CN108964817A - A kind of unloading of heterogeneous network combined calculation and resource allocation methods - Google Patents
A kind of unloading of heterogeneous network combined calculation and resource allocation methods Download PDFInfo
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- CN108964817A CN108964817A CN201810949323.3A CN201810949323A CN108964817A CN 108964817 A CN108964817 A CN 108964817A CN 201810949323 A CN201810949323 A CN 201810949323A CN 108964817 A CN108964817 A CN 108964817A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/391—Modelling the propagation channel
- H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B17/00—Monitoring; Testing
- H04B17/30—Monitoring; Testing of propagation channels
- H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
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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
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
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- 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
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- 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
- H04L67/50—Network services
- H04L67/51—Discovery or management thereof, e.g. service location protocol [SLP] or web services
Abstract
The present invention relates to a kind of unloading of heterogeneous network combined calculation and resource allocation methods, belong to wireless communication technology field.Method includes the following steps: S1: modeling user's calculating task characteristic；S2: modeling user calculates unloading decision variable and its qualifications；S3: modeling user's calculating task processing locality energy consumption and deadline；S4: modeling user's calculating task unloads the deadline；S5: energy consumption is completed in modeling user's calculating task unloading；S6: modeling total energy consumption；S7: modeling user's calculating task deadline and its qualifications；S8: being minimized based on system total energy consumption, is determined and is calculated unloading and resource allocation optimization strategy.The method of the invention can rationally utilize radio resource under the conditions of meeting the tolerance of user's calculating task maximum delay, by Optimal design and calculation unloading and resource allocation policy, realize that system total energy consumption minimizes.
Description
Technical field
The invention belongs to wireless communication technology fields, are related to heterogeneous network resource allocation techniques field, and in particular to a kind of
The unloading of heterogeneous network combined calculation and resource allocation methods.
Background technique
With computer, the high speed development of the communication technology, customer service demand is become more diversified, and next-generation communication network will
Towards isomerization trend development, and gradually, trend interconnects.To meet the market demand, occur in succession type appear it is wireless
Access technology (Radio Access Technologies, RATs), from Cellular Networks to local area network, from public network to professional net, from
Single business service network respectively has different characteristics to multi-media network and provides ability with business.Since single wireless connects
The business demand of different user can not be fully met by entering technology, and future communications network melts the efficient isomery for realizing a variety of RATs
It closes, provides diversified network service for user.
Mobile edge calculations technology is considered as a kind of mode that prospect is considerable in next generation wireless network, by by cloud
Computing capability moves near mobile device, assigns Radio Access Network (Radio Access Networks, RANs) powerful meter
Calculation ability, it can be achieved that at any time any place for mobile device provide calculating service.Mobile edge calculations technology is calculating
Low time delay, high bandwidth and computational flexibility are capable of providing in uninstall process.As mobile edge calculations technology typical case it
One, thin cloud was quickly grown in recent years, by by thin cloud server disposition at RANs access point, can mobile use in overlay network
Family provides calculating unloading service for user.Higher calculating cost is needed due to locally executing user's calculating task, and uses and calculates
Unloading scheme executes user task by cloud and is related to communicating cost, therefore how to divide calculating task and determine task unloading side
Case, to realize that wireless network resource efficiently utilizes and the optimization of system-computed and communication cost is current hot research problem.
In recent years, have the unloading of literature research heterogeneous network combined calculation and resource allocation problem.Document T.T.Nguyen
and B.L.Long.Joint computation offloading and resource allocation in cloud
based wireless hetnets[C].IEEE Global Communications Conference,Dec 2017,
Pp.1-6 proposes a kind of combined calculation unloading and resource allocation optimization plan for two layers multiple cell multi-user system
Slightly, under the restrictive condition for meeting bandwidth, computing resource and tolerable time delay, the energy consumption minimized target of maximum weighted is realized.
Document J.Zhang, W.Xia, F.Yan, and L.Shen.Joint computation offloading and resource
allocation optimization in heterogeneous networks with mobile edge computing
[J] .IEEE Access, vol.6, pp.19324-19337,2018, for the mobile edge calculations system of heterogeneous network, research
The distribution of mobile device upstream subchannels, upload transfers power distribution and computing resource scheduling problem, propose a kind of distribution
Combined calculation unloading and resource allocation optimization strategy.
