Method and system for slicing based on passive optical network architecture

Abstract

The application provides a slice division method and system based on a passive optical network architecture, the method comprising: performing global slice division on a network undirected graph based on a traffic matrix of the network undirected graph to obtain a plurality of subgraphs, wherein the network undirected graph comprises nodes and edges connecting the nodes, the nodes represent optical network units, the edges and weights of the edges represent communication relationships and traffic between the optical network units respectively, the subgraphs and the number of optical line terminals are the same, and the traffic matrix comprises traffic between the optical network units; performing local search optimization on each subgraph based on weights of edges connecting each node in the subgraph to obtain a target subgraph with minimized cut value, so as to output a plurality of target slices, wherein the cut value is obtained based on the weights of the edges connecting each node in the corresponding subgraph, and the target slice comprises the optical line terminal and a plurality of optical network units in the corresponding subgraph connected by the target subgraph. The application can improve the traffic diversion efficiency of the access network.

Description

Technical Field

[0001] This invention relates to the field of access network technology, and in particular to a slicing method and system based on a passive optical network architecture. Background Technology

[0002] In industrial internet scenarios, services with high terminal density place higher demands on network transmission capabilities. For example, in regional power supply communication networks, the traditional centralized power distribution method of the master station can no longer meet current needs, making intelligent and distributed power distribution design a development trend. Under the existing architecture, when PON (Passive Optical Network) is centrally deployed, OLT (Optical Line Terminal) pooling forms a resource pool, but network traffic still needs to be exchanged through the core router (CR), which can easily cause bandwidth congestion during peak user periods, and there is a bandwidth waste problem in the same area.

[0003] Traditional PON architectures suffer from insufficient resource scheduling flexibility, making it difficult to cope with traffic surges. Furthermore, when multiple services are accessed, the system is tightly coupled, resulting in poor compatibility. In addition, in existing multi-user, multi-service network transmissions, algorithms need to balance the globally optimal solution with time complexity to avoid increasing operating costs due to excessive complexity. Therefore, how to achieve efficient slicing and traffic routing within a more flexible PON architecture has become a pressing technical problem to be solved. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a slicing method and system based on a passive optical network architecture to eliminate or improve one or more defects existing in the prior art.

[0005] One aspect of the present invention provides a slicing method based on a passive optical network architecture, the method comprising the following steps:

[0006] The network undirected graph is globally sliced ​​and partitioned based on the traffic matrix of the network undirected graph to obtain multiple subgraphs. The network undirected graph includes each node and each edge connecting the nodes. The nodes represent optical network units in the passive optical network architecture. The edges and edge weights represent the communication relationship and traffic between optical network units, respectively. The number of optical line terminals communicating with each optical network unit in the subgraph and the passive optical network architecture is the same. The traffic matrix includes the traffic between each optical network unit.

[0007] Local search optimization is performed on each subgraph based on the weights of the edges connecting each node in each subgraph to obtain the target subgraph that minimizes each cutting value, so as to output multiple target slices for traffic diversion. The cutting value is obtained based on the weights of the edges connecting each node in the corresponding subgraph, and the target slice includes optical line terminals and multiple optical network units in the corresponding connected target subgraphs.

[0008] In some embodiments of the present invention, the flow matrix based on the undirected network graph is used to globally slice the undirected network graph to obtain multiple subgraphs, including:

[0009] The degree matrix is ​​calculated based on the flow matrix of the undirected network graph to obtain the flow characteristics of each node in the undirected network graph;

[0010] The normalized Laplacian matrix is ​​calculated based on the degree matrix to obtain the flow characteristics of multiple initial subgraphs, wherein the initial subgraphs include the flow characteristics of multiple nodes;

[0011] The traffic characteristics of the multiple initial subgraphs are sorted from smallest to largest, and the multiple initial subgraphs with smaller traffic characteristics that are ranked first or whose traffic characteristics are less than the first threshold are selected. The number of selected initial subgraphs is the same as the number of optical line terminals.

