CN116033564A - Multi-granularity FlexE slice scheduling method and medium in 5G energy Internet - Google Patents

Multi-granularity FlexE slice scheduling method and medium in 5G energy Internet Download PDF

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Publication number
CN116033564A
CN116033564A CN202211623989.2A CN202211623989A CN116033564A CN 116033564 A CN116033564 A CN 116033564A CN 202211623989 A CN202211623989 A CN 202211623989A CN 116033564 A CN116033564 A CN 116033564A
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service
slice
physical layer
calculating
traffic
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Inventor
马涛
周飞飞
张�浩
何迎利
蔡鹏�
管荑
李菁竹
葛红舞
卢岸
程程
周熠
罗衡森
李宇航
李芹
冯宝
南天
陆涛
丁雍
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Nari Information and Communication Technology Co
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
State Grid Electric Power Research Institute
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Nari Information and Communication Technology Co
Economic and Technological Research Institute of State Grid Shandong Electric Power Co Ltd
State Grid Electric Power Research Institute
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Abstract

The invention discloses a multi-granularity FlexE slice scheduling method and medium in a 5G energy Internet, which are used for calculating slice allocation priority weight values of services of flexible Ethernet clients in the energy Internet; calculating a service priority weight of a physical layer data carrying channel in a carrying network; calculating network resource requirements of slices corresponding to each service; carrying out the bearing control of the service slice according to the network resource requirement and the service priority weight of the physical layer data bearing channel; according to the slice allocation priority weight of the service, calculating the slice scheduling cost of the overall loadable service; establishing an optimization problem with the constraint of meeting the QoS requirement of the service and the goal of minimizing the slice scheduling cost; solving the optimization problem through a re-weighted message passing algorithm to obtain an optimal slice scheduling result. The invention further improves the execution efficiency and effect of FlexE slice scheduling, and improves the fairness and economy of scheduling technology.

Description

Multi-granularity FlexE slice scheduling method and medium in 5G energy Internet
Technical Field
The invention relates to a multi-granularity FlexE slice scheduling method and medium in a 5G energy Internet, and belongs to the technical field of FlexE network communication.
Background
With the extensive research and popularization of the fifth generation (5G) mobile communication system, the requirements of data rate, transmission delay and device connection of the 5G service in various scenes are greatly increased. Meanwhile, along with the development of new energy, the construction of the energy Internet is in urgent need of further improvement and promotion through a 5G technology. Due to the energy internet based on 5G, coexistence of various enhanced mobile broadband (eMBB), low-latency high-reliability (URLLC) and large-scale machine type connection (mctc) type services is necessarily faced, and different types of 5G services have different QoS indexes and network resource requirements. How to realize high-efficiency and flexible network resource management on different 5G services in the energy Internet based on the existing network resources and network architecture, and greatly influence the service effect of each service of the energy Internet, the overall resource utilization rate of the network, the scheduling flexibility, the bearing fairness and other performance indexes.
The FlexE is used as a new bearer network technology, and can provide a tunneled hardware isolation function on a PHY interface, so that transmission and management isolation of different service data based on different slices is ensured at a PHY layer, and the FlexE is matched with an upper layer application, and the capability of meeting multi-granularity QoS (quality of service) is realized when a plurality of services are carried. However, the conventional FlexE slice scheduling scheme is based on three simple management functions, which are not further used in combination, which limits the freedom and flexibility of overall slicing of network resources. In addition, the traditional FlexE resource selection method is based on simple successive comparison, has high computational complexity, is difficult to obtain an optimal scheduling scheme, and does not consider the overall priority of the service and the overall utilization rate of network resources.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a multi-granularity FlexE slice scheduling method and medium in the 5G energy Internet, and is used for flexibly supporting services of eMBB, URLLC, mMTC related services in the 5G energy Internet, such as video monitoring, abnormal electricity utilization detection, conventional electric power inspection, electric equipment internet of things interaction and the like, further improving the execution efficiency and effect of FlexE slice scheduling, and improving the fairness and economy of scheduling technology.
