CN106095529B - A kind of carrier wave emigration method under C-RAN framework - Google Patents

A kind of carrier wave emigration method under C-RAN framework Download PDF

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CN106095529B
CN106095529B CN201610403972.4A CN201610403972A CN106095529B CN 106095529 B CN106095529 B CN 106095529B CN 201610403972 A CN201610403972 A CN 201610403972A CN 106095529 B CN106095529 B CN 106095529B
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virtual machine
task
carrier
migration
carrier wave
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CN106095529A (en
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李兵兵
高炜委
李靖
汪珊珊
胡晔
王璐
李进
王江宏
李想
王超敏
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Xidian University
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    • 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
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • 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
    • G06F9/48—Program initiating; Program switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/485—Task life-cycle, e.g. stopping, restarting, resuming execution
    • G06F9/4856—Task life-cycle, e.g. stopping, restarting, resuming execution resumption being on a different machine, e.g. task migration, virtual machine migration
    • 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]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5072—Grid computing
    • 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
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/4557—Distribution of virtual machine instances; Migration and load balancing
    • 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

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Abstract

The invention discloses a kind of carrier wave emigration methods under C-RAN framework, which comprises occurs that CPU and memory comprehensive resources utilization rate are more than 80% on carrier Virtual machine in resource pool, or is lower than 20%, triggers carrier wave emigration；Determine the whole user tasks for needing to move out on the carrier Virtual machine of the certain customers' task and underload that need to move out on the carrier Virtual machine of overload；According to correlated variables, mathematic(al) representation, founding mathematical models are defined using energy consumption and migration cost as target based on migration two constraint conditions of time delay and resource；Mathematical model is solved based on improved max-min ant system, obtains the migration selection method of different user task.The present invention is conducive to balance the load of each carrier Virtual machine, and the process resource of each carrier wave is adequately utilized, reduces energy consumption, is advantageously implemented load balancing.

Description

A kind of carrier wave emigration method under C-RAN framework
Technical field
The invention belongs to a kind of carrier wave emigration methods under field of communication technology more particularly to C-RAN framework.
Background technique
Cloud radio access network (cloud-radio access network, C-RAN) is based on centralization processing, association It deals with, the green wireless access planar network architecture of real-time cloud computing architecture.Traditional base station is disposed by " maximum processing capability ", There is no the dynamic variation characteristics for considering network service, i.e. tidal effect, cause utilization rate of equipment and installations low, and electric power is caused to provide A large amount of wastes in source.Tidal effect can be coped with using the BBU baseband pool of centralized deployment, open dynamic carrier wave emigration function Afterwards, the whole usage quantity that can save carrier wave improves the utilization rate of carrier wave, and using baseband pool process resource is dynamically distributed, mentions Rise systematic entirety energy power dissipation ratio.The method of carrier wave emigration thinks that carrier wave emigration is to undertake under the existing framework in relation to C-RAN The migration of the virtual machine of carrier processing selects to be suitble to according to the triggering type of physical node and the load characteristic of virtual Multi-Mode Base Station The virtual Multi-Mode Base Station of migration, moves to it by online migration pattern for the virtual machine for being used for carrier processing on a server On his server, it to be used for load balancing；Or by the online migrating technology of virtual machine, a physics for undertaking carrier processing is taken Business device (physical machine) moves to another server by exchange network；When the carrier Virtual machine on server all moves to it After his server, it may be convenient to the attended operations such as be overhauled or be upgraded to it, or be turned off to reach energy-saving and emission-reduction Effect.
What the method for carrier wave emigration considered under the existing framework in relation to C-RAN is is from the decision of the load state of physical node No migration, balance be physical server load, do not fully consider the load state of each carrier Virtual machine thereon.Work as tide When nighttide effect occurs, it may appear that the number of users of the carrier service of some cells is excessive, and process resource is not enough, and other one The number of users of a little cell serves is seldom, and resource is mostly in idle state, cannot be very only by the mode of migration virtual machine Good guarantees each state of the carrier Virtual machine load in balance in resource pool, and the utilization rate of carrier wave is lower, whole energy consumption compared with It is high.
Summary of the invention
The purpose of the present invention is to provide a kind of carrier wave emigration methods under C-RAN framework, it is intended to solve existing related The method of carrier wave emigration exists under C-RAN framework cannot guarantee each load in resource pool well by way of migrating virtual machine State of the wave virtual machine load in balance, the utilization rate of carrier wave is lower, the whole higher problem of energy consumption.
