CN107248928B - Multi-objective optimization carrier migration objective baseband pool selection method based on conflict equalization - Google Patents

Multi-objective optimization carrier migration objective baseband pool selection method based on conflict equalization Download PDF

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CN107248928B
CN107248928B CN201710377189.XA CN201710377189A CN107248928B CN 107248928 B CN107248928 B CN 107248928B CN 201710377189 A CN201710377189 A CN 201710377189A CN 107248928 B CN107248928 B CN 107248928B
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migration
carrier
baseband pool
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pool
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CN107248928A (en
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李兵兵
钱鑫
陈文杰
高炜委
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Xidian University
Xian Cetc Xidian University Radar Technology Collaborative Innovation Research Institute Co Ltd
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Xian Cetc Xidian University Radar Technology Collaborative Innovation Research Institute Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/142—Network analysis or design using statistical or mathematical methods
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The invention belongs to the technical field of mobile communication, and discloses a multi-objective optimization carrier migration destination baseband pool selection method based on conflict equalization, which establishes a multi-objective optimization based migration destination baseband pool selection model for a carrier to be migrated after receiving a carrier migration signal; and solving the selection model established in the previous step by using an approximate ideal solution algorithm based on the dynamic objective weight value and the conflict equilibrium degree, and selecting the migration target baseband pool. The invention can eliminate the conflict between different optimization targets more fully, select the proper base band pool for migration, and better realize the maximization of the user service quality of the migration carrier, the minimization of the power consumption of the base band pool system and the energy consumption of the carrier migration at the same time, so that the cost performance of the carrier migration is higher.

Description

Multi-objective optimization carrier migration objective baseband pool selection method based on conflict equalization
Technical Field
The invention belongs to the technical field of mobile communication, and particularly relates to a multi-objective optimization carrier migration objective baseband pool selection method based on conflict equalization.
Background
With the development of mobile communication radio access networks, radio access devices are undergoing an evolution process from a conventional integrated base station to a distributed base station to a base station resource pool. The distributed base station separates the radio frequency unit from the base station, and the distributed base station and a far-end antenna are put together to form a Remote Radio Unit (RRU), while the original base station cabinet only leaves a baseband unit (BBU). On one hand, the RRU and the antenna are placed together, so that the attenuation of an antenna feeder is reduced, and the transmitting power of a base station can be reduced; on the other hand, the size of the BBU cabinet after the RRU is peeled off can be greatly reduced, and the RRU arranged on the ceiling is kept at a constant temperature by natural conditions, so that special air-conditioning equipment is not needed, and the energy consumption is further reduced. The concept of the base station resource pool is provided on the basis of a distributed base station, BBUs within a certain range are interconnected, and the baseband processing capacity of each BBU is shared to form a baseband resource pool which is distributed as required and is uniformly scheduled. Through reasonable planning, the base stations in the base station resource pool are not in the maximum traffic state at the same time, and the carrier processing resources of the baseband resource pool can not be allocated according to the sum of all the maximum requirements, so that the investment cost of an operator and the overall energy consumption of a network are reduced, and the overall utilization rate of the carrier processing resources is improved. Due to the rapid development of cloud computing technology, virtualization technology and virtual machine migration technology are gradually introduced into a base station resource pool. By combining with the virtualization technology, the carrier processing resources in the base station resource pool can be abstracted into a virtual machine form, and the baseband pool processing resources are extracted as required to form a corresponding virtual base station to process the baseband signals of the carriers, so that the utilization rate of the resources is improved, and the flexible allocation and unified scheduling of different carriers can be performed more conveniently with finer granularity. By combining a virtual machine migration technology, a virtual base station for processing carrier baseband signals can be migrated from one centralized baseband pool to another centralized baseband pool, so that carrier migration is realized; under the condition of insufficient carrier processing resources, the condition of insufficient carrier processing resources is relieved through carrier migration, and the communication service quality is improved; by means of carrier migration, all carriers on a certain physical server are migrated to other physical servers, maintenance operations such as overhaul or upgrade can be carried out on the physical server, or the physical server is powered off, so that the purposes of energy conservation and emission reduction are achieved. After triggering carrier migration, selecting a migration destination baseband pool for a carrier needing migration is faced. The carrier migration has the main significance that resources among different centralized baseband pools under a C-RAN framework can be further shared at the minimum cost, firstly, the user service quality on the migrated carrier can be improved, secondly, the resource utilization rate can be improved, and the power consumption of the whole baseband pool system is reduced. Therefore, three indexes need to be considered when selecting the migration destination baseband pool: user service quality of the migrating carrier, power consumption of the baseband pool system and carrier migration energy consumption. However, the current methods for selecting a baseband pool for carrier migration are optimized for a single target, and cannot achieve simultaneous optimization of multiple conflicting targets, so that the cost performance of carrier migration is low.
