation _uA(t), χ _uB(t), χ _uCt () also selects corresponding route, current MEO node M _uthen according to routing table RT _ut () sends data to next-hop node;

Step 7, after the time through a topological update cycle Δ T, in network, each node starts to re-execute above-mentioned steps one ~ step 6, ensures to be updated periodically link cost information, and then dynamic conditioning Route Selection, reach the load balancing of network and ensure the QoS of different business.

3., as claimed in claim 2 based on the multilayer satellite network method for routing of Load-aware, it is characterized in that, described step one specifically comprises:

The first step, based on the predictability and periodically of satellite motion track, all MEO nodes calculate in advance and on star the overall topology Γ (t) of storage networking within t topological update cycle;

Second step, when the topology update cycle arrives, obtains the overall topology Γ (t) of current t topology update cycle;

3rd step, each MEO node M _usub-clustering C is built according to Γ (t) _ut (), wherein cluster head is M _unode, C _ut () bunch interior nodes is by a series of LEO node L meeting following condition _uicomposition, i.e. L _uinode is at M _uwithin the scope of coverage and node M _ube nearest neighbours MEO node, sub-clustering element set is expressed as:

$C_u\left(t\right)=\{M_u,L_{u1},L_{u2},...,L_{u\left|C_u\right|}\},$

Wherein | C _u| be C _uthe number of LEO node in (t) bunch, L _uinode represents C _ui-th LEO node in (t) bunch.

4., as claimed in claim 2 based on the multilayer satellite network method for routing of Load-aware, it is characterized in that, described step 2 specifically comprises:

The first step, each cluster head MEO node M _uto C _ulEO node L in (t) bunch _uibroadcast packet is containing the HELLO bag of the IP of this node, geographical location information and bag sequence number;

Second step, the LEO node L in bunch _uireceive from C _u(t) cluster head M _uthe HELLO of node wraps and obtains the positional information of cluster head node, finds that cluster head node changes, then by new cluster head node M _ureplace original cluster head node;

3rd step, each LEO node L _uioutside newly arrived Business Stream λ is monitored within the topology update cycle _ui(t) and average queuing delay Q _ij(t) information, and to C _u(t) cluster head MEO node M _usend the packet comprising information;

4th step, M _unode collects C _uall LEO node L in (t) bunch _uipacket and according to Q _ijt () builds route subgraph ${\mathrm{RG}}_{M_u}\left(t\right)=\{V\left(M_u\right),E\left(M_u\right),w(i,j)\}.$

5., as claimed in claim 4 based on the multilayer satellite network method for routing of Load-aware, it is characterized in that, described 4th step specific implementation comprises:

Step one, M _unode obtains complete topology Γ (t) in the present topology update cycle, with M _unode is all neighbor node set V (M that root node obtains it _u) be connected M _ulink set E (the M of node and neighbor node _u);

Step 2, calculates link set E (M _u) in cost w (i, the j)=p (i of all links (i, j), j)+q (i, j), wherein p (i, the j) propagation delay that is link, q (i, j) be the queuing delay of link, p (i, j) calculates in advance according to the topological Γ (t) of network, and q (i, j)=Q _ij(t);

Step 3, synthesizes final route subgraph ${\mathrm{RG}}_{M_u}\left(t\right)=\{V\left(M_u\right),E\left(M_u\right),w(i,j)\}.$

6., as claimed in claim 2 based on the multilayer satellite network method for routing of Load-aware, it is characterized in that, described step 3 specifically comprises:

The first step, initial phase, each MEO node M _uto all neighbours MEO node M _kbroadcast packet contains and Q _ukthe packet of (t) information, and receive from M _kcomprising of node and Q _kuthe packet of (t) information;

Second step, when MEO node M _ureceive from M _kpacket after, to check in buffer memory whether containing this packet, if having, then delete this bag, and by M _ujoin the set Π corresponding to packet number n _nin, if do not have, then preserve this packet, and send to set Π _nouter neighbours MEO node;

3rd step, when each MEO node gets all link (i, j) ∈ E _tcost w (i, j) after, exchange process stops, and builds final routing diagram RG (t)={ V _t, E _t, w (i, j) }.