The above research determines the money of corresponding performance Function Optimization based on optimum theory by modeling particular network performance function
Source allocation strategy, but the less diversity for comprehensively considering heterogeneous network of existing research, multiple access characteristic and network resource status
Otherness the problems such as, it is difficult to realize network synthesis performance optimize.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of unloading of heterogeneous network combined calculation and resource allocation methods,
In the method, for the heterogeneous network scene comprising 1 macro base station and Multiple Small Cell Sites, it is assumed that all base station (Base
Station, BS) a certain number of thin cloud servers are disposed, comprehensively consider user's calculating task characteristic, user terminal and service
The factors such as device processing capacity, modeling total energy consumption are optimization aim, realize combined calculation unloading and resource allocation optimization strategy.
In order to achieve the above objectives, the invention provides the following technical scheme:
A kind of unloading of heterogeneous network combined calculation and resource allocation methods, specifically includes the following steps:
S1: modeling user's calculating task characteristic；
S2: modeling user calculates unloading decision variable and its qualifications；
S3: modeling user's calculating task processing locality energy consumption and deadline；
S4: modeling user's calculating task unloads the deadline；
S5: energy consumption is completed in modeling user's calculating task unloading；
S6: modeling total energy consumption；
S7: modeling user's calculating task deadline and its qualifications；
S8: being minimized based on system total energy consumption, is determined and is calculated unloading and resource allocation optimization strategy.
Further, the step S1 is specifically included: assuming that each user, which at a time generates one, may need to unload
The calculating task of completion, enables dmIndicate input data size required for executing the calculating task of user m, these input datas can
It can include program code, input file etc.；Enable cmIndicate the computational load of user m calculating task；It enablesIndicate that user m is calculated
The maximum delay tolerance of task, 1≤m≤M, wherein M is total number of users in network.
Further, the step S2 is specifically included:
Assuming that a certain number of thin cloud servers are disposed in all base stations, BS is enablednIndicate nth base station, δm,n∈{0,1}
Indicate whether the calculating task of user m is unloaded to BSnThin cloud complete calculate unloading decision variable, δm,n=1 indicates user m
Calculating task be unloaded to BSnThin cloud server complete calculate, otherwise, δm,n=0；
It completes to calculate on the thin cloud server of a BS assuming that the calculating task of each user can only be at most unloaded to, then
δm,nIt should meetEnable SnFor BSnOn thin cloud number of servers, then be unloaded to BS simultaneouslynNumber of users cannot surpass
Cross its thin cloud server sum, i.e. δm,nIt should meet1≤n≤N, wherein N is the sum of BS in network.
Further, the step S3 is specifically included: modeling user's calculating task processing locality energy consumptionAnd the deadline
According to formulaCalculate the calculating task processing locality energy consumption of user m, whereinIndicate that user m processing locality calculates
The power of battery of task consumption,Indicate the deadline of user m processing locality calculating task；According to formulaIt calculates and uses
The deadline of family m processing locality calculating task, whereinFor the processing locality rate of user m.
Further, the step S4 is specifically included: modeling user's calculating task unloads the deadlineAccording to formulaIt calculates user's calculating task and unloads the deadline, whereinIndicate that user m unloads calculating task to BSn's
The radio link transmission times of thin cloud server,Indicate the calculating task of user m in BSnThin cloud server calculating when
Between.
Further, modeling user m unloads calculating task to BSnThin cloud server radio link transmission timesAre as follows:Wherein, Rm,nIndicate access BSnUser m message transmission rate；According to formulaCalculate access BSnUser m message transmission rate, wherein W indicate subchannel bandwidth,
Pm,nIndicate access BSnUser m transimission power, gm,nAnd σ2Respectively indicate user m and BSnBetween channel gain and noise
Power；
The calculating task of user m is modeled in BSnThin cloud server the calculating timeAre as follows:Wherein, fnTable
Show BSnThin cloud server computing capability.
Further, the step S5 is specifically included: energy consumption is completed in modeling user's calculating task unloadingAccording to formulaIt calculates the unloading of user's calculating task and completes energy consumption, whereinIndicate that user m unloads calculating task to BSn
Thin cloud server transmission energy consumption,Indicate the calculating task of user m in BSnThin cloud server calculating energy consumption；According to
FormulaIt calculates user m and unloads calculating task to BSnThin cloud server transmission energy consumption；According to formulaThe calculating task of user m is calculated in BSnThin cloud server calculating energy consumption, whereinIndicate BSnThin cloud
The data of server calculate power consumption.
