CN110012039A - Task distribution and power control scheme in a kind of car networking based on ADMM - Google Patents
Task distribution and power control scheme in a kind of car networking based on ADMM Download PDFInfo
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- CN110012039A CN110012039A CN201810006519.9A CN201810006519A CN110012039A CN 110012039 A CN110012039 A CN 110012039A CN 201810006519 A CN201810006519 A CN 201810006519A CN 110012039 A CN110012039 A CN 110012039A
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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
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
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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
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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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
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
- H04L67/62—Establishing a time schedule for servicing the requests
-
- 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
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
- H04L67/63—Routing a service request depending on the request content or context
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
- H04W52/02—Power saving arrangements
- H04W52/0209—Power saving arrangements in terminal devices
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- 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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- 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
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The present invention relates to the mobile edge calculations scheme in a kind of car networking scene, under the premise of meeting delay requirement, distribution of computation tasks and transmitting power control problem to interior user equipment are optimized this method.Using energy loss of the equipment under the weighting of distribution of computation tasks rate as objective function, the data transfer model that user equipment and edge calculations node are obtained using Queueing Theory Method solves the optimization problem by the iteration of nonlinear fractional optimization and alternating direction multipliers method.In each round circulation, outer loop solves the problems, such as nonlinear fractional programming, and interior loop updates initial value and variable, until the result of iteration meets the threshold value of setting, the energy consumption for determining the task amount allotment ratio of each user equipment and being minimized.Technical solution provided by the invention can be effectively reduced the energy consumption of user equipment and meet the requirement of time delay, improve the computing capability of whole network.
Description
Technical field
The present invention relates to the mobile edge calculations schemes of wireless communication field, and in particular to a kind of applied to the logical of car networking
It overregulates user equipment distribution of computation tasks and transimission power and improves the method and system of user equipment energy consumption.
Background technique
As Internet of Things in the typical case of traffic and transport field, car networking realized in little or no human intervention
Immanent information sharing is carried out in vehicle, this is most important for realizing the following intelligent transportation system.On the one hand, car networking
A series of application programs required with stringent timeliness in the fields such as road safety, travelling assistance and automatic Pilot can be stimulated fast
Speed development；On the other hand, the relatively rich media Internet of Things application such as augmented reality, streaming media video and game on line is sent out rapidly
Exhibition causes great workload data to need to be buffered and handle, this demand largely calculates, communicates and storage resource.?
In traditional cloud computing model, the position of Cloud Server is far from demand side, and return path and core network ability are limited, this
Uncertain delay has been resulted in, Internet of Things has not been can guarantee and reliable service quality and Quality of experience is provided.
And as task processing method quick in Internet of Things, the edge calculations (VEC) of vehicle will calculate mode from distal end
Central distribution framework extend to distributed edge server.In car networking, calculate, communication and storage resource are assigned to
Close to the place of user, and it is dispersed in network edge.Car networking can be considered as a kind of useful supplement of traditional cloud computing.?
Network edge handles lower calculating demand and has the task of stringent timeliness limitation that can eliminate excessive network and gets over a little, this is not only
Reduce and calculate the response time, also alleviates the traffic congestion problem of the limited backhaul link of ability.Furthermore, it is understood that car networking
By being transferred to the excessive workload that consumes energy on the VEC node with higher computing capability and continuous energy supply, significantly
Improve the cruise duration of the interior user equipment such as smart phone and wearable device of limited battery capacity.Appointed by be suitable for
It is engaged in allocation strategy, the energy loss of local computing is to increase the energy consumption of data transmission, and by data transmission, edge service
Workload processing and transregional caused delay on device are reduced for cost.
Therefore, realize that distribution of computation tasks and power control under VEC scene are important problem.Firstly, since vehicle
Fast move channel conditions and network topology caused quickly to change, the task distribution ratio for making decision optimal is constrained in different delays
Rate is highly difficult.And vehicle may also leave the service range of roadside unit during data are transmitted or task is handled.Its
Secondary, the task distribution variable of different problems intercouples since VEC node computing capability is limited, goes out from the angle of energy efficiency
Hair, task allotment ratio must carry out combined optimization with power control.Finally, due to the work on user equipment and VEC node
Make task to change at random, is unable to get the optimum utilization scheme of determining calculating and the communication resource.