In order to achieve the above purpose, the present invention provides a multi-granularity FlexE slice scheduling method in 5G energy internet, comprising:
step (1), calculating slice allocation priority weight of the business of each flexible Ethernet client in the energy Internet based on the importance degree of the business and the transmission delay service quality requirement;
step (2), setting a service priority weight of a physical layer data carrying channel in a carrying network based on the number Nmax of idle time slots in the physical layer data carrying channel;
step (3), calculating the network resource requirement of each corresponding service slice based on the information transmission rate and the minimum network resource requirement required by each corresponding service slice;
step (4), carrying out the bearing control of the service slice according to the network resource requirement and the service priority weight of the physical layer data bearing channel;
step (5), according to the priority weight of the slice distribution of the service, calculating the slice scheduling cost of the overall loadable service;
step (6), establishing an optimization problem with the constraint of meeting QoS requirements of the service and the goal of minimizing slice scheduling cost; solving the optimization problem through a re-weighted message passing algorithm to obtain an optimal slice scheduling result.
Preferentially, step (1), calculating the distribution priority weight of the business of each flexible Ethernet client in the energy Internet based on the importance degree of the business and the transmission delay service quality requirement, and realizing the steps of:
step (11), sorting the business according to the importance degree of the business to obtain the flexible Ethernet slice service priority Si of the business i;
step (12), calculating a slice allocation priority weight alpha i of the service based on the transmission delay service quality requirement Ti and the FlexE slice service priority Si of the service:
αi＝Ti*Si。
preferentially, in the step (2), based on the number Nmax of idle time slots in the physical layer data carrying channel, the service priority weight of the physical layer data carrying channel in the carrying network is calculated, and the method is implemented by the following steps:
counting the number Nmax of idle time slots in a physical layer data carrying channel which is available in real time in a carrying network;
setting a service priority value beta j of a physical layer data bearing channel j in a bearing network, wherein the number Nmax of idle time slots in the physical layer data bearing channel is in direct proportion to the service priority value beta j of the physical layer data bearing channel j in the bearing network.
Preferably, step (3) calculates the network resource requirement of each service corresponding slice based on the information transmission rate and the minimum network resource requirement required by each service corresponding slice, by:
step (31), calculating an information transmission rate Ri of a service corresponding to the data to be carried:
information transmission rate ri=total data quantity D of traffic i/transmission delay quality of service requirement Ti;
step (32), calculating the minimum network resource requirement Ni required by the corresponding slice of each service i:
minimum network resource requirement ni=information transmission rate Ri +.i. bandwidth rate granularity for a single slot for each traffic i corresponds to the slice required.
Preferably, the data to be carried includes a minimum transmission rate requirement for the data, a minimum network bandwidth requirement, a tolerable maximum transmission delay and a minimum signal to interference plus noise ratio requirement.
Preferentially, in the step (4), according to the network resource requirement and the service priority weight of the physical layer data bearer channel, the bearer control of the service slice is executed, and the method is realized by the following steps:
if the minimum network resource requirement and ΣNi > Nmax required by the corresponding slice of all the services i, judging that the services corresponding to all the flexible Ethernet clients cannot be all served, and sorting the services according to the slice allocation priority weight value αi from small to large, and preferentially rejecting the corresponding services until the minimum network resource requirement and ΣNi < Nmax required by the corresponding slice of all the services i.
