WO2021036660A1 - 最短路径计算方法、路由获取装置及服务器 - Google Patents

最短路径计算方法、路由获取装置及服务器 Download PDF

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WO2021036660A1
WO2021036660A1 PCT/CN2020/105535 CN2020105535W WO2021036660A1 WO 2021036660 A1 WO2021036660 A1 WO 2021036660A1 CN 2020105535 W CN2020105535 W CN 2020105535W WO 2021036660 A1 WO2021036660 A1 WO 2021036660A1
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constraint
weight
index
necessary
shortest path
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PCT/CN2020/105535
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English (en)
French (fr)
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杨军
彭鑫
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中兴通讯股份有限公司
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Priority to US17/638,055 priority Critical patent/US20220278920A1/en
Priority to EP20857120.8A priority patent/EP4016940A4/en
Publication of WO2021036660A1 publication Critical patent/WO2021036660A1/zh

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H04L45/124—Shortest path evaluation using a combination of metrics
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/12—Shortest path evaluation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/02—Topology update or discovery
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L45/00—Routing or path finding of packets in data switching networks
    • H04L45/14—Routing performance; Theoretical aspects
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • the embodiments of the present application relate to the field of communication technologies, and in particular, to a shortest path calculation method, a route acquisition device, and a server.
  • Software-defined network (software-defined network, SDN) is a new type of network architecture. Its core is to separate the control plane and data plane of network devices, and realize centralized control of the network by introducing controllers, realizing a more open and flexible network Management and smarter and more convenient resource scheduling and operation and maintenance.
  • the SDN controller can use the link layer discovery protocol to obtain network resource information, including the real-time bandwidth and delay of the entire network topology and links; based on this information, the SDN controller can easily calculate the shortest (ie Optimal) path.
  • K Shortest Path Algorithm is one of the classic methods for calculating the shortest path with necessary constraints. Its principle is to use the shortest path algorithm to sequentially calculate the first K shortest paths in a given network topology. Check whether it meets the necessary constraints (that is, whether the path passes through all required nodes and links). If the k-th shortest path meets the necessary constraints, stop the calculation and return the path as the calculation result, otherwise Continue to calculate the (k+1)-th shortest path until it finds a path that satisfies the necessary constraints or stops after traversing all paths.
  • the embodiments of the present application provide a shortest path calculation method, a route acquisition device, and a server.
  • the embodiment of the present application provides a shortest path calculation method, including: obtaining network resource information according to a received routing request; wherein, the routing request includes a starting node, an ending node, index constraints, and necessary constraints;
  • the index constraint calculates the index constraint weight of each topology element in the network, and calculates the necessary constraint weight of each topology element according to the mandatory constraint and the resource information of the network; according to the index of each topology element
  • the constraint weight and the necessary constraint weight are used to calculate the shortest path from the start node to the end node that satisfies the necessary constraint.
  • the embodiment of the present application also provides a route acquisition device, including: a topology acquisition module, configured to acquire network resource information according to a received routing request; wherein, the routing request includes a start node, a termination node, index constraints, and Necessary constraints; a weight calculation module for calculating the index constraint weights of each topology element in the network according to the index constraints, and calculating the necessary constraints of each topology element according to the necessary constraints and the resource information of the network Constrained weight; a route calculation module, used to calculate the shortest path from the start node to the end node that satisfies the mandatory constraint based on the index constraint weight and the mandatory constraint weight of each of the topological elements.
  • a route acquisition device including: a topology acquisition module, configured to acquire network resource information according to a received routing request; wherein, the routing request includes a start node, a termination node, index constraints, and Necessary constraints; a weight calculation module for calculating the index constraint weights of each topology element
  • the embodiment of the present application also provides a server, including: at least one processor; and, a memory communicatively connected with the at least one processor; wherein the memory stores the memory that can be executed by the at least one processor; Instructions, the instructions are executed by the at least one processor, so that the at least one processor can execute the shortest path calculation method described above.
  • the embodiment of the present application also provides a computer-readable storage medium storing a computer program, and the above-mentioned shortest path calculation method when the computer program is executed by a processor.
  • Fig. 1 is a flowchart of the shortest path calculation method according to the first embodiment of the present application
  • Fig. 2 is a specific flowchart according to an example of step 102 in the first embodiment of the present application
  • FIG. 3 is a specific flowchart according to an example of step 103 in the first embodiment of the present application.
  • Fig. 5 is a flowchart of the shortest path calculation method according to the second embodiment of the present application.
  • Fig. 6 is a block diagram of a route acquisition device according to a fourth embodiment of the present application.
  • Fig. 7 is a block diagram of a server in a fifth embodiment according to the present application.
  • the algorithm is not designed for the necessary constraints, and its computational complexity is O(Kn 3 ), where n is the node of the network topology K is the number of shortest paths that have been found; therefore, when the network scale is large, that is, when the number of nodes in the network topology is large, the calculation efficiency of the algorithm will be very low, and it is difficult to meet the real-time requirements of routing calculation; In terms of routing calculations with necessary constraints, the solution process of the K-optimal algorithm contains too many invalid calculations. Based on the above findings, the inventor proposes the technical solution of the present application; that is, the shortest path calculation method of this embodiment is proposed for the route calculation of the shortest path that must be constrained in the network.
  • the first embodiment of the present application relates to a shortest path calculation method.
  • the specific process is shown in Figure 1.
  • Step 101 Obtain network resource information according to the received routing request; wherein, the routing request includes a start node, an end node, index constraints, and necessary constraints.
  • Step 102 Calculate the index constraint weight of each topology element in the network according to the index constraint, and calculate the necessary constraint weight of each topology element according to the necessary constraints and the resource information of the network.
  • Step 103 Calculate the shortest path from the start node to the end node that satisfies the necessary constraint based on the index constraint weight and the mandatory constraint weight of each topology element.
  • the network in this embodiment may be a software-defined network, referred to as SDN for short; the shortest path calculation method in this embodiment is applied to a route acquisition device, which may exist independently or integrated in an SDN controller.
  • the route request received by the route acquisition device includes a start node, an end node, index constraints, and mandatory constraints.