In summary, the problems of the prior art are as follows: the current method for selecting the baseband pool for the carrier migration purpose cannot realize the simultaneous optimization of a plurality of mutually conflicting objectives, so that the cost performance of the carrier migration is low.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a conflict balance-based multi-objective optimized carrier migration objective baseband pool selection method.
The invention is realized in such a way that a multi-objective optimization carrier migration destination baseband pool selection method based on conflict equalization is provided, wherein after receiving a carrier migration signal, the multi-objective optimization carrier migration destination baseband pool selection method based on conflict equalization establishes a multi-objective optimization based migration destination baseband pool selection model for a carrier to be migrated; and solving the selection model established in the previous step by using an approximate ideal solution algorithm based on the dynamic objective weight value and the conflict equilibrium degree, and selecting the migration target baseband pool.
Further, the establishing of the multi-objective optimization-based migration target baseband pool selection model comprises the following steps:
firstly, establishing a mathematical model of user service quality of a migration carrier of one of optimization targets;
secondly, establishing a mathematical model of the power consumption increment of a baseband pool system which is one of the optimization targets;
thirdly, establishing a mathematical model of carrier migration energy consumption of one of the optimization targets;
and fourthly, obtaining a migration target baseband pool selection model based on multi-objective optimization.
Further, the establishing of the mathematical model of the user service quality of the migration carrier specifically comprises the following steps:
(1) calculating jitter interference:
Figure GDA0001340555030000031
wherein
Figure GDA0001340555030000032
Representing the jitter interference resulting from carrier migration to the candidate destination baseband pool j,
Figure GDA0001340555030000033
Bjrepresenting the available migration bandwidth, T, from the source baseband pool to the candidate destination baseband pool jmaxRepresents the maximum acceptable downtime migration time, DjDenotes the distance, D, from the source baseband pool to the candidate destination baseband pool jmaxRepresenting maximum transmission distance, VM, of the fibremigIndicating the memory size, User, of the carrier being migratedmigIndicating the number of user services piggybacked on the carrier being migrated αmigAnd δ is a constant coefficient.
(2) Calculating the same-pool interference:
Figure GDA0001340555030000034
wherein
Figure GDA0001340555030000035
Indicates the same-pool interference, NC, generated by the carrier migration to the candidate destination baseband pool jmigRepresenting CPU resource demand, NM, after carrier migrationmigIndicates the memory resource demand, NB, after carrier migrationmigIndicating the bandwidth resource demand after carrier migration,
Figure GDA0001340555030000036
representing carrier k allocation in candidate target baseband pool jThe amount of resources of the CPU to be placed,
Figure GDA0001340555030000037
the amount of memory resources allocated to the carrier k in the candidate destination baseband pool j is represented,
Figure GDA0001340555030000038
indicates the bandwidth resources configured by the carrier k in the candidate destination baseband pool j,
Figure GDA0001340555030000039
represents the utilization rate, M, of the carrier k in the candidate destination baseband pool jjRepresenting the total number of carriers carried by the candidate destination baseband pool j αc、αmAnd αbIs a constant coefficient;
(3) calculating the user service quality of the migration carrier:
Figure GDA00013405550300000310
wherein
Figure GDA0001340555030000041
Indicating the user quality of service value of the migrating carrier when the carrier migrates to the candidate destination baseband pool j,
Figure GDA00013405550300000411
is a constant coefficient; j 1,2, N denotes the total number of baseband pools for candidate migration purposes.
Further, the establishing of the mathematical model of the power consumption increment of the baseband pool system is specifically performed according to the following steps:
(1) calculating the static power consumption of each baseband pool:
Figure GDA0001340555030000042
whereinRepresenting the static power consumption, VC, of the baseband pool jjTo representAmount of CPU resources, VM, configured for baseband pool jjRepresents the amount of memory resources, VB, allocated to the baseband pool jjDenoted as the bandwidth resource allocated for the baseband pool j, yc、γmAnd gammabIs a constant coefficient; j ═ s,1, 2.., N, s denote the source baseband pool;
(2) calculating the dynamic power consumption of the baseband pool;
Figure GDA0001340555030000044
whereinRepresenting the dynamic power consumption, gamma, of the baseband pool jcl、γml、γblEach of (1, 2, 3) is a constant coefficient, and j is s,1, 2.
