Routing and wavelength allocation method based on optical network and related equipment

Abstract

The invention provides a routing and wavelength allocation method based on an optical network and related equipment. The method comprises the following steps: acquiring a source node, a destination node, an earliest starting time, a latest starting time and a request duration; determining a shortest path set based on the source node and the destination node; determining a candidate path set in the shortest path set based on the earliest starting time, the latest starting time and the request duration; determining a renewable energy source proportion of each path in the candidate path set based on a pre-constructed renewable energy source proportion model; based on the renewable energy source proportion of each path in the candidate path set, a solution is determined, and the solution comprises a target path, a target wavelength and a target starting time period. The utilization rate of green energy is improved, and the service energy consumption is reduced.

Description

Technical Field

[0001] This application relates to the technical field of optical networks, and in particular, to a routing and wavelength allocation method and related devices based on optical networks. Background Art

[0002] With the rapid growth of optical network traffic demand and the related requirements of global green development, the efficient operation of optical networks faces two challenges: one is how to ensure meeting the service quality requirements, and the other is how to reduce greenhouse gas (GHG) emissions and improve the utilization efficiency of renewable energy.

[0003] However, the existing routing and wavelength allocation (RWA) technologies have many deficiencies. First, in terms of GHG emissions and energy utilization efficiency, the energy consumption of optical network devices includes static energy consumption (generated by the basic operation of the devices) and dynamic energy consumption (related to traffic). However, the existing technologies fail to fully consider the volatility and time distribution characteristics of the dynamic power consumption of devices and renewable energy (such as solar energy and wind energy), which makes it impossible to fully utilize renewable energy and naturally difficult to effectively reduce GHG emissions. Second, in terms of service quality and waiting delay, although the advance reservation (AR) service brings the possibility of optimizing resource allocation, however, how to reduce the service waiting delay and improve the service execution efficiency while fully utilizing the time window and energy resources is still a problem that the existing technologies have not overcome.

[0004] In addition, in terms of the balance of multi-objective optimization, the current RWA algorithms often only focus on a single objective, such as the shortest path or the lowest energy consumption, and lack an optimization scheme that comprehensively considers the utilization of green energy, service waiting delay, and service blocking rate. Summary of the Invention

[0005] In view of this, the purpose of this application is to propose a routing and wavelength allocation method and related devices based on optical networks.

[0006] As an aspect of this application, a routing and wavelength allocation method based on optical networks is provided, including:

[0007] Obtain the source node, destination node, earliest start time, latest start time, and request duration;

[0008] Based on the source node and destination node, determine the shortest path set;

[0009] Based on the earliest start time, latest start time, and request duration, determine the candidate path set in the shortest path set;

[0010] Based on the pre-constructed renewable energy proportion model, determine the renewable energy proportion of each path in the candidate path set;

[0011] Determine a solution based on the proportion of renewable energy for each path in the candidate path set, where the solution includes a target path, a target wavelength, and a target start time period.

[0012] Optionally, determine the candidate path set in the shortest path set based on the earliest start time, the latest start time, and the requested duration, including:

[0013] Determine the requested time period based on the earliest start time, the latest start time, and the requested duration;

[0014] Determine the candidate path set based on the requested time period.

[0015] Optionally, determine the candidate path set based on the requested time period, including:

[0016] For each path in the shortest path set, determine the wavelength occupancy of at least one link in the path based on the requested time period;

[0017] Determine the available time period of the available links in the path based on the wavelength occupancy of at least one link in each path;

[0018] Determine the continuous time period of the path based on the intersection of the available time periods of the available links in the path;

[0019] In response to the continuous time period of the path being not less than the requested duration, determine the candidate path set.

[0020] Optionally, determine the proportion of renewable energy for each path in the candidate path set based on a pre - constructed renewable energy proportion model, including:

[0021] According to the renewable energy proportion model, determine the first renewable energy proportion of the source node in the start time period and the second renewable energy proportion of the destination node in the start time period;

[0022] Determine the proportion of renewable energy for each path in the candidate path set according to the first renewable energy proportion and the second renewable energy proportion.

[0023] Optionally, determine a solution based on the proportion of renewable energy for each path in the candidate path set, including:

[0024] Obtain the target path according to the candidate path with the highest proportion of renewable energy;

[0025] Determine the target wavelength and the target start time period corresponding to the target path.

[0026] Optionally, determine a solution based on the proportion of renewable energy for each path in the candidate path set, including:

[0027] For each path in the candidate path set, determine the waiting delay of the path according to the start time period of the path and the earliest start time.

[0028] Normalize the renewable energy ratio of the path to obtain the first normalization index, and normalize the waiting delay of the path to obtain the second normalization index.

[0029] According to the technique for order preference by similarity to ideal solution, the first normalization index, and the second normalization index, determine the first distance to the positive ideal solution and the second distance to the negative ideal solution.

[0030] Determine the relative closeness according to the first distance and the second distance.

[0031] Obtain the solution according to the relative closeness.

[0032] Optionally, the above method further includes:

[0033] Determine the total greenhouse gas emissions of the solution according to the optical network model.

[0034] As a second aspect of the present application, there is provided a routing and wavelength assignment device based on an optical network, including: an acquisition module and a determination module;

[0035] The acquisition module is configured to acquire a source node, a destination node, an earliest start time, a latest start time, and a request duration.

[0036] The determination module is configured to determine a shortest path set based on the source node and the destination node.

[0037] The determination module is further configured to determine a candidate path set in the shortest path set based on the earliest start time, the latest start time, and the request duration.

[0038] The determination module is further configured to determine the renewable energy ratio of each path in the candidate path set based on a pre-constructed renewable energy ratio model.

[0039] Determine a solution based on the renewable energy ratio of each path in the candidate path set, where the solution includes a target path, a target wavelength, and a target start time period.

[0040] Optionally, the determination module is specifically configured to determine a request time period based on the earliest start time, the latest start time, and the request duration.

[0041] Determine the candidate path set based on the request time period.

[0042] Optionally, the determination module is further specifically configured to, for each path in the shortest path set, determine the wavelength occupancy of at least one link in the path based on the request time period.

[0043] Determine the available time periods of the available links in a path based on the wavelength occupancy of at least one link in each path;

[0044] Determine the continuous time period of a path based on the intersection of the available time periods of the available links in the path;

[0045] In response to the continuous time period of the path being not less than the requested duration, determine a set of candidate paths.

[0046] Optionally, the determining module is further specifically configured to determine a first renewable energy ratio of the source node in the starting time period and a second renewable energy ratio of the destination node in the starting time period according to a renewable energy ratio model;

[0047] Determine the renewable energy ratio of each path in the set of candidate paths according to the first renewable energy ratio and the second renewable energy ratio.

[0048] Optionally, the determining module is further specifically configured to obtain a target path according to the candidate path with the highest renewable energy ratio;

[0049] Determine the target wavelength and the target starting time period corresponding to the target path.

[0050] Optionally, the determining module is further specifically configured to, for each path in the set of candidate paths, determine the waiting delay of the path according to the starting time period of the path and the earliest starting time;

[0051] Perform normalization processing on the renewable energy ratio of the path to obtain a first normalization index, and perform normalization processing on the waiting delay of the path to obtain a second normalization index;

[0052] According to the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), the first normalization index, and the second normalization index, determine the first distance to the positive ideal solution and the second distance to the negative ideal solution;

[0053] Determine the relative closeness according to the first distance and the second distance;

[0054] Obtain a solution according to the relative closeness.

[0055] Optionally, the determining module is further configured to determine the total greenhouse gas emissions of the solution according to an optical network model.

[0056] As a third aspect of the present application, an electronic device includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, implements the routing and wavelength allocation method based on an optical network as described above.

[0057] As a fourth aspect of the present application, a non-transitory computer-readable storage medium is provided, which stores computer instructions, and the computer instructions are used to enable a computer to execute the above-mentioned optical network-based routing and wavelength allocation method provided in the present application.