Further, the step S6 is specifically included: modeling total energy consumption E, and the system total energy consumption is to complete in network
Energy needed for all user's calculating tasks, i.e.,
Further, the step S7 is specifically included: according to formulaCalculate user m
The calculating task deadline, should meet
Further, the step S8 is specifically included: comprehensively consider user's calculating task characteristic and calculates unloading qualifications,
It is minimized based on system total energy consumption, determines and calculate unloading and resource allocation optimization strategy, note
The beneficial effects of the present invention are: the method for the invention is meeting user's calculating task maximum delay tolerance condition
Under, radio resource can be rationally utilized, by Optimal design and calculation unloading and resource allocation policy, realizes that system total energy consumption is minimum
Change.
Detailed description of the invention
In order to keep the purpose of the present invention, technical scheme and beneficial effects clearer, the present invention provides following attached drawing and carries out
Illustrate:
Fig. 1 is the heterogeneous network scene schematic diagram for being deployed with thin cloud server；
Fig. 2 is the flow diagram of the method for the invention.
Specific embodiment
Below in conjunction with attached drawing, a preferred embodiment of the present invention will be described in detail.
Fig. 1 is the heterogeneous network scene schematic diagram for being deployed with thin cloud server, and Multiple Small Cell Sites is located in the heterogeneous network
In the network's coverage area of one macro base station, and there are multiple users in the heterogeneous network, and user is located at the net of multiple BS
The thin cloud server process being unloaded on suitable BS calculating may be selected according to the calculating task characteristic of user in network overlay area
Task；Modeling total energy consumption is to complete energy needed for all user's calculating tasks in network, minimum based on system total energy consumption
Change and realizes combined calculation unloading and resource allocation optimal policy.
Fig. 2 is the flow diagram of the method for the invention, as shown in Fig. 2, heterogeneous network combined calculation of the present invention
Unloading and resource allocation methods specifically includes the following steps:
1) user's calculating task characteristic is modeled:
Assuming that each user, which at a time generates one, may need to unload the calculating task completed, modeling user is calculated
Task characteristic, specifically: enable dmIndicate input data size required for executing the calculating task of user m, these input datas
It may include program code, input file etc.；Enable cmIndicate the computational load of user m calculating task；It enablesIndicate user m meter
The maximum delay tolerance of calculation task, 1≤m≤M, wherein M is total number of users in network.
2) building user calculates unloading decision variable, models qualifications:
Assuming that a certain number of thin cloud servers are disposed in all base stations, building user calculates unloading decision variable, specifically
Are as follows: enable BSnIndicate nth base station, δm,n∈ { 0,1 } indicates whether the calculating task of user m is unloaded to BSnThin cloud complete calculate
Unloading decision variable, δm,nThe calculating task of=1 expression user m is unloaded to BSnThin cloud server complete calculate, otherwise,
δm,n=0；It completes to calculate on the thin cloud server of a BS assuming that the calculating task of each user can only be at most unloaded to, then δm,n
It should meetEnable SnFor BSnOn thin cloud number of servers, then be unloaded to BS simultaneouslynNumber of users no more than it
Thin cloud server sum, i.e. δm,nIt should meet1≤n≤N, wherein N is the sum of BS in network.
3) user's calculating task processing locality energy consumption and deadline are modeled:
Model user's calculating task processing locality energy consumptionAnd the deadlineAccording to formulaCalculate user m
Calculating task processing locality energy consumption, whereinIndicate the power of battery of user m processing locality calculating task consumption,It indicates
The deadline of user's m processing locality calculating task；According to formulaCalculate the completion of user m processing locality calculating task
Time, whereinFor the processing locality rate of user m.
4) modeling user's calculating task unloads the deadline:
It models user's calculating task and unloads the deadlineAccording to formulaUser's calculating task is calculated to unload
Carry the deadline, whereinIndicate that user m unloads calculating task to BSnThin cloud server radio link transmission times,Indicate the calculating task of user m in BSnThin cloud server the calculating time.In turn, modeling user m unloading calculating task arrives
BSnThin cloud server radio link transmission timesAre as follows:Wherein, Rm,nTo access BSnUser m data
Transmission rate；According to formulaCalculate access BSnUser m message transmission rate, wherein
W is subchannel bandwidth, Pm,nTo access BSnUser m transimission power, gm,nAnd σ2Respectively indicate user m and BSnBetween letter
Road gain and noise power；The calculating task of user m is modeled in BSnThin cloud server the calculating timeAre as follows:
Wherein, fnFor BSnThin cloud server computing capability.