(3) Calculating the power consumption of the baseband pool system;
Figure GDA0001340555030000046
wherein P issysRepresents the total power consumption of the baseband pool system;
(4) calculating the increment of the power consumption of the baseband pool system;
Figure GDA0001340555030000047
wherein
Figure GDA0001340555030000048
Indicating the increment of power consumption of the baseband pool system after the carrier is migrated to the candidate destination baseband pool j,
Figure GDA0001340555030000049
represents the total power consumption of the baseband pool system after the carrier is migrated to the candidate destination baseband pool j,
Figure GDA00013405550300000410
represents the total power consumption of the baseband pool system before carrier migration, and j is 1, 2.
Further, the establishing of the mathematical model of the carrier migration energy consumption of one of the optimization objectives is as follows:
Figure GDA0001340555030000051
wherein And d, representing the migration energy consumption generated by the carrier migration to the candidate destination baseband pool j, wherein delta, epsilon and β are constant coefficients, and j is 1, 2.
Further, the obtaining of the migration target baseband pool selection model based on multi-objective optimization specifically includes:
Figure GDA0001340555030000054
Figure GDA0001340555030000055
wherein
Figure GDA0001340555030000056
The amount of CPU resources remaining in the baseband pool j representing the candidate migration destination,
Figure GDA0001340555030000057
the remaining amount of memory resources in the baseband pool j representing the candidate migration destination,
Figure GDA0001340555030000058
the amount of bandwidth resources remaining in the baseband pool j representing the candidate migration destination,
Figure GDA0001340555030000059
and representing the shutdown migration time of the carrier migration to the candidate migration destination baseband pool j.
Further, the approximate ideal solution algorithm based on the dynamic objective weight value and the conflict equilibrium degree is specifically performed according to the following steps:
step one, solving the variance of each optimized target attribute value according to a normalized decision matrix Y:
ξ thereinQVariance of user quality of service value indicating migrating carrier, ξPVariance representing incremental value of power consumption of baseband pool system, ξERepresenting the variance of the carrier migration energy consumption values.
Step two, according to the variance of the attribute values of the optimization targets, the weight value of each optimization target is obtained:
Figure GDA0001340555030000061
wherein ω isQWeight value of user service quality, omega, for migrating carriersPWeighted value, omega, for power increment in a baseband pool systemEWeighted value, omega, of energy consumption for carrier migrationQ+ωP+ωE＝1；
Step three, according to the positive ideal solution A+And calculating the difference between the optimal solutions of the optimization targets:
Figure GDA0001340555030000062
wherein
Figure GDA0001340555030000063
Representing the difference between the user quality of service positive ideal solution for the migrating carrier and the base band pool system power consumption increment positive ideal solution,
Figure GDA0001340555030000064
the difference value of the positive ideal solution of the power consumption increment of the baseband pool system and the positive ideal solution of the carrier migration energy consumption is shown,
Figure GDA0001340555030000065
representing the difference value of the user service quality positive ideal solution of the migration carrier and the carrier migration energy consumption positive ideal solution;
step four, calculating the difference value between the optimized target attribute values of the baseband pool of each candidate migration target according to the weighted normalized decision matrix Z:
whereinRepresenting the difference between the user quality of service value for the migrated carrier to the candidate destination baseband pool j and the baseband pool system power consumption incremental value,
Figure GDA0001340555030000068
representing the difference value of the power consumption increment value of the baseband pool system migrated to the candidate destination baseband pool j and the power consumption value of carrier migration,
Figure GDA0001340555030000069
representing the difference value between the user service quality value of the migration carrier migrated to the candidate target baseband pool j and the power consumption value of the baseband pool system, wherein j is 1, 2.
Step five, calculating the conflict balance degree of the baseband pool of each candidate migration destination:
Figure GDA0001340555030000071
wherein BaljIndicates the conflict balance degree, omega, of the base band pool j of the candidate destinationQPConflict balance weight value omega representing user service quality of migration carrier and power consumption increment of baseband pool systemPEConflict balance weight value omega for expressing power consumption increment of base band pool system and carrier migration energy consumptionQEConflict balance weight value omega representing user service quality of transferred carrier and carrier transfer energy consumptionQP+ωPE+ωQE＝1，j＝1,2,...,N；
Step six, calculating the relative closeness of the baseband pool of each candidate migration target and the positive ideal solution:
Figure GDA0001340555030000072
wherein C isjThe relative closeness of the baseband pool j for the candidate migration destination to the positive ideal solution,
Figure GDA0001340555030000073
the euclidean distance of the baseband pool j representing the candidate migration destination from the positive ideal solution,
Figure GDA0001340555030000074
the base band pool j representing the candidate migration destination is the euclidean distance from the negative ideal solution, j being 1, 2.