  • the index constraint refers to the service index, that is, the requirement for transmission quality.
  • the service index includes, for example, the number of forwarding hops, required bandwidth, delay, delay jitter, etc.
  • the mandatory constraint refers to the time that must pass from the start node to the end node Nodes and/or links, the nodes or links that must be passed here can be collectively referred to as mandatory elements.
  • the route obtaining device can obtain the resource information of the network according to the index constraints in the route request.
  • the network refers to the network where the start node and the end node are located.
  • the resource information of the network includes the topology of the network, the real-time bandwidth and time delay of each topology element, etc.; among them, the route acquisition device will obtain the satisfaction index from the network Constrained available topology.
  • the topology element is a node or link in the available topology.
  • step 102 includes two sub-steps.
  • Sub-step 1021 Obtain a routing strategy; wherein, the routing strategy at least includes: an index constraint strategy obtained according to index constraints, and a necessary constraint strategy obtained according to network resource information;
  • Sub-step 1022 Calculate the index constraint weight and the necessary constraint weight of each topology element based on the routing strategy; among them, calculate the index constraint weight according to the index constraint and the index constraint strategy, and calculate the necessary constraint according to the necessary constraint and the necessary constraint strategy. Weights.
  • a first selection strategy is pre-stored in the route acquisition device, and the first selection strategy is used to select a corresponding index constraint strategy for the index constraint in the route request. That is, the first selection strategy can be understood as a selection function, and the index constraint is used as the input parameter of the first selection strategy, and the first selection strategy determines the index constraint strategy corresponding to the index constraint by analyzing the index constraint. For example, if the indicator constraint includes a limitation on the number of forwarding hops, then the indicator constraint strategy corresponding to the indicator constraint determined by the first selection strategy may be a minimum hop strategy.
  • Different index constraints correspond to different index constraint strategies.
  • Different index constraint strategies include, for example, a minimum hop strategy, a bandwidth equalization strategy, a minimum delay strategy, and a minimum delay jitter strategy. More specifically, for example: when the index constraint strategy is the minimum hop strategy, the index constraint weight of all links can be set to a constant greater than 0, such as 100; when the index constraint strategy is a bandwidth equalization strategy, the index constraint weight is determined by The ratio of the bandwidth required by the service to the available bandwidth of the node or link is determined; when the index constraint strategy is the minimum delay strategy, the index constraint weight is determined by the delay of the node or link; when the index constraint strategy is delay jitter For the minimum strategy, the weight of the index constraint is determined by the delay jitter of the node or link.
  • a second selection strategy is also pre-stored in the route acquisition device, and the second selection strategy is used to select a corresponding mandatory constraint strategy for the current network.
  • the second selection strategy can be understood as a selection function
  • the resource information of the network is used as the input parameter of the second selection strategy
  • the second selection strategy determines the necessary constraint strategy by analyzing the resource information of the network;
  • the resource information of the network may include, for example, the number of forwarding hops, required bandwidth, delay, delay jitter, etc. of each topology element, and the distance between each topology element.
  • the index constraint weight and the necessary constraint weight can be calculated.
  • the index constraints of the route request include the number of forwarding hops h and the required bandwidth b. Delay d, delay jitter j, etc.
  • the mandatory element (necessary node or mandatory link) in the mandatory constraint of the route request is e, e ⁇ S E , where S E is the set of mandatory elements , And S E ⁇ G.
  • the topology element (node or link) to be investigated is g ⁇ G.
  • index constraint weight w I of g can be expressed as:
  • the function f is the index constraint strategy here. After substituting the forwarding hop count h, required bandwidth b, delay d, delay jitter j, etc. of each topology element into the function f, the index constraint weight w of each topology element can be obtained. I.
  • the mandatory constraint weight of each topological element g includes the sum of the mandatory constraint weights generated by each mandatory element e in the mandatory constraint on the topological element g; each mandatory element e is in the topology
  • the mandatory constraint weights generated on the elements are obtained based on the mandatory constraint strategy.
  • the necessary constraint weight w C (g) of each topological element g can be expressed as:
  • the mandatory constraint strategy is expressed by the following formula:
  • w 0 represents the statistical characteristic value of the index constraint weight of at least part of the topological elements.
  • at least part of the topological elements may include: in the set of topological elements, the topological elements directly connected to each mandatory element; that is, w 0 may be the index constraint weight of the topological element directly connected to each mandatory element Statistical feature value.
  • w 0 can also be the statistical characteristic value of the index constraint weight of all topological elements.
  • the statistical feature value may be one of the minimum, maximum, average, and median of the index constraint weight of each topological element.
  • c e represents the index constraint weight of the mandatory element and the ratio evaluation coefficient of the mandatory constraint weight, and c e is a real number greater than 0. Among them, the proportional evaluation coefficient can be set according to the actual situation.
  • At least part of the topological elements may include: topological elements directly connected to each necessary element in the set of topological elements. which is, It can be the baseline value of the distance evaluation coefficient of the essential element and the topological element directly connected to the essential element. But it’s not limited to this, It can also be the baseline value of the distance evaluation coefficient of the necessary elements and each topological element in the network.
  • the relevant information of the necessary element e includes, for example, the index constraint weight of the necessary element e, the distance between the necessary element e and the topological element, etc., which can be used to calculate the specific value of each parameter in the above formula (3).
  • the necessary constraint weight w C (g) of g can be expressed as follows.
  • c n and c l are the index constraint weights of the necessary nodes and the necessary links, and the proportional evaluation coefficients of the necessary constraint weights; Respectively indicate the evaluation coefficients of the distances of the necessary nodes, necessary links and topological elements; Respectively represent the baseline values of the distance evaluation coefficients of the necessary nodes, the necessary links, and the topological elements.
  • step 103 includes the following sub-steps.
  • sub-step 1031 the sum of the index constraint weight and the mandatory constraint weight of each topology element is used as the total weight of each topology element.
  • the shortest path that must be constrained from the start node to the end node is calculated according to the total weight of each topology element.
  • the total weight of each topological element is equal to the sum of the index constraint weight and the mandatory constraint weight; however, it is not limited to this; in other examples, the index constraint weight and the mandatory constraint weight can also be used as needed.