[0058] From the above content, it can be seen that the routing and wavelength allocation method based on optical network and related equipment provided by this application first determine the shortest path set between two nodes through the source node and the destination node, and then determine the candidate path set in the shortest path set through the earliest start time, the latest start time and the request duration. In this way, the service quality requirements are guaranteed, the service delay is reduced through the shortest path, and the time window and other resources are fully utilized through the earliest start time, the latest start time and the request duration. On the basis of ensuring that the service requirements are met, the service delay is reduced and the service quality is improved. Finally, the final solution is considered through the proportion of renewable energy in each path, which fully improves the utilization rate of green energy and reduces service energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0060] Figure 1 A schematic diagram of the architecture of a WDM network model provided in an embodiment of the present application;

[0061] Figure 2 A schematic diagram of the structure of an AR service model provided in an embodiment of the present application;

[0062] Figure 3 A schematic diagram of a flow chart of a routing and wavelength allocation method based on an optical network provided in an embodiment of the present application;

[0063] Figure 4 A schematic diagram of a flow chart of another optical network-based routing and wavelength allocation method provided in an embodiment of the present application;

[0064] Figure 5 A schematic diagram of a flow chart of another optical network-based routing and wavelength allocation method provided in an embodiment of the present application;

[0065] Figure 6 A schematic diagram of a flow chart of another optical network-based routing and wavelength allocation method provided in an embodiment of the present application;

[0066] Figure 7Schematic flowchart of yet another routing and wavelength assignment method based on an optical network provided by an embodiment of the present application;

[0067] Figure 8 Schematic flowchart of yet another routing and wavelength assignment method based on an optical network provided by an embodiment of the present application;

[0068] Figure 9 Schematic flowchart of yet another routing and wavelength assignment method based on an optical network provided by an embodiment of the present application;

[0069] Figure 10 Schematic diagram of a simulation result provided by an embodiment of the present application;

[0070] Figure 11 Schematic diagram of yet another simulation result provided by an embodiment of the present application;

[0071] Figure 12 Schematic diagram of yet another simulation result provided by an embodiment of the present application;

[0072] Figure 13 Schematic diagram of yet another simulation result provided by an embodiment of the present application;

[0073] Figure 14 Schematic diagram of yet another simulation result provided by an embodiment of the present application;

[0074] Figure 15 Schematic diagram of the composition of a routing and wavelength assignment device based on an optical network provided by an embodiment of the present application;

[0075] Figure 16 Schematic diagram of the composition of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0076] To make the objectives, technical solutions and advantages of the present application more clear and understandable, the following further describes the present application in detail with reference to specific embodiments and the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the scope of protection of the present application.

[0077] It should be noted that, in the embodiments of the present application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a concrete way. Unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the usual meaning understood by people with ordinary skills in the field to which the present application belongs. Summary of the Invention

[0079] The information and communication technology (ICT) industry has shown a rapid development trend in recent decades, especially in the application of optical networks. Optical networks have become a key element supporting modern communication infrastructure due to their high efficiency, low latency and high capacity. However, with the continuous expansion of the ICT industry, especially the widespread application of big data and cloud computing services, the industry's carbon emissions have also increased, gradually becoming one of the important sources of global greenhouse gas emissions.

[0080] Specifically, although the optical transmission technology of optical networks has efficient data transmission capabilities, key components in the network, such as optical switches, amplifiers, and transmitters, are still very energy-consuming, resulting in significant greenhouse gas emissions.

[0081] Secondly, as the world's reliance on renewable energy continues to increase, how to effectively integrate green energy such as solar energy and wind energy into optical networks and reduce reliance on fossil energy has become an urgent technical challenge. Especially when considering the geographical distribution and volatility of renewable energy, how to achieve dynamic resource scheduling in optical networks, especially optimizing the use of renewable energy in the time dimension to adapt to energy demand fluctuations in different time periods, is the key to solving this problem.

[0082] Although the optimized scheduling of green energy can reduce carbon emissions to a certain extent, it may lead to higher waiting delays, especially for services that require advance reservations. How to reduce carbon emissions while keeping waiting delays within an acceptable range and ensuring that the quality requirements of scheduled reservation services are met has become an important technical problem that needs to be solved urgently.

[0083] As can be seen from the above content, with the rapid growth of optical network traffic demand and the related needs of global green development, the efficient operation of optical networks faces two challenges: first, ensuring that service quality requirements are met. Second, reducing greenhouse gas (GHG) emissions and improving the utilization efficiency of renewable energy.

[0084] However, the existing routing and wavelength assignment (RWA) technology has many shortcomings. First, in terms of GHG emissions and energy efficiency, the energy consumption of optical network equipment includes static energy consumption (generated by the basic operation of the equipment) and dynamic energy consumption (related to traffic). However, the existing technology fails to fully consider the volatility and time distribution characteristics of the dynamic power consumption of the equipment and renewable energy (such as solar energy and wind energy), which makes it impossible to fully utilize renewable energy, and naturally it is difficult to effectively reduce GHG emissions. Second, in terms of service quality and waiting delay, although the advance reservation (AR) service has made it possible to optimize resource allocation, how to fully utilize time windows and energy resources while reducing service waiting delays and improving service execution efficiency is still a difficult problem that the existing technology has not yet solved.

[0085] In addition, in terms of balancing multi-objective optimization, current RWA algorithms often only focus on a single objective, such as the shortest path or minimum energy consumption, and lack an optimization solution that comprehensively considers green energy utilization, service waiting delay, and service blocking rate.

[0086] Based on this, the present application proposes a routing and wavelength allocation method based on an optical network and related equipment. First, the shortest path set between the two nodes is determined through the source node and the destination node, and then the candidate path set in the shortest path set is determined through the earliest start time, the latest start time and the request duration. In this way, the service quality requirements are guaranteed, the service delay is reduced through the shortest path, and the time window and other resources are fully utilized through the earliest start time, the latest start time and the request duration. On the basis of ensuring that the service requirements are met, the service delay is reduced and the service quality is improved. Finally, the final solution is considered through the proportion of renewable energy in each path, which fully improves the utilization rate of green energy and reduces service energy consumption.

[0087] Application Scenario

[0088] The routing and wavelength allocation method based on an optical network provided in the embodiment of the present application can be applied to relevant application scenarios of the optical network. In the optical network, the network topology is represented by G = (V, E), where V represents a set of nodes in the network topology, and each node v i Represents the i-th node in the network topology, i∈1,2,…,N, V=v 1 ,v 2 ,…,v N , N represents the total number of nodes.

[0089] Furthermore, as shown in the following formula:

[0090] E={e ij ∣v i ,v j ∈V}

[0091] Among them, the set E represents the bidirectional links between the above nodes, and each link e ij connects the node v i and the node v j .

[0092] It should be noted that the optical network can select Wavelength Division Multiplexing (WDM for short) as the physical bearer basis. WDM is a physical layer transmission technology that exponentially improves the fiber bandwidth by multiplexing optical signals of different wavelengths into a single optical fiber. WDM can provide a physical bearer basis for OTN and support large-scale service scheduling of the optical network.

[0093] As Figure 1 shown, it is a schematic diagram of the architecture of a WDM network model provided by this application. The WDM network model includes: multiple transponder (TSP), multiple erbium-doped fiber amplifiers (EDFA), multiple WDM terminal devices (WDMT), and multiple optical cross-connect devices (OXC). The above multiple devices are modeled based on the architecture of optical devices. In this architecture, OXC is a key component to support reconfigurable optical networks. Each node v i is equipped with an OXC, and the OXC of node v i is characterized by the following formula:

[0094]

[0095] In some embodiments, the WDMT device used in conjunction with OXC is used for wavelength multiplexing and demultiplexing, and is particularly suitable for point-to-point applications. The number of WDMT devices in node v i is determined by the degree D i of node v i , and the WDMT device in node v i is characterized by the following formula:

[0096]

[0097] In some embodiments, TSP is used to provide bidirectional conversion between optical wavelengths. To facilitate the description of the distribution of TSP in node v i , let represent the set of TSP in node v i , and this set includes all TSPs associated with each degree D i of node v i . Each TSP is used to convert the signal of node v i into a format suitable for optical fiber transmission, and for each direction d i|W| TSPs are required for processing, which is characterized by the following formula:

[0098]

[0099] where j represents the direction (i.e., adjacent node) connected to node v i and w represents the wavelength index, and W represents the set of all wavelengths in the optical fiber.

[0100] In some embodiments, the EDFA is deployed at periodic intervals of the optical fiber link, for example, it can be spaced 80 - 120 kilometers apart, and is used to amplify the optical signal and maintain the signal quality for long - distance transmission. For the link e i between node v j and node v ij with a length of l ij , the required number of EDFAs is proportional to the link length l ij . The EDFA on link e ij can be represented as and is characterized by the following formula:

[0101]

[0102] Figure 1 shows the schematic architecture of the WDM network model, including the relationships between four types of key devices: Each node is equipped with an OXC device, each directional link of each OXC is equipped with a WDMT, and the number of TSPs associated with each WDMT is determined by the wavelength. The OXC nodes are interconnected by optical fiber links, and the signals are amplified by EDFAs in the links to support long - distance transmission.

