WO2023019604A1 - 基于流量疏导的最小网络能耗优化方法及系统 - Google Patents

基于流量疏导的最小网络能耗优化方法及系统 Download PDF

Info

Publication number
WO2023019604A1
WO2023019604A1 PCT/CN2021/113992 CN2021113992W WO2023019604A1 WO 2023019604 A1 WO2023019604 A1 WO 2023019604A1 CN 2021113992 W CN2021113992 W CN 2021113992W WO 2023019604 A1 WO2023019604 A1 WO 2023019604A1
Authority
WO
WIPO (PCT)
Prior art keywords
service request
energy consumption
optical
path
network
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2021/113992
Other languages
English (en)
French (fr)
Inventor
陈伯文
江云飞
陈琪
马维克
刘玲
沈纲祥
高明义
向练
陈虹
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Suzhou University
Original Assignee
Suzhou University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Suzhou University filed Critical Suzhou University
Priority to US17/802,193 priority Critical patent/US12289208B2/en
Publication of WO2023019604A1 publication Critical patent/WO2023019604A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • H04L41/0833—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability for reduction of network energy consumption
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
    • H04B10/27—Arrangements for networking
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0803—Configuration setting
    • H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
    • H04L41/0836—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability to enhance reliability, e.g. reduce downtime
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5041—Network service management, e.g. ensuring proper service fulfilment according to agreements characterised by the time relationship between creation and deployment of a service
    • H04L41/5051—Service on demand, e.g. definition and deployment of services in real time
    • 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
    • H04L45/12—Shortest path evaluation
    • H04L45/122—Shortest path evaluation by minimising distances, e.g. by selecting a route with minimum of number of hops
    • 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
    • H04L45/38—Flow based routing