  • the routing strategy determined in sub-step 1021 can also include a value of K determined based on the resource information of the network, for example, the value of K is determined according to the network topology, sparsity, etc. ; Among them, K value is the upper limit of the number of shortest paths traversed.
  • the Dijkstra algorithm can be used to calculate the shortest path P 0 , where the length of the path is measured by the path cost.
  • the path cost represents the degree to which the path meets the routing requirements, and the path cost is the weight of all nodes and links on the path The sum, the greater the path cost, the lower the degree of satisfaction.
  • the sub-step 1032 includes the following sub-steps:
  • Substep 1032-1 the iterative calculation from the start node to the i th shortest path terminating node P i, P i, if present, into the substep 1032-2; if not, the process proceeds to substep 1032-3.
  • Substep 1032-2 P i checked satisfies a necessary constraint condition; if yes, the process proceeds to substep 1032-4; if not, into the sub-step 1032-5.
  • Sub-step 1032-3 returns a null value, and ends.
  • Substep 1032-4 returned as the shortest path P i calculated, and ends.
  • Sub-step 1032-5 judge whether i+1 is less than or equal to K; if yes, return to sub-step 1032-1; if not, end.
  • this embodiment is implemented based on the K-optimal path algorithm, but it is not limited to this. Any method that can use the total weight of each topology element to find the shortest path from the start node to the end node is applicable to This embodiment.
  • the implementation of this application obtains the necessary constraint strategy according to the resource information of the network, and calculates the necessary constraint weight of each topological element according to the necessary constraint and the necessary constraint strategy; according to the index constraint of each topological element
  • the weight and the necessary constraint weight determine the shortest path from the start node to the end node that satisfies the necessary constraint. That is, in the calculation of the shortest path, in addition to the index constraint weight as a consideration factor, the necessary constraint weight is also introduced, and the necessary constraint weight and the index constraint weight are used as the consideration factors to realize the routing calculation with the necessary constraint. Significantly improve the efficiency of routing calculations with necessary constraints in large-scale complex networks.
  • the topological elements directly connected to each of the mandatory elements are used to calculate the parameters in the mandatory constraint strategy, which can make the obtained mandatory constraint strategy more accurate.
  • the weights of links and nodes in the network are considered at the same time, which is more comprehensive than considering only the weights of links in the prior art.
  • the sum of the index constraint weight and the mandatory constraint weight is used as the total weight of the topological element, which provides a specific combination method in which the indicator constraint weight and the mandatory constraint weight are combined and considered.
  • the second embodiment of the present application relates to a shortest path calculation method.
  • the second embodiment is roughly the same as the first embodiment.
  • the main difference is: in the second embodiment of the present application, if the weight of each topology element is constrained and the weight must be constrained, the shortest path cannot be obtained, then the route is adjusted.
  • Strategy based on the adjusted routing strategy, recalculate the index constraint weight and the necessary constraint weight of each topology element, and calculate the starting node to the end node according to the recalculated index constraint weight and the necessary constraint weight of each topology element The shortest path that satisfies the necessary constraints.
  • Fig. 5 is a flowchart of the shortest path calculation method in the second embodiment; in which, compared with Fig. 4 in the first embodiment, step 104 is added to adjust the routing strategy.
  • the routing strategy is adjusted.
  • the mandatory constraint strategy can be adjusted, such as adjusting w 0 , c e (at least one of c n and c l) in formula (3) or At least one of them); or, the K value can be adjusted.
  • the shortest path calculation based on the K-optimal algorithm is taken as an example for illustration; in other examples, the shortest path can also be calculated based on other algorithms.
  • the routing strategy can also be adjusted, the total weight of each topology element is recalculated based on the adjusted routing strategy, and the shortest path is recalculated based on the other algorithm.
  • the third embodiment of the present application relates to a shortest path calculation method.
  • the third embodiment is substantially the same as the first or second embodiment, and the main difference is that: in the third embodiment of the present application, a specific calculation method of the proportional evaluation coefficient c e is provided.
  • the proportional evaluation coefficient c e may be determined according to a statistical feature value w 1 of the index constraint weight of each topological element; the statistical feature value w 1 is, for example, the variance.
  • a preset relationship between the variance of the index constraint weight of each topological element and the proportional evaluation coefficient is pre-stored in the route acquisition device; the variance of the index constraint weight of each topological element can be calculated first, and then the prediction of the coefficient is evaluated based on the variance and the ratio.
  • the relationship obtains the proportional evaluation coefficient c e corresponding to the variance; wherein, in the preset relationship, the greater the variance, the smaller the proportional evaluation coefficient; the preset relationship can be expressed in the form of a comparison table or in the form of a function.
  • DETAILED calculated ratio assessment factor provided by the present embodiment of the c E in accordance with the weight index constraint weights of each topology element current network statistical characteristic values w 1 is calculated to obtain the proportion of evaluation coefficients c E, may be such that the proportion of evaluation coefficients c obtained e can better meet the current network conditions, so that the calculated mandatory constraint weights are more accurate; and this helps to further improve the efficiency of routing calculations with mandatory constraints in large-scale and complex networks.
  • the fourth embodiment of the present application relates to a route acquisition device, as shown in FIG. 6, including:
  • the topology obtaining module 31 is configured to obtain the resource information of the network according to the received routing request; wherein, the routing request includes the start node, the end node, the index constraint and the necessary constraint.
  • the weight calculation module 32 is configured to calculate the index constraint weight of each topology element in the network according to the index constraint, and calculate the necessary constraint weight of each topology element according to the mandatory constraint and the resource information of the network .
  • the route calculation module 33 is configured to calculate the shortest path from the start node to the end node that satisfies the necessary constraint according to the index constraint weight and the necessary constraint weight of each of the topological elements.
  • this embodiment is an example of a device corresponding to the first, or second, or third embodiment, and this embodiment can be implemented in cooperation with the first, or second, or third embodiment.
  • the related technical details mentioned in the first, second, or third embodiments are still valid in this embodiment, and in order to reduce repetition, they will not be repeated here.