Preferentially, step (5), according to the slice allocation priority weight of the service, calculating the slice scheduling cost of the total loadable service, which is realized by the following steps:
according to the slice allocation priority weight alpha i of each service, the service priority weight beta j of the physical layer data carrying channel and the minimum network resource requirement Ni required by the corresponding slice of each service i, calculating the slice scheduling cost Cik:
Cik＝(αi+Ni)*βj。
preferentially, step (6) takes the service quality requirement of the service as constraint, takes the minimum slice scheduling cost as target, establishes an optimization problem, solves the optimization problem through a re-weighted message passing algorithm, and obtains an optimal slice scheduling result, and the method is realized by the following steps:
A. if the traffic-time slot allocation index xik =0 in the slice to be scheduled, determining that the idle time slot k is not allocated to the traffic i, and if the traffic-time slot allocation index xik =1 in the slice to be scheduled, determining that the idle time slot k is allocated to the traffic i;
B. the service quality demand time slot allocation quantity of the service is not less than the time slot quantity Ni and is used as constraint one;
C. assigning any free time slot k in each physical layer data carrying channel to one service slice at most as constraint two;
D. establishing a combination optimization problem based on 0-1 matching according to a basic theory of combination optimization by taking the minimum slice scheduling cost as an optimization target, and taking constraint one and constraint two as constraint conditions of the combination optimization problem;
E. based on the re-weighted message passing algorithm, the optimal slice scheduling result { xik }.
Preferably, based on a re-weighted message passing algorithm, an optimal slice scheduling result { xik }, is obtained by:
E1. combined optimization calculation message on correlation between traffic i and slot k when initializing t=0 times
Figure SMS_1
And a reverse combined optimization calculation message regarding the correlation between traffic i and time slot k>
Figure SMS_2
Figure SMS_3
Setting the value of a constant rho;
E2. optimizing calculation messages according to t-th time reverse combination regarding correlation between traffic f and time slot i
Figure SMS_4
And t-th time reverse combined optimization calculation message regarding correlation between traffic k and slot i>
Figure SMS_5
Combined optimization calculation message for calculating the correlation between traffic k and time slot i at time t+1st>
Figure SMS_6
Figure SMS_7
Wherein C is i，k C, cost of free time slot k in any allocable physical layer data carrying channel for service i i，f The cost of free slot k in any allocable physical layer data bearer channel for traffic f,
Figure SMS_8
the minimum number of time slots required for service i; E3. message according to t+1st time->
Figure SMS_9
And->
Figure SMS_10
Calculate t+th1-time message about the correlation between service i and time slot k>
Figure SMS_11
Figure SMS_12
In the method, in the process of the invention,
Figure SMS_13
the calculation message is optimized for the t +1 th time with respect to the combination of correlation between traffic i and slot k,
Figure SMS_14
optimizing calculation messages for t+1st time reverse combination regarding correlation between traffic l and time slot k,/>
Figure SMS_15
Optimizing the calculation message for the t+1st time reverse combination with respect to the correlation between traffic i and slot k;
E4. according to
Figure SMS_16
And->
Figure SMS_17
Calculating forward and reverse message sum between t+1st time user i and time slot k
Figure SMS_18
Figure SMS_19
E5. Calculation of t+1st partition factor result
Figure SMS_20
/>
Figure SMS_21
E6. If it is
Figure SMS_22
Obtaining an optimal slice scheduling result { xik }, ending the operation, otherwise increasing the value of t by 1, and entering a step E1;
if { xik } = 1, the service i selects the physical layer data bearer j corresponding to the time slot k.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the methods described above.
The invention has the beneficial effects that:
the invention provides a multi-granularity FlexE slice scheduling method and medium in a 5G energy Internet, which are used for calculating slice allocation priority weights of services of flexible Ethernet clients in the energy Internet; calculating a service priority weight of a physical layer data carrying channel in a carrying network; calculating network resource requirements of slices corresponding to each service; carrying out the bearing control of the service slice according to the network resource requirement and the service priority weight of the physical layer data bearing channel; according to the slice allocation priority weight of the service, calculating the slice scheduling cost of the overall loadable service; establishing an optimization problem with the constraint of meeting the QoS requirement of the service and the goal of minimizing the slice scheduling cost; solving the optimization problem through a re-weighted message passing algorithm to obtain an optimal slice scheduling result. The method is used for flexibly supporting services of eMBB, URLLC, mMTC related video monitoring, abnormal electricity utilization detection, conventional electric power inspection, electric power equipment internet of things interaction and the like in the 5G energy Internet, further improves the execution efficiency and effect of FlexE slice scheduling, and improves fairness and economy of scheduling technology.
