CN110417605A - A kind of mobile edge calculations node laying method based on network function virtualization - Google Patents
A kind of mobile edge calculations node laying method based on network function virtualization Download PDFInfo
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- CN110417605A CN110417605A CN201910871661.4A CN201910871661A CN110417605A CN 110417605 A CN110417605 A CN 110417605A CN 201910871661 A CN201910871661 A CN 201910871661A CN 110417605 A CN110417605 A CN 110417605A
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- 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
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- 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
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Abstract
The present invention relates to a kind of mobile edge calculations node laying methods based on network function virtualization, belong to mobile communication technology field.Initial placement decision is made by backtracking method first, provides the user with the service of good network initial stage.Then it is reply network load variation, needs to reasonably adjust node location on appropriate opportunity, therefore propose to determine adjustment opportunity based on user's evaluation and adjust decision using deeply learning method.Compared to other node laying methods, this method can successfully manage area load variation, long-term to provide good service for user, reduce operator's cost.
Description
Technical field
The invention belongs to mobile communication technology fields, are related to a kind of mobile edge calculations section based on network function virtualization
Point laying method.
Background technique
With the rapid development of mobile Internet and Internet of Things, higher, delay is required to require lower shifting computing capability
It is dynamic to apply (such as augmented reality, recognition of face, high definition video steaming) explosive growth.In order to preferably support following novel shifting
Dynamic application, solves the problems, such as that existing user equipment is limited to battery capacity and calculating, storage capacity, and provide lower prolong for user
Late, higher-quality service, mobile edge calculations (Mobile EdgeComputing, MEC) come into being.MEC passes through will be interior
Hold and computing capability sinks to network edge, so that original network edge node (such as macro base station, micro-base station) is saved as MEC
Part calculating task can be unloaded on MEC node by point, user equipment, to save equipment computing resource and energy.
MEC node is typically deployed at network edge, but does not have clear stipulaties deployed position.Operator's cost is limited, can only
The MEC node of limited quantity is placed, improper the problems such as being likely to cause uneven area load, request congestion is placed, to influence
User experience, and zero load can aggravate operator's cost loss.Meanwhile as time goes by, area load often generate compared with
Big variation, the MEC node location of script are also possible to deficiency to provide good service for nearby users.Therefore, user is faced
Demand and resource constraint, how effectively to place MEC node and reply permanent load variation becomes urgent problem to be solved.
To solve the above-mentioned problems, network function virtualization (Network Function Virtualization, NFV)
As one by making network equipment function be no longer dependent on specialized hardware, hardware resource for software and hardware decoupling and function modeling
It sufficiently can flexibly share, realize the quick key technology developed and dispose of new business, the pass increasingly closer by industry
Note.Virtual network function (Virtual Network Function, VNF) takes out MEC function, and placing in respective nodes should
VNF, so that MEC node gets rid of the constraint of fixed position, preferably because more preferably meeting user demand to dynamic load.
Inventor has found to have the following disadvantages: during studying the prior art
Existing MEC node laying method primarily focuses on raising QoS of customer, and less consideration operator cost,
It how to be the critical issue for needing to solve in MEC node laying method meeting user demand and minimizing operator's cost.This
Outside, existing research is mainly a certain number of MEC nodes of fixed placement, does not consider area load dynamic changes, and real
In the application of border, area load is dynamic change at any time.Therefore a kind of dynamic MEC node laying method is needed, can is
When, efficiently select suitable MEC node, provide good service for user.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of mobile edge calculations nodes based on network function virtualization
Laying method can minimize operator's cost while meeting user demand.
In order to achieve the above objectives, the invention provides the following technical scheme:
A kind of mobile edge calculations node laying method based on network function virtualization, firstly, collecting initial time area
Domain load mean value, and to construct network topological diagram, there is certain calculating capacity f in each MEC nodev, there are phases between each of the links
It should postpone dbv, the patient maximum delay of user is Dmax；
The solution space of each MEC node load routing condition is configured to the structure of tree；
Depth-first search is carried out to the tree, when there are overload situations in node or than optimal guards escorting prisoners, does not search for still further below,
Beta pruning is simultaneously recalled upwards；
MEC node location and corresponding routing are determined by obtained optimal solution.
Optionally, in the method, to prevent malice from evaluating or accidentally touching, request adjustment percentage r, centralized control are introduced
Device collects user's adjustment request, and when requesting number accounting to be more than r, request is communicated to network operator by Centralized Controller；
Network operator weighs Setup Cost and customer churn loss to determine whether carrying out MEC node tune at the secondary moment
It is whole.