The invention is realized in this way a kind of carrier wave emigration method under framework based on C-RAN, this method is related to mobile logical Letter field and computer field, carrier wave emigration method under the C-RAN framework the following steps are included:
Step 1, occurs that CPU and memory comprehensive resources utilization rate are more than 80% on carrier Virtual machine in resource pool, or is lower than 20%, trigger carrier wave emigration；
Step 2 determines the certain customers' task and underload for needing to move out on the carrier Virtual machine of overload The whole user tasks for needing to move out on carrier Virtual machine；
Step 3 is based on migration two constraint conditions of time delay and resource according to correlated variables, is with energy consumption and migration cost Target defines mathematic(al) representation, founding mathematical models；
Step 4 solves mathematical model based on improved max-min ant system, obtains the migration of different user task Selection method.
Further, the certain customers' task for needing to move out on the carrier Virtual machine of the overload is specifically to instigate carrier wave The resource utilization of virtual machine is more than the communication services such as 80% voice, data, the video of part user.
Further, the correlated variables includes virtual resource needed for handling moved out user task under w consensus standard Measure Taski(vrc,vrm), wherein vrcAnd vrmThe calculating needed for the user task moved out on carrier wave virtual machine i under w consensus standard Stock number and memory source amount；
The energy consumption of carrier Virtual machine is the sum of its CPU and energy consumption of memory, and mathematic(al) representation is as follows:
Wherein
WithRespectively carrier Virtual machine vmiCPU and memory usage, αcpu、γcpu、αramAnd γmemFor The specific constant of model.
Further, the migration cost mathematic(al) representation is as follows:
Costmig(Task(vmi),vmj)=CmigD(vmi,vmj)；
Wherein, Task (vmi) it is carrier Virtual machine vmiUpper the needing to migrate of the task, D (vmi,vmj) it is from virtual machine vmiIt arrives Virtual machine vmjTopology distance, CmigFor moving costs parameter, CmigIt is defined as follows:
f+M(Task(vmi))/B(vmi,vmj)；
Wherein, f is fixed constant, M (Task (vmi)) it is carrier Virtual machine vmiOn memory shared by being migrated for task, B (vmi,vmj) it is virtual machine vmiAnd vmjBetween transmission bandwidth；
Further, the mathematical model is defined as follows:
And
Wherein mathematic(al) representation Tmig (Taskj)≤TthFor task immigration delay constraint condition, Tmig (Taskj) it is carrier wave Task immigration time delay on virtual machine j, TthFor the largest tolerable time delay of user in communication, it is defined as 20ms；
Mathematic(al) representationWithFor resource constraint, indicate The resources requirement of migration task is less than the available volume of resources of purpose carrier Virtual machine, vmicAnd vmimRespectively carrier Virtual The amount of available computational resources and memory source amount of machine i；
N is the number of carrier Virtual machine total in resource pool, and M is the carrier Virtual machine set for needing to migrate task, and m is collection Carrier Virtual machine number in M is closed,Expression task can only finally move on a virtual machine.
Further, the improved max-min ant system is changed to the definition of volatility coefficient in pheromones formula Into improved form is as follows:
Wherein, the volatility coefficient ρ (t) of pheromones updates as follows:
Wherein, t is the number of iterations, γkuThe pheromones of carrier Virtual machine u virtual machine as a purpose are selected for migration task k, Δγku bestFor the pheromones incremental value of optimal solution, γmaxAnd γminThe upper bound and lower bound for pheromones, tmaxFor greatest iteration time Number, f (t-1) are the suitability degree functions for defining two optimization aims by normalization and weighting factor method, and value is arrived between 0 Between 1, ρinFor the initialization value of ρ, ρminFor the minimum value of ρ, n1For the real number between 0 to 1.
Carrier wave emigration method under C-RAN framework provided by the invention, by will partially be used on the cell carrier of load weight The task immigration that family is submitted avoids on the carrier wave of load weight user to the competing of resource to loading on lighter cell carrier It strives, guarantees the service quality of user, and when the number of users of certain cell carrier service for a long time is seldom, it can be by by the load The corresponding user task of wave moves to an other carrier Virtual machine all to close the carrier Virtual machine, reduces energy consumption.
The present invention obtains the migration selection method of different user task by founding mathematical models, solution.It is moved by task The mode of shifting is conducive to the load for balancing each carrier Virtual machine, the process resource of each carrier wave is adequately utilized, reduces Energy consumption, improves the service quality of user, while improving the load balancing degrees of resource pool entirety.