Summary of the invention
To solve above-mentioned deficiency of the prior art, the object of the present invention is to provide under a kind of scene suitable for car networking
The combined optimization algorithm of calculating task allotment ratio and transmitting power control in mobile edge calculations.By using calculation of the invention
Method, can be under the premise of guaranteeing time delay limitation, and reasonable distribution task to be calculated and transimission power are effectively reduced user's movement and set
Standby energy consumption.
Compared with the immediate prior art, the excellent effect that technical solution provided by the invention has is:
Invention describes how going to realize the having car networking edge calculations method of higher energy efficiency, by alternating direction multipliers method and
Nonlinear fractional optimization solves the problems, such as the minimum energy consumption, and in view of the energy including local computing and data transmission
Amount consumption and as local computing, data transmission, VEC node and roadside unit waiting time and it is transregional caused by postpone.
Under the constraint condition of VEC node computing capability, the objective function of fractional form and the optimized variable of coupling are proposed, it is difficult to form NP
Problem
Mode is calculated in order to preferably build multitask multiserver, introduces queueing theory.In the case where considering that queue is heterogeneous,
Derive the dynamic transmission model at user equipment and VEC node.And assume that the workload that each user equipment generates is obeyed
Poisson distribution, and the service time of any one user equipment and VEC node follows exponential distribution, thus in user equipment and
The multiplexed transport model of VEC node can be considered separately as M/M/1 queue and M/M/c queue.
Detailed description of the invention
Fig. 1 is car networking edge calculations system diagram provided by the invention；
Fig. 2 is that normalized energy is consumed with task allotment ratio variation diagram under different capacity provided by the invention；
Fig. 3 is that normalized energy is consumed with transimission power variation diagram under different task allotment ratio provided by the invention；
Fig. 4 is energy consumption and number of user equipment relational graph in algorithms of different provided by the invention；
Fig. 5 is that normalized energy is consumed with roadside unit service radius variation diagram under different capacity provided by the invention；
Fig. 6 is Algorithm Convergence provided by the invention and the number of iterations relational graph；
Fig. 7 is that normalized energy is consumed with task allotment ratio variation diagram under different user devices quantity provided by the invention.
Specific embodiment
Specific embodiments of the present invention will be described in further detail with reference to the accompanying drawing.
The following description and drawings fully show specific embodiments of the present invention, to enable those skilled in the art to
Practice them.Other embodiments may include structure, logic, it is electrical, process and other change.Embodiment
Only represent possible variation.Unless explicitly requested, otherwise individual component and function are optional, and the sequence operated can be with
Variation.The part of some embodiments and feature can be included in or replace part and the feature of other embodiments.This hair
The range of bright embodiment includes equivalent obtained by the entire scope of claims and all of claims
Object.Herein, these embodiments of the invention can individually or generally be indicated that this is only with term " invention "
For convenience, and if in fact disclosing the invention more than one, the range for being not meant to automatically limit the application is to appoint
What single invention or inventive concept.
Embodiment one,
The scene of present invention simulation multitask multiserver under car networking scene, it is contemplated that the faster movement speed of vehicle, to
Calculating task possibly can not in the service range end of transmission of a roadside unit, and while receiving calculated result can also exist it is transregional
Problem.By judging that task can be assigned to the ratio and transmission function that roadside unit calculates and coordination of tasks is distributed
Rate can reduce user equipment energy consumption under conditions of guaranteeing delay requirement.By the regulation of master controller, vehicle institute is determined
In the roadside unit that position belongs to, the passback of calculated result is completed.It needs to consider simultaneously to generate multitask due to multi-user, and
The computing capability and storage capacity of roadside unit it is limited and caused by the waiting time.Its system model figure is as shown in Figure 1, entire mistake
Journey includes the following contents:
1, can judgement complete data transmission in the service range of roadside unit.User equipmentIt is assigned to roadside unit
Task allotment ratio obey average arrival rate bePoisson process, vehicle is with speedMovement, away from roadside unit edge
Distance beWhen, data transmission periodIt need to be not more than.And the execution of entire edge calculations process is total
TimeNo more than the time that vehicle leaves the section.When meeting above-mentioned condition, with task allotment ratioIt will calculate
Task is sent to roadside unit.
