CN112383846A - Cloud-fog elastic optical network-oriented spectrum resource allocation method for advance reservation request - Google Patents

Cloud-fog elastic optical network-oriented spectrum resource allocation method for advance reservation request Download PDF

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CN112383846A
CN112383846A CN202011271498.7A CN202011271498A CN112383846A CN 112383846 A CN112383846 A CN 112383846A CN 202011271498 A CN202011271498 A CN 202011271498A CN 112383846 A CN112383846 A CN 112383846A
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spectrum
time
service request
path
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CN112383846B (en
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吴利杰
朱睿杰
杨燚
刘岩
安致嫄
舒新建
赵凌霄
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Zhengzhou University
State Grid Corp of China SGCC
Information and Telecommunication Branch of State Grid Henan Electric Power Co Ltd
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State Grid Corp of China SGCC
Information and Telecommunication Branch of State Grid Henan Electric Power Co Ltd
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04Q11/00—Selecting arrangements for multiplex systems
    • H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
    • H04Q11/0062—Network aspects
    • H—ELECTRICITY
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    • H04L45/12—Shortest path evaluation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q11/00—Selecting arrangements for multiplex systems
    • H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
    • H04Q11/0062—Network aspects
    • H04Q2011/0073—Provisions for forwarding or routing, e.g. lookup tables
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04Q—SELECTING
    • H04Q11/00—Selecting arrangements for multiplex systems
    • H04Q11/0001—Selecting arrangements for multiplex systems using optical switching
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Abstract

本发明公开了一种面向云‑雾弹性光网络提前预留请求的频谱资源分配方法，包括如下步骤：利用最短路径算法计算业务请求的k条最短候选路径；基于链路的频谱资源划分时间片和频谱槽，根据每个时间频谱单元的状态获取路径资源矩阵，根据路径资源矩阵获取处理业务请求所需的时间片和频谱槽数量；利用强化学习算法确认业务请求被分配的动作，根据动作获取奖励；根据奖励确认该分配方案是否有效，若有效，记录分配方案；所述分配方案包括业务请求调度的开始时间、最短候选路径、处理业务请求所需的时间片和频谱槽数量；依次遍历k条最短候选路径，选择产生最大奖励的分配方案。本发明具有很好的鲁棒性，可最大程度地提高频谱资源利用率。

Figure 202011271498

The invention discloses a spectrum resource allocation method for cloud-fog elastic optical network advance reservation request, comprising the steps of: calculating k shortest candidate paths of service requests by using a shortest path algorithm; dividing time slices based on link-based spectrum resources and spectrum slots, obtain the path resource matrix according to the state of each time-spectrum unit, and obtain the time slice and the number of spectrum slots required to process the service request according to the path resource matrix; use the reinforcement learning algorithm to confirm the action of the service request being allocated, and obtain according to the action Reward; confirm whether the allocation plan is valid according to the reward, and if valid, record the allocation plan; the allocation plan includes the start time of the service request scheduling, the shortest candidate path, the time slice required to process the service request and the number of spectrum slots; traverse k in turn The shortest candidate paths are selected, and the allocation scheme that yields the greatest reward is selected. The present invention has good robustness and can maximize the utilization rate of spectrum resources.

Figure 202011271498

Description

Cloud-fog elastic optical network-oriented spectrum resource allocation method for advance reservation request
Technical Field
The invention relates to the technical field of elastic optical networks and cloud-fog communication, in particular to a spectrum resource allocation method for a cloud-fog elastic optical network advance reservation request.
Background
With the rapid development of 5G communication, internet of things (IoT) and virtual reality technologies, traditional cloud computing cannot meet its needs with high latency and huge energy consumption. Edge computing is a good complement to cloud computing, being closer to the device and with lower latency, and the cooperation of cloud computing and edge computing can fuse their advantages and provide higher quality of service. Meanwhile, as the bandwidth requirements of service requests are more and more diversified, new requirements are provided for the network to have the capability of flexibly providing frequency spectrums.
Elastic Optical Networks (EONs) are the underlying networks that are expected to carry flexible requests between cloud computing and edge computing. Based on the OFDM technology, the substrate spectrum resources are cut into independent spectrum time slots, each spectrum time slot usually occupies 6.25GHz or 12.5GHz, and a plurality of spectrum time slots can be efficiently and flexibly provided for arriving requests. Therefore, the application of Elastic Optical Networks (EONs) allows cloud-edge computing and 5G technologies to better improve quality of life.