The invention provides a FlexE slice efficient scheduling technology for 5G energy Internet multi-granularity requirements, which improves the resource allocation flexibility and effectiveness of a carrier network slice scheduling technology, and improves the economy and fairness of network resource scheduling by optimizing the utilization rate of Slots in slices and the allocation opportunity of Client business;
drawings
Fig. 1 is a schematic block diagram of a second embodiment of the present invention.
Detailed Description
The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Example 1
The invention provides a multi-granularity FlexE slice scheduling method in a 5G energy Internet, which comprises the following steps:
step (1), calculating slice allocation priority weight of the business of each flexible Ethernet client in the energy Internet based on the importance degree of the business and the transmission delay service quality requirement;
step (2), setting a service priority weight of a physical layer data carrying channel in a carrying network based on the number Nmax of idle time slots in the physical layer data carrying channel;
step (3), calculating the network resource requirement of each corresponding service slice based on the information transmission rate and the minimum network resource requirement required by each corresponding service slice;
step (4), carrying out the bearing control of the service slice according to the network resource requirement and the service priority weight of the physical layer data bearing channel;
step (5), according to the priority weight of the slice distribution of the service, calculating the slice scheduling cost of the overall loadable service;
step (6), establishing an optimization problem with the constraint of meeting QoS requirements of the service and the goal of minimizing slice scheduling cost; solving the optimization problem through a re-weighted message passing algorithm to obtain an optimal slice scheduling result.
Further, in step (1) of the present embodiment, based on the importance degree of the service and the transmission delay service quality requirement, the allocation priority weight of the service of each flexible ethernet client in the energy internet is calculated, and the method is implemented by the following steps:
step (11), sorting the business according to the importance degree of the business to obtain the flexible Ethernet slice service priority Si of the business i;
step (12), calculating a slice allocation priority weight alpha i of the service based on the transmission delay service quality requirement Ti and the FlexE slice service priority Si of the service:
αi＝Ti*Si。
further, in step (2) of the present embodiment, based on the number Nmax of idle time slots in the physical layer data bearer, the service priority weight of the physical layer data bearer in the bearer network is calculated, and the method is implemented by the following steps:
counting the number Nmax of idle time slots in a physical layer data carrying channel which is available in real time in a carrying network;
setting a service priority value beta j of a physical layer data bearing channel j in a bearing network, wherein the number Nmax of idle time slots in the physical layer data bearing channel is in direct proportion to the service priority value beta j of the physical layer data bearing channel j in the bearing network.
Further, in step (3) of this embodiment, based on the information transmission rate and the minimum network resource requirement required by each service corresponding slice, the network resource requirement of each service corresponding slice is calculated, and the method is implemented by the following steps: the method is realized by the following steps: step (31), calculating an information transmission rate Ri of a service corresponding to the data to be carried:
information transmission rate ri=total data quantity D of traffic i/transmission delay quality of service requirement Ti;
step (32), calculating the minimum network resource requirement Ni required by the corresponding slice of each service i:
minimum network resource requirement ni=information transmission rate Ri +.i. bandwidth rate granularity for a single slot for each traffic i corresponds to the slice required.
Further, the data to be carried in this embodiment includes a minimum transmission rate requirement, a minimum network bandwidth requirement, a tolerable maximum transmission delay and a minimum signal-to-interference-and-noise ratio requirement.