Another object of the present invention is to provide a mobile communication network applying the conflict equalization-based carrier migration destination baseband pool selection method.
The invention has the advantages and positive effects that: as can be seen from fig. 4 to fig. 6, the present invention can eliminate conflicts between different optimization targets more thoroughly, so that more optimization targets can simultaneously approach to the corresponding optimal solution to the greatest extent, and a situation that a certain optimization target particularly approaches to the optimal solution does not exist, and the extent that other optimization targets approach to the corresponding optimal solution is relatively poor, so that the present invention can better achieve the purpose of simultaneously maximizing the user service quality of the migrating carrier, minimizing the power consumption of the baseband pool system and the carrier migrating energy consumption, and bring higher migration performance-to-price ratio. The invention can eliminate the conflict between different optimization targets more fully, select the proper base band pool for migration, and better realize the maximization of the user service quality of the migration carrier, the minimization of the power consumption of the base band pool system and the energy consumption of the carrier migration at the same time, so that the cost performance of the carrier migration is higher.
Drawings
Fig. 1 is a flowchart of a method for selecting a baseband pool for multi-objective optimized carrier migration based on collision balancing according to an embodiment of the present invention.
Fig. 2 is a flowchart for establishing a multi-objective optimization-based baseband pool selection model for carrier migration.
Fig. 3 is a flowchart of a calculation of an approximate ideal solution algorithm based on dynamic objective weight values and conflict equalization provided in the embodiment of the present invention.
Fig. 4 is a schematic diagram of user service quality of a migration carrier obtained by a method for selecting a baseband pool of each carrier migration destination according to an embodiment of the present invention under different carrier loads.
Fig. 5 is a schematic diagram of power consumption increment of a baseband pool system obtained by a baseband pool selection method for each carrier migration purpose provided in the embodiment of the present invention under different carrier loads.
Fig. 6 is a schematic diagram of carrier migration energy consumption obtained by the method for selecting a baseband pool for each carrier migration destination according to the embodiment of the present invention under different carrier loads.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
After receiving a carrier migration signal, establishing a migration target baseband pool selection model based on multi-objective optimization for a carrier to be migrated; and solving the selection model established in the previous step by using an approximate ideal solution algorithm based on the dynamic objective weight value and the conflict equilibrium degree, and selecting the migration target baseband pool.
The following detailed description of the principles of the invention is provided in connection with the accompanying drawings.
As shown in fig. 1, the method for selecting a multi-objective optimized carrier migration destination baseband pool based on collision balancing according to the embodiment of the present invention includes the following steps:
s101: receiving a carrier migration signal, and establishing a migration target baseband pool selection model based on multi-objective optimization for a carrier to be migrated;
s102: and solving a migration target baseband pool selection model based on multi-objective optimization by using an approximate ideal solution algorithm based on dynamic objective weight values and conflict equilibrium degrees to obtain a finally selected migration target baseband pool.
As shown in fig. 2, in step S101, a multi-objective optimization-based migration destination baseband pool selection model is established for a carrier to be migrated, and the method specifically includes the following steps:
s1011: establishing a mathematical model for optimizing the user service quality of one of the migration carriers of the target;
s1012: establishing a mathematical model of the power consumption increment of a baseband pool system which is one of optimization targets;
s1013: establishing a mathematical model of carrier migration energy consumption which is one of optimization targets;
s1014: and obtaining a migration target baseband pool selection model based on multi-objective optimization.
In step S1011, a mathematical model is established that optimizes the user qos of one of the migration carriers of the target, specifically executed according to the following steps:
(1) calculating jitter interference:
Figure GDA0001340555030000091
wherein
Figure GDA0001340555030000092
Representing the jitter interference resulting from carrier migration to the candidate destination baseband pool j,Bjrepresenting the available migration bandwidth, T, from the source baseband pool to the candidate destination baseband pool jmaxRepresents the maximum acceptable downtime migration time, DjDenotes the distance, D, from the source baseband pool to the candidate destination baseband pool jmaxRepresenting maximum transmission distance, VM, of the fibremigIndicating the memory size, User, of the carrier being migratedmigIndicating the number of user services piggybacked on the carrier being migrated αmigAnd δ is a constant coefficient.