5) energy consumption is completed in the unloading of modeling user's calculating task:
It models the unloading of user's calculating task and completes energy consumptionAccording to formulaIt calculates user and calculates and appoint
Energy consumption is completed in business unloading, whereinIndicate that user m unloads calculating task to BSnThin cloud server transmission energy consumption,Table
Show the calculating task of user m in BSnThin cloud server calculating energy consumption；According to formulaCalculate user m unloading
Calculating task is to BSnThin cloud server transmission energy consumption；According to formulaThe calculating task of user m is calculated in BSn
Thin cloud server calculating energy consumption, whereinIndicate BSnThin cloud server data calculate power consumption.
6) modeling total energy consumption:
Modeling total energy consumption E is to complete energy needed for all user's calculating tasks in network, i.e.,
7) user's calculating task deadline and its qualifications are modeled:
User's calculating task deadline and qualifications are modeled, specifically: according to formula
The calculating task deadline for calculating user m, it should meet
8) it is minimized based on system total energy consumption, determines and calculate unloading and resource allocation optimization strategy:
Comprehensively consider user's calculating task characteristic and calculate unloading qualifications, is minimized, determined based on system total energy consumption
Calculate unloading and resource allocation optimization strategy, note
Finally, it is stated that preferred embodiment above is only used to illustrate the technical scheme of the present invention and not to limit it, although logical
It crosses above preferred embodiment the present invention is described in detail, however, those skilled in the art should understand that, can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (10)
1. a kind of heterogeneous network combined calculation unloading and resource allocation methods, which is characterized in that this method specifically includes following step
It is rapid:
S1: modeling user's calculating task characteristic；
S2: modeling user calculates unloading decision variable and its qualifications；
S3: modeling user's calculating task processing locality energy consumption and deadline；
S4: modeling user's calculating task unloads the deadline；
S5: energy consumption is completed in modeling user's calculating task unloading；
S6: modeling total energy consumption；
S7: modeling user's calculating task deadline and its qualifications；
S8: being minimized based on system total energy consumption, is determined and is calculated unloading and resource allocation optimization strategy.
2. a kind of heterogeneous network combined calculation unloading according to claim 1 and resource allocation methods, which is characterized in that institute
It states step S1 to specifically include: assuming that each user, which at a time generates one, may need to unload the calculating task completed, enable
dmIndicate input data size required for executing the calculating task of user m；Enable cmIndicate the computational load of user m calculating task；
It enablesIndicate the maximum delay tolerance of user m calculating task, 1≤m≤M, wherein M is total number of users in network.
3. a kind of heterogeneous network combined calculation unloading according to claim 2 and resource allocation methods, which is characterized in that institute
Step S2 is stated to specifically include:
Assuming that a certain number of thin cloud servers are disposed in all base stations, BS is enablednIndicate nth base station, δm,n∈ { 0,1 } indicates to use
Whether the calculating task of family m is unloaded to BSnThin cloud complete calculate unloading decision variable, δm,n=1 indicates the calculating of user m
Task is unloaded to BSnThin cloud server complete calculate, otherwise, δm,n=0；
It completes to calculate on the thin cloud server of a BS assuming that the calculating task of each user can only be at most unloaded to, then δm,nIt answers
MeetEnable SnFor BSnOn thin cloud number of servers, then be unloaded to BS simultaneouslynNumber of users it is micro- no more than its
Cloud Server sum, i.e. δm,nIt should meet1≤n≤N, wherein N is the sum of BS in network.
4. a kind of heterogeneous network combined calculation unloading according to claim 3 and resource allocation methods, which is characterized in that institute
It states step S3 to specifically include: modeling user's calculating task processing locality energy consumptionAnd the deadlineAccording to formula
Calculate the calculating task processing locality energy consumption of user m, whereinIndicate the battery function of user m processing locality calculating task consumption
Rate,Indicate the deadline of user m processing locality calculating task；According to formulaUser m processing locality is calculated to calculate
The deadline of task, whereinFor the processing locality rate of user m.