Definitions

  • the present invention relates to the technical field of energy consumption optimization, in particular to a method and system for optimizing minimum network energy consumption based on flow grooming.
  • traffic grooming technology in IP over elastic optical networks will be used to solve this problem.
  • traffic grooming technology in the IP over elastic optical network solves the problem of serious waste of bandwidth resources in the optical channel, it has not yet reached the optimal configuration method in the process of resource allocation, and cannot achieve the minimum optical energy consumption components. Therefore, traffic grooming still faces major challenges in terms of bandwidth allocation and energy consumption.
  • Traffic grooming technology is how to integrate multiple low-speed service flows of different types and different rates into high-speed data flows, so as to perform data transmission in the same optical channel.
  • the traffic grooming method is considered to be an effective method to optimize the allocation of spectrum resources in optical networks and reduce the use of power components in optical networks. Therefore, it has wide application value in optical network.
  • the traditional traffic grooming method solidifies the resource allocation process, and often cannot rationally utilize the bandwidth resources in the optical channel, resulting in an increase in the number of optical energy consumption components in the elastic optical network. Therefore, routing and spectrum resource allocation based on traffic grooming is one of the key issues to optimize network energy consumption and improve network spectrum resource efficiency.
  • the technical problem to be solved by the present invention is to overcome the problem of how to improve the service quality of each service request in elastic optical network transmission in the prior art, and the problem of high network energy consumption, thereby providing a method that can improve the service quality of each service request Quality of service in elastic optical network transmission, making full use of spectrum resources in the network, reducing network energy consumption, and a minimum network energy consumption optimization method and system based on traffic grooming.
  • a minimum network energy consumption optimization method based on traffic grooming in the present invention includes: in the elastic optical network, a set of service request sets is generated, and the number of service requests is calculated according to the source node and destination node of each service request.
  • the objective function of the planning model in the process of allocating resources for each service request, sequentially judges whether the bandwidth capacity constraints, path uniqueness constraints, spectrum allocation constraints, and optical regenerator quantity constraints of a single spectrum slot are satisfied. If it is satisfied, the establishment of the business request is successful; if any item is not satisfied, the establishment of the business request fails.
  • the distance between any two nodes on the working path does not exceed the maximum transmission distance of light.
  • the energy consumption in the elastic optical network includes IP routing ports, optical transponders and optical regenerators.
  • the objective function includes calculating the number of optical channels in the network, corresponding to the total energy consumption of IP routing ports and optical transponders, and calculating the number of optical regenerators configured in the network, corresponding to optical regeneration The total energy consumption of the device.
  • the bandwidth capacity constraint condition of the single spectrum slot is: Among them, CR represents a set of service requests, K represents the set of k shortest paths obtained through calculation, C is the bandwidth capacity of each spectrum slot, L and W represent the set of optical fiber links in the optical network and the number of links on each link, respectively.
  • the set of spectrum gaps is a variable. If the service request (s, d) occupies the spectrum gap w on the link l in the k-th shortest path, the value of this variable is the amount of bandwidth resources occupied, otherwise the value is 0.
  • the path selection uniqueness constraint condition is: in is a binary variable. If the service request (s, d) uses the kth shortest path to complete the service transmission, the value of this variable is 1, otherwise it is 0.
  • the spectrum allocation constraints are: B sd represents the bandwidth requirement of the service, Indicates the set of link occupancy of service requests (s, d) on the k-th shortest path, Indicates a variable, if the service request (s, d) occupies the spectral gap w on the link l i (l j ) in the kth shortest path, the value of the variable is the occupied bandwidth resource, otherwise the value is 0 .
  • the constraints on the number of optical regenerators are:
  • Z i represents the total number of optical regenerator configurations on node i; the values of M and VL respectively represent a larger integer and the maximum bandwidth requirement that the optical regenerator can carry; is a binary variable. If the service request (s, d) is occupied by the reachable path (i, j) on the kth shortest path, the value of this variable is 1, otherwise the value is 0; is a binary variable, if the service request (s, d) sets an optical regenerator on the node i of the k-th shortest path, the value of this variable is 1, otherwise the value is 0; B sd represents the bandwidth demand of the service .
  • the service request is established successfully, and corresponding frequency spectrum resources are allocated for the service request.
  • the present invention also provides a minimum network energy consumption optimization system based on traffic grooming, including: a calculation module, used to generate a set of service requests in an elastic optical network, and calculate the set of service requests according to the source node and destination node of each service request The reachable set of nodes in the multiple shortest paths; the establishment module is used to establish the virtual reachable path of the source node and the destination node in the reachable set of nodes in the multiple shortest paths; The reachable path is to establish the objective function of the integer linear programming model of the minimum energy consumption.
  • a calculation module used to generate a set of service requests in an elastic optical network, and calculate the set of service requests according to the source node and destination node of each service request
  • the reachable set of nodes in the multiple shortest paths is used to establish the virtual reachable path of the source node and the destination node in the reachable set of nodes in the multiple shortest paths
  • the reachable path is to establish the objective function of the integer linear programming model of the minimum energy consumption.
  • the minimum network energy consumption optimization method and system based on traffic grooming described in the present invention in order to solve the energy consumption efficiency problem in the IP over elastic optical network, the present invention proposes an integer linear programming method on the basis of the above static network energy calculation , that is, to achieve optimization with the goal of minimizing energy consumption in a static network.
  • the proposed minimum energy consumption optimization method based on traffic grooming can greatly reduce the energy consumption generated by completing service requests, so that the problem of energy consumption efficiency in the network can be solved, thereby improving The transmission performance and quality of service of business requests in the IP over elastic optical network; at the same time, the number of optical regenerators in the network is reduced as much as possible, and the energy consumption efficiency of network service transmission is further improved.
  • Fig. 1 is the flowchart of the method for optimizing the minimum network energy consumption based on traffic grooming in the present invention
  • Fig. 2 a is the schematic diagram of given path of the present invention.
  • Fig. 2b is a schematic diagram of the reachable path of the present invention.
  • Fig. 3 is a schematic diagram of a six-node network topology diagram of the present invention.
  • Fig. 4 is a schematic diagram of service routing distribution in the network of the present invention.
  • Fig. 5 is a schematic diagram of traffic grooming of an optical fiber link according to the present invention.
  • this embodiment provides a method for optimizing the minimum network energy consumption based on traffic grooming, including: Step S1: In the elastic optical network, generate a set of service requests, according to the source node and The destination node calculates the reachable sets of nodes in the multiple shortest paths; step S2: in the reachable sets of nodes in the multiple shortest paths, establish a virtual reachable path between the source node and the destination node; step S3: based on the virtual reachable In the process of allocating resources for each service request, it is sequentially judged whether the bandwidth capacity constraints, path uniqueness constraints, and spectrum allocation constraints of a single spectrum gap are met. As well as the constraints on the number of optical regenerators, if all of them are satisfied, the establishment of the service request is successful; if any one is not satisfied, the establishment of the service request fails.
  • a set of service requests is generated in the elastic optical network, and multiple shortest The reachable set of nodes in the path is conducive to the establishment of a virtual reachable path; in the step S2, in the reachable set of nodes in the multiple shortest paths, the virtual reachable path of the source node and the destination node is established, which is conducive to In the static network, the optimization with the minimum energy consumption as the goal is realized; in the step S3, based on the objective function, based on the virtual reachable path, the objective function of the integer linear programming model of the minimum energy consumption is established, for each During the resource allocation process of the service request, it is sequentially judged whether the bandwidth capacity constraints, path uniqueness constraints, spectrum allocation constraints, and optical regenerator quantity constraints of a single spectrum gap are met.