2, the time delay executed in implementation procedure that counts mainly is made of transmission time, waiting time and calculating time.
1) calculating task is sent to roadside unit from interior user equipment as double bounce transmission.Wherein from interior mobile device to
The signal-to-noise ratio of interior transponder is, it is to the signal-to-noise ratio of roadside unit greatly from interior transponder, so total signal-to-noise ratio is.It is in the size of transmitted data packet, channel width beWhen, transmission timePass through following formula
It obtains:
2) proportional to beTask in local computing, the demand to computing resource is, cpu resource is accounted for
There is rate, the local computing capability of user equipment, then obtain the local computing time:
3) proportional to beTask be assigned to roadside unit calculating.Roadside unit equipped withA equivalent computing capability
ForServer.Due to a roadside unitService range in have multiple user device transmissions calculating tasks, it is total
Arrival rate be.?M/M/cOn the basis of queuing model and Erlang formula, obtains task to be calculated and exist
Roadside unitAverage calculation times:
Wherein,,,
4) due to roadside unitProcessing capacity it is limited, task to be calculated must wait in the queue.Roadside unitBiography
Defeated processing speed is, then each calculated result is in roadside unitThe average latency at place are as follows:
5) when being ready for sending calculated result, if vehicleHave moved to roadside unitService range except, meter
Master controller will be first sent to by calculating result, be then forwarded to vehicleThe roadside unit at place.During this
Transmission delay, in the average latency of master controllerWith in roadside unitWaiting timeIt is considered that
It is constant, since the data length of calculated result is much smaller than calculating task, calculated result is from roadside unitTo user equipment
Time delay can ignore.Therefore transregional delay can be expressed as:
6) the whole of mobile edge calculations process execute the timeIt can be expressed as:
3, in calculating process, the energy loss of user equipment mainly includes the energy consumption of local computing and the energy of data transmission
Consumption.
1) local computing powerIt is determined by the inherent characteristic of CPU and the complexity of workload, in the task computing interval
It can be regarded as constant, then the energy consumption of local computing are as follows:
2) user equipmentData transmission utilization measure be, then user equipment sends the energy damage of data to interior transponder
Consumption are as follows:
3) in edge calculations implementation procedure, the gross energy of user equipment is lost are as follows:
Embodiment two,
Optimization algorithm of the invention is divided into two layers of iterative process, and external iteration process solves nonlinear fractional optimization problem, internal layer
Iterative process is updated variable.Its target is to minimize roadside unitIn service rangeThe whole energy consumption of vehicle.It should
Problem is expressed as
s.t.
Due to different user equipmeniesTask distribution variable be coupling, therefore optimization aim is inseparable.To understand
The certainly problem introduces the local replica of optimal resource allocation strategy and defines local optimum variable, and it is separable for making objective function
:
s.t.
Therefore objective function can be broken down intoThe subproblem that can be solved parallel, the problem are non-convex problem.By further
Mathematic(al) manipulation, and objective function value is, the problem can be converted to convex optimization problem, and then can be in iteration mistake
It is optimised in journey.Restrictive condition is addedAfterwards in iteration each time, following problem
It is solved:
Work as restrictive conditionWhen being satisfied, acquired results are the optimal solution of the optimization problem.It is drawn by solving following augmentation
Ge Lang problem obtains the variable that each secondary internal layer iteration updates and this time optimal solution of outer loop process:
After being initialized to variable, in the iterative process of dual variable, meet objective function convergence, residual error convergence and it is right
The optimal solution of available this time outer loop after the mutation amount condition of convergence, and after the loop termination condition for meeting setting, it obtains
Obtain the optimal solution of required objective functionAnd best task allotment ratioAnd optimal transmission power。
For the present invention, We conducted a large amount of emulation experiments.Such as Fig. 2, withGrowth, i.e., more task quilts
It is assigned to edge calculations node to be calculated, energy loss first reduces to be increased afterwards.This is becauseWhen smaller, data transmission
Energy will be less than local computing energy, then with data transmission consumption energy be more than local computing energy.Fig. 3 is aobvious
The increase with transimission power is shown, transmission rate is increasing, however the growth rate that data transmit consumed energy will be faster than
The increase of transmission rate, therefore show as the monotone increasing of transmission energy loss.Fig. 4 reflects number of user equipment to three kinds not
With the influence of the energy loss under prioritization scheme.Fig. 5 is shown with the increase with roadside unit coverage area, energy loss
It gradually decreases and tends towards stability.In process shown in Fig. 6, show that the iteration of the algorithm can be received rapidly in 6 ~ 7 iteration
It holds back, it can be quickly obtained optimal solution.Fig. 7 is the relationship of energy loss and task allotment ratio under different user devices quantity.