There are often many service requests for mass data migration or mass data backup between cloud-edge data centers, and these mass data migration or backup service requests do not need to be responded to immediately, and they always have a certain deadline. These service requests are completed before the expiration date, e.g., 8 am the next day. Therefore, these requests are also referred to as Advance Reservation (AR) requests. Due to the introduction of the time domain, these requests can be delayed appropriately to relieve network resource pressure and avoid network congestion. For allocating an advance reservation request, not only the spectrum domain resources but also the time domain should be considered. The request may be successfully allocated if both time resources and spectrum resources meet the requirements.
Routing and Spectrum Allocation (RSA) issues have been a hot issue in EON. Although many researches have researched the problem of large-capacity data transmission in some aspects and most of the researches propose the traditional heuristic RSA algorithm, in static RSA and dynamic RSA, the traditional heuristic RSA algorithm cannot be continuously optimized and is limited by scalability, and the technical problems that service requests cannot be reasonably distributed and processed and the blocking rate is high exist.
Disclosure of Invention
The invention provides a spectrum resource allocation method facing a cloud-fog elastic optical network advance reservation request, which solves the problem of spectrum resource allocation of cross-data center transmission services such as data backup, application data synchronization and virtual machine migration in the existing Internet of things.
S1, for a service request
Figure BDA0002777827990000021
K shortest candidate paths of the service request r are calculated by using a shortest path algorithm, wherein,
Figure BDA0002777827990000022
representing the number of services carried by the service request r, s representing the source node, d representing the destinationNode of, taAnd tdRespectively representing the arrival time and the deadline of the service request r;
s2, dividing time slices and frequency spectrum slots based on frequency spectrum resources of each link, obtaining a path resource matrix corresponding to the shortest candidate path in the step S1 according to the state of each time frequency spectrum unit, and obtaining the number n of the time slices needed for processing the service request r according to the path resource matrixtAnd the number n of spectral slotsf；
S3, the number n of time slices obtained from the step S2tAnd the number n of spectral slotsfConfirming the action A allocated to the service request R in the path resource matrix obtained in the step S2 by using a reinforcement learning algorithm, and acquiring a corresponding reward R according to the action A;
s4, according to the reward R obtained in the step S3, whether the distribution scheme under the shortest candidate route is effective is confirmed, if yes, the distribution scheme under the shortest candidate route and the corresponding reward R are recorded, and then the step S5 is executed, and if not, the step S5 is directly executed;
the allocation scheme includes a start time t of scheduling of a service request rsThe shortest candidate path, the number of time slices n required for processing the service request rtAnd the number n of spectral slotsf；
S5, according to the method of steps S2-S4, traversing k shortest candidate paths in turn, and selecting the distribution scheme generating the maximum reward R as the distribution scheme of the service request R.
The invention has the beneficial effects that: for an incoming advance reservation request, the invention firstly finds k shortest candidate paths by using a shortest path method, traverses each candidate path and calculates available spectrum resources corresponding to each candidate path; different service time and the number of frequency spectrum slots can be allocated to each service request, then the optimal allocation scheme is selected by utilizing the deep neural network, meanwhile, a reward is obtained for each allocation scheme, and the optimal allocation scheme is decided according to the reward; the method has good robustness, can select a proper routing path for all the services of the advance reservation requests and allocate the optimal service time and spectrum resources for each advance reservation request, thereby maximizing the utilization rate of the spectrum resources and reducing the blocking rate and the initial time delay of the service requests.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart of the present invention.
Fig. 2 is a schematic diagram of the synthesis of the environmental state S.
Fig. 3 is a flow chart of the DQN algorithm.
Fig. 4 is a schematic diagram of a cluster.
FIG. 5 shows the time-frequency spectrum continuity TFcA statistical representation of the parameters in (1).
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 5 in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
A spectrum resource allocation method for a pre-reservation request in a cloud-cloud elastic optical network, as shown in fig. 1, includes the following steps:
s1, for a service request
Figure BDA0002777827990000031
K shortest candidate paths of the service request r are calculated by using a shortest path algorithm, wherein,
Figure BDA0002777827990000032
table s representing the number of services carried by a service request rIndicating a source node, d indicating a destination node, taAnd tdRespectively representing the arrival time and the deadline of the service request r;
the service request
Figure BDA0002777827990000033
For reserving the request service in advance, each shortest candidate path is composed of one or more links.