[0012] A clustering algorithm is used to cluster the traffic characteristics of multiple initial subgraphs after selection, so as to realize the global slicing of the undirected graph of the network and obtain multiple subgraphs after global slicing.

[0013] In some embodiments of the present invention, the step of performing local search optimization on each subgraph based on the weights of the edges connecting each node in each subgraph to obtain the target subgraph that minimizes each cut value includes:

[0014] For each subgraph, the node to be optimized is determined based on the flow gain generated after each node in the subgraph moves to other subgraphs in the plurality of subgraphs, and the flow gain is obtained based on the weight of each edge connected to the corresponding node.

[0015] The subgraph to be moved is determined based on the traffic gains generated after the node to be optimized is moved to each of the other subgraphs.

[0016] The node to be optimized is moved to the subgraph to be moved until the cut value of each subgraph is minimized, so as to achieve local search optimization of each subgraph, and the subgraph with the minimized cut value is taken as the target subgraph.

[0017] In some embodiments of the present invention, in the step of determining the node to be optimized based on the traffic gains generated after each node in the subgraph moves to other subgraphs in the plurality of subgraphs excluding the subgraph itself, the node corresponding to the maximum traffic gain is determined as the node to be optimized.

[0018] In the step of determining the subgraph to be moved based on the traffic gains generated after the node to be optimized moves to the other subgraphs, the subgraph corresponding to the minimum traffic gain is determined as the subgraph to be moved.

[0019] In some embodiments of the present invention, the flow gain is obtained by calculating the difference between the cut values ​​before and after the corresponding node moves. The cut value of the node is obtained by calculating the difference between the external weight and the internal weight of the node. The external weight is the sum of the weights of each edge between the node and each adjacent node belonging to a different subgraph. The internal weight is the sum of the weights of each edge between the node and each adjacent node belonging to the same subgraph.

[0020] In some embodiments of the present invention, the cutting value of the subgraph is the sum of the cutting values ​​of each node in the subgraph.

[0021] In some embodiments of the present invention, the number of nodes in each target subgraph reaches a second threshold.

[0022] Another aspect of the present invention provides a slicing system based on a passive optical network architecture. The system includes: multiple optical line terminals, multiple optical network units, an optical distribution network, multiple optical splitters, and a software-defined network controller. Each optical line terminal is communicatively connected to the multiple optical network units through the optical distribution network and corresponding optical splitters. The software-defined network controller includes a computer device and is connected to each optical line terminal and each optical network unit. The computer device includes a processor and a memory. The memory stores computer instructions. The processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the aforementioned method.

[0023] Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the aforementioned method.

[0024] Another aspect of the present invention provides a computer program product including computer instructions that, when executed by a processor, implement the steps of the aforementioned method.

[0025] The present invention relates to a slicing method and system based on a passive optical network architecture. By constructing a centralized passive optical network access network architecture and combining global slicing and local search optimization, it achieves dynamic adjustment and dynamic slicing of optical network units. While balancing the global optimal solution and reducing time complexity, it can flexibly and efficiently divert traffic according to different traffic scenarios, effectively improving the traffic diversion efficiency of the access network. This solves the problems of low traffic diversion efficiency and high algorithm complexity in existing access networks under high-density terminal scenarios, thereby solving the problem of bandwidth resource congestion and waste in the access network during peak user periods and improving bandwidth resource utilization.

[0026] Additional advantages, objects, and features of the invention will be set forth in part in the description which follows, and will also become apparent in part to those skilled in the art upon studying the description, or may be learned by practice of the invention. The objects and other advantages of the invention can be realized and obtained by means of the structures specifically pointed out in the description and drawings.

[0027] Those skilled in the art will understand that the objectives and advantages achievable with the present invention are not limited to those specifically described above, and that the above and other objectives achievable with the present invention will become clearer from the following detailed description. Attached Figure Description

[0028] The accompanying drawings, which are provided to further illustrate the invention and form part of this application, are not intended to limit the scope of the invention.

