CN107333267B - A kind of edge calculations method for 5G super-intensive networking scene - Google Patents

A kind of edge calculations method for 5G super-intensive networking scene Download PDF

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Publication number
CN107333267B
CN107333267B CN201710485333.1A CN201710485333A CN107333267B CN 107333267 B CN107333267 B CN 107333267B CN 201710485333 A CN201710485333 A CN 201710485333A CN 107333267 B CN107333267 B CN 107333267B
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user
subchannel
server
mec
formula
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CN107333267A (en
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欧阳昊
王鹏飞
杨杰
但黎琳
肖悦
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University of Electronic Science and Technology of China
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
    • H04W16/06—Hybrid resource partitioning, e.g. channel borrowing
    • H04W16/08—Load shedding arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/02—Resource partitioning among network components, e.g. reuse partitioning
    • H04W16/10—Dynamic resource partitioning
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • 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
    • H04W28/0231—Traffic management, e.g. flow control or congestion control based on communication conditions
    • H04W28/0236—Traffic management, e.g. flow control or congestion control based on communication conditions radio quality, e.g. interference, losses or delay

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention belongs to communicate 5G and edge calculations technical field, specifically a kind of edge calculations method for 5G super-intensive networking scene.Basic ideas of the invention are to place a MEC server in one small base station to provide edge calculations service come the phone user for the Microcell, while phone user can select the cloud computing service being connected with macro base station according to the characteristic of pending task.Beneficial effects of the present invention are that method of the invention can make user select most suitable calculating service, improve delay performance.

Description

A kind of edge calculations method for 5G super-intensive networking scene
Technical field
The invention belongs to communicate 5G and edge calculations (Mobile Edge Computing, MEC) technical field, specifically Say it is a kind of edge calculations method for 5G super-intensive networking scene；Invention is related to convex optimization (Convex Optimal), Super-intensive networking (Ultra-Density Network, UDN), edge calculations, the technologies such as cloud computing (Cloud Computing).
Background technique
Cloud computing is the technology that one kind is intended to break through mobile terminal computing capability (storage capacity) limitation.Terminal device selection Transfer to cloud to execute the calculating task (store tasks) of oneself with save itself limited computing resource (storage resource) and Energy consumption.
And edge calculations this concepts is relative to cloud computing.Since the computing capability of cloud server is very powerful, But the time delay from terminal to Cloud Server may be very big.And with the development of mobile communication technology and various new industries It rises.To delay performance, more stringent requirements are proposed for some applications in terminal, such as augmented reality (Augmented Reality, AR).Therefore in order to meet this demand, this concept of edge calculations is come into being.What it is different from cloud computing is side Edge, which is calculated, is inferior to the edge calculations server of cloud computing server in the neighbouring geographical location deployment computing capability of user.Come with this Reduce time delay caused by physical distance.
The access density of upcoming 5G mobile communication technology design is 106Every square kilometre, in order to meet such Pang Big access demand, 5G use super-intensive networking technology: a cell is made of a macro base station and a large amount of micro-base station, Each micro-base station is responsible for neighbouring phone user's service.But so many accessing user, it goes to meet with which type of technology The various capability requirements of terminal are still problem to be solved.
Summary of the invention
In view of the above-mentioned problems, the invention proposes a kind of edge calculations methods for 5G super-intensive networking scene, it is intended to Improve the computing capability and delay performance of access terminal in cellular system.Basic ideas of the invention are a small base station (Small Base Station, SBS) places a MEC server and provides edge calculations clothes come the phone user for the Microcell Business, while phone user can select and macro base station (Macro Base Station, MBS) according to the characteristic of pending task Connected cloud computing service.The program can make user select most suitable calculating service, improve delay performance.
Introducing system model first:
As shown in Figure 1, consider the cellular system of the super-intensive networking of a uplink, there are the macro base station MSB at a center, It is connected with cloud computing server and several small base station SBS, each SBS placed an edge calculations server；Each The phone user that small base station has several to access, each user have a pending task, and there are two attributes for each task: defeated Enter data volume and CPU execution cycle number, and each task can choose and be performed locally, execute on MEC server or cloud meter It calculates and is executed on server.Here by taking a small base station as an example: assuming that access phone user's number of small base station is N, having K a independent Subchannel is for distribution.User, the computing capability of MEC server, cloud computing server are respectively as follows: simultaneouslyFmes, Fcloud。 Other variables are as shown in the table:
Table 1
Method of the invention is divided into four steps:
The unloading strategy of S1, initialising subscriber:
First of all for the user task initialized, unloading strategy assumes first that the meter of subchannel and MEC server Calculation ability gives each user, and channel gain mean valueInstead of.And calculate the time delay of three kinds of unloading situations:
Wherein
So the unloading strategy of the initialization of each user is to take time delay the smallest a kind of tactful as just under three kinds of strategies Beginningization unloading strategy；
S2, the subchannel number that all users are planned with distribution:
In the case where the first step is given initial unloading strategy, it is necessary to carry out the distribution of subchannel to user.This Walk the subchannel number C for being assigned to each user of determinationiIt indicates, it should be noted that select the user being performed locally 0 sub-channels should be assigned to.
The number of users for offloading tasks to MEC server in the first step is N*, and assume that they divide equally MEC in this step The computing resource of server.Then compared to the first step,The two parameters have occurred that variation, after update Parameter be updated in (1) formula, the time delay of each user can be obtained
Introduce utility functionIt establishes and obtains CiMathematical model be expressed as follows
Here it proposes to propose a kind of suboptimum solution based on the thought divided and ruled: the user of all subchannels to be allocated is divided into two A subsetIt is first the two set distribution number of subchannels. Introduce two functionsThen C is obtainediMathematical model (3) transformation are as follows:
Two constraint conditions are introduced simultaneouslyThen model (4) changes are as follows:
The Lagrangian of (5) formula of construction:
According to
Introduce variable
Then have:
Dichotomy solution equation group can be used and obtain numerical solution C1,C2, as gatherThe son being assigned to Channel number.Then again by subclassTwo are divided into setBy C1, C2Respectively instead of this K in step is iterated distribution.The subchannel number C that each user is assigned can finally be calculatedi。
S3, subchannel distribution is carried out to all users:
In the assigned subchannel number C that user has been determinediLater, it needs to distribute specific subchannel for user, is every The algorithm steps that a user distributes subchannel are as follows:
Step1, user is chosen
Step2, C is arrived for 1i, iteration execution:
By subchannelDistribute to user i；
Hik=0, i ∈ { 1,2..N }；
Step3, step Step1 and step Step2 is repeated until subchannel is assigned in all users；
S4, computational resource allocation is carried out to all users for being discharged in MEC server:
In the case where unloading tactful subchannel distribution strategy and all determining.It there remains last part: MEC computing resource Allocation strategy.And the allocation algorithm of MEC computing resource is identical as step 2, is all to use the thought for algorithm of dividing and ruling by the meter of MEC Resource -- cpu frequency is divided into two parts and is iterated distribution for calculation.Here it no longer repeats.
Beneficial effects of the present invention are that method of the invention can make user select most suitable calculating service, are improved Delay performance.
Detailed description of the invention
Fig. 1 is the cellular system type schematic diagram of super-intensive networking.
Specific embodiment
Summary is described in detail the solution of the present invention, and details are not described herein.