Further, in step (4) of the present embodiment, according to the network resource requirement and the service priority weight of the physical layer data bearer, the bearer control of the service slice is performed, which is implemented by the following steps:
in order to ensure that the allocated network resources in the flexible ethernet slices can meet the quality of service requirements of the service, it is necessary that the network resource bandwidth of the service in each flexible ethernet client does not exceed the total amount of idle resources that can be provided in the bearer network at the moment. In step (4), it is determined whether all services corresponding to the flexible ethernet clients are served according to QoS requirements according to Ni and the number Nmax of idle slots in the physical layer data bearer channel:
if the minimum network resource requirement and ΣNi > Nmax required by the corresponding slice of all the services i, judging that the services corresponding to all the flexible Ethernet clients cannot be all served, and sorting the services according to the slice allocation priority weight value αi from small to large, and preferentially rejecting the corresponding services until the minimum network resource requirement and ΣNi < Nmax required by the corresponding slice of all the services i.
Further, in step (5) of this embodiment, according to the priority weight assigned to the slices of the service, the slice scheduling cost of the total loadable service is calculated, and the method is implemented by the following steps:
according to the slice allocation priority weight alpha i of each service, the service priority weight beta j and Ni of the physical layer data carrying channel, calculating the slice scheduling cost Cik:
the slice scheduling cost Cik is the sum of the costs of each service i occupying an idle slot k in any allocable physical layer data carrying channel, and if the slot k belongs to the physical layer carrying channel j, cik= (αi+ni) ×βj.
Further, in step (6) in this embodiment, with the requirement of satisfying the service quality of the service as a constraint, with the objective of minimizing the slice scheduling cost, an optimization problem is established, and the optimization problem is solved by a re-weighted message passing algorithm, so as to obtain an optimal slice scheduling result, which is implemented by the following steps:
A. determining an optimization variable as traffic-slot allocation index xik =0 or 1 in the slice to be scheduled:
if the traffic-time slot allocation index xik =0 in the slice to be scheduled, determining that the idle time slot k is not allocated to the traffic i, and if the traffic-time slot allocation index xik =1 in the slice to be scheduled, determining that the idle time slot k is allocated to the traffic i;
B. the service quality demand time slot allocation quantity of the service is not less than the time slot quantity Ni and is used as constraint one;
C. assigning any free time slot k in each physical layer data carrying channel to one service slice at most as constraint two;
D. establishing a combination optimization problem based on 0-1 matching according to a basic theory of combination optimization by taking the minimum slice scheduling cost as an optimization target, and taking constraint one and constraint two as constraint conditions of the combination optimization problem;
E. based on the re-weighted message passing algorithm, the optimal slice scheduling result { xik }.
Preferably, based on a re-weighted message passing algorithm, an optimal slice scheduling result { xik }, is obtained by:
E1. combined optimization calculation message on correlation between traffic i and slot k when initializing t=0 times
Figure SMS_23
And a reverse combined optimization calculation message regarding the correlation between traffic i and time slot k>
Figure SMS_24
Figure SMS_25
Setting the value of a constant rho; the value of ρ is set in advance, usually to 0.8.
E2. Optimizing calculation messages according to t-th time reverse combination regarding correlation between traffic f and time slot i
Figure SMS_26
And t-th time reverse combined optimization calculation message regarding correlation between traffic k and slot i>
Figure SMS_27
Combined optimization calculation message for calculating the correlation between traffic k and time slot i at time t+1st>
Figure SMS_28
Figure SMS_29
Wherein C is i，k C, cost of free time slot k in any allocable physical layer data carrying channel for service i i，f The cost of free slot k in any allocable physical layer data bearer channel for traffic f,
Figure SMS_30
the minimum number of time slots required for service i;
E3. according to the t+1th time message
Figure SMS_31
And->
Figure SMS_32
Calculating the t+1st message about the correlation between traffic i and time slot k +.>
Figure SMS_33
Figure SMS_34
In the method, in the process of the invention,
Figure SMS_35
optimizing calculation messages for t+1st time with respect to the combination of correlations between traffic i and time slot k,/>
Figure SMS_36
Optimizing calculation messages for t+1st time reverse combination regarding correlation between traffic l and time slot k,/>
Figure SMS_37
Optimizing the calculation message for the t+1st time reverse combination with respect to the correlation between traffic i and slot k;
E4. according to
Figure SMS_38
And->
Figure SMS_39
Calculating forward and reverse message sum between t+1st time user i and time slot k
Figure SMS_40
Figure SMS_41
E5. Calculation of t+1st partition factor result
Figure SMS_42
Figure SMS_43
E6. If the distribution factor results obtained by two continuous calculations are equal, namely
Figure SMS_44
Obtaining an optimal slice scheduling result { xik }, ending the operation, otherwise increasing the value of t by 1, and entering a step E1;
if { xik } = 1, the service i selects the physical layer data carrying channel j corresponding to the time slot k, and flexible ethernet slice flexible scheduling for multi-granularity network resource requirement is completed.