Optionally, in the method, Q network is designed using neural network to be fitted Q function；For in neural network
Parameter is denoted as θ, is worth update to obtain parameter realization by updating neural network Q:
And when carrying out decision by Q network in the case where given current state s, it only need to be with the current maximum movement of Q value
As output action:
Q network is fitted Q function, including an input layer, a hidden layer, an output layer using three-layer neural network；
Each layer is made of several neurons；Activation primitive is relu, and parameter updating method is Backward error propagation；
Determine that Centralized Controller is intelligent body agent, the key component in frame is environment and movement；Environment needs to observe
Current area load and MEC node location information is made corresponding reward value to the movement provided and is fed back, and dynamic according to this
Work makes update to environment；State space: s=[workload, mec_ser], workload array are each node loads,
Mec_ser array is each node M EC activation situation；Motion space: a=[a0,a1,…,an], indicate MEC node location and road
By；Immediately reward: reward=- (r1+r2)+penalty r1 is cost caused by the total live-vertex quantity of NextState,
R2 is the cost of NextState activation (closing-is active) number of nodes, and (r1+r2) advantage is, when the certain MEC node of needs is gone
Processing load can preferentially guarantee the node activated, rather than reactivate another node after closing again；
When iterating to certain number, model is trained；
By current state input, trained model, acquisition corresponding decision determine MEC node location and road after adjustment
By minimizing operator's cumulative cost while meeting user demand.
The beneficial effects of the present invention are:
(1) a kind of MEC node initial placement method based on backtracking method, provides good initial stage network body for user
It tests, while minimizing operator's cost, it is more accurate compared to other algorithm decisions effective.
(2) a kind of adjustment opportunity based on user's evaluation determines method, and from user experience, user files a request, fortune
Battalion quotient weighs, and while meeting user demand, reduces Setup Cost and customer churn, compared to other methods of adjustment,
It can be realized user and operator's two-win, reduce operator's pressure, it is more humanized.
(3) a kind of MEC node method of adjustment based on deeply study, according to current environment adjustment MEC node
Position successfully manages user experience decline caused by area load changes compared to other methods of adjustment, meanwhile, reduce operation
Quotient's cumulative cost.
(4) a kind of mobile edge calculations node dynamic laying method based on network function virtualization, compared to other sections
Point laying method, provides good service for a long time for user, successfully manages area load variation, reduces operator's cost.
Other advantages, target and feature of the invention will be illustrated in the following description to a certain extent, and
And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke
To be instructed from the practice of the present invention.Target of the invention and other advantages can be realized by following specification and
It obtains.
Detailed description of the invention
To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention is made below in conjunction with attached drawing excellent
The detailed description of choosing, in which:
Fig. 1 is a kind of scene of the mobile edge calculations node dynamic laying method based on network function virtualization of the present invention
Figure；
Fig. 2 is a kind of flow chart that method is determined based on the adjustment opportunity of user's evaluation of the present invention；
Fig. 3 is a kind of process of the mobile edge calculations node dynamic laying method based on network function virtualization of the present invention
Figure.
Specific embodiment
Illustrate embodiments of the present invention below by way of specific specific example, those skilled in the art can be by this specification
Other advantages and efficacy of the present invention can be easily understood for disclosed content.The present invention can also pass through in addition different specific realities
The mode of applying is embodied or practiced, the various details in this specification can also based on different viewpoints and application, without departing from
Various modifications or alterations are carried out under spirit of the invention.It should be noted that diagram provided in following embodiment is only to show
Meaning mode illustrates basic conception of the invention, and in the absence of conflict, the feature in following embodiment and embodiment can phase
Mutually combination.
Wherein, the drawings are for illustrative purposes only and are merely schematic diagrams, rather than pictorial diagram, should not be understood as to this
The limitation of invention；Embodiment in order to better illustrate the present invention, the certain components of attached drawing have omission, zoom in or out, not
Represent the size of actual product；It will be understood by those skilled in the art that certain known features and its explanation may be omitted and be in attached drawing
It is understood that.
The same or similar label correspond to the same or similar components in the attached drawing of the embodiment of the present invention；It is retouched in of the invention
In stating, it is to be understood that if there is the orientation or positional relationship of the instructions such as term " on ", "lower", "left", "right", "front", "rear"
To be based on the orientation or positional relationship shown in the drawings, be merely for convenience of description of the present invention and simplification of the description, rather than indicate or
It implies that signified device or element must have a particular orientation, be constructed and operated in a specific orientation, therefore is described in attached drawing
The term of positional relationship only for illustration, is not considered as limiting the invention, for the ordinary skill of this field
For personnel, the concrete meaning of above-mentioned term can be understood as the case may be.
Fig. 1 is usage scenario figure of the invention, is specifically included:
Client layer: there are user various service requests, maximum to endure delay, evaluate service.