Detailed description of the invention
Fig. 1 is the carrier wave emigration method flow diagram under C-RAN framework provided in an embodiment of the present invention.
Fig. 2 is the topology diagram of C-RAN framework downloading wavelength-division cluster provided in an embodiment of the present invention.
Fig. 3 is schematic diagram of the user task provided in an embodiment of the present invention in cluster internal migration.
Fig. 4 is the schematic diagram that user task provided in an embodiment of the present invention migrates between cluster.
Fig. 5 is resource pool energy consumption pair before and after the migration obtained based on improvement MMAS (max-min ant system) optimization algorithm Than figure.
Fig. 6 is load balancing degrees pair before and after the migration obtained based on improvement MMAS (max-min ant system) optimization algorithm Than figure.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to Limit the present invention.
It is lighter small to load that the present invention passes through the task immigration that certain customers submit on the cell carrier that will load weight On area's carrier wave, guarantee QoS of customer, and when certain cell carrier for a long time service number of users it is seldom when, can pass through by The corresponding user task of the carrier wave moves to an other carrier Virtual machine all to close the carrier Virtual machine, reduces energy Consumption.By founding mathematical models, solution obtains the migration selection method of different user task.
Application principle of the invention is explained in detail with reference to the accompanying drawing.
As shown in Figure 1, carrier wave emigration method under the C-RAN framework of the embodiment of the present invention the following steps are included:
Occur that CPU and memory comprehensive resources utilization rate are more than 80% on carrier Virtual machine in S101, resource pool, or is lower than 20%, trigger carrier wave emigration；
S102, the load for determining the certain customers' task and underload that need to move out on the carrier Virtual machine of overload The whole user tasks for needing to move out on wave virtual machine；
S103, cost using energy consumption and is migrated as mesh based on migration two constraint conditions of time delay and resource according to correlated variables Mark defines mathematic(al) representation, founding mathematical models；
S104, mathematical model is solved based on improved max-min ant system, obtains the migration choosing of different user task Selection method.
Application principle of the invention is further described combined with specific embodiments below.
The embodiment of the present invention provides the carrier wave emigration method under C-RAN framework, comprising the following steps:
Occur that CPU and memory comprehensive resources utilization rate are more than 80% on carrier Virtual machine in S1, resource pool, or is lower than 20%, trigger carrier wave emigration.
It should be noted that carrier Virtual machine described in step S1 specifically refers to the carrier processing resource for a cell The virtual machine formed after virtualization.The configuration of each carrier Virtual resource is different according to the average portfolio of cell.
The CPU and memory comprehensive resources utilization rate of carrier Virtual machine are specifically referred to:
Wherein, vcutilAnd vmutilFor the CPU and memory source usage amount of carrier Virtual machine, vmcpuAnd vmmemFor carrier wave void The CPU of quasi- machine and memory source configuration.
S2, the carrier wave for determining the certain customers' task and underload that need to move out on the carrier Virtual machine of overload The whole user tasks for needing to move out on virtual machine.
It should be noted that the user task in step S2 specifically refer to the business such as voice that cell carrier is handled in real time, Data, video traffic etc..
It should be further noted that the part for needing to move out on the carrier Virtual machine of overload described in step S2 is used Family task be specifically instigate carrier Virtual machine resource utilization be more than 80% part user task.
S3, cost using energy consumption and is migrated as mesh based on migration two constraint conditions of time delay and resource according to correlated variables Mark defines mathematic(al) representation, founding mathematical models.
It should be noted that correlated variables described in step S3 includes handling moved out user task under w consensus standard Required virtual resource amount Taski(vrc,vrm), wherein vrcAnd vrmFor the user to move out on carrier wave virtual machine i under w consensus standard The amount of computational resources and memory source amount that task needs.
The energy consumption of carrier Virtual machine is the sum of its CPU and energy consumption of memory, and mathematic(al) representation is as follows:
Wherein
WithRespectively carrier Virtual machine vmiCPU and memory usage.αcpu、γcpu、αramAnd γmemFor The specific constant of model.