  • Allocate corresponding spectrum resources for the service request if any item is not satisfied, the establishment of the service request will fail, and the whole process is conducive to improving the service quality of each service request in elastic optical network transmission and making full use of the spectrum in the network resources, and reduce the number of optical channels in the network by channeling traffic for each service request.
  • E and P respectively represent the total energy consumption of the service request per unit time and the total power consumption of the network, t represents the unit time, expressed in seconds;
  • N I , NT and NR represent IP routing ports, optical transponders and optical regenerators respectively PI , PT and PR represent the power of IP routing ports, optical transponders and optical regenerators respectively.
  • the integer linear programming model is the optimal method to solve the problem.
  • the network topology the length of each link, the occupancy of spectrum resources, the number of IP service requests, and the bandwidth requirements are given.
  • the optimization goal is to minimize the energy consumption in the network, that is, reduce the IP routing ports, optical transponders and optical regeneration as much as possible.
  • the number of optical regenerators thus establishing an integer linear programming model (ILP_RP) based on the minimum number of optical regenerator configurations based on traffic grooming.
  • a set of service requests CR is generated, and each service request (s, d, B sd ) ⁇ CR, where N, L, W represents the set of optical switching nodes, the set of optical fiber links, and the set of spectrum gaps on each link in the optical network; s and d represent the source node and destination node of each service request, and B sd represents the bandwidth of the service need.
  • K represents the set of k shortest paths calculated by the KSP algorithm.
  • the integer linear programming model with the least energy consumption here will have an input variable Indicates the reachability of nodes in the kth shortest path of a service request (source node s, destination node d).
  • step S2 when establishing a virtual reachable path between the source node and the destination node, the distance between any two nodes on the working path does not exceed the maximum transmission distance of light.
  • Figure 2b shows the respective virtual reachable links when the maximum optical transmission distance is 2 hops and 3 hops. Assuming that the maximum transmission distance of light is 3 hops, it can be seen from the figure that node s and nodes 1, 2, and 3 are reachable, then and The values are all 1, and and The value of is 0.
  • the optimized objective function minimizes the energy consumption in the IP over elastic optical network.
  • the objective function of the integer linear programming model is mainly used to optimize the energy consumption of the IP over elastic optical network.
  • the optimization objective function can be expressed by the following formula:
  • the energy consumption in the IP over elastic optical network is mainly composed of IP routing ports, optical transponders and optical regenerators, Indicates whether the spectrum gap w on the link l is occupied by one or some service requests, Z i indicates the total number of optical regenerator configurations on node i.
  • the objective function is mainly composed of two parts. The first part represents the calculation of the number of optical channels in the network, corresponding to the total energy consumption of IP routing ports and optical transponders; the second part represents the calculation of the number of regenerators configured in the network, corresponding to the optical The total energy consumption of the regenerator.
  • the values of PI , PT and PR represent the power of the IP routing port, the power of the optical transponder, and the power of the optical regenerator, respectively.
  • N, L, and W respectively represent a set of optical switching nodes, a set of optical fiber links, and a set of spectrum gaps on each link in the optical network.
  • t represents the unit time, represented by 1 second.
  • the bandwidth capacity constraint condition of the single spectrum gap is:
  • CR represents a set of service requests
  • K represents the set of k shortest paths obtained through calculation
  • C is the bandwidth capacity of each spectrum slot
  • L and W represent the set of optical fiber links in the optical network and the number of links on each link, respectively.
  • a collection of spectral gaps is a variable, if the service request (s, d) occupies the spectral gap w on the link l in the kth shortest path, this variable takes the value of the occupied bandwidth resource, otherwise it takes the value 0.
  • K represents the set of k shortest paths calculated by the KSP algorithm; at this time, the constraints ensure that the sum of the spectrum resources occupied by all service requests on a spectrum slot of a certain link will not exceed the bandwidth of the spectrum slot Carrying capacity.
  • the path selection uniqueness constraint condition is:
  • B sd represents the bandwidth requirement of the service
  • Indicates a variable if the service request (s, d) occupies the spectral gap w on the link l i (l j ) in the kth shortest path, the value of the variable is the occupied bandwidth resource, otherwise the value is 0 .
  • the constraint condition (5) ensures that each service request will occupy the required bandwidth resources on the corresponding working path
  • the constraint condition (6) guarantees the constraint condition of spectrum consistency, that is, each link on the working path
  • the spectrum slot numbers occupied by the above are the same.
  • Z i represents the total number of optical regenerator configurations on node i; the values of M and VL respectively represent a larger integer and the maximum bandwidth requirement that the optical regenerator can carry; is a binary variable. If the service request (s, d) is occupied by the reachable path (i, j) on the kth shortest path, the value of this variable is 1, otherwise the value is 0; is a binary variable, if the service request (s, d) sets an optical regenerator on node i of the k-th shortest path, the value of this variable is 1, otherwise the value is 0; B sd represents the bandwidth demand of the service .
  • Constraint (7) ensures that the number of regenerators on each node is limited; constraint (8)-(10) guarantees flow constraints, and constraint (8) ensures that when the source node s and destination node d When a service request enters the network, the traffic of the optical regenerator will be generated on the corresponding reachable path of the source node; at the same time, the constraint condition (9) ensures that the generated traffic terminates at the destination node d on the corresponding reachable path; the constraint condition (10) then ensures that for any intermediate node, the size of the incoming traffic is equal to the outgoing traffic.
  • Constraint (11) guarantees that for the kth shortest path of the service request from s to d, according to the known reachable path, the corresponding optical regenerator is placed on the intermediate node between the source node s and the destination node d, so that Between s and d is reachable.
  • Constraint condition (12) guarantees that for the kth shortest path of the service request (s, d), optical regenerators are set on all reachable paths (excluding the source end).
  • Constraint condition (13) ensures the final number of optical regenerators placed on each node.
  • the value on the optical fiber link represents the link length (unit: km), and each optical fiber link is bidirectional. It is assumed that each optical fiber link has 5 spectrum slots, and the bandwidth capacity of each spectrum slot is 100Gbps.
  • the method of the invention allocates optimal resources in the network for the three service requests after executing the constraint conditions, so that the network energy consumption is the lowest. As shown in Figure 4, first select 0-1-2-3 as the working path for CR 1 (0,3,40), and allocate corresponding spectrum resources for it; then select 0-1-2-3 for CR 2 (0,2,120).
  • FIG. 1-2 is used as the working path, channeling part of the bandwidth requirement of 20Gbps to the remaining spectrum space in the spectrum gap 1 of links 0-1 and 1-2, and establishing a new optical channel for the bandwidth requirement of 100Gbps; finally, CR 3 ( 1, 4, 40) Select 1-2-4 as the working path, perform traffic grooming on spectrum gap 1 of link 1-2, and establish a new optical channel on link 2-4.
  • Figure 5 shows the spectrum resource occupancy in link 1-2, part of the bandwidth requirements of CR 1 (0,3,40), CR 2 (0,2,120) and CR 3 (1,4,40) are allocated to the spectrum gap 1, and occupy a single spectrum slot for CR 2 (0,2,120).
  • the maximum optical transmission distance of the optical regenerator is set to 2000km, it is only necessary to set an optical regenerator at node 2 for the service request CR 1 (0,3,40) to restore the optical signal.
  • the required IP routing ports, optical The minimum number of transponders and optical regenerators makes the energy consumption of the IPover elastic optical network the lowest.
  • this embodiment provides a minimum network energy consumption optimization system based on traffic grooming.
  • the problem-solving principle is similar to that of the minimum network energy consumption optimization method based on traffic grooming, and repeated descriptions will not be repeated.
  • This embodiment provides a minimum network energy consumption optimization system based on traffic grooming, including:
  • the calculation module is used to generate a set of service request sets in the elastic optical network, and calculate the reachable sets of nodes in multiple shortest paths according to the source node and destination node of each service request;
  • a judging module configured to establish an objective function of an integer linear programming model of minimum energy consumption based on the virtual reachable path, and in the process of allocating resources for each service request, sequentially judge whether the bandwidth capacity constraints of a single spectrum slot are met, If the path uniqueness constraints, spectrum allocation constraints, and optical regenerator quantity constraints are all satisfied, the service request is successfully established; if any one is not satisfied, the service request fails to be established.
  • the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions
  • the device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computing Systems (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Data Exchanges In Wide-Area Networks (AREA)