(2) Calculating the same-pool interference:
Figure GDA0001340555030000094
whereinIndicates the same-pool interference, NC, generated by the carrier migration to the candidate destination baseband pool jmigRepresenting CPU resource demand, NM, after carrier migrationmigIndicates the memory resource demand, NB, after carrier migrationmigIndicating the bandwidth resource demand after carrier migration,
Figure GDA0001340555030000101
represents the amount of CPU resources allocated to carrier k in the candidate destination baseband pool j,
Figure GDA0001340555030000102
the amount of memory resources allocated to the carrier k in the candidate destination baseband pool j is represented,
Figure GDA0001340555030000103
indicates the bandwidth resources configured by the carrier k in the candidate destination baseband pool j,
Figure GDA0001340555030000104
represents the utilization rate, M, of the carrier k in the candidate destination baseband pool jjRepresenting the total number of carriers carried by the candidate destination baseband pool j αc、αmAnd αbIs a constant coefficient.
(3) Calculating the user service quality of the migration carrier:
Figure GDA0001340555030000105
whereinIndicating the user quality of service value of the migrating carrier when the carrier migrates to the candidate destination baseband pool j,
Figure GDA00013405550300001011
is a constant coefficient; j 1,2, N denotes the total number of baseband pools for candidate migration purposes.
In step S1012, a mathematical model of the power consumption increment of the baseband pool system, which is one of the optimization objectives, is established, and specifically executed according to the following steps:
(1) calculating the static power consumption of each baseband pool:
Figure GDA0001340555030000107
wherein
Figure GDA0001340555030000108
Representing the static power consumption, VC, of the baseband pool jjRepresents the amount of CPU resources, VM, allocated by the baseband pool jjRepresents the amount of memory resources, VB, allocated to the baseband pool jjDenoted as the bandwidth resource allocated for the baseband pool j, yc、γmAnd gammabIs a constant coefficient; j ═ s,1, 2., N, s denote the source baseband pool.
(2) Calculating the dynamic power consumption of the baseband pool;
Figure GDA0001340555030000109
wherein
Figure GDA00013405550300001010
Representing the dynamic power consumption, gamma, of the baseband pool jcl、γml、γblEach of (1, 2, and 3) is a constant coefficient, and j is s,1, 2.
(3) Calculating the power consumption of the baseband pool system;
Figure GDA0001340555030000111
wherein P issysRepresenting the total power consumption of the baseband pool system.
(4) Calculating the increment of the power consumption of the baseband pool system;
Figure GDA0001340555030000112
wherein
Figure GDA0001340555030000113
Indicating the increment of power consumption of the baseband pool system after the carrier is migrated to the candidate destination baseband pool j,
Figure GDA0001340555030000114
represents the total power consumption of the baseband pool system after the carrier is migrated to the candidate destination baseband pool j,
Figure GDA0001340555030000115
represents the total power consumption of the baseband pool system before carrier migration, and j is 1, 2.
In step S1013, a mathematical model of carrier migration energy consumption, which is one of optimization objectives, is established as follows:
Figure GDA0001340555030000116
wherein
Figure GDA0001340555030000117
Figure GDA0001340555030000118
And d, representing the migration energy consumption generated by the carrier migration to the candidate destination baseband pool j, wherein delta, epsilon and β are constant coefficients, and j is 1, 2.
In step S1014, a migration target baseband pool selection model based on multi-objective optimization is obtained, specifically as follows:
Figure GDA0001340555030000119
Figure GDA00013405550300001110
whereinThe amount of CPU resources remaining in the baseband pool j representing the candidate migration destination,
Figure GDA00013405550300001112
the remaining amount of memory resources in the baseband pool j representing the candidate migration destination,
Figure GDA0001340555030000121
the amount of bandwidth resources remaining in the baseband pool j representing the candidate migration destination,and representing the shutdown migration time of the carrier migration to the candidate migration destination baseband pool j.
As shown in fig. 3, in step S102, an approximate ideal solution algorithm based on dynamic objective weight values and conflict equilibrium is used to solve the multi-objective optimization-based migration destination baseband pool selection model, which is specifically executed according to the following steps:
s10201: constructing a decision matrix X:
Figure GDA0001340555030000123
wherein xjQUser quality of service value of migrating carrier representing migration of carrier to candidate destination baseband pool j
Figure GDA0001340555030000124
xjPBaseband system power consumption increment representing carrier migration to candidate destination baseband pool j
Figure GDA0001340555030000125
xjERepresenting the carrier migration energy consumption value of the carrier migration to the candidate target baseband pool j
Figure GDA0001340555030000126
j＝1,2,...,N。
S10202: carrying out normalization processing on the decision matrix X to obtain a normalized decision matrix Y:
Figure GDA0001340555030000127
whereinj＝1,2,...,N。
S10203: and solving the variance of each optimized target attribute value according to the normalized decision matrix Y:
Figure GDA0001340555030000129
ξ thereinQVariance of user quality of service value indicating migrating carrier, ξPVariance representing incremental value of power consumption of baseband pool system, ξERepresenting the variance of the carrier migration energy consumption values.