A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the methods described above.
The combination optimization problem based on 0-1 matching is established according to the basic theory of combination optimization, and this step is the prior art, and this embodiment will not be described in detail.
Example two
As shown in fig. 1, the energy internet based on the 5G technology includes various application services based on eMBB, URLLC, mMTC, including but not limited to power internet of things/sensor networks, video camera-based power grid monitoring, anomaly detection for electricity usage, regular power equipment inspection, and the like. The method is applicable to different 5G typical application scenarios for different services, and has multi-granularity data transmission requirements and QoS. The invention is based on three functions of channelization, binding and sub-rate of a FlexE shimm module in the FlexE technology, and the reallocation of the fixed standard rate of a PHY layer of a bearing network is realized by combined application. The time slot allocation method based on ReMPA is utilized to achieve flexible scheduling of network resources, so that virtual network slices meeting the multi-granularity data rate requirements of different energy Internet services are formed and are used for independent management and transmission of each service.
The business comprises power IoT, video monitoring, anomaly detection and power inspection;
FlexE slices include FlexE slice A, flexE slice B, flexE slices C, …, flexE slice I;
FlexE Slot frames include Slot1, slot2, …, slot k, slot k+1, …;
the Ethernet physical layer comprises a FlexE PHY a, a FlexE PHY b, … and a FlexE PHY j;
the invention provides a FlexE slice efficient scheduling technology for 5G energy Internet multi-granularity requirements, which comprises the following steps:
step (1), calculating slice allocation priority weight of business of each flexible Ethernet client in the energy Internet;
step (2), calculating the service priority weight of the physical layer data bearing channel in the bearing network;
step (3), calculating network resource requirements of corresponding slices of each service;
step (4), carrying out the bearing control of the service slice according to the network resource requirement and the service priority weight of the physical layer data bearing channel;
step (5), according to the priority weight of the slice distribution of the service, calculating the slice scheduling cost of the overall loadable service;
step (6), establishing an optimization problem with the constraint of meeting QoS requirements of the service and the goal of minimizing slice scheduling cost; solving the optimization problem through a re-weighted message passing algorithm to obtain an optimal slice scheduling result.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present invention, and it should be noted that modifications and variations could be made by those skilled in the art without departing from the technical principles of the present invention, and such modifications and variations should also be regarded as being within the scope of the invention.

Claims (10)

  1. The multi-granularity FlexE slice scheduling method in the 1.5G energy Internet is characterized by comprising the following steps of:
    step (1), calculating slice allocation priority weight of the business of each flexible Ethernet client in the energy Internet based on the importance degree of the business and the transmission delay service quality requirement;
    step (2), setting a service priority weight of a physical layer data carrying channel in a carrying network based on the number Nmax of idle time slots in the physical layer data carrying channel;
    step (3), calculating the network resource requirement of each corresponding service slice based on the information transmission rate and the minimum network resource requirement required by each corresponding service slice;
    step (4), carrying out the bearing control of the service slice according to the network resource requirement and the service priority weight of the physical layer data bearing channel;
    step (5), according to the priority weight of the slice distribution of the service, calculating the slice scheduling cost of the overall loadable service;
    step (6), establishing an optimization problem with the constraint of meeting QoS requirements of the service and the goal of minimizing slice scheduling cost; solving the optimization problem through a re-weighted message passing algorithm to obtain an optimal slice scheduling result.