[0103] For the convenience of understanding this application, the following explains the possible technical terms:

[0104] Routing and Wavelength Assignment (RWA): With the development of modern optical fiber communication networks, the demand for transmission bandwidth in optical networks continues to grow. Especially driven by high-bandwidth applications such as big data, cloud computing, and high-definition video, how to transmit data efficiently and with low latency has become a core issue in optical network design. As one of the most important technologies in optical networks, routing and wavelength assignment technology determines the transmission path of data streams in the network and the allocation of wavelength resources. With the expansion of the scale of optical networks, the optimization of RWA is no longer just a resource allocation issue, but also involves how to improve network reliability, reduce energy consumption, reduce network latency, and reduce overall carbon emissions. In traditional RWA, routing and wavelength assignment are usually performed independently, that is, first select a suitable routing path, and then select a wavelength for each route. However, with the sharp increase in data traffic in optical networks, how to make the best choice among many routes and wavelengths, avoid frequent resource conflicts, and maximize resource utilization efficiency has become a technical problem that needs to be solved urgently. Especially in a high-load environment, the efficiency of RWA directly affects the performance and user experience of the entire optical network.

[0105] The core purpose of routing and wavelength allocation technology is to select appropriate paths and wavelengths for data transmission in optical networks according to different communication requirements. Traditional RWA technology mainly focuses on how to select the optimal routing path in the network and allocate appropriate wavelengths to it to ensure the normal operation of the network. Existing RWA technologies can be divided into two categories: static RWA and dynamic RWA. In static RWA, the routing path and wavelength of each data flow are pre-set before the traffic arrives. Generally, static RWA is suitable for situations where the network load is low and the traffic demand is relatively fixed. Although the static RWA algorithm is relatively simple and can effectively avoid repeated allocation of routing and wavelength resources, it cannot flexibly respond to traffic fluctuations. When the network traffic demand changes, the static solution will waste or be insufficient. Dynamic RWA dynamically selects routes and wavelengths in the network according to real-time traffic demand. In the case of large traffic fluctuations and uneven network load, dynamic RWA can flexibly adjust the routing and wavelength allocation in the network to improve the utilization of network resources. Dynamic RWA algorithms usually require more complex scheduling and resource management strategies to cope with the challenges brought by traffic changes.

[0106] Advance reservation service technology: With the development of optical network technology, advance reservation service (AR) technology has played an important role in meeting the needs of high-priority, time-sensitive and high-quality applications. Advance reservation service allows users to request network resources in advance within a specified time window, ensuring that stable and high-quality network connections can be provided within the predetermined time range.

[0107] Compared with the Instant Reservation (IR) service, a major advantage of AR service is its flexibility and schedulability in time. By reserving network resources in advance, the network can predict future service needs and schedule when resources are available, avoiding network congestion or delays caused by resource competition. In some application scenarios with strict service quality requirements, pre-reserved services can provide higher guarantees.

[0108] Advance reservation service technology refers to reserving network resources for specific users through network scheduling algorithms within a certain time period. Usually, AR service requests require the specification of a start time and duration to ensure that the network can provide sufficient bandwidth and resources within that time period. In optical networks, the implementation of AR services relies on effective routing and wavelength allocation strategies to ensure resource availability and service quality within the scheduled time.

[0109] Research on advance reservation (AR) technology is progressing, with key areas including resource reservation and scheduling, dynamic scheduling and timeliness guarantee, and scheduling optimization combined with renewable energy. To ensure the availability of AR services, the network must allocate appropriate bandwidth, wavelength, and routing for each request. Existing scheduling strategies usually allocate resources based on factors such as network topology, traffic demand, and wavelength availability. In particular, in service requests with different latency requirements, the scheduling algorithm needs to avoid resource conflicts as much as possible to ensure that the quality of service requirements are met. However, in practical applications, especially when the network load is high, how to reasonably adjust resource allocation to avoid excessive congestion of network resources or excessive latency is still a difficult point. With the increasing demand for green networks, some studies have begun to focus on how to combine renewable energy with AR service scheduling. Through intelligent scheduling, AR service requests can be scheduled in time periods suitable for renewable energy supply, thereby reducing dependence on non-renewable energy. This can not only optimize energy consumption, but also effectively reduce carbon emissions, and promote the development of optical networks towards green and low-carbon directions. At the same time, in order to further reduce carbon emissions, some studies have proposed green scheduling algorithms specifically for AR services, aiming to dynamically adjust the service time and resource allocation of AR service requests according to the supply of renewable energy in the time period, reduce dependence on non-renewable energy, and thus reduce overall carbon emissions.

[0110] In some embodiments, an AR service request is represented as a five-tuple (s, d, L, R, τ), where s, d ∈ V represent the source node and the destination node respectively, τ represents the duration of the request, and L and R represent the earliest and latest possible start times of the request respectively. We define the time period between L and R as the startup window, within which the request must start and satisfy the condition R - L ≥ τ, that is, the request is allowed to start at any time within this window. Assume that each service request needs to be assigned a wavelength to ensure the quality of service.

[0111] Greenhouse gas emission model: The greenhouse gas (GHG) emission model mainly consists of two parts: the device power consumption model and the relationship between energy consumption and GHG emissions. The device power consumption model describes the energy consumption characteristics of various devices in the optical network and the impact of the traffic generated by service requests on the device power consumption; the relationship between energy consumption and GHG emissions quantifies the contribution of different energy structures to the greenhouse gas emissions. This model provides a basis for the green optimization of optical networks.

[0112] The power consumption of optical network devices can be divided into two parts: static power consumption and dynamic power consumption. Among them, static power consumption refers to the basic operating power consumption of the device, which is independent of the activity state or traffic load of the device and remains fixed all the time; dynamic power consumption is affected by the traffic intensity, including whether there is traffic transmission and the size of the allocated bandwidth. Existing research shows that there are certain differences in the power consumption of optical network devices produced by different suppliers, but overall, the power consumption of TSP (optical transponder) is positively correlated with the data processing rate, while the power consumption of other types of devices (such as OXC, WDMT, EDFA) is mainly composed of the static part. In this study, it is assumed that the dynamic energy consumption of TSP is linearly related to the number of its active transponders, so as to construct a comprehensive device power consumption model combining static and dynamic power consumption.

[0113] Specifically, the total power consumption of the optical cross-connect device (OXC) is determined by its static power consumption, and an OXC is configured at each node in the network, and its power consumption can be expressed as:

[0114]

[0115] The power consumption of the WDM terminal device (WDMT) is proportional to the connection degree of the node. For each additional directional connection, the static power consumption of a WDMT will increase accordingly, and its power consumption is expressed as:

[0116]

[0117] The power consumption of the optical transponder (TSP) includes two parts: static power consumption and dynamic power consumption. The latter is directly affected by the data traffic. When the transponder is in the data transmission state, additional dynamic power consumption will be generated, and its formula is:

[0118]

[0119] Among them, γ represents whether the TSP transmits data (1 for transmission, 0 for non - transmission), and b TSP is the additional power consumption during data transmission.

[0120] An erbium - doped fiber amplifier (EDFA) is deployed on the optical fiber link. Its power consumption is proportional to the number of amplifiers on the link, and the number of amplifiers is determined by the link length l ij and the deployment spacing L of the amplifiers EDFA of the link e ij The total power consumption of the link is expressed as:

[0121]

[0122] The GHG emissions are affected by both the total power consumption of the equipment and the energy structure. As the core infrastructure for long - distance transmission, the energy supply of the optical network is usually composed of a mixture of renewable energy and non - renewable energy. To further quantify the impact of the energy structure on GHG emissions, the concept of the proportion of renewable energy (PRE) is introduced in the model, which represents the proportion of renewable energy at each node during different time periods Satisfying On this basis, the total GHG emissions can be expressed as:

[0123]

[0124] Among them, α represents the GHG emission intensity of renewable energy, with the unit of kgCO 2 e / kWh; β represents the GHG emission intensity of non - renewable energy, with the unit of kgCO 2 e / kWh; T represents the set of all time periods.

[0125] Through the above model, the optimization of GHG emissions in the optical network can not only perform static analysis based on the device power consumption, but also dynamically adjust the resource allocation strategy by combining the time - varying proportion of renewable energy, so as to achieve green and low - carbon network operation while meeting service requests. This model provides a comprehensive and accurate quantification method for the comprehensive impact of device power consumption and energy structure on GHG emissions in the optical network, and helps to guide the design and optimization of future green optical networks.

[0126] As Figure 2 shown, it is a schematic structural diagram of an AR service model provided by this application. Among them, a represents the time when the request arrives. L represents the earliest possible start time of the request, R represents the latest possible start time, and request1 and request2 represent the request duration.