S2, dividing time slices and frequency spectrum slots based on frequency spectrum resources of each link, respectively obtaining a path resource matrix corresponding to the shortest candidate path in the step S1 according to the state of each time frequency spectrum unit, and obtaining the number n of the time slices needed for processing the service request r according to the path resource matrixtAnd the number n of spectral slotsfThe method comprises the following steps:
s21, dividing the link on the shortest candidate path into time slices and frequency spectrum slots, establishing time frequency spectrum units based on the time slices and the frequency spectrum slots, and respectively confirming the state of each time frequency spectrum unit on the link;
the expression of the state of the time spectrum unit is as follows:
Figure BDA0002777827990000034
wherein S is(t,f)Representing a time-spectral unit u(t,f)State of (1), time spectrum unit u(t,f)Is composed of the t-th time slice and the f-th frequency spectrum slot.
S22, confirming the link resource matrix of the link according to the state of each time spectrum unit on the link obtained in the step S21;
the expression of the link resource matrix is:
Figure BDA0002777827990000041
in the formula of UlA link resource matrix representing the link/,
Figure BDA0002777827990000042
representing a time-spectrum unit u on a link l(T,F)T represents the number of time slices on link l; f denotes the number of spectrum slots on link i.
S23, confirming the link resource matrix of each link on the shortest candidate path according to the methods of S21 and S22, and confirming the path resource matrix of the shortest candidate path according to the link resource matrix;
the expression of the path resource matrix is as follows:
Figure BDA0002777827990000043
in the formula of UPA path resource matrix representing the shortest candidate path P, L representing all link sets comprised by the shortest candidate path P,
Figure BDA0002777827990000044
representing the time-spectrum unit u on all links in the shortest candidate path P(T,F)The state of (1).
The path resource matrix represents the state of each time spectrum unit in the shortest candidate path, and the available spectrum resources in the shortest candidate path can be quickly identified according to the path resource matrix.
S24, calculating the service duration time Deltat required by the service request r according to the path resource matrix, and calculating the number n of the time slices required by the service request r according to the service duration time DeltattAnd the number n of spectral slotsf；
The number of time slices ntThe calculation formula of (2) is as follows:
Figure BDA0002777827990000045
wherein τ represents the size of a time slice, and Δ t represents the service duration of the service request r;
the service duration Δ t is obtained by processing the service request r by respectively trying different start times and using available spectrum resources in the path resource matrix according to the following constraint conditions:
max△t＝td-ta；
ta≤ts≤td；
τ≤△t≤td-ts；
in the formula, tsRepresents the starting time of the scheduling of the service request r;
the calculation formula of the service duration time Δ t is as follows:
△t＝te-ts；
in the formula, teRepresenting the end time of the service request r scheduling;
the number n of spectrum slotsfThe calculation formula of (2) is as follows:
Figure BDA0002777827990000051
in the formula, FslotRepresenting the capacity of a spectrum bin, GB representing the guard bandwidth [. ]]Indicating that the whole is taken.
In this embodiment, the capacity F of one spectrum slotslotAt 12.5GHZ, the size of a time slice τ is one hour.
S3, the number n of time slices obtained from the step S2tAnd the number n of spectral slotsfConfirming the action A allocated to the service request R in the path resource matrix obtained in the step S2 by using a reinforcement learning algorithm, and acquiring a corresponding reward R according to the action A;
in this embodiment, the reinforcement learning algorithm is a DQN algorithm, and step S4 includes the following steps:
s31, as shown in FIG. 2, establishing a resource environment according to the path resource matrix established in step S2, and the number n of time slices required by the service request rtAnd the number n of spectral slotsfAnd establishing a request environment corresponding to the resource environment, and synthesizing the resource environment and the request environment to obtain an environment state S.
And S32, inputting the environment state S obtained in the step S31 into the evaluate network of the DQN algorithm to obtain an action A, wherein the action A represents the position of the service request r to be distributed in the path resource matrix.
And S33, judging and calculating the reward R corresponding to the position according to the reward mechanism.
The reward mechanism of the reward R is as follows:
Figure BDA0002777827990000052
in the formula, SRU represents a spectrum resource utilization value, and TSAE represents a time spectrum allocation efficiency; the smaller the spectrum resource utilization value SRU, the better, indicating that more resources may be left for subsequent requests, and therefore, the smaller the SRU,
Figure BDA0002777827990000053
the larger, i.e. the more awards R; the larger the time-spectrum allocation efficiency TSAE, the better, indicating less spectrum fragmentation in the path resource matrix, i.e., more available resources.