The result is consistent with the conclusion of Fig. 2, Fig. 4.
The above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, although referring to above-described embodiment pair
The present invention is described in detail, those of ordinary skill in the art still can to a specific embodiment of the invention into
Row modification perhaps equivalent replacement these without departing from any modification of spirit and scope of the invention or equivalent replacement, applying
Within pending claims of the invention.
Claims (4)
1. under a kind of car networking edge calculations scene, being based on the distribution of computation tasks and power control of alternating direction multipliers method (ADMM)
Scheme processed, which is characterized in that the method has following steps:
1) it determines whether to complete data transmission before vehicle leaves roadside unit service range；
2) it is optimized by the iterative process of nonlinear fractional optimization and alternating direction multipliers method, acquisition makes energy lossThe smallest distribution of computation tasks ratio and transimission power；
3) server of roadside unit calculates the task of distribution, and under the control of master controller, by the knot of calculating
Fruit is sent to interior mobile device by roadside unit.
2. the deterministic process as described in claim 1 step 1), which is characterized in that from user equipmentTo roadside unit's
Task amount allotment ratio obeys average arrival ratePoisson process, determine whether to leave roadside unit in vehicle describedService range before complete data transmission during, further comprise:
1) enter roadside unit in vehicleService range when, by speedWith vehicle at a distance from roadside unit edge
Determine the maximum tolerance time, work as data transmission periodWhen less than the value, it can carry out data transmission；
2) in data transmission periodAfter meeting the requirements, if the execution time of entire edge calculations processNo more than vehicle
Leave the time in the section, then certain task allotment ratio is pressedCalculating task is sent to roadside unit；
Above-mentioned deterministic process, which is characterized in that according to task amount allotment ratio, time delay and energy consumption are by local computing process sum number
It is constituted according to transmission and edge calculations process two parts, further comprises:
1) distributing for task is forwarded to interior transponder from interior mobile device first, then interior transponder maximum transmitted function
This task is sent roadside unit by rate, and overall process is double bounce transmission；The signal-to-noise ratio of double bounce respectively indicates are as follows:
Wherein,WithThe transimission power of mobile device and transponder has been respectively represented,WithIndicate slave mobile device
Channel gain to transponder and from transponder to roadside unit is usedIndicate the one-sided power spectrum density of white Gaussian noise, and
Obtain the total signal-to-noise ratio of double bounce:
And then it is for the size transmittedData packet, when channel width isWhen, transmission timeIt is obtained by following formula
It arrives:
2) for the task in local computing, local computing timeDemand by task to be calculated to computing resource, the local computing capability of mobile device, occupation rate of the task to be calculated to cpu resource, average arrival rate isWith task amount allotment ratioExport:
3) being calculated for being assigned to the server of roadside unit for task, wait that device to be serviced calculates from difference
The task amount of mobile device has total arrival rate；Roadside unitHaveA equivalent server, each clothes
Business device computing capability be,M/M/cOn the basis of queuing model and Erlang formula, calculating task is obtained in roadside list
MemberAverage handling time:
Wherein,,
,
Due to roadside unitProcessing capacity it is limited, task to be calculated must wait in queue, then by roadside unit
It handles and sends result to user equipment, therefore each calculated result is in roadside unitThe average latency at place
Are as follows:
WhereinFor roadside unitTransmission and processing speed, due to calculated result data length be much smaller than calculating task, meter
Result is calculated from roadside unitTo user equipmentTime delay can ignore；When being ready for sending calculated result, if vehicleHave moved to roadside unitCoverage area except, calculated result will be first sent to master controller, then
It is forwarded to vehicleThe roadside unit at place；Transmission delay during this, in the average etc. of master controller
To the timeWith in roadside unitWaiting timeIt may be considered constant, therefore transregional delay can be expressed as:
4) to the execution time of the entire mobile edge calculations process, have
。
3. the prioritization scheme as described in claim 1 step 2, which is characterized in that user equipmentEnergy loss should include
The energy consumption of local computing and the energy consumption of transmission data；DefinitionFor local computing power, it consolidates depending on CPU's
There is the complexity of characteristic and workload, constant can be considered as during task execution；
User equipment is obtained underLocal computing energy consumption:
User equipment is obtained by following formulaThe energy loss of data is sent to interior transponder:
User equipment is obtained by following formulaGross energy loss:
Energy optimization scheme is distribution of computation tasks and power control scheme based on ADMM, and target is to minimize roadside unitIn service rangeThe whole energy consumption of vehicle defines optimized variable set, wherein
,, then the optimization problem are as follows:
s.t.