[0029] Figure 1 This is a schematic diagram of a passive optical network architecture in one embodiment of the present invention;

[0030] Figure 2 This is a flowchart illustrating a slicing method based on a passive optical network architecture in one embodiment of the present invention.

[0031] Figure 3 This is a schematic diagram of an undirected network graph in one embodiment of the present invention;

[0032] Figure 4 This is a schematic diagram illustrating a specific process of a local search optimization step in one embodiment of the present invention. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0034] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0035] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0036] It should also be noted that, unless otherwise specified, the term "connection" in this article can refer not only to a direct connection, but also to an indirect connection involving an intermediary.

[0037] In the following description, embodiments of the invention will be illustrated with reference to the accompanying drawings. In the drawings, the same reference numerals represent the same or similar parts, or the same or similar steps.

[0038] To address the issue of low traffic routing efficiency in existing access networks under high-density terminal scenarios, this invention provides a slicing method and system based on a passive optical network (PON) architecture. By constructing a centralized PON access network architecture and combining global slicing and local search optimization, dynamic adjustment and dynamic slicing of optical network units are achieved. This balances the global optimal solution and reduces time complexity while enabling flexible and efficient traffic routing according to different traffic scenarios, effectively improving the traffic routing efficiency of the access network. This solves the problem of bandwidth resource congestion and waste during peak user periods and improves resource utilization.

[0039] Figure 1 This is a schematic diagram of a passive optical network architecture according to an embodiment of the present invention. Figure 1As shown, taking the industrial internet park access network as an example, the passive optical network architecture proposed in this embodiment of the invention is actually a flexible centralized passive optical network architecture. This PON architecture includes multiple optical line terminals, multiple optical network units (ONUs) that are communicatively connected to each optical line terminal, an optical distribution network (ODN), multiple optical splitters, and a software defined network (SDN) controller. The optical distribution network is used to dynamically connect the optical network units and the optical line terminals. Each optical line terminal is connected to a group of optical network units (multiple optical network units) through the optical distribution network and various optical splitters, and is connected to the metropolitan area network through a core router. The optical network units can be equipped with tunable lasers. The SDN controller is connected to each optical line terminal and each optical network unit, realizing centralized management and control of the entire network. It can intelligently adjust the connection relationship and resource allocation between the OLT and ONU based on network traffic and other information. The aforementioned centralized PON architecture breaks the static binding and fixed connection relationship between optical line terminals and optical network units in the traditional PON architecture. It can dynamically allocate resources according to the real-time needs of different users, thereby improving network efficiency and resource utilization.

[0040] Figure 2 This is a flowchart illustrating a slicing method based on a passive optical network architecture according to an embodiment of the present invention. Figure 2 As shown, the method includes the following steps:

[0041] Step S210: The software-defined network controller performs global slicing of the undirected network graph based on the traffic matrix of the undirected network graph to obtain multiple subgraphs. The undirected network graph includes each node and each edge connecting the nodes. The nodes represent optical network units, and the edges and edge weights represent the communication relationship and traffic between optical network units, respectively. The number of subgraphs is the same as the number of optical line terminals. The traffic matrix includes the traffic between each optical network unit.

[0042] Figure 3 This is a schematic diagram of an undirected graph in one embodiment of the present invention. Each optical network unit (ONU) in the passive optical network architecture is treated as a node, resulting in a node set V. The communication relationship between any two ONUs (actually, bidirectional communication between two ONUs is achieved through their respective connected optical line terminals) is treated as an edge connecting the two nodes, resulting in an edge set E. The node set and edge set together form the undirected graph G(V,E). This undirected graph can be visualized as follows: Figure 3As shown, ONU1 to ONU9 are 9 nodes. The undirected graph in this embodiment is a weighted graph, where the weight of each edge can be the amount of traffic between the two nodes connected by that edge. The traffic can exhibit characteristics such as power-law network features.