The calculation formula of the frequency spectrum resource utilization value SRU is as follows:
SRU＝(te-ts)×nt×h(r)；
where h (r) represents the number of route hops from source node s to destination node d;
the calculation formula of the time spectrum allocation efficiency TSAE is as follows:
TSAE＝Cs×Ri×TFc；
in the formula, CsDenotes the size of the cluster, RiIndicating resource idleness, TFcRepresents temporal spectral continuity;
the calculation of the time spectrum allocation efficiency TSAE comprehensively considers two factors of a cluster and a resource idleness degree on the basis of the time spectrum continuity, so that the spectrum fragmentation can be reduced, and the spectrum resources are utilized to the maximum extent.
The cluster is divided into a position assigned by the service request r and a surrounding areaThe time and frequency spectrum units are connected to form a cluster with the size CsI.e. the number of time-spectrum units in the cluster; resource idleness degree RiRepresenting the fraction of time spectrum units in the path resource matrix that are free. As shown in fig. 4, if the allocated location of the service request is available block 1, cluster 1 is formed, and the size C of cluster 1s64; if the service request is allocated the available block 2, cluster 2 is formed, and the size C of cluster 2s17; since the number of time spectrum units in available block 1 and available block 2 is the same, the resource idleness R in both casesiSame as Ri＝0.32。
The time-frequency spectrum continuity TFcThe calculation formula of (2) is as follows:
Figure BDA0002777827990000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002777827990000062
and
Figure BDA0002777827990000063
representing the number of available spectral blocks, num, along the time axis and the spectral axis, respectively2uIndicating the number of two consecutive spectral units (along the time axis and the spectral axis, respectively).
Time-frequency spectrum continuity TFcRepresents the situation of spectral fragmentation in the path resource matrix, as shown in fig. 5, the corresponding TF in fig. 5c＝1.08。
S4, according to the reward R obtained in the step S3, whether the distribution scheme under the shortest candidate route is effective is confirmed, if yes, the distribution scheme under the shortest candidate route and the corresponding reward R are recorded, and then the step S5 is executed, and if not, the step S5 is directly executed; the allocation scheme includes a start time t of scheduling of a service request rsThe shortest candidate path, the number of time slices n required for processing the service request rtAnd the number n of spectral slotsf。
Whether the position allocated in step S3 is occupied can be determined according to the sign of the reward R, and if the reward R is positive, the allocation scheme is valid, and if the reward R is negative, the allocation scheme is invalid.
Preferably, after recording the distribution scheme under the shortest candidate path, the environment state S is synchronously updated according to the action a to obtain a new environment state S_And the experience (S, A, R, S)_) And storing the updated network parameter into an experience pool of the evaluate network, judging whether the set time for updating the network parameter is reached, if so, updating the network parameter, and if not, directly executing the step S5.
As shown in fig. 3, the DQN algorithm includes two networks, namely an evaluate network and a target network, respectively, and the evaluate network is used to calculate an estimated Q value, denoted as Qevaluate(ii) a the target network is used for calculating an actual Q value, which is marked as Qtarget. As shown in fig. 3, according to the set time for updating the network parameters, the evaluate network and the target network extract part of experience (S, a, R, S) from the experience pool at intervals_) The evaluate network obtains Q according to the environment state Sevaluate(S, A), the target network according to the new environment state S_To obtain Qtarget(S_,A_) Then calculating a loss function from the two Q values, wherein A_Indicating a new environmental state according to S-The estimated new action.
The loss function is Qevaluate(S, A) and Qtarget(S_,A_) The specific formula of the mean square error L is as follows:
L＝E((Qtarget(S_,A_)-Qevaluate(S,A))2)；
the evaluate network updates the network parameters by adopting a gradient descent method, and the target network copies the updated parameters of the evaluate network, which is the prior art and is not described in detail in this embodiment.
And S5, traversing the k shortest candidate paths in sequence according to the method of the steps S2-S4, and then using the allocation scheme of the maximum reward R generated by the elastic optical network as the spectrum resource allocation scheme of the service request R.