Network layer: being dispersed with many macro base stations and small base station, these base stations constitute network node, these network nodes are all
There are network function virtualized infrastructure solution (NFV Infrastructure, NFVI), to support the placement of VNF,
Physical resource pool is constituted in the physical server that macro base station is placed, by NFV technology, realizes the virtualization of resource, and pass through
Layout and distribution VNF, distribute to subnetwork node for the VNF with MEC function, make MEC node
Control layer: by software defined network (Software Defined Network, SDN) technology, user's request is collected
Information, the routing of centralized management network, centralized management VNF distribution.
Fig. 2 is the flow chart that method is determined based on the adjustment opportunity of user's evaluation, is described in detail below:
When user obtain less than satisfied service, have certain probability and be lost to other network operators, cause potential benefit
The loss of profit, referred to as customer churn loss, are expressed as Clost=n_rej (t) wPlost.It is deposited between user and network operator
In SLA agreement, user calls to be serviced by the MEC that network operator provides, and after using service, evaluate-be satisfied with to service
Or it is dissatisfied, it is dissatisfied then be to issue adjustment request to network operator, it is desirable to network operator's readjustment MEC node location
And quantity, increase user experience.
Evaluating in view of certain user's malice or accidentally touching leads to the tradeoff of network operator's frequent progress, brings unnecessary
Cost, influences the user for really having demand, introduces request adjustment percentage r, and SDN controller collects user's adjustment request, when asking
When several accountings of asking for help are more than r, request is communicated to network operator by SDN.
After network operator receives user's adjustment request, it is adjusted the tradeoff of cost and customer churn loss, it is final to make
The decision whether being adjusted at the moment out.
Fig. 3 is a kind of stream of mobile edge calculations node dynamic laying method based on network function virtualization of the invention
Cheng Tu, the method meets user network initial stage service experience under the premise of meeting user demand, with initial placement, using use
Family evaluation method determines adjustment opportunity, determines node method of adjustment using deeply learning method, successfully manages area load
While variation, operator's cost is minimized.
Part1: collecting initial time area load mean value, and to construct network topological diagram, each MEC node exists certain
Calculate capacity fv, there are phase delay d between each of the linksbv, the patient maximum delay of user is Dmax。
Further, the solution space of each node load routing condition is configured to the structure of tree.
Further, depth-first search is carried out to tree, when overload situations occurs in node or than optimal guards escorting prisoners, does not search still further below
Rope, beta pruning are simultaneously recalled upwards.
Further, initial MEC node location and corresponding routing are determined by obtained optimal solution.
Since area load is variation, user satisfaction can also generate variation therewith, and user satisfaction is user's loyalty
The important prerequisite of degree, for MEC environment as novel business environment, user is services pay, and consumer loyalty degree directly affects profit.When
User obtains less than satisfied service, has certain probability and is lost to other network operators, causes the loss of potential profit, claims
For customer churn loss.User satisfaction declines in order to prevent, needs to carry out the adjustment of MEC node location.
Part2: each moment terminates, and collects the user's evaluation using service.
Further, to prevent malice from evaluating or accidentally touching, request adjustment percentage r is introduced, Centralized Controller collects user's tune
Whole request, when requesting number accounting to be more than r, request is communicated to network operator by Centralized Controller.
Further, network operator weighs Setup Cost and customer churn loss to determine whether carrying out MEC at the secondary moment
Node adjustment.
After determining adjustment opportunity, it is thus necessary to determine that how to be adjusted, while to meet user demand, minimize operator
Cost.
Part3: deeply learning method, training pattern are used.
One Q network is designed using neural network to be fitted Q function.For the parameter in neural network, it is denoted as θ, to Q value
It must update and obtain parameter realization by updating neural network:
And when carrying out decision by Q network in the case where given current state s, it only need to be with the current maximum movement of Q value
As output action:
Q network is fitted Q function, including an input layer, a hidden layer, an output layer using three-layer neural network.
Each layer is made of several neurons.Activation primitive is relu, and parameter updating method is Backward error propagation.Every time according to ε-
The movement of greedy policy selection breaks data correlation using experience replay, accelerates algorithmic statement.
Further, determine that Centralized Controller is intelligent body agent, the key component in frame is environment and movement.Environment
It needs to observe current area load and MEC node location information, corresponding reward value is made to the movement provided and is fed back, and
Update is made to environment according to the movement.State space: s=[workload, mec_ser], workload array are each sections
Point load, mec_ser array are each node M EC activation situations.Motion space: a=[a0,a1,…,an], indicate MEC node
Position and routing.Immediately reward: reward=- (r1+r2)+penalty r1 is that the total live-vertex quantity of NextState causes
Cost, r2 is the cost of NextState activation (close-active) number of nodes, and (r1+r2) advantage is, certain when needing
MEC node goes processing to load, and can preferentially guarantee the node activated, rather than reactivate another section after closing again
Point.