[0043] In some embodiments, step S210, which involves globally slicing the undirected network graph based on the traffic matrix of the undirected graph to obtain multiple subgraphs, specifically includes the following steps:

[0044] The degree matrix is ​​calculated based on the flow matrix of the undirected network graph to obtain the flow characteristics of each node in the undirected network graph;

[0045] The normalized Laplacian matrix is ​​calculated based on the degree matrix to obtain the flow characteristics of multiple initial subgraphs, wherein the initial subgraphs include the flow characteristics of multiple nodes;

[0046] The traffic characteristics of the multiple initial subgraphs are sorted from smallest to largest, and the multiple initial subgraphs with smaller traffic characteristics that are ranked first or whose traffic characteristics are less than the first threshold are selected. The number of selected initial subgraphs is the same as the number of optical line terminals.

[0047] A clustering algorithm is used to cluster the traffic characteristics of multiple initial subgraphs after selection, so as to realize the global slicing of the undirected graph of the network and obtain multiple subgraphs after global slicing.

[0048] Since communication between any two optical network units in the entire network is mutual, this embodiment defines an N*N traffic matrix T, which can be represented as:

[0049]

[0050] Where i represents a node, j represents a neighboring node connected to node i, T(i,j) represents the traffic transmitted from node i to j, T(j,i) represents the traffic transmitted from node j to i, and N represents the number of nodes in the network. The formula for calculating the degree matrix can be expressed as:

[0051]

[0052] Among them, D i Let represent the element corresponding to node i in the degree matrix D, i.e., the flow characteristic of node i; n represents the number of neighboring nodes j of node i. The formula for calculating the normalized Laplace matrix L can be expressed as:

[0053]

[0054] Here, I represents the identity matrix. Then, the multiple eigenvectors in L are sorted in ascending order, and the top M smallest eigenvectors, or the M smallest eigenvectors whose values ​​are less than a first threshold, are selected. Each eigenvector represents the traffic characteristics of an initial subgraph and includes the traffic characteristics of multiple nodes. M represents the number of optical line terminals in the network, thus obtaining M subgraphs. The clustering algorithm can be K-means clustering, etc.

[0055] Step S220: Based on the weights of the edges connecting each node in each subgraph, perform local search optimization on each subgraph to obtain the target subgraph that minimizes each cutting value, so as to output multiple target slices for traffic diversion. The cutting value is obtained based on the weights of the edges connecting each node in the corresponding subgraph, and the target slice includes optical line terminals and multiple optical network units in the corresponding connected target subgraphs.

[0056] In graph theory, the cut value is a concept related to cut sets in a graph. Given a weighted graph G = (V, E), a cut set is a subset C of the edge set E. Removing edges from cut set C from graph G will divide G into multiple disconnected subgraphs. Therefore, for cut set C, after dividing the graph into different subgraphs, the edges in the cut set are the edges connecting the different subgraphs. The cut value is the sum of the weights of all these edges in cut set C, used to measure the degree to which the cut set "cuts" the graph, reflecting the "tightness" or "cost" of the connections between different subgraphs. In this embodiment, the cut value of each subgraph is defined as the sum of the cut values ​​of each node in that subgraph. The cut value of each node is defined as the difference between the external weight and the internal weight of that node in the subgraph, which can be expressed as:

[0057] DW i =OW i -IW i

[0058] Among them, DW i OW represents the cut value of node i. i IW represents the external weights of node i. i This represents the internal weight of node i.

[0059] The final target slices divided in this step are the target subgraphs that minimize each cutting value. Each target subgraph contains multiple optimized nodes (optical network units), such as... Figure 3 The diagram shows three groups formed by the division by two dashed lines, each group corresponding to a target subgraph. Multiple optical network elements and corresponding optical line terminations are connected within each target subgraph, resulting in the final output of the SDN controller as shown below. Figure 1The multiple logical slices vPON1 to n shown can dynamically adjust the connection relationship between optical line terminals and optical network units and the number of optical network units connected to each optical line terminal based on different traffic demands in the network. While balancing the global optimal solution and reducing time complexity, it effectively improves the traffic routing efficiency of the access network.