[0127] Renewable Energy Application Technology: With the increasing severity of the global energy crisis and environmental pollution problems, reducing greenhouse gas emissions and promoting the use of green energy have become important goals for governments and global enterprises. Traditional energy supply methods rely on the combustion of fossil fuels, which not only leads to a large amount of carbon dioxide emissions but also exacerbates the risk of climate change. Against this background, the application of renewable energy (such as solar energy, wind energy, water energy, etc.) has received increasing attention and become an important direction to replace traditional energy.

[0128] In communication networks, especially in critical infrastructures such as optical networks and data centers, energy consumption has become an important operating cost and environmental burden. With the continuous increase in network traffic and the demand for high-performance computing resources in data centers, how to effectively integrate renewable energy to reduce the carbon footprint and energy consumption has become a key challenge in promoting the sustainable development of optical networks. As a source of green electricity, renewable energy has the advantage of zero carbon emissions, but its power supply stability and predictability are poor, and there are problems of uneven geographical distribution and strong volatility. How to effectively integrate this fluctuating energy into optical networks and dynamically adjust the energy supply to meet the network's needs has become a technical problem to be solved urgently. At the same time, there are imbalances in energy demand around the world. In some regions, solar and wind energy resources are abundant, while in others they are scarce. Therefore, how to achieve intelligent scheduling based on the geographical distribution of energy resources, combined with the actual network traffic demand and the temporal changes of renewable energy, to maximize the use of green energy and reduce dependence on traditional non-renewable energy has become an important topic in optical network research.

[0129] Renewable energy application technology mainly involves how to effectively integrate green energy such as solar energy and wind energy into optical networks and optimize their usage efficiency. Research in this field mainly focuses on the following aspects:

[0130] First of all, in response to the volatility and uncertainty of renewable energy, many studies have proposed energy scheduling schemes based on prediction models. By predicting the power generation of renewable energy, the network can make advance resource scheduling plans to cope with the instability of energy supply. For example, during periods when solar and wind energy resources are abundant, the network can preferentially use this green energy for data transmission and network operation, reducing dependence on traditional fossil energy. Through this dynamic scheduling strategy, optical networks can maximize the use of renewable energy, improve energy usage efficiency, and reduce carbon emissions.

[0131] Secondly, due to the geographical distribution differences of renewable energy, how to perform intelligent scheduling according to the energy supply situation in different regions is also an important research direction at present. The green energy resources available to optical network nodes in different regions are different. How to use optimization algorithms to allocate green energy from resource-rich regions to resource-scarce regions, thereby improving the overall energy efficiency of the network, has become the focus of attention of many scholars. These intelligent scheduling and resource management technologies usually combine advanced methods such as machine learning and artificial intelligence to predict energy production and consumption, and on this basis, achieve cross-regional optimal energy scheduling.

[0132] In addition, how to solve the problem of the stable operation of optical networks when the supply of renewable energy is insufficient is also a difficult point in current technologies. To ensure that optical networks can continue to operate when renewable energy is insufficient, researchers have proposed a hybrid energy supply scheme that combines the dual supply modes of renewable energy and non-renewable energy. This scheme can automatically switch to traditional energy when the supply of green energy is insufficient, ensuring the stability of network services.

[0133] After introducing the basic principle of this application, the various non-limiting implementation manners of this application will be specifically introduced below.

[0134] Figure 3 It is a schematic flowchart of a routing and wavelength allocation method based on an optical network provided by an embodiment of this application. As Figure 3 shown, it specifically includes the following steps:

[0135] S101. Obtain the source node, destination node, earliest start time, latest start time, and request duration.

[0136] Among them, the source node s, destination node d, earliest start time L, latest start time R, and request duration τ are included in the request data.

[0137] In some embodiments, the request data may be the input data of an AR request. In addition to the above content, the input data of the AR request further includes: as Figure 1 shown, the network topology diagram, and the proportion of renewable energy (PRE) of each node in the topology at each time period.

[0138] S102. Determine the shortest path set based on the source node and destination node.

[0139] In some embodiments, the first k shortest paths from the source node s to the destination node d are calculated by the KSP algorithm, and the above first k shortest paths are stored in the shortest path set P. Among them, the value of k can be set according to the actual situation, and this application does not limit this.

[0140] S103. Determine a candidate path set in the shortest path set based on the earliest start time, the latest start time, and the requested duration.

[0141] In some embodiments, by retrieving the wavelength occupancy status of the wavelength, link, and start time period corresponding to each path in the above shortest path set, identify a continuous available time period of length τ within the start time period, and store the start time in set T. Immediately afterwards, take the intersection of the time period availability of all links in each of the above paths to determine the continuous available time period of each path. If there is a continuous available time period that meets the request conditions, store the path corresponding to this time period, the wavelength corresponding to this path, and the start time period in the candidate path set Z. The paths stored in the candidate path set are also called candidate paths. If there is no path in the shortest path set that meets the above conditions, reject the request data obtained this time.

[0142] S104. Determine the proportion of renewable energy of each path in the candidate path set based on a pre-constructed renewable energy proportion model.

[0143] Among them, the proportion of renewable energy (PRE) represents the proportion of the electricity generated by renewable energy in the total electricity generation per unit. The proportion of the electricity generated by renewable energy is inversely proportional to the carbon intensity. When the proportion of the electricity generated by renewable energy is relatively high, the carbon intensity decreases to enhance the environmental sustainability. Denote the node v in the optical network i The PRE at time period t.

[0144] It should be noted that renewable energy generation has significant time volatility, and its characteristics are affected by factors such as weather conditions, temperature, and terrain. This volatility may change significantly over time. In the pre-constructed renewable energy proportion model, the time variation trend of renewable energy can be predicted based on predictable renewable energy (such as weather forecasts). To facilitate the discrete modeling of renewable energy generation, the time dimension is divided into multiple discrete time periods. In the renewable energy proportion model, the PREs of different wavelengths associated with the same node v i are assumed to be the same, and it is assumed that the power supply is sufficient in each time period.

[0145] In some embodiments, determine the PRE of the source node and the PRE of the destination node of the start time period corresponding to each path in the candidate path set through the renewable energy proportion model.

[0146] S105. Determine a solution based on the proportion of renewable energy of each path in the candidate path set.

[0147] Among them, the solution includes a target path, a target wavelength, and a target start time period.

[0148] It can be understood that each path in the candidate path set can be used as a feasible solution. In order to make the final solution have the best effect, it is necessary to further determine the final solution from multiple solutions in the candidate path set. Among them, the final solution is also used as the service mapping result corresponding to the request data, and can also be called the service mapping result of the AR request. After determining the final solution, the occupancy status of the links, start time period, and wavelength in the network topology is updated accordingly.

[0149] In some embodiments, the path with the highest sum of the PRE of the source node and the PRE of the destination node in the start time period in the candidate path set, and the wavelength and start time period corresponding to this path are used as the solution. This path is also called the target path, the wavelength corresponding to this path is called the target wavelength, and the start time period corresponding to this path is called the target start time period.

[0150] Exemplarily, taking the candidate path set including path A, path B, and path C as an example. The PRE of the source node of path A is 0.6, the PRE of the destination node is 0.5, and the sum of the two is 1.1. The PRE of the source node of path B is 0.8, the PRE of the destination node is 0.7, and the sum of the two is 1.5. The PRE of the source node of path C is 0.6, the PRE of the destination node is 0.7, and the sum of the two is 1.3. Select the path with the largest sum of the two as the target path, that is, select path B as the target path, the wavelength corresponding to path B as the target wavelength, and the start time period corresponding to path B as the target start time period.

[0151] In some embodiments, as Figure 4 shown, based on the earliest start time, the latest start time, and the request duration, determine the candidate path set in the shortest path set, which can be specifically implemented as the following steps:

[0152] S201. Based on the earliest start time, the latest start time, and the request duration, determine the request time period.

[0153] Among them, the request time period is the start time period plus the request duration.

[0154] In some embodiments, taking the earliest start time L as 10:00, the latest start time R as 11:00, and the request duration τ as 30 minutes as an example, the start time period corresponding to the request data is [10:00, 11:00], denoted as [L, R]. Further, the request time period corresponding to the request is [10:00, 11:30] to ensure coverage of all possible service end times (for example, the end time corresponding to the latest start time R is R + τ), and the request time period is denoted as [L, R + τ].

[0155] S202. Determine a candidate path set based on the request time period.

[0156] In some embodiments, as Figure 5 shown, determining a candidate path set based on the request time period can be specifically implemented as the following steps:

[0157] S2021. For each path in the shortest path set, determine the wavelength occupancy of at least one link in the path based on the request time period.