It is as follows to migrate cost mathematic(al) representation:
Costmig(Task(vmi),vmj)=CmigD(vmi,vmj),
Wherein, Costmig (Task (vmi),vmj) it is carrier Virtual machine vmiOn task task (vmi) move to carrier wave Virtual machine vmjUpper required migration cost, D (vmi,vmj) it is from virtual machine vmiTo virtual machine vmjTopology distance, CmigTo move Move cost parameter.CmigIt is defined as follows:
f+M(Task(vmi))/B(vmi,vmj),
Wherein, f is fixed constant, M (Task (vmi)) it is carrier Virtual machine vmiOn memory shared by being migrated for task, B (vmi,vmj) it is virtual machine vmiAnd vmjBetween transmission bandwidth.
It should be further noted that mathematical model described in step S3 is defined as follows:
And
Wherein, mathematic(al) representation Tmig (Taskj)≤TthFor task immigration delay constraint condition, Tmig (Taskj) it is to carry Task immigration time delay on wave virtual machine j, TthFor the largest tolerable time delay of user in communication, it is defined as 20ms；
Mathematic(al) representationWithFor resource constraint, indicate to move The resources requirement of shifting task is less than the available volume of resources of purpose carrier Virtual machine, vmicAnd vmimRespectively carrier Virtual machine i Amount of available computational resources and memory source amount；
N is the number of carrier Virtual machine total in resource pool, and M is the carrier Virtual machine set for needing to migrate task, and m is collection Carrier Virtual machine number in M is closed,Expression task can only finally move on a virtual machine.
S4, mathematical model is solved based on improved max-min ant system, obtains the migration selection of different user task Method.
It should be noted that improved max-min ant system described in step S4 refers to, to being waved in pheromones formula The definition of hair coefficient improves, and improved form is as follows:
Wherein, the volatility coefficient ρ (t) of pheromones updates as follows:
Wherein, t is the number of iterations, γkuThe pheromones of carrier Virtual machine u virtual machine as a purpose are selected for migration task k, Δγku bestFor the pheromones incremental value of optimal solution, γmaxAnd γminThe upper bound and lower bound for pheromones, tmaxFor greatest iteration time Number, f (t-1) are the suitability degree functions for defining two optimization aims by normalization and weighting factor method, and value is arrived between 0 Between 1, ρinFor the initialization value of ρ, ρminFor the minimum value of ρ, n1For the real number between 0 to 1.
Fig. 2 gives the topology diagram of the carrier wave sub-clustering under C-RAN framework, the carrier wave in resource pool in the form of cluster into Row management, each cluster contain the carrier Virtual machine of protocol type of the same race, and each carrier Virtual machine is according to its cell serviced Average traffic and have different resource distributions.
Fig. 3 and Fig. 4 give user task migration schematic diagram, when by step S4 be calculated it is each need migrate appoint It is engaged in after selected purpose carrier Virtual machine, under the network topology structure based on sub-clustering, in fact it could happen that cluster internal migration and cluster Between migrate two kinds of situations.Since across cluster migration will pass through multistage switching network, the migration cost of consuming is big, therefore, when in cluster There are when the suitable carrier virtual machine of acceptable migration task, in order to reduce migration cost, cluster internal migration will be selected, otherwise, Suitable purpose carrier Virtual machine will be found by way of migrating between cluster.Monitoring resource module monitors first are to a cluster memory Carrier Virtual machine meet migration trigger condition, then informing removal management module according to migration task resources requirement and The constraint condition that migration time delay should meet, by the migration selection method for establishing seismic responses calculated different user task.
Fig. 5 gives resource pool energy before and after the migration obtained based on improvement MMAS (max-min ant system) optimization algorithm Consume comparison diagram.Resource pool same kind carrier Virtual machine number is arranged in emulation to configure by basic, normal, high 1:1:1, every kind of configuration packet The carrier Virtual machine of equally distributed different resource utilization rate is contained.It can be seen that the total energy consumption of resource pool reduces after migration, Because realizing the readjustment of task by way of migration, the low carrier Virtual machine of resource utilization is turned off, has mitigated simultaneously Competition of the user to resource on the high carrier wave of load.When carrier Virtual machine number total in resource pool increases, need The user task of migration increases therewith, and also more by migrating reduced energy consumption.