The invention firstly establishes a two-dimensional resource model of frequency domain and time domain facing the service of the advance reservation request, carries out interaction with the environment through reinforcement learning, scores the frequency spectrum resource allocation scheme to optimize the allocation of frequency spectrum resources, and then updates the state of the corresponding time frequency spectrum unit according to the determined allocation scheme to prepare for the arrival of the next service request.
Since Deep Reinforcement Learning (DRL) shows the potential for successful Learning strategies for combinatorial and distributed problems, the present invention relies on obtaining feedback and rewards from the environment, and the DQN algorithm can learn the optimization strategy step by step, and is therefore well suited for decision-making problems. In the research of the static Spectrum Allocation strategy of the advance reservation request service, the optimal solution of the computing Resource of the Integer Linear Programming (ILP), the DRDA method and three traditional heuristic algorithms are compared, the performance of the DRDA in the static RSA problem is tested, and the simulation result shows that the performance of the DRDA method is very close to the optimal solution of the Resource computed by the ILP. In the research of dynamic Spectrum Allocation strategy, the invention provides a Time Spectrum Allocation Efficiency (TSAE) measurement standard for measuring the available resource state in an elastic optical network, a DQN algorithm Allocation scheme is adopted for scoring, simulation test and large-scale network experiment are adopted for comparing DRDA with three traditional heuristic algorithms from three aspects of average TSAE, request blocking rate and average initial delay, and the result shows that the DRDA method has good robustness, and compared with other three heuristic algorithms, the DRDA method keeps lower initial delay while obtaining the lowest blocking rate.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (10)

1.一种面向云-雾弹性光网络提前预留请求的频谱资源分配方法，其特征在于，包括如下步骤：1. A spectrum resource allocation method for cloud-fog elastic optical network reservation request in advance, is characterized in that, comprises the steps: S1，对于一个业务请求
Figure FDA0002777827980000011
利用最短路径算法计算出业务请求r的k条最短候选路径，其中，
Figure FDA0002777827980000012
表示业务请求r所携带的业务数量，s表示源节点，d表示目的节点，ta和td分别表示业务请求r的到达时间和截止时间；S1, for a service request
Figure FDA0002777827980000011
Use the shortest path algorithm to calculate the k shortest candidate paths of the service request r, where,
Figure FDA0002777827980000012
Represents the number of services carried by the service request r, s represents the source node, d represents the destination node, t a and t d represent the arrival time and deadline of the service request r, respectively; S2，基于每条链路的频谱资源划分时间片和频谱槽，根据每个时间频谱单元的状态获取步骤S1中所述最短候选路径所对应的路径资源矩阵，根据路径资源矩阵获取处理业务请求r所需的时间片数量nt和频谱槽数量nf；S2: Divide time slices and spectrum slots based on the spectrum resources of each link, obtain the path resource matrix corresponding to the shortest candidate path in step S1 according to the state of each time spectrum unit, and obtain the processing service request r according to the path resource matrix the required number of time slices n t and the number of spectral bins n f ; S3，根据步骤S2所得到的时间片数量nt和频谱槽数量nf利用强化学习算法确认业务请求r在步骤S2所得到的路径资源矩阵中被分配的动作A，根据动作A获取对应的奖励R；S3, according to the number of time slices n t and the number of spectrum slots n f obtained in step S2, use the reinforcement learning algorithm to confirm the action A that the service request r is allocated in the path resource matrix obtained in step S2, and obtain the corresponding reward according to the action A R; S4，根据步骤S3所述得到的奖励R确认该最短候选路径下的分配方案是否有效，若有效，记录该最短候选路径下的分配方案和对应的奖励R后执行步骤S5，若无效，直接执行步骤S5；S4, confirm whether the allocation scheme under the shortest candidate path is valid according to the reward R obtained in step S3, if valid, record the allocation scheme under the shortest candidate path and the corresponding reward R and then execute step S5, if invalid, execute directly Step S5; 所述分配方案包括业务请求r调度的开始时间ts、该最短候选路径、处理业务请求r所需的时间片数量nt和频谱槽数量nf；The allocation scheme includes the scheduled start time ts of the service request r, the shortest candidate path, the number of time slices nt required to process the service request r, and the number of frequency spectrum slots nf ; S5，按照步骤S2-S4的方法，依次遍历k条最短候选路径，选择产生最大奖励R的分配方案作为业务请求r的分配方案。