[0060] In some embodiments, step S220, which involves performing local search optimization on each subgraph based on the weights of the edges connecting each node in each subgraph to obtain the target subgraph that minimizes each cut value, specifically includes the following steps:

[0061] For each subgraph, the node to be optimized is determined based on the flow gain generated after each node in the subgraph is moved (divided) to other subgraphs in the plurality of subgraphs other than the current subgraph. The flow gain is obtained based on the weight of each edge connected to the corresponding node.

[0062] The subgraph to be moved is determined based on the traffic gains generated after the node to be optimized is moved (divided) to the other subgraphs;

[0063] The node to be optimized is moved (partitioned) to the subgraph to be moved until the cut value of each subgraph is minimized, so as to achieve local search optimization of each subgraph, and the subgraph with the minimized cut value is taken as the target subgraph.

[0064] In some embodiments, in the step of determining the node to be optimized based on the traffic gains generated after each node in the subgraph moves to other subgraphs in the plurality of subgraphs, the node corresponding to the maximum traffic gain is determined as the node to be optimized; in the step of determining the subgraph to be moved based on the traffic gains generated after the node to be optimized moves to the other subgraphs, the subgraph corresponding to the minimum traffic gain is determined as the subgraph to be moved.

[0065] In some embodiments, the flow gain is obtained by calculating the difference between the cut values ​​before and after the corresponding node moves. The cut value of the node is obtained by calculating the difference between the external weight and the internal weight of the node. The external weight is the sum of the weights of each edge between the node and each adjacent node belonging to a different subgraph. The internal weight is the sum of the weights of each edge between the node and each adjacent node belonging to the same subgraph.

[0066] The flow gain in this step is the node cut value DW before and after the node movement. i The difference. The internal weight IW of node i. i Specifically, this is represented by node i and the subgraph G that belongs to the same subgraph as node i. x The sum of the weights (flows) of each edge between each adjacent node x can be expressed as:

[0067]

[0068] External weights OW of node i i Specifically, this means that node i and node i belong to different subgraphs G. y The sum of the weights (flows) of each edge between each adjacent node y can be expressed as:

[0069]

[0070] Here, V represents the set of nodes in the undirected graph of the network. The subgraph to which a node belongs changes before and after the move, and according to the above formula, this causes changes in the node's internal and external weights, i.e., the node's cut value changes, resulting in traffic gain. During the local search optimization process for each slice (i.e., each subgraph) obtained after global slicing in step S210, for each node in each subgraph to be optimized, the node generating the largest traffic gain in each subgraph is selected as the node to be optimized. The purpose is to prioritize removing the node that has the greatest impact on the cut value of each subgraph; that is, if the node remains in its original subgraph, it will lead to more cross-subgraph traffic. After determining the nodes to be optimized in each subgraph, it is determined which other subgraph the node to be optimized should move to. At this point, the subgraph generating the smallest traffic gain during the move is selected as the subgraph to be moved to. This is because the subgraph corresponding to the minimum traffic gain has the best "compatibility" with the node to be optimized. In other words, after the node to be optimized is assigned to this subgraph, the communication traffic between this node and the ONUs in the subgraph is maximized, while the communication traffic between this node and the ONUs in other subgraphs is minimized, thereby minimizing the segmentation value of the subgraph to which the node belongs before and after the move. Then, each node to be optimized is moved to its corresponding subgraph, thus achieving local search optimization.

[0071] Figure 4 This is a schematic diagram illustrating a specific implementation process of the local search optimization step in one embodiment of the present invention. For example... Figure 4 As shown, in this example, optimizing each subgraph to have a node count that meets the second threshold is considered as minimizing the cut value for each subgraph. In other words, each subgraph optimized through local search with a node count meeting the second threshold is the target subgraph. Therefore, in this example, the local search optimization process specifically includes the following steps:

[0072] For each subgraph obtained in step S210, if the number of nodes in the subgraph is greater than the second threshold k, then traverse each node in the subgraph.