WithTo limit the arrival rate of workloadWithRespectively no more than user equipmentWith
Roadside unitProcessing speed,Ensure that transimission power is no more than the maximum transmission power of user equipment,WithRespectively
It is limited for data transmission and the delay of task computation process,For task allotment ratioBoundary limitation；
In P1, because of different user equipmeniesTask distribution variable be coupling, therefore optimization aim is inseparable
's；In order to solve this problem, it further includes steps of
1) local replica of optimal resource allocation strategy is introduced；Local optimum variable is indicated using one group of new variable, is definedWithRespectively asWithLocal variable, then the set of local optimized variable is defined as, wherein,,
Then the suboptimal problems of P1 can be expressed as:
s.t.
2) P2 makes objective function by introducing local variableIt is separable, objective function is decomposed
ForThe combined optimization problem of a subproblem that can be solved parallel, these dispersions can be expressed as
s.t.
Objective function P3 is still a non-convex problem, and the molecule of P3 and denominator are respectively defined as:
And it definesOptimal objective function value as P3:
WhereinWithOptimal local computing task allotment ratio and power control strategy are respectively represented；
3) according to nonlinear fractional optimization problem, optimal objective value is obtainedSufficient and necessary condition be: and if only if equation
It sets up, i.e., by solving the problems, such as following to obtain optimal local optimized variableWith:
s.t.
4) local variable set is defined for each user equipment, and defined function:
It can be expressed as a result, about the convex optimization problem of P2:
s.t.
5) the optimal variables collection for being associated with P5 is defined,
In the iterative process each time of the iterative algorithm described in claim 1 step 2, problem below is solved:
s.t.
Wherein optimal solutionIt is obtained in back iteration, works as restrictive conditionWhen being satisfied,It is required optimization problem P1
One group of optimal solution.
4. the prioritization scheme as described in claim 1 step 2, which is characterized in that for the iterative process, definition is corresponded to
It is closed in the Set of Lagrangian Multipliers of equation P 6, define normal numberConvergence rate is adjusted, then
The augmentation lagrange formula of P6 can be expressed as
The iterative process includes two layers of circulation, and outer circulation is nonlinear fractional optimization problem, indicates the number of iterations with n；Inside follow
Ring is the update of original variable and dual variable, indicates the number of iterations with t,
Further comprise:
1) to task allotment ratio, transimission powerAnd optimal solutionTermination condition is arranged in initialization；
2) optimized variable set is updated, give the optimal solution of n-th outer circulation, and then obtain
Obtain the transimission power of each user equipment；Local variableWithUpdate can be broken down into and can solve parallelA subproblem；User equipment is calculated according to the following formulaThe optimal task assignment ratio of acquisition in the t times when circulationAnd transimission power:
3) it updates；Is obtained according to the following formulaGlobal optimum's task allotment ratio when secondary interior circulation:
Is obtained according to the following formulaLagrange multiplier when secondary interior circulation:
4) optimal solution is updated；It is full when t tends to infimum in the initializaing variable of ADMM and the iterative process of dual variable
Foot-eye function convergence, residual error convergence and the dual variable condition of convergence；It is obtained when the interior loop termination of nth iterationWith, thenThe optimal solution of secondary iterationIt obtains as the following formula:
5) loop termination；When n-th outer loop meetsWhen, pass through
Following formula obtains optimal task assignment ratio, optimal transmission powerAnd optimal solution:
。
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