5. a kind of heterogeneous network combined calculation unloading according to claim 4 and resource allocation methods, which is characterized in that institute
It states step S4 to specifically include: modeling user's calculating task unloading deadlineAccording to formulaCalculate user
Calculating task unloads the deadline, whereinIndicate that user m unloads calculating task to BSnThin cloud server Radio Link
Transmission time,Indicate the calculating task of user m in BSnThin cloud server the calculating time.
6. a kind of heterogeneous network combined calculation unloading according to claim 5 and resource allocation methods, which is characterized in that
It models user m and unloads calculating task to BSnThin cloud server radio link transmission timesAre as follows:Its
In, Rm,nIndicate access BSnUser m message transmission rate；According to formulaCalculate access
BSnUser m message transmission rate, wherein W indicate subchannel bandwidth, Pm,nIndicate access BSnUser m transimission power,
gm,nAnd σ2Respectively indicate user m and BSnBetween channel gain and noise power；
The calculating task of user m is modeled in BSnThin cloud server the calculating timeAre as follows:Wherein, fnIndicate BSn
Thin cloud server computing capability.
7. a kind of heterogeneous network combined calculation unloading according to claim 6 and resource allocation methods, which is characterized in that institute
State step S5 to specifically include: energy consumption is completed in modeling user's calculating task unloadingAccording to formulaIt calculates
Energy consumption is completed in the unloading of user's calculating task, whereinIndicate that user m unloads calculating task to BSnThin cloud server transmission
Energy consumption,Indicate the calculating task of user m in BSnThin cloud server calculating energy consumption；According to formulaMeter
It calculates user m and unloads calculating task to BSnThin cloud server transmission energy consumption；According to formulaCalculate user m's
Calculating task is in BSnThin cloud server calculating energy consumption, whereinIndicate BSnThin cloud server data calculate power
Consumption.
8. a kind of heterogeneous network combined calculation unloading according to claim 7 and resource allocation methods, which is characterized in that institute
State step S6 to specifically include: modeling total energy consumption E, the system total energy consumption are all user's calculating task institutes in completion network
The energy needed, i.e.,
9. a kind of heterogeneous network combined calculation unloading according to claim 8 and resource allocation methods, which is characterized in that institute
It states step S7 to specifically include: according to formulaWhen calculating the calculating task completion of user m
Between, it should meet
10. a kind of heterogeneous network combined calculation unloading according to claim 9 and resource allocation methods, which is characterized in that
The step S7 is specifically included: being comprehensively considered user's calculating task characteristic and is calculated unloading qualifications, is based on system total energy consumption
It minimizes, determines and calculate unloading and resource allocation optimization strategy, note
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| CN112004239A (en) * | 2020-08-11 | 2020-11-27 | 中国科学院计算机网络信息中心 | Computing unloading method and system based on cloud edge cooperation |
| CN112395089A (en) * | 2020-11-19 | 2021-02-23 | 联通智网科技有限公司 | Cloud heterogeneous computing method and device |
| CN112689296A (en) * | 2020-12-14 | 2021-04-20 | 山东师范大学 | Edge calculation and cache method and system in heterogeneous IoT network |
| CN112689296B (en) * | 2020-12-14 | 2022-06-24 | 山东师范大学 | Edge calculation and cache method and system in heterogeneous IoT network |
| CN112769910B (en) * | 2020-12-29 | 2022-07-19 | 杭州电子科技大学 | Fog calculation task unloading method based on dynamic voltage regulation technology |
| CN112769910A (en) * | 2020-12-29 | 2021-05-07 | 杭州电子科技大学 | Fog calculation task unloading method based on dynamic voltage regulation technology |
| CN115100898A (en) * | 2022-05-31 | 2022-09-23 | 东南大学 | Cooperative computing task unloading method for urban intelligent parking management system |
| CN115100898B (en) * | 2022-05-31 | 2023-09-12 | 东南大学 | Collaborative computing task unloading method of urban intelligent parking management system |
| CN116541153A (en) * | 2023-07-06 | 2023-08-04 | 南昌工程学院 | Task scheduling method and system for edge calculation, readable storage medium and computer |
| CN116541153B (en) * | 2023-07-06 | 2023-10-03 | 南昌工程学院 | Task scheduling method and system for edge calculation, readable storage medium and computer |
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