[0073] If the node generates the largest traffic gain after being moved (divided) into all other unoptimized subgraphs in the subgraphs, then the node is identified as a node to be optimized; in this example, subgraphs whose number of nodes is not the second threshold k are considered unoptimized graphs.

[0074] Traverse all unoptimized subgraphs except the current subgraph. If the traffic gain generated by moving (dividing) the node to be optimized to other unoptimized subgraphs is the smallest, then the unoptimized subgraph is determined as the subgraph to be moved.

[0075] The nodes to be optimized are moved (partitioned) to the subgraph to be moved, and the subgraph to which the node belongs is updated, until the number of nodes in all subgraphs is optimized to exactly the second threshold k. In this example, the subgraph with the number of nodes at the second threshold k is considered an optimized subgraph.

[0076] The steps outlined above in this example effectively reduce time complexity, balancing the pursuit of a globally optimal solution with the reduction in time complexity, while simultaneously achieving flexible and efficient traffic routing. Specifically, the complexity of the global slice partitioning in step S210 is O(MN). 2 The time complexity of the local search optimization in the above steps is O(kM). 2 Therefore, the overall complexity of the algorithm described in this example is O(max(MN)). 2 ,kM 2 The time complexity of the existing traditional dynamic ONU slicing (DONUSA) algorithm is O(N). 3 Compared to other methods, this approach has lower complexity, especially in large-scale, high-density ONU terminal network scenarios where the values ​​of M and k are relatively small. The algorithm complexity of this approach can be approximated as O(N). 2 As can be seen, the algorithm complexity of this method is greatly reduced.

[0077] In other examples, there may be subgraphs in each subgraph obtained after the global slicing partitioning in step S210 where the number of nodes meets the second threshold. However, the slicing partitioning of such subgraphs may not be globally optimal. Therefore, the subgraph can also be optimized according to the specific implementation steps of step S220 described above to obtain the globally optimal solution for each subgraph, but this may increase the algorithm complexity.

[0078] In summary, to achieve efficient slicing and traffic routing under a flexible centralized PON architecture, the slicing method based on a passive optical network architecture provided in this invention achieves a balance between global optimal solution and time complexity, as well as efficient dynamic slicing. This enables efficient routing of access network traffic in high-density terminal scenarios, reduces network latency and bandwidth waste, and lowers algorithm complexity. It is well-suited for resource allocation and traffic management in high-density terminal scenarios and is applicable to scenarios with high reliability and real-time requirements, such as smart grids and industrial IoT.

[0079] Corresponding to the above method, the present invention also provides a slicing system based on a passive optical network architecture. The system includes multiple optical line terminals, multiple optical network units, an optical distribution network, multiple optical splitters, and a software-defined network controller. Each optical line terminal is communicatively connected to multiple optical network units through the optical distribution network and corresponding optical splitters. The software-defined network controller includes a computer device and is connected to each optical line terminal and each optical network unit. The computer device includes a processor and a memory. The memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the system implements the steps of the aforementioned method.

[0080] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the aforementioned method. The computer-readable storage medium may be a tangible storage medium, such as random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, register, floppy disk, hard disk, removable storage disk, CD-ROM, or any other form of storage medium known in the art.

[0081] This invention also provides a computer program product, including computer instructions that, when executed by a processor, implement the steps of the aforementioned method.

[0082] Those skilled in the art will understand that the exemplary components, systems, and methods described in conjunction with the embodiments disclosed herein can be implemented in hardware, software, or a combination of both. Whether implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention. When implemented in hardware, it can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this invention are programs or code segments used to perform the desired tasks. The programs or code segments can be stored in a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried in a carrier wave.

[0083] It should be clarified that the present invention is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present invention is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of the present invention.

[0084] In this invention, features described and / or illustrated for one embodiment may be used in the same or similar manner in one or more other embodiments, and / or combined with or in place of features of other embodiments.