S5, according to the method of steps S2-S4, traverse the k shortest candidate paths in sequence, and select the allocation scheme that generates the maximum reward R as the allocation scheme of the service request r. 2.根据权利要求1所述的面向云-雾弹性光网络提前预留请求的频谱资源分配方法，其特征在于，所述步骤S2包括如下步骤：2. The spectrum resource allocation method for cloud-fog elastic optical network advance reservation request according to claim 1, wherein the step S2 comprises the following steps: S21，对最短候选路径上的链路进行时间片和频谱槽划分，基于时间片和频谱槽建立时间频谱单元，分别确认链路上每个时间频谱单元的状态；S21, the link on the shortest candidate path is divided into time slices and spectrum slots, and time-spectrum units are established based on the time slices and spectrum slots, and the status of each time-spectrum unit on the link is confirmed respectively; S22，根据步骤S21所得到的链路上每个时间频谱单元的状态确认该链路的链路资源矩阵；S22, confirming the link resource matrix of the link according to the state of each time spectrum unit on the link obtained in step S21; S23，按照步骤S21和步骤S22的方法分别确认最短候选路径上每条链路的链路资源矩阵，根据链路资源矩阵确认该最短候选路径的路径资源矩阵；S23, confirm the link resource matrix of each link on the shortest candidate path according to the method of step S21 and step S22 respectively, confirm the path resource matrix of this shortest candidate path according to the link resource matrix; S24，根据路径资源矩阵计算业务请求r所需要的服务持续时间△t，并根据服务持续时间△t计算该业务请求r所需的时间片数量nt和频谱槽数量nf。S24: Calculate the service duration Δt required by the service request r according to the path resource matrix, and calculate the number of time slices nt and the number of spectrum slots nf required by the service request r according to the service duration Δt. 3.根据权利要求2所述的面向云-雾弹性光网络提前预留请求的频谱资源分配方法，其特征在于，在步骤S24中，所述时间片数量nt的计算公式为：3. The spectrum resource allocation method for cloud-fog elastic optical network advance reservation request according to claim 2, is characterized in that, in step S24, the calculation formula of described time slice quantity n t is:
Figure FDA0002777827980000021
Figure FDA0002777827980000021
 式中，τ表示一个时间片的大小，△t表示业务请求r的服务持续时间；In the formula, τ represents the size of a time slice, and Δt represents the service duration of the service request r; 所述频谱槽数量nf的计算公式为：The calculation formula of the number of spectrum slots n f is:
Figure FDA0002777827980000022
Figure FDA0002777827980000022
 式中，Fslot表示一个频谱槽的容量，GB表示保护带宽，[*]表示取上整。In the formula, F slot represents the capacity of a spectrum slot, GB represents the guard bandwidth, and [*] represents the rounding up. 4.根据权利要求2或3所述的面向云-雾弹性光网络提前预留请求的频谱资源分配方法，其特征在于，所述服务持续时间△t根据如下约束条件，并通过分别尝试不同的开始时间对业务请求r进行处理的方法获取，所述约束条件为：4. The spectrum resource allocation method for cloud-fog elastic optical network advance reservation request according to claim 2 or 3, characterized in that, the service duration Δt is based on the following constraints, and by trying different The method for processing the service request r at the start time is obtained, and the constraint conditions are: max△t＝td-ta；maxΔt=t d −t a ; ta≤ts≤td；t a ≤t s ≤t d ; τ≤△t≤td-ts；τ≤Δt≤t d -t s ; 式中，ts表示业务请求r调度的开始时间，τ表示一个时间片的大小；In the formula, ts represents the start time of the service request r scheduling, and τ represents the size of a time slice; 所述服务持续时间△t的计算公式为：The calculation formula of the service duration Δt is: △t＝te-ts；Δt=t e −t s ; 式中，te表示业务请求r调度的结束时间。In the formula, t e represents the end time of the service request r scheduling. 5.