Abstract

本发明涉及一种基于流量疏导的最小网络能耗优化方法及系统,包括：在弹性光网络中，产生一组业务请求集合，根据每个业务请求的源节点和目的节点计算多条最短路径中的节点可达集合；在多条最短路径中的节点可达集合中，建立源节点和目的节点的虚拟可达路径；基于所述虚拟可达路径，建立最小能量消耗的整数线性规划模型的目标函数，为每个业务请求分配资源的过程中，依次判断是否满足单个频谱间隙的带宽容量约束条件、路径唯一性约束条件、频谱分配约束条件以及光再生器数量约束条件，若均满足，则业务请求建立成功；若有任意一项不满足，则业务请求建立失败。本发明有利于提高网络业务传输的能耗利用效率。

Description

基于流量疏导的最小网络能耗优化方法及系统 技术领域
本发明涉及能量消耗优化的技术领域，尤其是指一种基于流量疏导的最小网络能耗优化方法及系统。
背景技术
随着网络带宽和移动用户数量的快速增长，以及云计算、数据中心、视频点播等不同应用业务的出现，网络中的数据流量进入爆炸性增长阶段，给基础网络传输造成极大的困难与挑战。因此，如何提高网络的带宽资源效率，以及如何保证网络的能耗效率将成为研究的技术难题。传统光网络中一般使用固定频谱宽度的方式，这样使得带宽资源无法得有效利用，造成效率低、灵活性差、严重浪费。通过采用频谱灵活切片方式，可以实现带宽资源的灵活分配，从而提高频谱资源的利用率。为提高和改善网络带宽资源分配的灵活性，以及提高网络能耗效率、减少网络能耗元件的使用，将采用IP over弹性光网络中的流量疏导技术来解决这一问题。然而，虽然IP over弹性光网络中的流量疏导技术解决了光通道中带宽资源的浪费严重这一难题，但是在资源分配的过程中尚未达到最佳的配置方法，不能达到最小化光能耗元件数量的目的，所以流量疏导的带宽分配和能量消耗方面仍然面临着重大挑战。
流量疏导技术是如何将多个不同类型和不同速率的低速业务流汇集成高速数据流，从而在同一个光通道内进行数据传输。流量疏导方法被认为是一种优化光网络中频谱资源分配、减少光网络中功率元件使用的有效方法。因此，在光网络中具有广泛应用价值。然而，传统的流量疏导方法固化了资源分配的流程，往往不能合理利用光通道内的带宽资源，造成弹性光网络中 的光能耗元件使用数量增加。因此，基于流量疏导的路由与频谱资源分配问题是优化网络的能耗与提高网络频谱资源效率的关键的问题之一。一方面，在传统的IP over弹性光网络中，由于一个光通道只能承载相同业务请求，不需要考虑光通道中的剩余可用频谱资源，只需要考虑简单的路由与频谱资源分配，在频谱分配时满足频谱连续性和连续性即可。另一方面，在弹性光网络中，充分利用光通道内的空闲频谱资源，不仅需要考虑路由和频谱分配的问题，而且在频谱分配的过程中需要将不同的业务请求同时疏导到同一光通道内，以达到提高频谱资源效率和能量消耗效率，这样会造成网络资源分配中的复杂性。
发明内容
为此，本发明所要解决的技术问题在于克服现有技术中如何提高每个业务请求在弹性光网络传输中的服务质量，且网络能量消耗高的问题，从而提供一种可以提高每个业务请求在弹性光网络传输中的服务质量，充分利用网络中的频谱资源，降低网络能量消耗的基于流量疏导的最小网络能耗优化方法及系统。
为解决上述技术问题，本发明的一种基于流量疏导的最小网络能耗优化方法，包括：在弹性光网络中，产生一组业务请求集合，根据每个业务请求的源节点和目的节点计算多条最短路径中的节点可达集合；在多条最短路径中的节点可达集合中，建立源节点和目的节点的虚拟可达路径；基于所述虚拟可达路径，建立最小能量消耗的整数线性规划模型的目标函数，为每个业务请求分配资源的过程中，依次判断是否满足单个频谱间隙的带宽容量约束条件、路径唯一性约束条件、频谱分配约束条件以及光再生器数量约束条件，若均满足，则业务请求建立成功；若有任意一项不满足，则业务请求建立失败。
在本发明的一个实施例中，建立源节点和目的节点的虚拟可达路径时，工作路径上的任意两个节点之间的距离不超过光的最大传输距离。
在本发明的一个实施例中，所述弹性光网络中的能量消耗包括IP路由 端口、光转发器和光再生器。
在本发明的一个实施例中，所述目标函数包括计算获得网络中的光通道数，对应IP路由端口和光转发器的总能耗以及计算网络中配置的光再生器的个数，对应光再生器的总能耗。
在本发明的一个实施例中，所述单个频谱间隙的带宽容量约束条件为：
Figure PCTCN2021113992-appb-000001
其中CR表示一组业务请求集合，K表示计算获得k条最短路径的集合，C为每个频谱隙的带宽容量，L和W分别代表光网络中的光纤链路集合和每条链路上的频谱间隙集合，是一个变量，若业务请求(s,d)占用了第k条最短路径中链路l上的频谱间隙w，该变量取值为占用的带宽资源量，否则取值为0。
在本发明的一个实施例中，所述路径选择唯一性约束条件为：
Figure PCTCN2021113992-appb-000002
其中
Figure PCTCN2021113992-appb-000003
是一个二进制变量，若业务请求(s,d)采用第k条最短路径来完成业务传输，该变量取值为1，否则取值为0。
在本发明的一个实施例中，所述频谱分配约束条件为：
Figure PCTCN2021113992-appb-000004
Figure PCTCN2021113992-appb-000005
B sd代表该业务的带宽需求，
Figure PCTCN2021113992-appb-000006
表示业务请求(s,d)在第k条最短路径上链路占用情况的集合，
Figure PCTCN2021113992-appb-000007
Figure PCTCN2021113992-appb-000008
表示一个变量，若业务请求(s,d)占用了第k条最短路径中链路l i(l j)上的频谱间隙w，该变量取值为占用的带宽资源量，否则取值为0。
在本发明的一个实施例中，所述光再生器数量约束条件为：
Figure PCTCN2021113992-appb-000009
Figure PCTCN2021113992-appb-000010
Figure PCTCN2021113992-appb-000011
Figure PCTCN2021113992-appb-000012
其中Z i表示节点i上的光再生器配置的总个数；M和VL的数值分别表示一个较大的整数以及光再生器可以承载的最大带宽需求；
Figure PCTCN2021113992-appb-000013
是一个二进制变量，若业务请求(s,d)采用第k条最短路径上的可达路径(i,j)进行占用，该 变量取值为1，否则取值为0；
Figure PCTCN2021113992-appb-000014
是一个二进制变量，若业务请求(s,d)在第k条最短路径的节点i上设置了光再生器，该变量取值为1，否则取值为0；B sd代表该业务的带宽需求。
在本发明的一个实施例中，所述业务请求建立成功，为所述业务请求分配相应的频谱资源。
本发明还提供了一种基于流量疏导的最小网络能耗优化系统，包括：计算模块，用于在弹性光网络中，产生一组业务请求集合，根据每个业务请求的源节点和目的节点计算多条最短路径中的节点可达集合；建立模块，用于在多条最短路径中的节点可达集合中，建立源节点和目的节点的虚拟可达路径；判断模块，用于基于所述虚拟可达路径，建立最小能量消耗的整数线性规划模型的目标函数，为每个业务请求分配资源的过程中，依次判断是否满足单个频谱间隙的带宽容量约束条件、路径唯一性约束条件、频谱分配约束条件以及光再生器数量约束条件，若均满足，则业务请求建立成功；若有任意一项不满足，则业务请求建立失败。
本发明的上述技术方案相比现有技术具有以下优点：