  • the related technical details mentioned in this embodiment can also be applied to the first, second, or third embodiment.
  • modules involved in this embodiment are all logical modules.
  • a logical unit can be a physical unit, a part of a physical unit, or multiple physical units. The combination of units is realized.
  • this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.
  • the fifth embodiment of the present application relates to a server, as shown in FIG. 7, including:
  • At least one processor 41 and,
  • the memory 42 stores instructions that can be executed by the at least one processor 41, and the instructions are executed by the at least one processor 41, so that the at least one processor 41 can execute the foregoing shortest path calculation method.
  • the memory 42 and the processor 41 are connected in a bus manner.
  • the bus may include any number of interconnected buses and bridges, and the bus connects one or more various circuits of the processor 41 and the memory 42 together.
  • the bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits, etc., which are all known in the art, and therefore, no further description will be given herein.
  • the bus interface provides an interface between the bus and the transceiver.
  • the transceiver may be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on the transmission medium.
  • the data processed by the processor 41 is transmitted on the wireless medium through the antenna. Further, the antenna also receives the data and transmits the data to the processor 41.
  • the processor 41 is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions.
  • the memory 42 may be used to store data used by the processor 41 when performing operations.
  • the sixth embodiment of the present application relates to a computer-readable storage medium storing a computer program.
  • the computer program is executed by the processor, the above method embodiment is realized.
  • the program is stored in a storage medium and includes several instructions to enable a device ( It may be a single-chip microcomputer, a chip, etc.) or a processor (processor) to execute all or part of the steps of the methods described in the embodiments of the present application.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical disks and other media that can store program codes. .

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Abstract

本申请实施例公开了一种最短路径计算方法、路由获取装置及服务器。最短路径计算方法包括：根据接收的路由请求获取网络的资源信息；其中，路由请求包括起始节点、终止节点、指标约束及必经约束；根据指标约束计算网络中各拓扑元素的指标约束权重，并根据必经约束和网络的资源信息计算各拓扑元素的必经约束权重；根据各拓扑元素的指标约束权重和必经约束权重，计算起始节点到终止节点的满足必经约束的最短路径。

Description

最短路径计算方法、路由获取装置及服务器
相关申请的交叉引用
本申请基于申请号为201910816009.2、申请日为2019年8月30日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此以引入方式并入本申请。
技术领域
本申请实施例涉及通信技术领域，特别涉及一种最短路径计算方法、路由获取装置及服务器。
背景技术