Fig. 6 gives load balancing before and after the migration obtained based on improvement MMAS (max-min ant system) optimization algorithm Spend comparison diagram.Load balancing degrees is defined as:
uiFor the resource utilization of carrier Virtual machine i,For the mean value of the resource utilization of all carrier Virtual machines, n is money The number of the carrier Virtual machine for the total protocol type of the same race opened in the pond of source.The value of B is smaller, the resource of each carrier Virtual machine Utilization value is more close, and load balancing degrees are better.Before migrating it can be seen from analogous diagram, resource pool configures different carrier Virtuals In the case where machine number, load balancing degrees be in one similar in level.The value of B reduces after migration, this is because by moving It moves, the carrier Virtual organ for keeping resource utilization low closes, and will be on the carrier Virtual machine of its whole task and high load condition Partial task moves on resource utilization carrier Virtual machine under normal condition, makes the load being ultimately under open state The resource utilization of wave virtual machine is in an approximately equal state, improves load balancing degrees.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all in essence of the invention Made any modifications, equivalent replacements, and improvements etc., should all be included in the protection scope of the present invention within mind and principle.

Claims (2)

1. a kind of carrier wave emigration method under C-RAN framework, which is characterized in that the carrier wave emigration method under the C-RAN framework The following steps are included:
Step 1, occurs that CPU and memory comprehensive resources utilization rate are more than 80% on carrier Virtual machine in resource pool, or is lower than 20%, trigger carrier wave emigration；
Step 2 determines the carrier wave of the certain customers' task and underload that need to move out on the carrier Virtual machine of overload The whole user tasks for needing to move out on virtual machine；
Step 3, according to correlated variables, based on migration two constraint conditions of time delay and resource, using energy consumption and migration cost as mesh Mark defines mathematic(al) representation, founding mathematical models；
The correlated variables includes virtual resource amount Task needed for handling moved out user task under w consensus standardi(vrc, vrm), wherein vrcAnd vrmThe amount of computational resources that is needed for the user task moved out on carrier wave virtual machine i under w consensus standard and interior Deposit stock number；
The energy consumption of carrier Virtual machine is the sum of its CPU and energy consumption of memory, and mathematic(al) representation is as follows:
Wherein
WithRespectively carrier Virtual machine vmiCPU and memory usage, αcpu、γcpu、αramAnd γmemFor model Specific constant；
The migration cost mathematic(al) representation of task is as follows:
Costmig(Task(vmi),vmj)=CmigD(vmi,vmj)；
Wherein, Task (vmi) it is carrier Virtual machine vmiUpper the needing to migrate of the task, D (vmi,vmj) it is from virtual machine vmiTo virtual Machine vmjTopology distance, CmigFor moving costs parameter, CmigIt is defined as follows:
f+M(Task(vmi))/B(vmi,vmj)；
Wherein, f is fixed constant, M (Task (vmi)) it is carrier Virtual machine vmiOn memory shared by being migrated for task, B (vmi,vmj) it is virtual machine vmiAnd vmjBetween transmission bandwidth；
The mathematical model is defined as follows:
And
Wherein, mathematic(al) representation Tmig (Taskj)≤TthFor task immigration delay constraint condition, Tmig (Taskj) it is carrier Virtual Task immigration time delay on machine j, TthFor the largest tolerable time delay of user in communication, it is defined as 20ms；
Mathematic(al) representationWithFor resource constraint, indicate that migration is appointed The resources requirement of business is less than the available volume of resources of purpose carrier Virtual machine, vmicAnd vmimRespectively carrier Virtual machine i's can With amount of computational resources and memory source amount；
N is the number of carrier Virtual machine total in resource pool, and M is the carrier Virtual machine set for needing to migrate task, and m is set M Middle carrier Virtual machine number,Expression task can only finally move on a virtual machine；
Step 4 solves mathematical model based on improved max-min ant system, obtains the migration selection of different user task Method；
The improved max-min ant system is improved to the definition of volatility coefficient in pheromones formula, improved Form is as follows:
Wherein, the volatility coefficient ρ (t) of pheromones updates as follows:
Wherein, t is the number of iterations, γkuThe pheromones of carrier Virtual machine u virtual machine as a purpose, Δ are selected for migration task k γku bestFor the pheromones incremental value of optimal solution, γmaxAnd γminThe upper bound and lower bound for pheromones, tmaxFor greatest iteration time Number, f (t-1) are the suitability degree functions for defining two optimization aims by normalization and weighting factor method, and value is arrived between 0 Between 1, ρinFor the initialization value of ρ, ρminFor the minimum value of ρ, n1For the real number between 0 to 1.
2. the carrier wave emigration method under C-RAN framework as described in claim 1, which is characterized in that the load of the overload The certain customers' task for needing to move out on wave virtual machine be specifically instigate carrier Virtual machine resource utilization be more than 80% that The communication service of the voice, data, video of certain customers.
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