Further, the certain number of iteration, is trained model.
Further, by current state input, trained model, acquisition corresponding decision determine MEC node after adjustment
Position and routing minimize operator's cumulative cost while meeting user demand.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of the technical program, should all be covered in the present invention
Scope of the claims in.
Claims (3)
1. a kind of mobile edge calculations node laying method based on network function virtualization, it is characterised in that:
Firstly, collecting initial time area load mean value, and to construct network topological diagram, each MEC node has certain calculate
Capacity fv, there are phase delay d between each of the linksbv, the patient maximum delay of user is Dmax；
The solution space of each MEC node load routing condition is configured to the structure of tree；
Depth-first search is carried out to the tree, when overload situations occurs in node or than optimal guards escorting prisoners, does not search for still further below, beta pruning
And recall upwards；
MEC node location and corresponding routing are determined by obtained optimal solution.
2. a kind of mobile edge calculations node laying method based on network function virtualization according to claim 1,
It is characterized in that: in the method, to prevent malice from evaluating or accidentally touching, introducing request adjustment percentage r, Centralized Controller is collected
User's adjustment request, when requesting number accounting to be more than r, request is communicated to network operator by Centralized Controller；
Network operator weighs Setup Cost and customer churn loss to determine whether carrying out the adjustment of MEC node at the secondary moment.
3. a kind of mobile edge calculations node laying method based on network function virtualization according to claim 1,
It is characterized in that: in the method, Q network being designed using neural network to be fitted Q function；For the parameter in neural network,
It is denoted as θ, is worth update to obtain parameter realization by updating neural network Q:
And when carrying out decision by Q network in the case where given current state s, only need to using the current maximum movement of Q value as
Output action:
Q network is fitted Q function, including an input layer, a hidden layer, an output layer using three-layer neural network；It is each
Layer is made of several neurons；Activation primitive is relu, and parameter updating method is Backward error propagation；
Determine that Centralized Controller is intelligent body agent, the key component in frame is environment and movement；Environment needs to observe current
Area load and MEC node location information, corresponding reward value is made to the movement provided and is fed back, and according to the movement pair
Environment makes update；State space: s=[workload, mec_ser], workload array are each node load, mec_
Ser array is each node M EC activation situation；Motion space: a=[a0,a1,…,an], indicate MEC node location and routing；
Immediately reward: reward=- (r1+r2)+penalty r1 is cost caused by the total live-vertex quantity of NextState, and r2 is
The cost of NextState activation (closing-is active) number of nodes, (r1+r2) advantage are, when the certain MEC node of needs goes to handle
Load can preferentially guarantee the node activated, rather than reactivate another node after closing again；
When iterating to certain number, model is trained；
By current state input, trained model, acquisition corresponding decision determine MEC node location and routing after adjustment,
Operator's cumulative cost is minimized while meeting user demand.
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Cited By (4)
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| CN110856183A (en) * | 2019-11-18 | 2020-02-28 | 南京航空航天大学 | Edge server deployment method based on heterogeneous load complementation and application |
| CN112953806A (en) * | 2019-12-10 | 2021-06-11 | 中国电信股份有限公司 | Method, device and system for determining service type and installation path and storage medium |
| CN113504999A (en) * | 2021-08-05 | 2021-10-15 | 重庆大学 | Scheduling and resource allocation method for high-performance hierarchical federated edge learning |
| US11743344B1 (en) | 2022-03-15 | 2023-08-29 | International Business Machines Corporation | Edge resource processing |
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110856183A (en) * | 2019-11-18 | 2020-02-28 | 南京航空航天大学 | Edge server deployment method based on heterogeneous load complementation and application |
| CN110856183B (en) * | 2019-11-18 | 2021-04-16 | 南京航空航天大学 | Edge server deployment method based on heterogeneous load complementation and application |
| CN112953806A (en) * | 2019-12-10 | 2021-06-11 | 中国电信股份有限公司 | Method, device and system for determining service type and installation path and storage medium |
| CN112953806B (en) * | 2019-12-10 | 2022-04-01 | 中国电信股份有限公司 | Method, device and system for determining service type and installation path and storage medium |
| CN113504999A (en) * | 2021-08-05 | 2021-10-15 | 重庆大学 | Scheduling and resource allocation method for high-performance hierarchical federated edge learning |
| CN113504999B (en) * | 2021-08-05 | 2023-07-04 | 重庆大学 | Scheduling and resource allocation method for high-performance hierarchical federal edge learning |
| US11743344B1 (en) | 2022-03-15 | 2023-08-29 | International Business Machines Corporation | Edge resource processing |
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