软件定义网络(software defined network，SDN)是一种新型的网络架构，其核心是将网络设备的控制平面和数据平面分离，通过引入控制器实现网络的集中控制，实现更开放、更灵活的网络管理和更智能、更便捷的资源调度和运维。SDN控制器可利用链路层发现协议获取网络资源信息，包括全网拓扑和链路的实时带宽、时延等信息；基于这些信息，SDN控制器可以方便的计算出满足业务需求的最短(即最优)路径。
随着通信业务种类越来越多样化，路由的约束也越来越多，除了来自业务传输质量的要求(如带宽、时延、时延抖动等)外，还可能指定一些必经节点或者链路。例如由于信息安全要求，某些业务流要求必须经过指定的路由交换设备或者链路。另外，某些跨国通信可能会要求绕开一些政治敏感地区或者要求必须经过指定区域的网络或者特定的网络节点。这就要求控制器在路由计算过程中添加必经约束(包括必经节点和必经链路)。
K优算法(K Shortest Path Algorithm)是计算带必经约束的最短路径的经典方法之一，其原理是利用最短路径算法依次算出给定网络拓扑中的前K条最短路径，每算出一条路径就检验其是否满足必经约束(即该路径是否经过要求的所有必经节点和必经链路)，如果第k条最短路径满足必经约束，则停止计算并将该路径作为计算结果返回，否则继续计算第(k+1)条最短路径，直至找到满足必经约束的路径或者遍历完所有路径后停止。
发明内容
本申请实施方式提供一种最短路径计算方法、路由获取装置及服务器。
本申请的实施方式提供了一种最短路径计算方法，包括：根据接收的路由请求获取网络的资源信息；其中，所述路由请求包括起始节点、终止节点、指标约束及必经约束；根据所述指标约束计算所述网络中各拓扑元素的指标约束权重，并根据所述必经约束和所述网络的资源信息计算各所述拓扑元素的必经约束权重；根据各所述拓扑元素的指标约束权重和必经约束权重，计算所述起始节点到所述终止节点的满足所述必经约束的最短路径。
本申请的实施方式还提供了一种路由获取装置，包括：拓扑获取模块，用于根据接收的路由请求获取网络的资源信息；其中，所述路由请求包括起始节点、终止节点、指标约束及必经约束；权重计算模块，用于根据所述指标约束计算所述网络中各拓扑元素的指标约束权重，并根据所述必经约束和所述网络的资源信息计算各所述拓扑元素的必经约束权重；路由计算模块，用于根据各所述拓扑元素的指标约束权重和必经约束权重，计算所述起始节点到所述终止节点的满足所述必经约束的最短路径。
本申请的实施方式还提供了一种服务器，包括：至少一个处理器；以及，与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行上述最短路径计算方法。
本申请的实施方式还提供了一种计算机可读存储介质，存储有计算机程序，所述计算机程序被处理器执行时上述最短路径计算方法。
附图说明
一个或多个实施例通过与之对应的附图中的图片进行示例性说明，这些示例性说明并不构成对实施例的限定，附图中具有相同参考数字标号的元件表示为类似的元件，除非有特别申明，附图中的图不构成比例限制。
图1是根据本申请第一实施例中最短路径计算方法的流程图；
图2是根据本申请第一实施例中步骤102的一个例子的具体流程图；
图3是根据本申请第一实施例中步骤103的一个例子的具体流程图；
图4是根据本申请第一实施例中子步骤1032的一个例子的具体流程图；
图5是根据本申请第二实施例中最短路径计算方法的流程图；
图6是根据本申请第四实施例中路由获取装置的方框图；
图7是根据本申请第五实施例中服务器的方框图。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合附图对本申请的各实施方式进行详细的阐述。然而，本领域的普通技术人员可以理解，在本申请各实施方式中，为了使读者更好地理解本申请而提出了许多技术细节。但是，即使没有这些技术细节和基于以下各实施方式的种种变化和修改，也可以实现本申请所要求保护的技术方案。以下各个实施例的划分是为了描述方便，不应对本申请的具体实现方式构成任何限定，各个实施例在不矛盾的前提下可以相互结合相互引用。
目前的K优算法虽然在理论上可以保证获得满足必经约束的最短路径，但该算法并非针对必经约束而设计的，其计算复杂度为O(Kn 3)，其中n为网络拓扑的节点数，K为已找到的最短路径数；因此当网络规模较大时，即网络拓扑的节点数较大时，该算法的计算效率将会非常低，难以满足路由计算的实时性要求；而对于带必经约束的路由计算而言，K优算法的求解过程包含了太多的无效计算。基于上述发现，发明人提出了本申请的技术方案；即，本实施例的最短路径计算方法，是针对网络中带必经约束的最短路径的路由计算而提出的。
本申请的第一实施方式涉及一种最短路径计算方法。具体流程如图1所示。
步骤101，根据接收的路由请求获取网络的资源信息；其中，路由请求包括起始节点、终止节点、指标约束及必经约束。
步骤102，根据指标约束计算网络中各拓扑元素的指标约束权重，并根据必经约束和网络的资源信息计算各拓扑元素的必经约束权重。
步骤103，根据各拓扑元素的指标约束权重和必经约束权重，计算起始节点到终止节点的满足必经约束的最短路径。
下面对本实施方式的最短路径计算方法的实现细节进行具体的说明，以下内容仅为方便理解提供的实现细节，并非实施本方案的必须。
本实施例中的网络可以是软件定义网络，简称为SDN；本实施例中的最短路径计算方法应用于路由获取装置，该路由获取装置可以独立存在，也可以集成在SDN控制器中。
在步骤101中，路由获取装置接收的路由请求包括起始节点、终止节点、指标约束及必经约束。其中，指标约束是指业务指标，即对传输质量的要求，业务指标例如包括转发跳数、需求带宽、时延、时延抖动等；必经约束是指从起始节点到终止节点必须经过的节点和/或链 路，这里将必须经过的节点或链路可以统称为必经元素。
路由获取装置可以根据路由请求中的指标约束获取网络的资源信息。该网络是指起始节点和终止节点所在的网路，网络的资源信息包括网络的拓扑结构、各拓扑元素的实时带宽、时延等信息；其中，路由获取装置会从网络中会获取满足指标约束的可用拓扑。其中，拓扑元素为该可用拓扑中的节点或链路。
如图2的例子中，步骤102包括两个子步骤。
子步骤1021，获取路由策略；其中，路由策略至少包括：根据指标约束获取的指标约束策略、以及根据网络的资源信息获取的必经约束策略；
子步骤1022，基于路由策略计算各拓扑元素的指标约束权重和必经约束权重；其中，根据指标约束与指标约束策略计算得到指标约束权重、根据必经约束与必经约束策略计算得到必经约束权重。
子步骤1021中，具体的，路由获取装置中预存有第一选择策略，该第一选择策略用于为路由请求中的指标约束选择对应的指标约束策略。即，该第一选择策略可以理解为一个选择函数，将指标约束作为该第一选择策略的输入参数，第一选择策略通过对该指标约束的分析来确定该指标约束对应的指标约束策略。例如，如果该指标约束中包含对转发跳数的限定，那么，该第一选择策略确定出的该指标约束对应的指标约束策略可以是最小跳策略。