本发明所述的基于流量疏导的最小网络能耗优化方法及系统，为了解决IP over弹性光网络中存在的能耗效率问题，本发明在上述静态网络能量计算的基础上，提出整数线性规划方法，即在静态网络中实现以最小能量消耗为目标的优化。本发明在IP over弹性光网络中，所提出的基于流量疏导的最小能量消耗优化方法，可以极大地降低完成业务请求所产生的能量消耗，使得网络中的能耗效率问题得以解决，从而提高了IP over弹性光网络中业务请求的传输性能和服务质量；同时尽可能减少网络中光再生器的使用数量，进一步提高了网络业务传输的能耗利用效率。
附图说明
为了使本发明的内容更容易被清楚的理解，下面根据本发明的具体实施例并结合附图，对本发明作进一步详细的说明，其中
图1是本发明基于流量疏导的最小网络能耗优化方法流程图；
图2a是本发明给定路径的示意图；
图2b是本发明可达路径的示意图；
图3是本发明六节点网络拓扑图示意图；
图4是本发明网络中的业务路由分配情况示意图；
图5是本发明光纤链路的流量疏导情况示意图。
具体实施方式
实施例一
如图1所示，本实施例提供一种基于流量疏导的最小网络能耗优化方法，包括：步骤S1：在弹性光网络中，产生一组业务请求集合，根据每个业务请求的源节点和目的节点计算多条最短路径中的节点可达集合；步骤S2：在多条最短路径中的节点可达集合中，建立源节点和目的节点的虚拟可达路径；步骤S3：基于所述虚拟可达路径，建立最小能量消耗的整数线性规划模型的目标函数，为每个业务请求分配资源的过程中，依次判断是否满足单个频谱间隙的带宽容量约束条件、路径唯一性约束条件、频谱分配约束条件以及光再生器数量约束条件，若均满足，则业务请求建立成功；若有任意一项不满足，则业务请求建立失败。
本实施例所述基于流量疏导的最小网络能耗优化方法，所述步骤S1中，在弹性光网络中，产生一组业务请求集合，根据每个业务请求的源节点和目的节点计算多条最短路径中的节点可达集合，从而有利于建立虚拟可达路径；所述步骤S2中，在多条最短路径中的节点可达集合中，建立源节点和目的节点的虚拟可达路径，有利于在静态网络中实现以最小能量消耗为目标的优化；所述步骤S3中，基于所述目标函数，基于所述虚拟可达路径，建立最小能量消耗的整数线性规划模型的目标函数，为每个业务请求分配资源的过程中，依次判断是否满足单个频谱间隙的带宽容量约束条件、路径唯一性约束条件、频谱分配约束条件以及光再生器数量约束条件，若均满足，则业务请求建立成功，从而为所述业务请求分配相应的频谱资源；若有任意一 项不满足，则业务请求建立失败，整个过程有利于提高每个业务请求在弹性光网络传输中的服务质量，充分利用网络中的频谱资源，通过对各个业务请求进行流量疏导，从而减少网络中光通道的数量。
在IPover静态网络模型中，业务请求在占用相应的频谱资源后不会释放这些资源，因此网络的总能耗即所有网络功耗元件的功率之和。所以，每一条业务请求的总能耗可以计算为：
E＝P×t＝(N I×P I+N T×P T+N R×P R)×t      (1)
其中E和P分别表示业务请求单位时间内的总能耗和网络总功耗，t表示单位时间，用秒表示；N I、N T和N R分别表示IP路由端口、光转发器和光再生器的数量，P I、P T和P R则分别表示IP路由端口、光转发器和光再生器的功率大小。
对于IPover弹性光网络中的能量消耗问题，整数线性规划模型是解决该问题的最优方法。在IPover弹性光网络中，给定网络拓扑结构、每条链路长度、频谱资源占用情况以及IP业务请求的数量和带宽需求。这里，给定充足的网络频谱资源，以确保所有的IP业务均能通过网络进行数据传输，优化目标是最小化网络中的能量消耗，即尽可能地减少IP路由端口、光转发器以及光再生器的数量，从而建立了基于流量疏导的最小数量光再生器配置整数线性规划模型(ILP_RP)。
所述步骤S1中，在IP over弹性光网络G(N,L,W)中，产生一组业务请求集合CR，每个业务请求(s,d,B sd)∈CR，其中N、L、W分别代表光网络中的光交换节点集合、光纤链路集合、每条链路上的频谱间隙集合；s和d分别代表每个业务请求的源节点和目的节点，B sd代表该业务的带宽需求。K表示通过KSP算法计算获得k条最短路径的集合。设定|N|、|L|、|W|的数值，分别表示网络中节点的数目、每条链路上频谱间隙的数目，以及单个频谱间隙的带宽容量；设置P I、P T、P R、M、VL的数值，分别表示IP路由端口的功率、光转发器的功率、光再生器的功率、一个较大的整数以及光再生器可以承载的最大带宽需求。
Figure PCTCN2021113992-appb-000015
表示业务请求(s,d)在第k条最短路径上链路占用情况 的集合。
这里的能耗最小的整数线性规划模型会有一个输入变量
Figure PCTCN2021113992-appb-000016
表示某业务请求(源节点s，目的节点d)第k条最短路径中的节点可达情况。
所述步骤S2中，建立源节点和目的节点的虚拟可达路径时，工作路径上的任意两个节点之间的距离不超过光的最大传输距离。如图2a的给定路径图中，如果工作路径上的任意两个节点i和j之间的距离不超过光的最大传输距离，则会在这两个节点之间建立一条虚拟的可达路径。图2b中则给出了光的最大传输距离为2跳和3跳的情况下各自的虚拟可达链路。假设光的最大传输距离为3跳，由图可知，节点s和节点1、2、3之间是可达的，则
Figure PCTCN2021113992-appb-000017
和
Figure PCTCN2021113992-appb-000018
的取值都为1，而
Figure PCTCN2021113992-appb-000019
和
Figure PCTCN2021113992-appb-000020
的取值为0。
所述步骤S3中，由于本发明主要解决IP over弹性光网络中能耗元件的能量消耗问题，优化的目标函数使IP over弹性光网络中的能量消耗最小化。整数线性规划模型的目标函数主要用于优化IP over弹性光网络的能量消耗，优化目标函数可用如下式子表示：
最小化：
Figure PCTCN2021113992-appb-000021
这里IP over弹性光网络中的能量消耗主要由IP路由端口、光转发器和光再生器组成，
Figure PCTCN2021113992-appb-000022
表示链路l上的频谱间隙w是否被某一或某些业务请求占用，Z i表示节点i上的光再生器配置的总个数。该目标函数主要由两部分组成，第一部分表示计算获得网络中的光通道数，对应IP路由端口和光转发器的总能耗；第二部分表示计算网络中配置的再生器的个数，对应光再生器的总能耗。这里P I、P T和P R的数值，分别表示IP路由端口的功率、光转发器的功率、光再生器的功率。N、L、W分别代表光网络中的光交换节点集合、光纤链路集合、每条链路上的频谱间隙集合。这里t表示单位时间，用1秒表示。
对IP over弹性光网络中的频谱资源进行分配与优化时须满足以下约束条件，具体地：
所述单个频谱间隙的带宽容量约束条件为：
Figure PCTCN2021113992-appb-000023
其中CR表示一组业务请求集合，K表示计算获得k条最短路径的集合，C为每个频谱隙的带宽容量，L和W分别代表光网络中的光纤链路集合和每条链路上的频谱间隙集合，
Figure PCTCN2021113992-appb-000024
是一个变量，若业务请求(s,d)占用了第k条最短路径中链路l上的频谱间隙w，该变量取值为占用的带宽资源量，否则取值为0。另外，K表示通过KSP算法计算获得k条最短路径的集合；此时所述约束条件确保所有业务请求在某一链路某一频谱间隙上占用的频谱资源之和不会超过该频谱间隙的带宽承载能力。
所述路径选择唯一性约束条件为：