7., as claimed in claim 2 based on the multilayer satellite network method for routing of Load-aware, it is characterized in that, described step 4 specifically comprises:

The first step, in compute cluster, LEO node newly arrives the total amount λ of business _u(t)=Σ λ _ui(t);

Second step, definition LEO/MEO multitiered network characteristic parameter φ _m, φ _l, and perform following calculating:

${\mathrm{\&phi;}}_M=\frac{1}{8}\underset{j=1}{\overset{\left(S_M-1\right)/2}{\&Sigma;}}\left({\left(\frac{1}{3}\right)}^{j-1}+{\left(\frac{1}{3}\right)}^{P_M/2+j-1}\right)+\frac{1}{4}\underset{i=1}{\overset{P_M/2-1}{\&Sigma;}}\underset{j=1}{\overset{\left(P_M-1\right)/2}{\&Sigma;}}{\left(\frac{1}{3}\right)}^{i+j-1},$

${\mathrm{\&phi;}}_L=\frac{1}{8}\underset{j=1}{\overset{(S_L-1)/2}{\&Sigma;}}({\left(\frac{1}{3}\right)}^{j-1}+{\left(\frac{1}{3}\right)}^{P_L/2+j-1})+\frac{1}{4}\underset{i=1}{\overset{P_L/2-1}{\&Sigma;}}\underset{j=1}{\overset{(P_L-1)/2}{\&Sigma;}}{\left(\frac{1}{3}\right)}^{i+j-1};$

3rd step, the average Congestion Level SPCC Θ of compute cluster interior nodes _u(t):

${\mathrm{\&Theta;}}_u\left(t\right)=h_M(\frac{{\mathrm{\&lambda;}}_M\left(t\right)}{C_M-{\mathrm{\&lambda;}}_M\left(t\right)}+d_M{\mathrm{\&lambda;}}_M\left(t\right))+h_L(\frac{{\mathrm{\&lambda;}}_L\left(t\right)}{C_L-{\mathrm{\&lambda;}}_L\left(t\right)}+d_L{\mathrm{\&lambda;}}_L\left(t\right));$

Wherein λ _m(t), C _m, d _mbe respectively the business average arrival rate of actual arrival MEO layer link, the transmission capacity of MEO layer inter-satellite link and propagation delay, λ _l(t), C _l, d _lbe respectively the business average arrival rate of actual arrival LEO layer link, the transmission capacity of MEO layer inter-satellite link and propagation delay, λ _m(t), λ _lt () comprises external user and newly arrives business and the business for neighbors relaying, and calculate with following formula:

${\mathrm{\&lambda;}}_L\left(t\right)=\frac{{\mathrm{\&lambda;}}_u\left(t\right)(1-{\mathrm{\&chi;}}_u(t)){\mathrm{\&phi;}}_L}{\left|C_u\right|},$

${\mathrm{\&lambda;}}_M\left(t\right)=\frac{{\mathrm{\&lambda;}}_u\left(t\right){\mathrm{\&chi;}}_u\left(t\right){\mathrm{\&phi;}}_M}{\left|C_u\right|};$

4th step, solves following equation equation and namely obtains optimum shunting factor χ _u(t):

$\frac{\&part;\mathrm{\&Theta;}}{\&part;{\mathrm{\&lambda;}}_M\left(t\right)}=\frac{\&part;\mathrm{\&Theta;}}{\&part;{\mathrm{\&lambda;}}_L\left(t\right)}.$

8., as claimed in claim 2 based on the multilayer satellite network method for routing of Load-aware, it is characterized in that, in described step 6, business is divided into three classes according to the requirement of time delay: delay sensitive business; Time delay tolerance service; Ensure business as possible.

9. one kind as described in claim 1-8 any one based on the application of the multilayer satellite network method for routing of Load-aware at the two-layer satellite network of LEO/MEO.