[0158] In some embodiments, by retrieving the wavelength, link, and wavelength occupancy status of the request time period [L, R + τ] corresponding to each path in the above shortest path set, identify a continuous available time period of length τ within the request time period [L, R + τ], and store the start time in the set T, where the available time period is also referred to as the idle time period.

[0159] Exemplarily, taking the earliest start time L as 10:00, the latest start time R as 11:00, the request duration τ as 30 minutes, and the path including link l 1 and link l 2 and link l 3 as an example. Among them, the available time period of link l 1 is [10:00, 12:00], the available time period of link l 2 is [10:30, 13:00], and the available time period of link l 3 is [14:00, 16:00]. From the above, it can be known that the request time period is [10:00, 11:30], and the start time 14:00 of the available time period of link l 3 is later than the earliest start time 10:00 in the request time period. Therefore, it can be determined that the wavelength occupancy of link l 1 is available, the wavelength occupancy of link l 2 is available, and the wavelength occupancy of link l 3 is unavailable. Correspondingly, a link with an available wavelength occupancy is also referred to as an available link, and a link with an unavailable wavelength occupancy is also referred to as an unavailable link.

[0160] S2022. Determine the available time periods of the available links in the path based on the wavelength occupancy of at least one link in each path.

[0161] In some embodiments, by the wavelength occupancy of at least one link in each path, eliminate the available time periods of the unavailable links in the path to obtain the available time periods of the available links in the path.

[0162] S2023. Determine the continuous time period of the path based on the intersection of the available time periods of the available links in the path.

[0163] In some embodiments, continuing with the previous example, the available links l 1 and l 2 in the path have the following available time periods respectively: link l 1 [10:00, 12:00], link l 2 [10:30, 13:00]. The intersection of their available time periods is used as the continuous time period of the path, which is [10:30, 12:00], and the duration of the continuous time period is 90 minutes.

[0164] S2024. In response to the continuous time period of the path being not less than the requested duration, determine the candidate path set.

[0165] It can be understood that to ensure that the continuous time period can meet the service request, the continuous time period needs to be not less than the requested duration. Therefore, store the paths in the shortest path set whose continuous time periods are not less than the requested duration into the candidate path set. Among them, the paths in the candidate path set are also called candidate paths.

[0166] In some embodiments, continuing with the previous example, the duration of the continuous time period of the path is 90 minutes, which is greater than the requested duration of 30 minutes. Therefore, store the path, the corresponding wavelength, and the continuous time period of the path into the candidate path set Z. If there is no path in the shortest path set that meets the above conditions, reject the requested data obtained this time.

[0167] In some embodiments, as Figure 6 shown, based on the pre-constructed renewable energy ratio model, determine the renewable energy ratio of each path in the candidate path set, which can be specifically implemented as the following steps:

[0168] S301. According to the renewable energy ratio model, determine the first renewable energy ratio of the source node in the starting time period and the second renewable energy ratio of the destination node in the starting time period.

[0169] S302. Determine the renewable energy ratio of each path in the candidate path set according to the first renewable energy ratio and the second renewable energy ratio.

[0170] Among them, the renewable energy ratio of each path is determined according to the following formula:

[0171]

[0172] In the above formula, C 1 is the renewable energy ratio, t s is the start time period, τ is the request duration, is the first renewable energy ratio, is the second renewable energy ratio.

[0173] In some embodiments, as Figure 7 shown, based on the renewable energy ratio of each path in the candidate path set, determine a solution, which can be specifically implemented as the following steps:

[0174] S401. Obtain the target path according to the candidate path with the highest renewable energy ratio.

[0175] It can be understood that each path in the candidate path set and the corresponding wavelength and start time period can be used as a feasible candidate solution. In order to make the final solution have the best effect, it is necessary to further determine the final solution from multiple candidate solutions in the candidate path set.

[0176] In some embodiments, the path with the highest sum of the PRE of the source node and the PRE of the destination node in the start time period of the candidate path set is used as the target path to ensure the improvement of the utilization rate of renewable energy on the basis of low-latency service allocation.

[0177] S402. Determine the target wavelength and target start time period corresponding to the target path.

[0178] In some embodiments, taking the candidate path set including path A, path B, and path C as an example. The PRE of the source node of path A is 0.6, the PRE of the destination node is 0.5, and the sum of the two is 1.1. The PRE of the source node of path B is 0.8, the PRE of the destination node is 0.7, and the sum of the two is 1.5. The PRE of the source node of path C is 0.6, the PRE of the destination node is 0.7, and the sum of the two is 1.3. Select the path with the largest sum of the two as the target path, that is, select path B as the target path, the wavelength corresponding to path B as the target wavelength, and the start time period corresponding to path B as the target start time period.

[0179] As Figure 8As shown in the figure, it is a schematic flowchart of another routing and wavelength assignment method based on an optical network provided by an embodiment of the present application. First, the KSP algorithm is used to calculate k shortest paths, and the paths are traversed to determine whether there are unprocessed paths. If there are unprocessed paths, it is determined whether there are unprocessed wavelengths in the path. Further, if there are unprocessed wavelengths, it is determined whether there are unprocessed links. If there are unprocessed links, check and determine the time period availability (available time period) of the link, and determine whether the time period meets the conditions. If the conditions are met, store the candidate solution corresponding to the path. Finally, calculate the total PRE of each candidate solution (the PRE of the earliest start point and the PRE of the latest start point), and select the candidate solution with the highest total PRE as the final solution, and update the occupancy status of network resources.

[0180] It should be noted that the method shown by Figure 8 is also called the Renewable Energy-Aware RWA Algorithm (RE for short), which can reduce greenhouse gas (GHG) emissions related to advance reservation (AR) requests in optical networks.

[0181] As can be seen from the above, the time complexity of the RE algorithm is mainly determined by nested loops, involving the time period availability and PRE calculation of candidate paths, wavelengths, and links. Its time complexity can be expressed as:

[0182] O(k·|W|·|L|·|T|)

[0183] where k represents the number of candidate paths in the candidate path set, |W| represents the total number of wavelengths, |L| represents the average number of links in the candidate paths, and |T| represents the number of time periods to be checked.

[0184] In some embodiments, to ensure the flexibility of the time scheduling of service requests, during the process of determining the solution, not only the proportion of renewable energy used by the service request is considered, but also the waiting delay of the service request is considered. As Figure 9 shown, based on the proportion of renewable energy of each path in the candidate path set, determining the solution can be specifically implemented as the following steps:

[0185] S501. For each path in the candidate path set, determine the waiting delay of the path according to the start time period of the path and the earliest start time.

[0186] In some embodiments, the waiting delay of the path is determined according to the following formula:

[0187] C 2 = t s - L

[0188] Among them, C 2 represents the waiting delay of the path, t s represents the start time period of the path, and L represents the earliest start time.

[0189] S502. Normalize the renewable energy ratio of the path to obtain a first normalization index, and normalize the waiting delay of the path to obtain a second normalization index.

[0190] It should be noted that the renewable energy ratio of the path can be determined by Figure 6 the corresponding method, or can also be determined by other methods, and this application does not limit this.

[0191] In some embodiments, it is necessary to normalize the renewable energy ratio C 1 and the waiting delay C 2 to the interval [0, 1] to obtain the normalization index of the renewable energy ratio and the normalization index of the waiting delay which can be achieved through the following formula:

[0192]

[0193] S503. According to the technique for order preference by similarity to ideal solution, the first normalization index, and the second normalization index, determine the first distance to the positive ideal solution and the second distance to the negative ideal solution.

[0194] Among them, the technique for order preference by similarity to ideal solution (Technique for Order Preference by Similarity to Ideal Solution, abbreviated as TOPSIS) has a core idea of determining the relative superiority and inferiority ranking of solutions by calculating the distances between each solution (the solution corresponding to the path in the candidate path set) and the ideal optimal solution (positive ideal solution) and the ideal worst solution (negative ideal solution) (that is, the first distance to the positive ideal solution and the second distance to the negative ideal solution).

[0195] In some embodiments, the positive ideal solution P * and the negative ideal solution P - of the path are determined according to the following formula:

[0196]

[0197] Immediately afterwards, determine the first distance to the positive ideal solution according to the following formula and the second distance to the negative ideal solution

[0198]

[0199] S504. Determine the relative proximity based on the first distance and the second distance.

[0200] In some embodiments, the relative proximity M i is determined according to the following formula:

[0201]

[0202] S505. Obtain a solution according to the relative proximity.