S10204: according to the variance of the attribute values of the optimization targets, the weight value of each optimization target is obtained:
Figure GDA0001340555030000131
wherein ω isQWeight value of user service quality, omega, for migrating carriersPWeighted value, omega, for power increment in a baseband pool systemEWeighted value, omega, of energy consumption for carrier migrationQ+ωP+ωE＝1。
S10205: then, according to the dynamic objective weight values of the optimization targets obtained in the previous step, a weighted normalized decision matrix Z is obtained:
Figure GDA0001340555030000132
s10206: determining a positive ideal solution A+And negative ideal solution A-：
Figure GDA0001340555030000133
Where j is 1, 2.
S10207: according to the positive ideal solution A+And calculating the difference between the optimal solutions of the optimization targets:
Figure GDA0001340555030000134
wherein sigma'QPAnd representing the difference value sigma 'between the user service quality positive ideal solution of the migration carrier and the power consumption increment positive ideal solution of the base band pool system'PERepresenting the difference value sigma 'of the positive ideal solution of the power consumption increment of the baseband pool system and the positive ideal solution of the carrier migration energy consumption'QEThe difference between the user service quality positive ideal solution of the migrating carrier and the carrier migration energy consumption positive ideal solution is represented.
S10208: calculating the difference value between the optimized target attribute values of the baseband pool of each candidate migration target according to the weighted normalized decision matrix Z:
Figure GDA0001340555030000141
wherein
Figure GDA0001340555030000142
Representing the difference between the user quality of service value for the migrated carrier to the candidate destination baseband pool j and the baseband pool system power consumption incremental value,
Figure GDA0001340555030000143
representing the difference value of the power consumption increment value of the baseband pool system migrated to the candidate destination baseband pool j and the power consumption value of carrier migration,
Figure GDA0001340555030000144
and representing the difference value between the user service quality value of the migration carrier migrated to the candidate target baseband pool j and the power consumption value of the baseband pool system, wherein j is 1, 2.
S10209: calculating the conflict balance degree of the baseband pool of each candidate migration destination:
Figure GDA0001340555030000145
wherein BaljIndicates the conflict balance degree, omega, of the base band pool j of the candidate destinationQPConflict balance weight value omega representing user service quality of migration carrier and power consumption increment of baseband pool systemPEConflict balance weight value omega for expressing power consumption increment of base band pool system and carrier migration energy consumptionQEConflict balance weight value omega representing user service quality of transferred carrier and carrier transfer energy consumptionQP+ωPE+ωQE＝1，j＝1,2,...,N。
S10210: respectively calculating Euclidean distances between the baseband pool of each candidate migration destination and the positive ideal solution and the negative ideal solution
Figure GDA0001340555030000146
Figure GDA0001340555030000147
Wherein
Figure GDA0001340555030000148
The euclidean distance of the baseband pool j representing the candidate migration destination from the positive ideal solution,
Figure GDA0001340555030000149
the base band pool j representing the candidate migration destination is the euclidean distance from the negative ideal solution, j being 1, 2.
S10211: calculating the relative closeness of the baseband pool of each candidate migration target and the positive ideal solution:
wherein C isjThe relative closeness of the baseband pool j, which is the candidate migration destination, to the positive ideal solution, j is 1, 2.
S10212: and selecting the baseband pool of the candidate migration destination with the maximum relative proximity value as the baseband pool of the carrier migration destination.
The application effect of the present invention will be described in detail with reference to the simulation.
To test the performance of the invention, the parameters were set as follows: omegaQP＝ωPE＝ωQE1/3; the number of the baseband pools for the candidate migration destination is 100; the resource utilization rate of each part of the source baseband pool is 60-80%. Selecting a multi-objective optimization migration destination baseband pool selection method (MOP-TT) based on a traditional approximate ideal solution algorithm, a migration destination baseband pool selection method (SOP-MAQ) for maximizing user service quality of a migration carrier, a migration destination baseband pool selection method (SOP-MIP) for minimizing energy consumption increment of a baseband pool system and a migration destination baseband pool selection method (SOP-MIE) for minimizing energy consumption of carrier migration as comparison methods, and performing 100 Monte Carlo experiment simulations to obtain the user service quality of the migration carrier shown in figure 4, the power consumption increment of the baseband pool system shown in figure 5 and the energy consumption of carrier migration shown in figure 6.