Claims (1)

1. a kind of edge calculations method for 5G super-intensive networking scene, this method is used for the cellular system of super-intensive networking, If the system includes the macro base station MSB and several small base station SBS at the center that is connected with cloud computing server, Each SBS placed an edge calculations server MEC；The phone user that each small base station has several to access, each user There is a pending task, there are two attributes for each task: input data amount and CPU execution cycle number, and each task can It is performed locally, executed on execution or cloud computing server on MEC server, the access phone user of a base station with selection Number is N, has K independent subchannels for distributing, while the computing capability difference of user, MEC server, cloud computing server Are as follows:WithCharacterized by comprising the following steps:
The unloading strategy of S1, initialising subscriber:
The user task initialized in order to obtain, unloading strategy assume first that the computing capability of subchannel and MEC server is equal Give each user, and channel gain mean valueInstead of；Use is unloaded to by the acquisition of following formula 1- formula 3 respectively simultaneously Family, the time delay in the case of three kinds of MEC server and cloud computing server:
Wherein, τ is time delay, and subscript ue is user, mec is MEC server, subscript cloud is cloud computing server, αiFor user i Task input data amount, βiFor the execution cycle number of the task of user i,For the cpu frequency of user,For MEC clothes The cpu frequency of business device,For the cpu frequency of cloud computing server, K is the subchannel number of Microcell, and B is subchannel bandwidth, σ2For thermal noise power, P is user emission power, diFor user i with The distance of SBS, γ are large-scale fading coefficient；
The unloading strategy of the initialization of each user is to take time delay the smallest a kind of tactful as initialization unloading under three kinds of strategies Tactful then unloading strategy according to the building initialization of formula 1 are as follows:
S2, subchannel number is distributed to all users:
The subchannel number C that each user of determination is assigned toiIt indicates, it is assumed that offload tasks to the user of MEC server Number is N*, and they divide equally the computing resource of MEC server, then relative to formula 1,The two parameters have been sent out Variation has been given birth to, updated parameter is brought into formula 1, the time delay of each user can be obtained
Introducing utility function isIt establishes and obtains CiThe following formula 3 of mathematical model:
The user of all subchannels to be allocated is divided into two subsetsWithAnd meetFor subsetWithDistribute number of subchannels:
Introduce two functionsFormula 3 is changed are as follows:
Two constraint conditions are introduced simultaneouslyFormula 4 is changed are as follows:
Construct Lagrangian:
According to
Introduce variable
It can obtain:
Numerical solution C is obtained using equation group shown in dichotomy solution formula 81、C2, as gatherThe son being assigned to Channel number, then again by subclassTwo are divided into setBy C1、C2Respectively instead of K in this step is iterated calculating, finally obtains the subchannel number C that each user is assignedi；
S3, subchannel distribution is carried out to all users:
If user gathersTask input data set Α={ α of user1,α2,α3,…,αN, subchannel SetUser is assigned subchannel number setInclude:
S31, user is chosen
S32, C is arrived for 1i, iteration execution:
By subchannelAfter distributing to user i, then by Hik=0, i ∈ { 1,2..N }；
S33, step S31 and step S32 is repeated until subchannel is assigned in all users；
S4, computational resource allocation is carried out to all users for being discharged in MEC server.
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