不同的指标约束对应于不同的指标约束策略。不同的指标约束策略例如包括最小跳策略、带宽均衡策略、时延最小策略、时延抖动最小策略等。更具体的，例如：当指标约束策略为最小跳策略时，所有链路的指标约束权重均可以设为一大于0的常数，例如100；当指标约束策略为带宽均衡策略时，指标约束权重由业务所需带宽与该节点或者链路的可用带宽之比确定；当指标约束策略为时延最小策略时，指标约束权重由该节点或者链路的时延确定；当指标约束策略为时延抖动最小策略，指标约束权重由该节点或者链路的时延抖动决定。
同理，路由获取装置中还预存有第二选择策略，该第二选择策略用于为当前的该网络选择对应的必经约束策略。即，该第二选择策略可以理解为一个选择函数，将该网络的资源信息作为该第二选择策略的输入参数，第二选择策略通过对该网络的资源信息的分析来确定必经约束策略；该网络的资源信息例如可以包括各拓扑元素的转发跳数、需求带宽、时延、时延抖动等，各拓扑元素之间的距离等。
子步骤1022中，可以计算出指标约束权重和必经约束权重。为描述方便，引入如下标记符号：网络G＝G(V,E)，其中V为G中的节点集，E为G中的链路集；路由请求的指标约束包括转发跳数h、需求带宽b、时延d、时延抖动j等；路由请求的必经约束中的必经元素(必 经节点或者必经链路)为e，e∈S E，其中，S E是必经元素集，且S E∈G。设待考察的拓扑元素(节点或者链路)为g∈G。
在一个例子中，g的指标约束权重w I可表示为：
w I＝f(h,b,d,j,…)            公式(1)
其中，函数f为这里的指标约束策略，将各拓扑元素的转发跳数h、需求带宽b、时延d、时延抖动j等代入该函数f计算后可以得到各拓扑元素的指标约束权重w I。
在一个例子中，每个拓扑元素g的必经约束权重包括，必经约束中的各必经元素e在该拓扑元素g上产生的必经约束权重的总和；每个必经元素e在拓扑元素上产生的必经约束权重基于必经约束策略得到。
每个拓扑元素g的必经约束权重w C(g)可表示为：
Figure PCTCN2020105535-appb-000001
其中，函数
Figure PCTCN2020105535-appb-000002
为这里的必经约束策略。
在一个例子中，必经约束策略以如下公式表示：
Figure PCTCN2020105535-appb-000003
以下为对公式(3)中各参数的说明。
w 0表示至少部分拓扑元素的指标约束权重的统计特征值。在一个例子中，至少部分拓扑元素可以包括：拓扑元素的集合中，与各必经元素直接相连的拓扑元素；即，w 0可以是与各必经元素直接相连的拓扑元素的指标约束权重的统计特征值。然不限于此，w 0也可以是所有拓扑元素的指标约束权重的统计特征值。其中，统计特征值可以是各拓扑元素的指标约束权重的最小值、最大值、平均值、中位数的其中之一。
c e表示必经元素的指标约束权重和必经约束权重的比例评估系数，且c e是大于0的实数。其中，比例评估系数可以根据实际情况设定。
Figure PCTCN2020105535-appb-000004
表示必经元素与拓扑元素的远近程度评估系数。在一个例子中，可以以必经元素与拓扑元素的距离来衡量，距离越大，远近程度评估系数越大。
Figure PCTCN2020105535-appb-000005
表示必经元素与至少部分拓扑元素的远近程度评估系数的基线值。在一个例子中，至少部分拓扑元素可以包括：拓扑元素的集合中，与各必经元素直接相连的拓扑元素。即，
Figure PCTCN2020105535-appb-000006
可以是必经元素和与必经元素直接相连的拓扑元素的远近程度评估系数的基线值。然不限于此，
Figure PCTCN2020105535-appb-000007
也可以是必经元素和网络中每个拓扑元素的远近程度评估系数的基线值。
其中，函数
Figure PCTCN2020105535-appb-000008
即表示必经约束策略，将每个必经元素e的相关信息代入上述函数
Figure PCTCN2020105535-appb-000009
就可以计算出每个必经元素在拓扑元素上产生的必经约束权重
Figure PCTCN2020105535-appb-000010
必经元素e的相关信息例如包括必经元素e的指标约束权重、必经元素e与拓扑元素的距离等可用于计算出上述公式(3)中各参数具体值的信息。
如果必经约束中同时包含必经节点和必经链路；设必经节点n∈S N，必经节点集S N∈V；必经链路l∈S L，必经链路集S L∈E，其中，S E＝S N∪S L。那么，对应于上述公式(2)和公式(3)，g的必经约束权重w C(g)可以表示为如下。
Figure PCTCN2020105535-appb-000011
Figure PCTCN2020105535-appb-000012
Figure PCTCN2020105535-appb-000013
其中，c n、c l分别为必经节点、必经链路的指标约束权重和必经约束权重的比例评估系数；
Figure PCTCN2020105535-appb-000014
分别表示必经节点、必经链路与拓扑元素的远近程度评估系数；
Figure PCTCN2020105535-appb-000015
分别表示必经节点、必经链路与各拓扑元素的远近程度评估系数的基线值。
在一个例子中，如图3所示，在步骤103包含如下子步骤。
子步骤1031，将各拓扑元素的指标约束权重和必经约束权重之和作为各拓扑元素的总权重。
子步骤1032，根据各拓扑元素的总权重计算起始节点到终止节点的经过必经约束的最短路径。
在子步骤1031中，每个拓扑元素的总权重等于指标约束权重和必经约束权重之和；然并不以此为限；在其他例子中，也可以根据需要，为指标约束权重和必经约束权重设置不同的权重系数，即，拓扑元素的总权重＝指标约束权重与其对应的权重系数之积+必经约束权重与其对应的权重系数之积。
在子步骤1032中，可以基于K优路径算法实现；子步骤1021中确定的路由策略还可以包括基于网络的资源信息确定的K值，例如，根据网络的拓扑规模、稀疏度等来确定K值；其中，K值为遍历的最短路径数的上限值。
具体的，可以利用Dijkstra算法计算最短路径P 0，其中，路径的长短由路径代价来衡量，路径代价代表了该路径满足路由要求的程度，且路径代价是该路径上所有节点和链路的权重之和，路径代价越大，满足程度越低。K优算法的基本思想就是基于最短路径P 0，利用迭代方法逐条计算次优路径(即第二最短路径、第三最短路径……)；每获得一条次优路径P i，首 先检查P i是否满足必经约束条件，若满足则将P i作为计算结果返回，若不满足，则判断i+1与K的大小关系，若i+1≦K，则继续计算P i+1条次优路径，若未能找到P i+1条次优路径，则表示当前网络拓扑中由起始节点到终止节点只有i条可达路径，则返回会空值并结束计算；若i+1>K，则表示在当前的路由策略下，无法找到满足必经约束的路径。请参考图4，子步骤1032包括如下子步骤：