根据权利要求1所述的面向云-雾弹性光网络提前预留请求的频谱资源分配方法，其特征在于，所述步骤S3包括如下步骤：5. The spectrum resource allocation method for cloud-fog elastic optical network advance reservation request according to claim 1, wherein the step S3 comprises the following steps: S31，根据步骤S2所建立的路径资源矩阵建立资源环境，根据业务请求r所需的时间片数量nt和频谱槽数量nf建立与资源环境相对应的请求环境，将资源环境和请求环境进行合成得到环境状态S；S31, establish a resource environment according to the path resource matrix established in step S2, establish a request environment corresponding to the resource environment according to the number of time slices n t and the number of frequency spectrum slots n f required by the service request r, and perform the resource environment and the request environment. Synthesized to obtain the environmental state S; S32，将步骤S31所得到的环境状态S输入强化学习算法的evaluate网络得到动作A，所述动作A表示业务请求r在所述路径资源矩阵中将被分配的位置；S32, the environmental state S obtained in step S31 is input into the evaluate network of the reinforcement learning algorithm to obtain action A, and the action A represents the position where the service request r will be allocated in the path resource matrix; S33，根据奖励机制判断并计算该位置所对应的奖励R。S33, judge and calculate the reward R corresponding to the position according to the reward mechanism. 6.根据权利要求5所述的面向云-雾弹性光网络提前预留请求的频谱资源分配方法，其特征在于，在步骤S33中，所述奖励机制为：6. The spectrum resource allocation method for cloud-fog elastic optical network advance reservation request according to claim 5, characterized in that, in step S33, the reward mechanism is:
Figure FDA0002777827980000023
Figure FDA0002777827980000023
 式中，SRU表示频谱资源利用值，TSAE表示时间频谱分配效率。In the formula, SRU represents the spectrum resource utilization value, and TSAE represents the time spectrum allocation efficiency. 7.根据权利要求6所述的面向云-雾弹性光网络提前预留请求的频谱资源分配方法，其特征在于，所述频谱资源利用值SRU的计算公式为：7. The spectrum resource allocation method for cloud-fog elastic optical network advance reservation request according to claim 6, wherein the calculation formula of the spectrum resource utilization value SRU is: SRU＝(te-ts)×nt×h(r)；SRU=(t e -t s )×n t ×h(r); 式中，h(r)表示从源节点s到目的节点d的路由跳数，ts和te分别表示业务请求r调度的开始时间和结束时间。In the formula, h(r) represents the number of routing hops from the source node s to the destination node d, and ts and te represent the start time and end time of the service request r scheduling, respectively. 8.根据权利要求6所述的面向云-雾弹性光网络提前预留请求的频谱资源分配方法，其特征在于，所述时间频谱分配效率TSAE的计算公式为：8. The spectrum resource allocation method for cloud-fog elastic optical network advance reservation request according to claim 6, wherein the calculation formula of the time spectrum allocation efficiency TSAE is: TSAE＝Cs×Ri×TFc；TSAE=C s ×R i ×TF c ; 式中，Cs表示簇的大小，Ri表示资源闲置度，TFc表示时间频谱连续度。In the formula, C s represents the size of the cluster, R i represents the resource idleness, and TF c represents the time-spectrum continuity. 9.根据权利要求8所述的面向云-雾弹性光网络提前预留请求的频谱资源分配方法，其特征在于，所述时间频谱连续度TFc的计算公式为：9. The spectrum resource allocation method for cloud-fog elastic optical network advance reservation request according to claim 8, wherein the calculation formula of the time spectrum continuity TF c is:
Figure FDA0002777827980000031
Figure FDA0002777827980000031
 式中，
Figure FDA0002777827980000032
和
Figure FDA0002777827980000033
分别表示沿着时间轴和频谱轴的可用频谱块数量，num2u表示连续的两个频谱单元的个数。In the formula,
Figure FDA0002777827980000032
and
Figure FDA0002777827980000033
represents the number of available spectrum blocks along the time axis and spectrum axis, respectively, and num 2u represents the number of two consecutive spectrum units. 10.根据权利要求1所述的面向云-雾弹性光网络提前预留请求的频谱资源分配方法，其特征在于，在步骤S4中，在记录下该最短候选路径下的分配方案和对应的奖励R后，根据动作A同步更新环境状态S，得到新环境状态S_，并将经验(S,A,R,S_)存储进经验池中，然后判断是否到达所设定的更新网络参数时间，如果是，利用梯度下降法更新网络参数，否则，直接执行步骤S5。10. The spectrum resource allocation method for cloud-fog elastic optical network advance reservation request according to claim 1, wherein in step S4, the allocation scheme and corresponding reward under the shortest candidate path are recorded After R, update the environment state S synchronously according to the action A, obtain the new environment state S _ , store the experience (S, A, R, S _ ) in the experience pool, and then judge whether the set time for updating network parameters is reached , if yes, use the gradient descent method to update the network parameters, otherwise, go to step S5 directly.
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