[0203] In some embodiments, the candidate solution with the highest relative proximity among the candidate solutions corresponding to the paths in the candidate path set is used as the final solution, which is specifically determined by the following formula:

[0204] M max = max(M i )

[0205] In some embodiments, the above method further includes: determining the total greenhouse gas emissions of the solution according to the optical network model.

[0206] Wherein, the total greenhouse gas emissions satisfy the following expression:

[0207]

[0208] Wherein, G is the total greenhouse gas emissions, α represents the greenhouse gas emission intensity of renewable energy, with the unit of kgCO 2 e / kWh; β represents the greenhouse gas emission intensity of non-renewable energy, with the unit of kgCO 2 e / kWh, T represents the set of available time periods, t is the target start time period, V represents the set of nodes in the optical network model, and each node v i represents the i-th node in the optical network model, i ∈ 1, 2,..., N, E represents the set of bidirectional links between the node sets, and e ij represents each link, represents the proportion of renewable energy of node v i in the target start time period, represents the power consumption of the optical cross-connect device, represents the power consumption of the wavelength division multiplexing terminal device, represents the power consumption of the optical transponder, represents the power consumption of the erbium-doped fiber amplifier on each link.

[0209] In some embodiments, after determining the solution, update the relevant information such as the wavelength, link, and time period occupancy status in the network topology according to the solution.

[0210] It should be noted that Figure 9The corresponding method is also known as the Renewable Energy and Waiting Delay Aware RWA algorithm (REWD algorithm), which aims to optimize the utilization rate of renewable energy and waiting delay for Advance Reservation (AR) requests. This algorithm not only focuses on the proportion of renewable energy used in service requests but also comprehensively considers the time scheduling flexibility of service requests to better balance environmental benefits and service quality.

[0211] In some embodiments, to verify the performance of the RE and REWD algorithms proposed in this application, two scheduling algorithms are selected as comparison benchmarks through simulation studies: (1) the Minimum Waiting Delay Wavelength Assignment algorithm (WD algorithm); (2) the First Fit Wavelength Assignment algorithm (FF algorithm). The main difference between the WD algorithm and the RE algorithm is that after finding all available solutions, the WD algorithm selects the solution that can minimize the waiting delay of AR requests. The FF algorithm traverses paths, wavelengths, and time periods and immediately uses the available solution once it is found without collecting multiple sets of solutions for comparison. The worst-case time complexity of the FF algorithm is the same as that of the RE algorithm, but due to the introduction of an early termination mechanism in the FF algorithm, its actual running time is significantly lower than the theoretical worst-case complexity. Similarly, the worst-case time complexity of the WD algorithm is also the same as that of the RE algorithm, but its actual number of calculation steps is less than that of the RE and REWD algorithms.

[0212] During the simulation, NSFNet (comprising 14 nodes) is adopted as the network topology. Each optical fiber supports 80 wavelengths by default, and 20 of them are used for AR requests. The generation of AR requests is based on the Poisson traffic model, and 10,000 AR operations are simulated. The default traffic load of the network is set to 0.6. The duration and start time window of AR requests follow an exponential distribution, and the mean of the distribution is determined by a predefined scaling factor. The duration scaling factor is set to 300, which is called the "request duration", and the start time window scaling factor is defined as the "request duration" plus 100. For each request, the source-destination node pair is randomly selected. Only AR requests are concerned in the simulation, while immediate requests (IR requests) are ignored to focus on the research issues of this application.

[0213] In terms of energy consumption, only the dynamic energy consumption of the devices is compared because the static energy consumption of the devices is not affected by the amount of data processed in the network. In addition, according to the TSP dynamic power consumption data, the dynamic power consumption of TSP is calculated as 33.3W. The PRE value of each node fluctuates between 0 and 1 at different time periods, with a default mean of 0.5 and a standard deviation of 0.3, and the changing trend is gradually adjusted by a small random step size. The greenhouse gas emission intensities of renewable energy (α) and non-renewable energy (β) are set to 0.01 and 1 respectively. All algorithms use KSP as the basic routing algorithm and select 3 candidate shortest routing paths. All simulations are completed on an open-source optical network simulation platform.

[0214] In some embodiments, as Figure 10 shown, the simulation results under different network traffic load conditions are presented. Among them, as Figure 10 (a) shows, the RE algorithm performs optimally in terms of dynamic greenhouse gas emissions, followed by the REWD algorithm, while the WD and FF algorithms have higher emissions. This indicates that the RE and REWD algorithms have significant advantages in reducing greenhouse gas emissions, with the RE algorithm showing particularly prominent emission reduction effects. When the traffic load is 0.45, the RE algorithm reduces emissions by 18.78% compared to the FF algorithm and 17.26% compared to the WD algorithm. The REWD algorithm reduces emissions by 12.84% and 11.21% respectively. As the service arrival rate increases, due to the limited network resources (such as the PRE value), the dynamic greenhouse gas emissions of all algorithms increase. At high traffic loads, the emissions of REWD and WD tend to be the same, indicating that REWD's ability to select high-PRE time periods has declined, and its resource allocation pattern gradually approaches that of the WD algorithm. For WD and FF, the intersection of emissions at lower traffic loads indicates sufficient resources, and at this time, reducing greenhouse gas emissions is not the main optimization goal of the two algorithms.

[0215] As Figure 10 (b) shows, as the traffic load increases, the average service waiting delay of the four algorithms all increases. The dashed line represents the maximum waiting delay of AR requests, which is equal to the average starting window length (400). The WD algorithm preferentially reduces the waiting delay. When the service demand is low and the wavelength and time period resources are sufficient, its average waiting delay can be almost close to 0. When the traffic load is 0.45, the waiting delay of the WD algorithm is only 0.5% of the starting window length, while the waiting delays of RE and REWD are 54.8% and 20.5% respectively. The waiting delay of the FF algorithm is 61.8%. Nevertheless, since AR requests only need to be completed before the specified deadline, these waiting delays are all within an acceptable range.

[0216] As Figure 10 (c) shows, Figure 10 (c) reveals the relationship between traffic load and the average service blocking probability. The blocking probabilities of RE and REWD are between those of FF and WD, with FF having the highest blocking probability and WD the lowest. FF selects the first available solution, which easily leads to frequent use of some links, thus forming a resource bottleneck and resulting in a higher blocking probability. While REWD performs better than FF in balancing the utilization of renewable energy and delay, its blocking probability is slightly higher than that of WD.

[0217] As Figure 10 (d) shows, Figure 10(d) shows the relationship between traffic load and average routing hop count. When the traffic load is low, the routing hop counts of the four algorithms change little, indicating that hop count optimization is not the main goal of the algorithms when resources are sufficient.

[0218] In some embodiments, such as Figure 11 shown in the variation with the request duration, the performance of each algorithm in the network. As Figure 11 (a) shows, in terms of dynamic greenhouse gas emissions, the emissions of the four algorithms are maintained in the order of RE < REWD < WD < FF. When the request duration is 100, the RE algorithm reduces emissions by 24.47% and 24.44% compared with the FF and WD algorithms respectively, while the REWD algorithm reduces emissions by 14.9% and 14.87% respectively. When the request duration increases to 300, resources become scarce, and the emission reduction effects of the RE algorithm are 16.76% and 13.58% respectively, while those of the REWD are 11.62% and 8.24% respectively. These results indicate that the RE and REWD algorithms have more significant emission reduction effects when the request duration is short and service requests are more flexible.

[0219] As Figure 11 (b) shows, in terms of average waiting delay, when the request duration is the independent variable, the maximum average waiting delay of AR requests has a linear relationship with the request duration (the dashed line indicates). When the request duration is 100, the average waiting delays of the WD, RE, REWD, and FF algorithms are 0%, 53.5%, 19%, and 58% of the maximum waiting delay respectively. When the request duration is 300, these values become 9.25%, 65.3%, 34%, and 68.3% respectively. This shows that the WD algorithm performs strongly in reducing request waiting delay. The waiting delay of the RE algorithm is second only to that of the FF, but higher than that of the WD and REWD, indicating that the emission reduction effect of the RE algorithm comes at the cost of increased waiting delay. The REWD achieves a good balance between reducing greenhouse gas emissions and waiting delay. Although the average waiting delays of the RE and REWD algorithms are higher than that of the WD, they are still within an acceptable range.

[0220] As Figure 11 (c) shows, in terms of blocking probability. The WD algorithm has the lowest blocking probability, followed by REWD, RE, and FF. When the request duration is 100, the blocking probabilities of all algorithms are 0, indicating that the network resources are sufficient to meet the transmission requirements at this time. When the request duration increases to 300, the blocking probabilities of the RE and REWD are 3.27% and 6.5% lower than that of the FF respectively, but 6.3% and 3.07% higher than that of the WD. Even under higher resource demands, the RE and REWD still show relatively low blocking rates.