子步骤1032-1，迭代计算从起始节点到终止节点的第i条最短路径P i，若P i存在，进入子步骤1032-2；若不存在，则进入子步骤1032-3。
子步骤1032-2，检查P i是否满足必经约束条件；若是，则进入子步骤1032-4；若否，进入子步骤1032-5。
子步骤1032-3，返回空值，并结束。
子步骤1032-4，返回P i作为计算得到的最短路径，并结束。
子步骤1032-5，判断i+1是否小于或等于K；若是，返回子步骤1032-1；若否，则结束。
需要说明的是，本实施例中基于K优路径算法实现，然并不以此为限，任何可以利用各拓扑元素的总权重寻找从起始节点到终止节点的最短路径的方法，均适用于本实施例。
本申请实施方式相对于现有技术而言，根据网络的资源信息获取必经约束策略，并根据必经约束与必经约束策略计算各拓扑元素的必经约束权重；根据各拓扑元素的指标约束权重和必经约束权重，确定起始节点到终止节点的满足必经约束的最短路径。即，在最短路径的计算中除了将指标约束权重作为考量因素，还引入了必经约束权重，将必经约束权重与指标约束权重一同作为考量因素来实现带必经约束的路由计算，从而可以显著提高大规模复杂网络中带必经约束的路由计算的效率。
在一些实施例中，以与各所述必经元素直接相连的拓扑元素，来计算必经约束策略中的参数，可以使得得到的必经约束策略更准确。
在一些实施例中，在最短路径的计算中，同时考虑网络中的链路和节点的权重，相较于现有技术中只考虑链路的权重而言，更加全面。
在一些实施例中，将指标约束权重和必经约束权重之和作为拓扑元素的总权重，提供了指标约束权重和必经约束权重被结合起来考量的一种具体结合方式。
本申请的第二实施方式涉及一种最短路径计算方法。第二实施方式与第一实施方式大致相同，主要区别之处在于：在本申请第二实施方式中，如果根据各拓扑元素的指标约束权重和必经约束权重，无法得到最短路径，则调整路由策略，基于调整后的路由策略重新计算各拓扑元素的指标约束权重和必经约束权重，并根据重新计算出的各拓扑元素的指标约束权重 和必经约束权重，计算起始节点到终止节点的满足必经约束的最短路径。
如图5所示为第二实施例中最短路径计算方法的流程图；其中，与第一实施例中的图4相比，增加了步骤104，调整路由策略。
具体的，当子步骤1032-5的判断结果为否时，表示在当前的路由策略下，无法找到从起始节点到终止节点的满足必经约束的最短路径；此时，进入步骤104，调整路由策略；然后返回子步骤1022，从而可以基于调整后的路由策略重新计算各拓扑元素的指标约束权重和必经约束权重；并根据重新计算出的各拓扑元素的指标约束权重和必经约束权重，计算起始节点到终止节点的满足必经约束的最短路径。其中，如果在当前的路由策略下无法得到满足约束条件的最短路径，就可以调整路由策略后重新计算，直至找到满足约束条件的最短路径为止。
在步骤104中，调整路由策略，具体可以调整必经约束策略，如调整公式(3)中的w 0、c e(c n、c l的至少其中之一)或
Figure PCTCN2020105535-appb-000016
的至少其中之一)；或者，可以调整K值。
需要说明的是，本实施例中在计算出各拓扑元素的总权重后，以基于K优算法计算最短路径为例进行说明；在其他例子中，也可以基于其他算法计算最短路径，当无法得到最短路径时，也可以调整路由策略，基于调整后的路由策略重新计算各拓扑元素的总权重，并基于该其他算法重新计算最短路径。
本申请的第三实施方式涉及一种最短路径计算方法。第三实施方式与第一或二实施方式大致相同，主要区别之处在于：在本申请第三实施方式中，提供了比例评估系数c e的一种具体计算方式。
本实施例中，比例评估系数c e可以根据各拓扑元素的指标约束权重的一个统计特征值w 1来确定；该统计特征值w 1例如是方差。
具体的，路由获取装置内预存有各拓扑元素的指标约束权重的方差与比例评估系数的预设关系；可以先计算出各拓扑元素的指标约束权重的方差，再根据方差与比例评估系数的预设关系得到该方差对应的比例评估系数c e；其中，该预设关系中，方差越大，比例评估系数越小；该预设关系可以以对照表形式表示，或者以函数形式表示。
本实施例提供的比例评估系数c e的具体计算方式中，根据当前网络中各拓扑元素的指标约束权重的统计特征值w 1来计算得到比例评估系数c e，可以使得得到的比例评估系数c e能够较好地符合当前网络的情况，从而使得计算出来的必经约束权重更加准确；进而有助于进一步提升大规模复杂网络中带必经约束的路由计算的效率。
上面各种方法的步骤划分，只是为了描述清楚，实现时可以合并为一个步骤或者对某些步骤进行拆分，分解为多个步骤，只要包括相同的逻辑关系，都在本专利的保护范围内；对算法中或者流程中添加无关紧要的修改或者引入无关紧要的设计，但不改变其算法和流程的核心设计都在该专利的保护范围内。
本申请第四实施方式涉及一种路由获取装置，如图6所示，包括：
拓扑获取模块31，用于根据接收的路由请求获取网络的资源信息；其中，所述路由请求包括起始节点、终止节点、指标约束及必经约束。
权重计算模块32，用于根据所述指标约束计算所述网络中各拓扑元素的指标约束权重，并根据所述必经约束和所述网络的资源信息计算各所述拓扑元素的必经约束权重。
路由计算模块33，用于根据各所述拓扑元素的指标约束权重和必经约束权重，计算所述起始节点到所述终止节点的满足所述必经约束的最短路径。
不难发现，本实施方式为与第一、或第二、或第三实施方式相对应的装置实施例，本实施方式可与第一、或第二、或第三实施方式互相配合实施。第一、或第二、或第三实施方式中提到的相关技术细节在本实施方式中依然有效，为了减少重复，这里不再赘述。相应地，本实施方式中提到的相关技术细节也可应用在第一、或第二、或第三实施方式中。
值得一提的是，本实施方式中所涉及到的各模块均为逻辑模块，在实际应用中，一个逻辑单元可以是一个物理单元，也可以是一个物理单元的一部分，还可以以多个物理单元的组合实现。此外，为了突出本申请的创新部分，本实施方式中并没有将与解决本申请所提出的技术问题关系不太密切的单元引入，但这并不表明本实施方式中不存在其它的单元。
本申请第五实施方式涉及一种服务器，如图7所示，包括：
至少一个处理器41；以及，
与所述至少一个处理器41通信连接的存储器42；其中，
所述存储器42存储有可被所述至少一个处理器41执行的指令，所述指令被所述至少一个处理器41执行，以使所述至少一个处理器41能够执行上述最短路径计算方法。
其中，存储器42和处理器41采用总线方式连接，总线可以包括任意数量的互联的总线和桥，总线将一个或多个处理器41和存储器42的各种电路连接在一起。总线还可以将诸如外围设备、稳压器和功率管理电路等之类的各种其他电路连接在一起，这些都是本领域所公知的，因此，本文不再对其进行进一步描述。总线接口在总线和收发机之间提供接口。收发机可以是一个元件，也可以是多个元件，比如多个接收器和发送器，提供用于在传输介质上与各种其他装置通信的单元。经处理器41处理的数据通过天线在无线介质上进行传输，进一 步，天线还接收数据并将数据传送给处理器41。