As can be seen from fig. 4 to fig. 6, the present invention can eliminate conflicts between different optimization targets more thoroughly, so that more optimization targets can simultaneously approach to the corresponding optimal solution to the greatest extent, and a situation that a certain optimization target particularly approaches to the optimal solution does not exist, and the extent that other optimization targets approach to the corresponding optimal solution is relatively poor, so that the present invention can better achieve the purpose of simultaneously maximizing the user service quality of the migrating carrier, minimizing the power consumption of the baseband pool system and the carrier migrating energy consumption, and bring higher migration performance-to-price ratio.
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 and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (2)

1. A multi-objective optimization carrier migration destination baseband pool selection method based on conflict equalization is characterized in that after receiving a carrier migration signal, the multi-objective optimization carrier migration destination baseband pool selection method based on conflict equalization establishes a migration destination baseband pool selection model based on multi-objective optimization for a carrier to be migrated; solving the selection model established in the previous step by using an approximate ideal solution algorithm based on the dynamic objective weight value and the conflict equilibrium degree, and selecting a migration target baseband pool;
the establishment of the multi-objective optimization-based migration target baseband pool selection model comprises the following steps:
firstly, establishing a mathematical model of user service quality of a migration carrier of one of optimization targets;
secondly, establishing a mathematical model of the power consumption increment of a baseband pool system which is one of the optimization targets;
thirdly, establishing a mathematical model of carrier migration energy consumption of one of the optimization targets;
fourthly, obtaining a migration target baseband pool selection model based on multi-objective optimization;
the establishing of the mathematical model of the user service quality of the migration carrier specifically comprises the following steps:
(1) calculating jitter interference:
Figure FDA0002324411170000011
wherein
Figure FDA0002324411170000012
Representing the jitter interference resulting from carrier migration to the candidate destination baseband pool j,
Figure FDA0002324411170000013
Bjrepresenting the available migration bandwidth, T, from the source baseband pool to the candidate destination baseband pool jmaxRepresents the maximum acceptable downtime migration time, DjDenotes the distance, D, from the source baseband pool to the candidate destination baseband pool jmaxRepresenting maximum transmission distance, VM, of the fibremigIndicating the memory size, User, of the carrier being migratedmigIndicating the number of user services piggybacked on the carrier being migrated αmigAnd δ is a constant coefficient;
(2) calculating the same-pool interference:
Figure FDA0002324411170000014
whereinIndicates the same-pool interference, NC, generated by the carrier migration to the candidate destination baseband pool jmigRepresenting CPU resource demand, NM, after carrier migrationmigIndicates the memory resource demand, NB, after carrier migrationmigIndicating the bandwidth resource demand after carrier migration,
Figure FDA0002324411170000022
represents the amount of CPU resources allocated to carrier k in the candidate destination baseband pool j,
Figure FDA0002324411170000023
the amount of memory resources allocated to the carrier k in the candidate destination baseband pool j is represented,
Figure FDA0002324411170000024
indicates the bandwidth resources configured by the carrier k in the candidate destination baseband pool j,
Figure FDA0002324411170000025
represents the utilization rate, M, of the carrier k in the candidate destination baseband pool jjRepresenting the total number of carriers carried by the candidate destination baseband pool j αc、αmAnd αbIs a constant coefficient;
(3) calculating the user service quality of the migration carrier:
Figure FDA0002324411170000026
wherein
Figure FDA0002324411170000027
Indicating the user quality of service value of the migrating carrier when the carrier migrates to the candidate destination baseband pool j,
Figure FDA0002324411170000028
is a constant coefficient; j ═ 1, 2., N denotes the total number of baseband pools for candidate migration purposes;
the establishing of the mathematical model of the power consumption increment of the baseband pool system is specifically carried out according to the following steps:
(1) calculating the static power consumption of each baseband pool:
Figure FDA0002324411170000029
wherein
Figure FDA00023244111700000210
Representing the static power consumption, VC, of the baseband pool jjRepresents the amount of CPU resources, VM, allocated by the baseband pool jjRepresents the amount of memory resources, VB, allocated to the baseband pool jjDenoted as the bandwidth resource allocated for the baseband pool j, yc、γmAnd gammabIs a constant coefficient; j ═ s,1, 2.., N, s denote the source baseband pool;
(2) calculating the dynamic power consumption of the baseband pool;
Figure FDA00023244111700000211
wherein
Figure FDA00023244111700000212
Representing the dynamic power consumption, gamma, of the baseband pool jcl、γml、γblEach of (1, 2, 3) is a constant coefficient, and j is s,1, 2.