Figure PCTCN2021113992-appb-000025
其中，
Figure PCTCN2021113992-appb-000026
是一个二进制变量，若业务请求(s,d)采用第k条最短路径来完成业务传输，该变量取值为1，否则取值为0。此时约束条件确保每一个业务请求都会选取1条最短路径进行频谱资源分配，满足每一个业务请求的带宽资源。
所述频谱分配约束条件为：
Figure PCTCN2021113992-appb-000027
Figure PCTCN2021113992-appb-000028
其中B sd代表该业务的带宽需求，
Figure PCTCN2021113992-appb-000029
表示业务请求(s,d)在第k条最短路径上链路占用情况的集合，
Figure PCTCN2021113992-appb-000030
表示一个变量，若业务请求(s,d)占用了第k条最短路径中链路l i(l j)上的频谱间隙w，该变量取值为占用的带宽资源量，否则取值为0。此时约束条件(5)确保每一业务请求会在相应的工作路径上占用所需的带宽资源，以及约束条件(6)保证了频谱一致性的约束条件，即使得工作路径上各条链路上所占用的频谱间隙编号是相同的。
所述光再生器数量约束条件为：
Figure PCTCN2021113992-appb-000031
Figure PCTCN2021113992-appb-000032
Figure PCTCN2021113992-appb-000033
Figure PCTCN2021113992-appb-000034
Figure PCTCN2021113992-appb-000035
Figure PCTCN2021113992-appb-000036
Figure PCTCN2021113992-appb-000037
其中Z i表示节点i上的光再生器配置的总个数；M和VL的数值分别表示一个较大的整数以及光再生器可以承载的最大带宽需求；
Figure PCTCN2021113992-appb-000038
是一个二进制变量，若业务请求(s,d)采用第k条最短路径上的可达路径(i,j)进行占用，该变量取值为1，否则取值为0；
Figure PCTCN2021113992-appb-000039
是一个二进制变量，若业务请求(s,d)在第k条最短路径的节点i上设置了光再生器，该变量取值为1，否则取值为0；B sd代表该业务的带宽需求。约束条件(7)保证了每个节点上再生器的数量有一定限制；约束条件(8)-(10)保证了流量约束，其中约束条件(8)确保了当源节点s和目的节点d的业务请求进入网络时，会在源节点相应的可达路径上生成光再生器的流量；与此同时，约束条件(9)确保生成流量在相应的可达路径上终止于目的节点d；约束条件(10)则确保对于任何中间节点，流入流量的大小等于流出流量。约束条件(11)保证了对于从s到d的业务请求的第k条最短路径，根据已知可达路径在源节点s和目的节点d之间的中间节点上放置相应的光再生器，使得s和d之间是可达的。约束条件(12)保证了对于业务请求(s,d)的第k条最短路径，为其所有可达路径上(除去源端)设置光再生器。约束条件(13)确保了最终各个节点上放置光再生器的数量。
通过以上约束条件，可以找出在IPover弹性光网络中基于流量疏导的最小网络能耗优化方法，从而实现该发明整数线性规划的优化目标函数。
下面结合相关的实例对本发明中具体的实施方法进行详细阐述：
如图3所示的网络拓扑为例，光纤链路上的数值代表链路长度(单位： km)，每条光纤链路是双向的。设定每条光纤链路有5个频谱间隙，每个频谱间隙的带宽容量为100Gbps。
在IPover弹性光网络中生成一组业务请求集合CR∈{CR 1(0,3,40),CR 2(0,2,120),CR 3(1,4,40)}。
确立并执行该发明中提出的基于最小能量消耗的线性整数规划模型的目标函数，
Figure PCTCN2021113992-appb-000040
确立并执行IP over弹性光网络中基于流量疏导的最小网络能耗优化方法的不同约束条件。在为每个业务请求分配资源的过程中，要满足单个频谱间隙的带宽容量约束条件(见公式(3))、路径唯一性约束条件(见公式(4))、频谱分配约束条件(见公式(5)和(6))以及光再生器数量约束条件(见公式(7)-(13))。
经过上述步骤，即可在基于目标条件下为网络中的业务请求CR 1(0,3,40)、CR 2(0,2,120)、CR 3(1,4,40)分配相应的频谱资源。为了减少频谱占用，本发明方法在为每个业务寻找工作路径时，计算出每个业务的k条最短路径作为其备选路径。设K＝2，由图3可知，CR 1(0,3,40)的K条最短路径分别为0-5-4-3和0-1-2-3；CR 2(0,2,120)的k条最短路径分别为0-1-2和0-5-4-2，CR 3(1,4,40)的K条最短路径分别为1-2-4和1-5-4。由于三个业务请求的备选路径多次经过链路0-1和链路1-2，在选择工作路径时会尽可能在这两条链路上进行流量疏导。本发明方法在执行完约束条件后，会为这三个业务请求在网络中分配最优的资源，使得网络能耗最低。如图4所示，首先为CR 1(0,3,40)选择0-1-2-3作为工作路径，并为其分配相应的频谱资源；然后为CR 2(0,2,120)选择0-1-2作为工作路径，将部分带宽需求20Gbps疏导到链路0-1和1-2的频谱间隙1中的剩余频谱空间，并为100Gbps的带宽需求建立新的光通道；最后为CR 3(1,4,40)选择1-2-4作为工作路径，在链路1-2的频谱间隙1上进行流量疏导，并在链路2-4上建立新的光通道。图5为链路1-2中的频谱资源占用情况，CR 1(0,3,40)、CR 2(0,2,120)和CR 3(1,4,40)的部分带宽需求疏导到频谱间隙1中，并为CR 2(0,2,120)单独占用一个频谱间隙。设定光 再生器的最大光传输距离为2000km，只需在节点2为业务请求CR 1(0,3,40)设置一个光再生器进行光信号的恢复。此时，为一组业务请求CR 1(0,3,40)、CR 2(0,2,120)和CR 3(1,4,40)分配最优的频谱资源，所需要的IP路由端口、光转发器和光再生器数量最小，使得IPover弹性光网络的能量消耗最低。
实施例二
基于同一发明构思，本实施例提供了一种基于流量疏导的最小网络能耗优化系统，其解决问题的原理与所述基于流量疏导的最小网络能耗优化方法类似，重复之处不再赘述。
本实施例提供一种基于流量疏导的最小网络能耗优化系统，包括：
计算模块，用于在弹性光网络中，产生一组业务请求集合，根据每个业务请求的源节点和目的节点计算多条最短路径中的节点可达集合；
建立模块，用于在多条最短路径中的节点可达集合中，建立源节点和目的节点的虚拟可达路径；
判断模块，用于基于所述虚拟可达路径，建立最小能量消耗的整数线性规划模型的目标函数，为每个业务请求分配资源的过程中，依次判断是否满足单个频谱间隙的带宽容量约束条件、路径唯一性约束条件、频谱分配约束条件以及光再生器数量约束条件，若均满足，则业务请求建立成功；若有任意一项不满足，则业务请求建立失败。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流 程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
显然，上述实施例仅仅是为清楚地说明所作的举例，并非对实施方式的限定。对于所属领域的普通技术人员来说，在上述说明的基础上还可以做出其它不同形式变化或变动。这里无需也无法对所有的实施方式予以穷举。而由此所引伸出的显而易见的变化或变动仍处于本发明创造的保护范围之中。