处理器41负责管理总线和通常的处理，还可以提供各种功能，包括定时，外围接口，电压调节、电源管理以及其他控制功能。而存储器42可以被用于存储处理器41在执行操作时所使用的数据。
本申请第六实施方式涉及一种计算机可读存储介质，存储有计算机程序。计算机程序被处理器执行时实现上述方法实施例。
即，本领域技术人员可以理解，实现上述实施例方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序存储在一个存储介质中，包括若干指令用以使得一个设备(可以是单片机，芯片等)或处理器(processor)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或者光盘等各种可以存储程序代码的介质。
本领域的普通技术人员可以理解，上述各实施方式是实现本申请的具体实施例，而在实际应用中，可以在形式上和细节上对其作各种改变，而不偏离本申请的精神和范围。

Claims (15)

  1. 一种最短路径计算方法，其特征在于，包括：
    根据接收的路由请求获取网络的资源信息；其中，所述路由请求包括起始节点、终止节点、指标约束及必经约束；
    根据所述指标约束计算所述网络中各拓扑元素的指标约束权重，并根据所述必经约束和所述网络的资源信息计算各所述拓扑元素的必经约束权重；
    根据各所述拓扑元素的指标约束权重和必经约束权重，计算所述起始节点到所述终止节点的满足所述必经约束的最短路径。
  2. 根据权利要求1所述的最短路径计算方法，其特征在于，所述根据所述指标约束计算所述网络中各拓扑元素的指标约束权重，并根据所述必经约束和所述网络的资源信息计算各所述拓扑元素的必经约束权重，包括：
    获取路由策略；其中，所述路由策略至少包括，根据所述指标约束获取的指标约束策略、以及根据所述网络的资源信息获取的必经约束策略；
    基于所述路由策略计算各所述拓扑元素的指标约束权重和必经约束权重；其中，根据所述指标约束与所述指标约束策略计算得到所述指标约束权重、根据所述必经约束与所述必经约束策略计算得到所述必经约束权重。
  3. 根据权利要求2所述的最短路径计算方法，其特征在于，所述根据各所述拓扑元素的指标约束权重和必经约束权重，计算所述起始节点到所述终止节点的满足所述必经约束的最短路径中，如果根据各所述拓扑元素的指标约束权重和必经约束权重，无法得到所述最短路径，则调整所述路由策略，基于调整后的所述路由策略重新计算各所述拓扑元素的指标约束权重和必经约束权重，并根据重新计算出的各所述拓扑元素的指标约束权重和必经约束权重，计算所述起始节点到所述终止节点的满足所述必经约束的最短路径。
  4. 根据权利要求2或3所述的最短路径计算方法，其特征在于，每个所述拓扑元素的必经约束权重包括，所述必经约束中的各必经元素在所述拓扑元素上产生的必经约束权重的总和；每个所述必经元素在所述拓扑元素上产生的必经约束权重基于所述必经约束策略得到。
  5. 根据权利要求4所述的最短路径计算方法，其特征在于，所述必经约束策略以如下公式表示：
    Figure PCTCN2020105535-appb-100001
    其中，
    Figure PCTCN2020105535-appb-100002
    表示所述必经约束策略，e表示所述必经元素，g表示所述拓扑元素，G表示 所述拓扑元素的集合；w 0表示至少部分所述拓扑元素的指标约束权重的一个统计特征值，c e表示所述必经元素的指标约束权重和必经约束权重的比例评估系数，且c e是大于0的实数；
    Figure PCTCN2020105535-appb-100003
    表示所述必经元素与所述拓扑元素的远近程度评估系数，
    Figure PCTCN2020105535-appb-100004
    表示所述必经元素与至少部分所述拓扑元素的远近程度评估系数的基线值。
  6. 根据权利要求5所述的最短路径计算方法，其特征在于，所述至少部分所述拓扑元素包括：所述拓扑元素的集合中与各所述必经元素直接相连的拓扑元素。
  7. 根据权利要求5所述的最短路径计算方法，其特征在于，所述统计特征值w 0为各所述拓扑元素的指标约束权重的最小值、最大值、平均值、中位数的其中之一。
  8. 根据权利要求5所述的最短路径计算方法，其特征在于，所述比例评估系数c e的计算方式包括：
    计算各所述拓扑元素的指标约束权重的一个统计特征值w 1；
    根据统计特征值和比例评估系数的预设关系，获取所述统计特征值w 1对应的比例评估系数c e。
  9. 根据权利要求8所述的最短路径计算方法，其特征在于，所述统计特征值w 1为各所述拓扑元素的指标约束权重的方差；且在所述预设关系中，所述方差越大，所述比例评估系数越小。
  10. 根据权利要求1所述的最短路径计算方法，其特征在于，所述拓扑元素包括链路和节点。
  11. 根据权利要求1所述的最短路径计算方法，其特征在于，所述根据各所述拓扑元素的指标约束权重和必经约束权重，确定所述起始节点到所述终止节点的经过所述必经约束的最短路径，包括：
    将各所述拓扑元素的指标约束权重和必经约束权重之和作为各所述拓扑元素的总权重；
    根据各所述拓扑元素的总权重计算所述起始节点到所述终止节点的经过所述必经约束的最短路径。
  12. 根据权利要求11所述的最短路径计算方法，其特征在于，所述根据各所述拓扑元素的总权重计算所述起始节点到所述终止节点的经过所述必经约束的最短路径中，基于K优路径算法实现。
  13. 一种路由获取装置，其特征在于，包括：
    拓扑获取模块，用于根据接收的路由请求获取网络的资源信息；其中，所述路由请求包 括起始节点、终止节点、指标约束及必经约束；
    权重计算模块，用于根据所述指标约束计算所述网络中各拓扑元素的指标约束权重，并根据所述必经约束和所述网络的资源信息计算各所述拓扑元素的必经约束权重；
    路由计算模块，用于根据各所述拓扑元素的指标约束权重和必经约束权重，计算所述起始节点到所述终止节点的满足所述必经约束的最短路径。
  14. 一种服务器，其特征在于，包括：
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如权利要求1至12中任一所述的最短路径计算方法。
  15. 一种计算机可读存储介质，存储有计算机程序，其特征在于，所述计算机程序被处理器执行时实现权利要求1至12中任一项所述的最短路径计算方法。
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