[0221] As Figure 11As shown in (d), as the request duration increases, the average number of routing hops of the four algorithms does not change significantly. When the request duration exceeds 240, as the resource demand of the service increases, the average number of routing hops shows a slight fluctuation.

[0222] In some embodiments, Figure 12 shows the influence of different numbers of available wavelengths on the algorithm performance under the condition that the traffic load is 0.5. As Figure 12 shown in (a), under different numbers of wavelengths, the rankings of dynamic greenhouse gas emissions are consistent, further verifying the effectiveness of RE and REWD in reducing carbon emissions. As the number of available wavelengths increases, the expansion of the resource selection range helps to reduce dynamic greenhouse gas emissions. When the available wavelength is 17, the emissions of the RE and REWD algorithms are reduced by 17.43% and 12.19% respectively compared with the FF algorithm, and are reduced by 13.5% and 8% respectively compared with the WD algorithm. When the available wavelength increases to 23, these values further increase. RE and REWD are reduced by 18.96% and 12.83% respectively compared with FF, and are reduced by 17.6% and 11.3% respectively compared with WD.

[0223] As Figure 12 shown in (b), as the wavelength resources increase, the average waiting delay of all algorithms decreases. When the available wavelength increases from 17 to 23, the waiting delays of the WD, RE, REWD, and FF algorithms decrease from 5.5%, 63%, 31.25%, and 66% of the maximum waiting delay to 2.5%, 54.8%, 20.25%, and 63.25% respectively. The waiting delays of RE and REWD are higher than that of WD, but significantly lower than that of FF.

[0224] As Figure 12 shown in (c), Figure 12 (c) clearly reflects the trend that the network blocking rate decreases as the wavelength resources increase. When the number of wavelengths reaches 23, the blocking rates of RE and REWD are reduced by 1.56% and 2.63% respectively compared with FF, while they increase by 1.34% and 0.27% respectively compared with WD.

[0225] Figure 12 (d) shows the relationship between the number of available wavelengths and the average number of routing hops. When the resources are sufficient, there is no obvious difference in the average number of routing hops of the four algorithms.

[0226] In some embodiments, Figure 13 shows the simulation results under different PRE mean values. As Figure 13As shown in (a), with the increase in the average value of PRE, the average dynamic greenhouse gas emissions decrease significantly, indicating the key role of the overall renewable energy utilization rate in reducing carbon emissions. When the average value of PRE reaches 0.9 (high renewable energy availability), the RE and REWD algorithms reduce by 31.58% and 21.93% respectively compared to the FF algorithm, and reduce by 29.5% and 19.5% respectively compared to the WD algorithm.

[0227] As Figure 13 As shown in (b), under the condition of the change in the average value of PRE, the average waiting delays of RE and REWD fluctuate slightly, while those of WD and FF remain unchanged. When the average value of PRE is 0.9, the average waiting delays of the WD, RE, REWD, and FF algorithms are 1%, 58.17%, 23.06%, and 63.75% of the maximum waiting delay respectively.

[0228] As Figure 13 As shown in (c), Figure 13 As shown in (c), the blocking probabilities of FF and WD are basically not affected by the change in the average value of PRE. However, for RE and REWD, due to their association with the optimization objective of PRE, the blocking probabilities fluctuate slightly but remain stable between those of FF and WD. When the average value of PRE is 0.9, the blocking probabilities of RE and REWD are 3.39% and 1.06% respectively, while those of FF and WD are 5.76% and 0.22% respectively.

[0229] As Figure 13 As shown in (d), there is no obvious difference in the average routing hop counts of the four algorithms.

[0230] In some embodiments, Figure 14 shows the simulation results under the condition of the change in the standard deviation of PRE (the traffic load is set to 0.5). As Figure 14 As shown in (a), with the increase in the standard deviation of PRE, the dynamic greenhouse gas emissions of RE and REWD gradually decrease, while those of the FF and WD algorithms fluctuate. When the standard deviation reaches 0.7, the RE and REWD algorithms reduce by 26.31% and 18.69% respectively compared to FF, and reduce by 23.4% and 15.4% respectively compared to WD.

[0231] As Figure 14 As shown in (b), with the change in the standard deviation of PRE, the average waiting delays of RE and REWD fluctuate slightly, while the average waiting delays of FF and WD remain stable. When the standard deviation is 0.7, the average waiting delays of the WD, RE, REWD, and FF algorithms account for 1%, 57.5%, 22.5%, and 63.75% of the maximum waiting delay respectively.

[0232] As Figure 14As shown in (c), the blocking probabilities of FF and WD remain unchanged, while those of RE and REWD fluctuate slightly, with average values ​​of 3.29% and 1.08%, respectively.

[0233] like Figure 14 As shown in (d), changes in the PRE standard deviation have almost no effect on the average number of routing hops for the service.

[0234] In summary, the optical network-based routing and wavelength allocation method and related equipment provided by the present application first determine the shortest path set between the two nodes through the source node and the destination node, and then determine the candidate path set in the shortest path set through the earliest start time, the latest start time and the request duration. In this way, the service quality requirements are guaranteed, the service delay is reduced through the shortest path, and the time window and other resources are fully utilized through the earliest start time, the latest start time and the request duration. On the basis of ensuring that the service requirements are met, the service delay is reduced and the service quality is improved. Finally, the final solution is considered through the proportion of renewable energy in each path, which fully improves the utilization rate of green energy and reduces service energy consumption.

[0235] In addition, this application also has the following beneficial effects:

[0236] 1. Further optimized the flexibility of energy scheduling

[0237] The present invention not only considers shutting down idle network elements to reduce power consumption, but also proposes a dynamic scheduling method based on the time dimension, which can optimize resource allocation according to the volatility and time distribution characteristics of renewable energy. This dynamic energy management strategy significantly improves the utilization efficiency of green energy, while the existing EEM-RSA mainly stays in static allocation or simple dynamic scenarios, and does not deeply analyze the impact of energy fluctuation characteristics on scheduling optimization.

[0238] 2. Enhanced service quality assurance mechanism

[0239] The present invention provides better guarantee for service quality (such as waiting delay and blocking probability) while reducing energy consumption. Through the improved heuristic algorithm, the present invention can further reduce the carbon footprint of the network while ensuring service quality. Although EEM-RSA proposes a compromise solution, it fails to find the optimal balance between waiting time and energy consumption.

[0240] 3. Support more types of service requests

[0241] EEM-RSA mainly focuses on multicast services in static and dynamic scenarios, while the present invention further expands the scope of application, especially for the scheduling optimization of Advance Reservation (AR), enabling it to be applied in a wider range of scenarios and enhancing the generality and flexibility of the algorithm.

[0242] 4. Optimization by combining the diversity and spatio-temporal distribution of renewable energy

[0243] The present invention takes into account the distribution differences of renewable energy in terms of geographical location and time, and matches the energy supply and demand fluctuations through a scheduling optimization algorithm, making energy use more flexible and efficient. In contrast, the green energy selection method (GEA-DS) of EEM-RSA only stays at the static priority evaluation of nodes and does not further utilize the time-dynamic characteristics of renewable energy.

[0244] 5. More significant reduction effect on environmental impact

[0245] By combining dynamic energy scheduling and resource optimization strategies, the present invention is superior to EEM-RSA in reducing greenhouse gas emissions. Although the green energy awareness method of EEM-RSA reduces emissions to a certain extent, its fixed selection mechanism limits the possibility of further optimization, while the strategy of the present invention has better performance in this field.

[0246] It should be noted that the method of the embodiment of the present application can be executed by a single device, such as a computer or a server, etc. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In such a distributed scenario, one of these multiple devices can only execute one or more steps of the method of the embodiment of the present application, and these multiple devices will interact with each other to complete the described method.

[0247] It should be noted that some embodiments of the present application have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be executed in a different order from that in the above embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0248] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a routing and wavelength allocation device based on an optical network.

[0249] Reference Figure 15, the routing and wavelength assignment device based on an optical network includes: an acquisition module 1501 and a determination module 1502;

[0250] The acquisition module 1501 is configured to acquire a source node, a destination node, an earliest start time, a latest start time, and a request duration;

[0251] The determination module 1502 is configured to determine a shortest path set based on the source node and the destination node;

[0252] The determination module 1502 is further configured to determine a candidate path set in the shortest path set based on the earliest start time, the latest start time, and the request duration;

[0253] The determination module 1502 is further configured to determine the renewable energy ratio of each path in the candidate path set based on a pre-constructed renewable energy ratio model;

[0254] The determination module 1502 is further configured to determine a solution based on the renewable energy ratio of each path in the candidate path set, where the solution includes a target path, a target wavelength, and a target start time period.