Claims (10)

  1. 一种基于流量疏导的最小网络能耗优化方法，其特征在于，包括如下步骤：
    步骤S1：在弹性光网络中，产生一组业务请求集合，根据每个业务请求的源节点和目的节点计算多条最短路径中的节点可达集合；
    步骤S2：在多条最短路径中的节点可达集合中，建立源节点和目的节点的虚拟可达路径；
    步骤S3：基于所述虚拟可达路径，建立最小能量消耗的整数线性规划模型的目标函数，为每个业务请求分配资源的过程中，依次判断是否满足单个频谱间隙的带宽容量约束条件、路径唯一性约束条件、频谱分配约束条件以及光再生器数量约束条件，若均满足，则业务请求建立成功；若有任意一项不满足，则业务请求建立失败。
  2. 根据权利要求1所述的基于流量疏导的最小网络能耗优化方法，其特征在于：建立源节点和目的节点的虚拟可达路径时，工作路径上的任意两个节点之间的距离不超过光的最大传输距离。
  3. 根据权利要求1所述的基于流量疏导的最小网络能耗优化方法，其特征在于：所述弹性光网络中的能量消耗包括IP路由端口、光转发器和光再生器。
  4. 根据权利要求1所述的基于流量疏导的最小网络能耗优化方法，其特征在于：所述目标函数包括计算获得网络中的光通道数，对应IP路由端口和光转发器的总能耗以及计算网络中配置的光再生器的个数，对应光再生器的总能耗。
  5. 根据权利要求1所述的基于流量疏导的最小网络能耗优化方法，其特征在于：所述单个频谱间隙的带宽容量约束条件为：
    Figure PCTCN2021113992-appb-100001
    其中CR表示一组业务请求集合，K表示计 算获得k条最短路径的集合，C为每个频谱隙的带宽容量，L和W分别代表光网络中的光纤链路集合和每条链路上的频谱间隙集合，
    Figure PCTCN2021113992-appb-100002
    是一个变量，若业务请求(s,d)占用了第k条最短路径中链路l上的频谱间隙w，该变量取值为占用的带宽资源量，否则取值为0。
  6. 根据权利要求1所述的基于流量疏导的最小网络能耗优化方法，其特征在于：所述路径选择唯一性约束条件为：
    Figure PCTCN2021113992-appb-100003
    其中
    Figure PCTCN2021113992-appb-100004
    是一个二进制变量，若业务请求(s,d)采用第k条最短路径来完成业务传输，该变量取值为1，否则取值为0。
  7. 根据权利要求1所述的基于流量疏导的最小网络能耗优化方法，其特征在于：所述频谱分配约束条件为
    Figure PCTCN2021113992-appb-100005
    Figure PCTCN2021113992-appb-100006
    B sd代表该业务的带宽需求，
    Figure PCTCN2021113992-appb-100007
    表示业务请求(s,d)在第k条最短路径上链路占用情况的集合，表示一个变量，若业务请求(s,d)占用了第k条最短路径中链路l i(l j)上的频谱间隙w，该变量取值为占用的带宽资源量，否则取值为0。
  8. 根据权利要求1所述的基于流量疏导的最小网络能耗优化方法，其特征在于：所述光再生器数量约束条件为：
    Figure PCTCN2021113992-appb-100008
    Figure PCTCN2021113992-appb-100009
    Figure PCTCN2021113992-appb-100010
    Figure PCTCN2021113992-appb-100011
    其中Z i表示节点i上的光再生器配置的总个数；M和VL的数值分别表示一个较大的整数以及光再生器可以承载的最大带宽需求；
    Figure PCTCN2021113992-appb-100012
    是一个二进制变量，若业务请求(s,d)采用第k条最短路径上的可达路径(i,j)进行占用，该变量取值为1，否则取值为0；
    Figure PCTCN2021113992-appb-100013
    是一个二进制变量，若业务请求(s,d)在第k条最短路径的节点i上设置了光再生器，该变量取值为1，否则取值为0；B sd代表该业务的带宽需求。
  9. 根据权利要求1所述的基于流量疏导的最小网络能耗优化方法，其特征在于：所述业务请求建立成功，为所述业务请求分配相应的频谱资源。
  10. 一种基于流量疏导的最小网络能耗优化系统，其特征在于，包括：
    计算模块，用于在弹性光网络中，产生一组业务请求集合，根据每个业务请求的源节点和目的节点计算多条最短路径中的节点可达集合；
    建立模块，用于在多条最短路径中的节点可达集合中，建立源节点和目的节点的虚拟可达路径；
    判断模块，用于基于所述虚拟可达路径，建立最小能量消耗的整数线性规划模型的目标函数，为每个业务请求分配资源的过程中，依次判断是否满足单个频谱间隙的带宽容量约束条件、路径唯一性约束条件、频谱分配约束条件以及光再生器数量约束条件，若均满足，则业务请求建立成功；若有任意一项不满足，则业务请求建立失败。
PCT/CN2021/113992 2021-08-16 2021-08-23 基于流量疏导的最小网络能耗优化方法及系统 Ceased WO2023019604A1 (zh)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US17/802,193 US12289208B2 (en) 2021-08-16 2021-08-23 Optimization method and system for minimizing network energy consumption based on traffic grooming

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202110938102.8A CN113489617B (zh) 2021-08-16 2021-08-16 基于流量疏导的最小网络能耗优化方法及系统
CN202110938102.8 2021-08-16