  2. 2. The method for multi-granularity FlexE slice scheduling in 5G energy internet according to claim 1, wherein,
    step (1), calculating the service distribution priority weight of each flexible Ethernet client in the energy Internet based on the importance degree of the service and the transmission delay service quality requirement, and realizing the steps as follows:
    step (11), sorting the business according to the importance degree of the business to obtain the flexible Ethernet slice service priority Si of the business i;
    step (12), calculating a slice allocation priority weight alpha i of the service based on the transmission delay service quality requirement Ti and the FlexE slice service priority Si of the service:
    αi＝Ti*Si。
  3. 3. the method for multi-granularity FlexE slice scheduling in 5G energy internet according to claim 2, wherein,
    in the step (2), based on the number Nmax of idle time slots in the physical layer data carrying channel, the service priority weight of the physical layer data carrying channel in the carrying network is calculated, and the method is realized by the following steps:
    counting the number Nmax of idle time slots in a physical layer data carrying channel which is available in real time in a carrying network;
    setting a service priority value beta j of a physical layer data bearing channel j in a bearing network, wherein the number Nmax of idle time slots in the physical layer data bearing channel is in direct proportion to the service priority value beta j of the physical layer data bearing channel j in the bearing network.
  4. 4. The method for scheduling multi-granularity FlexE slices in the 5G energy internet according to claim 3 wherein step (3) is implemented by calculating the network resource requirements of each corresponding slice of the service based on the information transmission rate and the minimum network resource requirements required by each corresponding slice of the service by:
    step (31), calculating an information transmission rate Ri of a service corresponding to the data to be carried:
    information transmission rate ri=total data quantity D of traffic i/transmission delay quality of service requirement Ti;
    step (32), calculating the minimum network resource requirement Ni required by the corresponding slice of each service i:
    minimum network resource requirement ni=information transmission rate Ri +.i. bandwidth rate granularity for a single slot for each traffic i corresponds to the slice required.
  5. 5. The method for multi-granularity FlexE slice scheduling in 5G energy internet according to claim 4 wherein,
    the data to be carried includes a minimum transmission rate requirement for the data, a minimum network bandwidth requirement, a tolerable maximum transmission delay and a minimum signal to interference plus noise ratio requirement.
  6. 6. The method for multi-granularity FlexE slice scheduling in 5G energy internet according to claim 4 wherein,
    in the step (4), according to the network resource requirement and the service priority weight of the physical layer data carrying channel, carrying out the carrying control of the service slice, and realizing the following steps:
    if the minimum network resource requirement and ΣNi > Nmax required by the corresponding slice of all the services i, judging that the services corresponding to all the flexible Ethernet clients cannot be all served, and sorting the services according to the slice allocation priority weight value αi from small to large, and preferentially rejecting the corresponding services until the minimum network resource requirement and ΣNi < Nmax required by the corresponding slice of all the services i.