[0255] In some embodiments, the determination module 1502 is specifically configured to determine a request time period based on the earliest start time, the latest start time, and the request duration;

[0256] Based on the request time period, determine the candidate path set.

[0257] In some embodiments, the determination module 1502 is further specifically configured to, for each path in the shortest path set, determine the wavelength occupancy of at least one link in the path based on the request time period;

[0258] Based on the wavelength occupancy of at least one link in each path, determine the available time period of the available links in the path;

[0259] Based on the intersection of the available time periods of the available links in the path, determine the continuous time period of the path;

[0260] In response to the continuous time period of the path being not less than the request duration, determine the candidate path set.

[0261] In some embodiments, the determination module 1502 is further specifically configured to determine a first renewable energy ratio of the source node at the start time period and a second renewable energy ratio of the destination node at the start time period according to the renewable energy ratio model;

[0262] According to the first renewable energy ratio and the second renewable energy ratio, determine the renewable energy ratio of each path in the candidate path set.

[0263] In some embodiments, the determining module 1502 is further specifically configured to obtain a target path according to the candidate path with the highest proportion of renewable energy;

[0264] Determine the target wavelength and the target start time period corresponding to the target path.

[0265] In some embodiments, the determining module 1502 is further specifically configured to, for each path in the candidate path set, determine the waiting delay of the path according to the start time period of the path and the earliest start time;

[0266] Perform normalization processing on the renewable energy proportion of the path to obtain a first normalization index, and perform normalization processing on the waiting delay of the path to obtain a second normalization index;

[0267] According to the technique for order preference by similarity to ideal solution, the first normalization index, and the second normalization index, determine the first distance to the positive ideal solution and the second distance to the negative ideal solution;

[0268] Determine the relative closeness according to the first distance and the second distance;

[0269] Obtain a solution according to the relative closeness.

[0270] In some embodiments, the determining module 1502 is further configured to determine the total greenhouse gas emissions of the solution according to the optical network model.

[0271] For the sake of convenience of description, when describing the above device, various modules are described separately according to their functions. Of course, when implementing the present application, the functions of each module can be implemented in one or more software and / or hardware.

[0272] The device in the above embodiment is used to implement the corresponding routing and wavelength allocation method based on an optical network in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0273] Based on the same inventive concept, corresponding to the method in any of the above embodiments, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the routing and wavelength allocation method based on an optical network described in any of the above embodiments when executing the program.

[0274] Figure 16FIG. 0 shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1610, a memory 1620, an input / output interface 1630, a communication interface 1640, and a bus 1650. Among them, the processor 1610, the memory 1620, the input / output interface 1630, and the communication interface 1640 are communicatively connected to each other inside the device through the bus 1650.

[0275] The processor 1610 may be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0276] The memory 1620 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1620 may store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1620 and are called and executed by the processor 1610.

[0277] The input / output interface 1630 is used to connect to an input / output module to implement information input and output. The input / output module may be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0278] The communication interface 1640 is used to connect to a communication module (not shown in the figure) to implement communication interaction between this device and other devices. Among them, the communication module may implement communication in a wired manner (such as USB, network cable, etc.) or in a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0279] The bus 1650 includes a path for transmitting information between various components of the device (such as the processor 1610, the memory 1620, the input / output interface 1630, and the communication interface 1640).

[0280] It should be noted that although the above device only shows the processor 1610, the memory 1620, the input / output interface 1630, the communication interface 1640, and the bus 1650, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solution of the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0281] The electronic device of the above embodiment is used to implement the corresponding routing and wavelength allocation method based on the optical network in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0282] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a non-transitory computer-readable storage medium. The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the routing and wavelength allocation method based on the optical network as described in any of the foregoing embodiments.

[0283] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0284] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the routing and wavelength allocation method based on the optical network as described in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0285] Based on the same inventive concept, corresponding to the optical network-based routing and wavelength allocation method described in any of the above embodiments, the present disclosure also provides a computer program product, which includes a computer program. In some embodiments, the computer program is executable by one or more processors to cause the processors to execute the optical network-based routing and wavelength allocation method. Corresponding to the execution subjects corresponding to the steps in each embodiment of the optical network-based routing and wavelength allocation method, the processors executing the corresponding steps may belong to the corresponding execution subjects.

[0286] The computer program product of the above embodiment is used to cause a processor to execute the optical network-based routing and wavelength allocation method described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0287] Those of ordinary skill in the art should understand that: The discussion of any of the above embodiments is only exemplary and is not intended to imply that the scope of the present application (including the claims) is limited to these examples; Under the concept of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present application as described above, and they are not provided in detail for the sake of brevity.

[0288] In addition, for simplicity of explanation and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. In addition, the device may be shown in block diagram form to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application will be implemented (that is, these details should be completely within the understanding of those skilled in the art). In the case where specific details (such as circuits) are set forth to describe the exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0289] Although the present application has been described in conjunction with specific embodiments of the present application, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) may be used in the embodiments discussed.

[0290] Embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application shall be included within the protection scope of the present application.

Claims

1. A routing and wavelength allocation method based on an optical network, characterized in that: The method comprises: Get the source node, destination node, earliest start time, latest start time and request duration; Determine a shortest path set based on the source node and the destination node; Determine a candidate path set in the shortest path set based on the earliest start time, the latest start time, and the request duration; Determining the renewable energy proportion of each path in the candidate path set based on a pre-built renewable energy proportion model; Based on the renewable energy proportion of each path in the candidate path set, a solution is determined, where the solution includes a target path, a target wavelength, and a target starting time period.

2. The method according to claim 1, characterized in that The determining, based on the earliest start time, the latest start time, and the request duration, a set of candidate paths in the shortest path set comprises: Determine a requested time period based on the earliest start time, the latest start time, and the requested duration; Based on the request time period, the candidate path set is determined.

3. The method according to claim 2, characterized in that The determining the candidate path set based on the request time period includes: For each path in the shortest path set, determining, based on the request time period, a wavelength occupancy status of at least one link in the path; Determine an available time period of an available link in the path based on the wavelength occupancy of at least one link in each path; Determining a continuous time period of the path based on an intersection of available time periods of available links in the path; In response to the continuous time period of the path being not less than the request duration, the candidate path set is determined.

4. The method according to claim 1, characterized in that: The step of determining the renewable energy proportion of each path in the candidate path set based on a pre-built renewable energy proportion model includes: Determining, according to the renewable energy proportion model, a first renewable energy proportion of the source node in the starting time period and a second renewable energy proportion of the destination node in the starting time period; The renewable energy proportion of each path in the candidate path set is determined according to the first renewable energy proportion and the second renewable energy proportion.

5. The method according to claim 1, characterized in that: The determining of a solution based on the proportion of renewable energy in each path in the candidate path set includes: Obtaining a target path according to the candidate path with the highest proportion of renewable energy; The target wavelength and the target starting time period corresponding to the target path are determined.

6. The method according to claim 1, characterized in that The determining of a solution based on the proportion of renewable energy in each path in the candidate path set includes: For each path in the candidate path set, determining a waiting delay of the path according to the start time period and the earliest start time of the path; Normalizing the renewable energy proportion of the path to obtain a first normalized index, and normalizing the waiting delay of the path to obtain a second normalized index; Determine a first distance of a positive ideal solution and a second distance of a negative ideal solution according to a ranking preference technique based on an ideal solution, the first normalized index, and the second normalized index; determining a relative proximity based on the first distance and the second distance; According to the relative proximity, the solution is obtained.

7. The method according to claim 1, characterized in that The method further comprises: Based on the optical network model, determine the total greenhouse gas emissions of the solution.

8. A routing and wavelength allocation device based on an optical network, characterized in that: The device comprises: an acquisition module and a determination module; The acquisition module is used to obtain the source node, the destination node, the earliest start time, the latest start time and the request duration; The determination module is used to determine a shortest path set based on the source node and the destination node; The determination module is further configured to determine a candidate path set in the shortest path set based on the earliest start time, the latest start time, and the request duration; The determination module is further used to determine the renewable energy proportion of each path in the candidate path set based on a pre-built renewable energy proportion model; Based on the renewable energy proportion of each path in the candidate path set, a solution is determined, where the solution includes a target path, a target wavelength, and a target starting time period.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the program. 10 . A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to claim 1 .

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