Publications (1)

Publication Number Publication Date
WO2023019604A1 true WO2023019604A1 (zh) 2023-02-23

Family

ID=77946601

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/113992 Ceased WO2023019604A1 (zh) 2021-08-16 2021-08-23 基于流量疏导的最小网络能耗优化方法及系统

Country Status (3)

Country Link
US (1) US12289208B2 (zh)
CN (1) CN113489617B (zh)
WO (1) WO2023019604A1 (zh)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116708447A (zh) * 2023-08-08 2023-09-05 天津市奥讯通电缆科技发展有限公司 一种综合分叉电缆的数据耦合方法
WO2026045207A1 (zh) * 2024-09-02 2026-03-05 烽火通信科技股份有限公司 一种otn网络业务故障恢复方法及系统

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US12135603B2 (en) * 2022-08-17 2024-11-05 Red Hat, Inc. Monitoring energy consumption associated with users of a distributed computing system using tracing
CN115914891B (zh) * 2022-10-31 2024-09-06 苏州大学 数据中心弹性光网络距离自适应的流量分配方法和系统
CN116887080B (zh) * 2023-07-20 2024-08-02 苏州大学 云边弹性光网络中频谱和计算资源占用联合卸载方法
CN119849211B (zh) * 2025-03-19 2025-05-30 国网安徽省电力有限公司经济技术研究院 基于网络通信的小型化开关柜二次电缆轻量化优化方法
CN120216446B (zh) * 2025-05-30 2025-08-08 上海云燧科技有限公司 一种面向ai处理器硬件部件的互联网络设计方法及介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140099119A1 (en) * 2012-10-08 2014-04-10 Futurewei Technologies, Inc. Transport Functions Virtualization for Wavelength Division Multiplexing (WDM)-based Optical Networks
CN104092609A (zh) * 2014-07-24 2014-10-08 苏州大学 一种IP over WDM网络构建方法及装置
CN109245918A (zh) * 2018-08-20 2019-01-18 中天宽带技术有限公司 软件定义频谱灵活光网络能耗优化方法及其系统
CN110234128A (zh) * 2019-06-12 2019-09-13 苏州大学 无线接入频谱灵活光网络的能耗优化方法、系统

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2012083311A1 (en) * 2010-12-17 2012-06-21 University Of Kansas A digital subcarrier optical network utilizing digital subcarrier cross-connects with increased energy efficiency
US9031406B2 (en) * 2010-12-17 2015-05-12 University Of Kansas Digital subcarrier cross-connect switching

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140099119A1 (en) * 2012-10-08 2014-04-10 Futurewei Technologies, Inc. Transport Functions Virtualization for Wavelength Division Multiplexing (WDM)-based Optical Networks
CN104092609A (zh) * 2014-07-24 2014-10-08 苏州大学 一种IP over WDM网络构建方法及装置
CN109245918A (zh) * 2018-08-20 2019-01-18 中天宽带技术有限公司 软件定义频谱灵活光网络能耗优化方法及其系统
CN110234128A (zh) * 2019-06-12 2019-09-13 苏州大学 无线接入频谱灵活光网络的能耗优化方法、系统

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
ZHAO, YONGLI ET AL.: "Energy Efficiency With Sliceable Multi-Flow Transponders and Elastic Regenerators in Survivable Virtual Optical Networks", IEEE TRANSACTIONS ON COMMUNICATIONS, vol. 64, no. 6, 30 June 2016 (2016-06-30), XP011613847, DOI: 10.1109/TCOMM.2016.2554110 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116708447A (zh) * 2023-08-08 2023-09-05 天津市奥讯通电缆科技发展有限公司 一种综合分叉电缆的数据耦合方法
CN116708447B (zh) * 2023-08-08 2023-10-20 天津市奥讯通电缆科技发展有限公司 一种综合分叉电缆的数据耦合方法
WO2026045207A1 (zh) * 2024-09-02 2026-03-05 烽火通信科技股份有限公司 一种otn网络业务故障恢复方法及系统

Also Published As

Publication number Publication date
CN113489617A (zh) 2021-10-08
CN113489617B (zh) 2022-12-30
US12289208B2 (en) 2025-04-29
US20230308352A1 (en) 2023-09-28

Similar Documents

Publication Publication Date Title
CN113489617B (zh) 基于流量疏导的最小网络能耗优化方法及系统
CN113784373B (zh) 云边协同网络中时延和频谱占用联合优化方法及系统
Zhang et al. On the parallelization of spectrum defragmentation reconfigurations in elastic optical networks
Carpio et al. Balancing the migration of virtual network functions with replications in data centers
CN105827528B (zh) 一种适用于频谱灵活光网络的路由选择方法
CN107426110B (zh) 频谱灵活光网络中自适应负载均衡能耗优化方法及系统
Shvedov et al. Determining shortest paths between two arbitrary nodes in a composite transport network using segment routing
CN105245307B (zh) 用于在通信网络中确定通信路径的方法及设备
US20250168116A1 (en) Path Determining Method and Related Device
CN107026803A (zh) 基于共享保护的路由与频谱分配方法和系统
CN113242176A (zh) 一种端到端多路径快速计算方法及装置
Valkanis et al. A traffic prediction assisted routing algorithm for elastic optical networks
CN116887080B (zh) 云边弹性光网络中频谱和计算资源占用联合卸载方法
CN107124376A (zh) 一种弹性光网络(eon)中基于分段调制的频谱分配方案
CN115914891B (zh) 数据中心弹性光网络距离自适应的流量分配方法和系统
CN107204935B (zh) 频谱灵活光网络的故障概率和负载均衡折衷方法和系统
CN117119335A (zh) 一种基于qkd的光网络优化方法及装置
Kantarci et al. Resilient design of a cloud system over an optical backbone
Li et al. Path-protection-based routing and wavelength assignment in wavelength-division multiplexing optical networks under a scheduled traffic model
Guler et al. Virtual multicast tree embedding over elastic optical networks
Tabota et al. Greedy-based VNF placement algorithm for dynamic multipath service chaining
Koulougli et al. Optimized FlexEthernet for inter-domain traffic restoration
CN105721309B (zh) 一种软件定义传送网中的频谱资源优化方法及装置
Paira et al. Mdc-rsa: A novel online survivable rsa technique in data center oriented eon
Wu et al. Cost-Efficient Design for 5G-Enabled MEC Servers under Uncertain User Demands

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21953845

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21953845

Country of ref document: EP

Kind code of ref document: A1

WWG Wipo information: grant in national office

Ref document number: 17802193

Country of ref document: US