(3) Calculating the power consumption of the baseband pool system;
Figure FDA0002324411170000031
wherein P issysRepresents the total power consumption of the baseband pool system;
(4) calculating the increment of the power consumption of the baseband pool system;
Figure FDA0002324411170000032
wherein
Figure FDA0002324411170000033
Indicating the increment of power consumption of the baseband pool system after the carrier is migrated to the candidate destination baseband pool j,
Figure FDA0002324411170000034
represents the total power consumption of the baseband pool system after the carrier is migrated to the candidate destination baseband pool j,
Figure FDA0002324411170000035
represents the total power consumption of the baseband pool system before carrier migration, j is 1, 2.
The establishing of the mathematical model of the carrier migration energy consumption of one of the optimization targets is as follows:
Figure FDA0002324411170000036
wherein
Figure FDA0002324411170000037
Figure FDA0002324411170000038
Representing the migration energy consumption generated when the carrier is migrated to the candidate target baseband pool j, wherein delta, epsilon and β are constant coefficients, and j is 1, 2.. multidot.N;
the migration target baseband pool selection model based on multi-objective optimization is obtained as follows:
Figure FDA0002324411170000039
wherein
Figure FDA00023244111700000310
Remaining CPU resources in baseband pool j representing candidate migration destinationsThe amount of the compound (A) is,
Figure FDA00023244111700000311
the remaining amount of memory resources in the baseband pool j representing the candidate migration destination,the amount of bandwidth resources remaining in the baseband pool j representing the candidate migration destination,representing the shutdown migration time of the carrier migration to the candidate migration destination baseband pool j;
the approximate ideal solution algorithm based on the dynamic objective weight value and the conflict equilibrium degree is specifically carried out according to the following steps:
step one, solving the variance of each optimized target attribute value according to a normalized decision matrix Y:
Figure FDA0002324411170000042
ξ thereinQVariance of user quality of service value indicating migrating carrier, ξPVariance representing incremental value of power consumption of baseband pool system, ξEA variance representing a carrier migration energy consumption value;
step two, according to the variance of the attribute values of the optimization targets, the weight value of each optimization target is obtained:
Figure FDA0002324411170000043
wherein ω isQWeight value of user service quality, omega, for migrating carriersPWeighted value, omega, for power increment in a baseband pool systemEWeighted value, omega, of energy consumption for carrier migrationQ+ωP+ωE＝1；
Step three, according to the positive ideal solution A+And calculating the difference between the optimal solutions of the optimization targets:
Figure FDA0002324411170000044
wherein sigma'QPAnd representing the difference value sigma 'between the user service quality positive ideal solution of the migration carrier and the power consumption increment positive ideal solution of the base band pool system'PERepresenting the difference value sigma 'of the positive ideal solution of the power consumption increment of the baseband pool system and the positive ideal solution of the carrier migration energy consumption'QERepresenting the difference value of the user service quality positive ideal solution of the migration carrier and the carrier migration energy consumption positive ideal solution;
step four, calculating the difference value between the optimized target attribute values of the baseband pool of each candidate migration target according to the weighted normalized decision matrix Z:
Figure FDA0002324411170000051
wherein
Figure FDA0002324411170000052
Representing the difference between the user quality of service value for the migrated carrier to the candidate destination baseband pool j and the baseband pool system power consumption incremental value,
Figure FDA0002324411170000053
representing the difference value of the power consumption increment value of the baseband pool system migrated to the candidate destination baseband pool j and the power consumption value of carrier migration,
Figure FDA0002324411170000054
representing the difference value between the user service quality value of the migration carrier migrated to the candidate target baseband pool j and the power consumption value of the baseband pool system, wherein j is 1, 2.
Step five, calculating the conflict balance degree of the baseband pool of each candidate migration destination:
wherein BaljRepresenting migration to a candidateConflict balance, omega, of destination baseband pool jQPConflict balance weight value omega representing user service quality of migration carrier and power consumption increment of baseband pool systemPEConflict balance weight value omega for expressing power consumption increment of base band pool system and carrier migration energy consumptionQEConflict balance weight value omega representing user service quality of transferred carrier and carrier transfer energy consumptionQP+ωPE+ωQE＝1，j＝1,2,...,N；
Step six, calculating the relative closeness of the baseband pool of each candidate migration target and the positive ideal solution:
Figure FDA0002324411170000056
wherein C isjThe relative closeness of the baseband pool j for the candidate migration destination to the positive ideal solution,
Figure FDA0002324411170000057
the euclidean distance of the baseband pool j representing the candidate migration destination from the positive ideal solution,the base band pool j representing the candidate migration destination is the euclidean distance from the negative ideal solution, j being 1, 2.
2. A mobile communication network applying the method for selecting a baseband pool for carrier migration destination based on collision balancing as claimed in claim 1.
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