  7. 7. The method for multi-granularity FlexE slice scheduling in 5G energy internet of claim 6 wherein,
    and (5) calculating the slice scheduling cost of the total loadable service according to the slice allocation priority weight of the service, wherein the method is realized by the following steps:
    according to the slice allocation priority weight alpha i of each service, the service priority weight beta j of the physical layer data bearing channel j and the minimum network resource requirement Ni required by the corresponding slice of each service i, calculating the slice scheduling cost Cik:
    Cik＝(αi+Ni)*βj。
  8. 8. the method for multi-granularity FlexE slice scheduling in 5G energy internet of claim 7 wherein,
    and (6) establishing an optimization problem with the requirement of meeting the service quality of service of the service as a constraint and with the aim of minimizing the slice scheduling cost, solving the optimization problem through a re-weighted message transfer algorithm to obtain an optimal slice scheduling result, and realizing the method by the following steps:
    A. if the traffic-time slot allocation index xik =0 in the slice to be scheduled, determining that the idle time slot k is not allocated to the traffic i, and if the traffic-time slot allocation index xik =1 in the slice to be scheduled, determining that the idle time slot k is allocated to the traffic i;
    B. the service quality demand time slot allocation quantity of the service is not less than the time slot quantity Ni and is used as constraint one;
    C. assigning any free time slot k in each physical layer data carrying channel to one service slice at most as constraint two;
    D. establishing a combination optimization problem based on 0-1 matching according to a basic theory of combination optimization by taking the minimum slice scheduling cost as an optimization target, and taking constraint one and constraint two as constraint conditions of the combination optimization problem;
    E. based on the re-weighted message passing algorithm, the optimal slice scheduling result { xik }.
  9. 9. The method for multi-granularity FlexE slice scheduling in 5G energy internet of claim 8 wherein,
    based on a re-weighted message passing algorithm, an optimal slice scheduling result { xik }, is obtained by the following steps:
    E1. combined optimization calculation message on correlation between traffic i and slot k when initializing t=0 times
    Figure FDA0003999904420000031
    And a reverse combined optimization calculation message regarding the correlation between traffic i and time slot k>
    Figure FDA0003999904420000032
    Figure FDA0003999904420000033
    Setting the value of a constant rho;
    E2. optimizing calculation messages according to t-th time reverse combination regarding correlation between traffic f and time slot i
    Figure FDA0003999904420000034
    And t-th time reverse combined optimization calculation message regarding correlation between traffic k and slot i>
    Figure FDA0003999904420000035
    Combined optimization calculation message for calculating the correlation between traffic k and time slot i at time t+1st>
    Figure FDA00039999044200000321
    Figure FDA0003999904420000036
    Wherein C is i，k C, occupying the cost of the free time slot k in any allocable physical layer data bearing channel for the service i i，f The cost of occupying free slot k in any allocable physical layer data bearer channel for service f,
    Figure FDA0003999904420000037
    the minimum number of time slots required for service i; />
    E3. According to the t+1th time message
    Figure FDA0003999904420000038
    And->
    Figure FDA0003999904420000039
    Calculating the t+1st message about the correlation between traffic i and time slot k +.>
    Figure FDA00039999044200000310
    Figure FDA00039999044200000311
    In the method, in the process of the invention,
    Figure FDA00039999044200000312
    optimizing calculation messages for t+1st time with respect to the combination of correlations between traffic i and time slot k,/>
    Figure FDA00039999044200000313
    Optimizing calculation messages for t+1st time reverse combination regarding correlation between traffic l and time slot k,/>
    Figure FDA00039999044200000314
    Optimizing the calculation message for the t+1st time reverse combination with respect to the correlation between traffic i and slot k;
    E4. according to
    Figure FDA00039999044200000315
    And->
    Figure FDA00039999044200000316
    Calculating forward and reverse messages and ++1 th time between user i and time slot k>
    Figure FDA00039999044200000317
    Figure FDA00039999044200000318
    E5. Calculation of t+1st partition factor result
    Figure FDA00039999044200000319
    Figure FDA00039999044200000320
    E6. If it is
    Figure FDA0003999904420000041
    Obtaining an optimal slice scheduling result { xik }, ending the operation, otherwise increasing the value of t by 1, and entering a step E1;
    if { xik } = 1, the service i selects the physical layer data bearer j corresponding to the time slot k.
  10. 10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any one of claims 1 to 9.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117459993A (en) * 2023-12-22 2024-01-26 深圳国人无线通信有限公司 Method and device for determining service priority in dedicated service channel
CN117459993B (en) * 2023-12-22 2024-03-15 深圳国人无线通信有限公司 Method and device for determining service priority in dedicated service channel

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