[0085] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations of the embodiments of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A slicing method based on a passive optical network architecture, characterized in that, The method includes: The network undirected graph is globally sliced ​​and partitioned based on the traffic matrix of the network undirected graph to obtain multiple subgraphs. The network undirected graph includes each node and each edge connecting the nodes. The nodes represent optical network units in the passive optical network architecture. The edges and edge weights represent the communication relationship and traffic between optical network units, respectively. The number of optical line terminals communicating with each optical network unit in the subgraph and the passive optical network architecture is the same. The traffic matrix includes the traffic between each optical network unit. Based on the weights of the edges connecting each node in each subgraph, local search optimization is performed on each subgraph to obtain the target subgraph that minimizes each cutting value, so as to output multiple target slices for traffic diversion. The cutting value of the subgraph is the sum of the cutting values ​​of each node in the subgraph, and the target slice includes optical line terminals and multiple optical network units in the corresponding connected target subgraphs. The process of performing local search optimization on each subgraph based on the weights of the edges connecting each node in each subgraph to obtain the target subgraph that minimizes each cut value includes: For each subgraph, the nodes to be optimized are determined based on the traffic gains generated after each node in the subgraph moves to other subgraphs outside the current subgraph. The traffic gains are obtained by calculating the difference between the cut values ​​before and after the corresponding node moves. The cut values ​​of a node are obtained by calculating the difference between the external weight and the internal weight of the node. The external weight is the sum of the weights of all edges between the node and its adjacent nodes belonging to different subgraphs. The internal weight is the sum of the weights of all edges between the node and its adjacent nodes belonging to the same subgraph. The node with the maximum traffic gain is determined as the node to be optimized. The subgraph to be moved is determined based on the traffic gains generated after the node to be optimized is moved to each of the other subgraphs, wherein the subgraph corresponding to the minimum traffic gain is determined as the subgraph to be moved. The node to be optimized is moved to the subgraph to be moved until the cut value of each subgraph is minimized, so as to achieve local search optimization of each subgraph, and the subgraph with the minimized cut value is taken as the target subgraph.

2. The method of claim 1, wherein, The flow matrix based on the undirected network graph is used to globally slice the undirected network graph, resulting in multiple subgraphs, including: The degree matrix is ​​calculated based on the flow matrix of the undirected network graph to obtain the flow characteristics of each node in the undirected network graph; The normalized Laplacian matrix is ​​calculated based on the degree matrix to obtain the flow characteristics of multiple initial subgraphs, wherein the initial subgraphs include the flow characteristics of multiple nodes; The traffic characteristics of the multiple initial subgraphs are sorted from smallest to largest, and the multiple initial subgraphs with smaller traffic characteristics that are ranked first or whose traffic characteristics are less than the first threshold are selected. The number of selected initial subgraphs is the same as the number of optical line terminals. A clustering algorithm is used to cluster the traffic characteristics of multiple initial subgraphs after selection, so as to realize the global slicing of the undirected graph of the network and obtain multiple subgraphs after global slicing.

3. The method of claim 1, wherein, The number of nodes in each target subgraph reaches the second threshold. 4.A slice partition system based on a passive optical network architecture, characterized in that, include: The system comprises multiple optical line terminals (OLTs), multiple optical network units (ONUs), an optical distribution network (ODN), multiple optical splitters, and a software-defined network controller (SDN). Each OLT is communicatively connected to the multiple ONUs through the ODN and its corresponding optical splitter. The SDN includes a computer device and is connected to each OLT and each ONU. The computer device includes a processor, a memory, and computer instructions stored in the memory. The processor executes the computer instructions. When the computer instructions are executed, the system implements the steps of the method as described in any one of claims 1 to 3.

5. A computer readable storage medium having stored thereon a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the method as described in any one of claims 1 to 3.

6. A computer program product comprising computer instructions, characterized in that, When executed by a processor, the computer instructions implement the steps of the method according to any one of claims 1 to 3.

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