CN110062026A - Mobile edge calculations resources in network distribution and calculating unloading combined optimization scheme - Google Patents

Mobile edge calculations resources in network distribution and calculating unloading combined optimization scheme Download PDF

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CN110062026A
CN110062026A CN201910199126.9A CN201910199126A CN110062026A CN 110062026 A CN110062026 A CN 110062026A CN 201910199126 A CN201910199126 A CN 201910199126A CN 110062026 A CN110062026 A CN 110062026A
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unloading
calculating
cost
base station
mobile
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徐昌彪
刘远祥
刘杨
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Chongqing University of Post and Telecommunications
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Chongqing University of Post and Telecommunications
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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
    • H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
    • 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

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  • Computer Networks & Wireless Communication (AREA)
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Abstract

The invention discloses wireless bandwidth and computing resource co-allocation schemes in mobile edge calculations network, belong to wireless communication and mobile edge calculations field, solves resource contention and problem of load balancing under the mostly mobile Edge Server deployment scenario of multi-user in heterogeneous wireless network.Scheme specifically includes: macro base station controller collects the calculating unloading solicited message that all user terminals are sent in this time slot, and notifies all MEC server report Current resource residue situations in its compass of competency；According to acquired information, user's calculating task and MEC server resource are carried out first fit by macro base station controller；Formulate wireless bandwidth and computational resource allocation rule in MEC server；It establishes Cooperative reference and exports final matching strategy collection.The present invention has taken into account the characteristic of each user, significantly reduces the charge costs for calculating unloading, has saved mobile client energy consumption.In identical quantity MEC server disposition, the present invention program can receive more calculating unloading tasks, balance the communication between server and computational load, improve the task execution efficiency of system.

Description

Mobile edge calculations resources in network distribution and calculating unloading combined optimization scheme
Technical field
The present invention relates to wireless communication technique and mobile edge calculations field, and in particular to a kind of mobile edge calculations net Resource allocation and calculating unloading combined optimization scheme in network.
Background technique
The appearance of mobile edge calculations provides a kind of effective solution scheme for resource-constrained mobile device.Movement is set The standby mobile edge that all or part of computation-intensive task can be unloaded to computing resource relative abundance by wireless channel Calculation server (Mobile Edge Computing, MEC) reduces processing locality task time delay and energy consumption.But execute meter Additional propagation delay time and energy consumption may be brought by calculating unloading, determine that best unloading decision is a research in MEC network Emphasis.In addition, the calculating capacity of wireless signal-path band width and MEC server belongs to limited resources, the nothing in multi-user's MEC system Line electricity and computing resource are supplied to user equipment (User Equipment, UE) in a shared manner.Different users have not Same calculating task and quality of service requirement, so having radio and the computational resource allocation side of differentiation for different UE Formula, calculates unloading and server resource allocation combined optimization is considered as one of the most significant problems for improving MEC network performance.
Have scholar in recent years and has been extensive research, document 1 [Chen M, Hao around mobile edge calculations network Y.Task Offloading for Mobile Edge Computing in Software Defined Ultra-dense Network [J] .IEEE Journal on SelectedAreas in Communications, 2018:1-1.] have studied it is super Intensive software defines the unloading problem of the task under network frame, regard task execution delay as optimization aim, proposing one kind has The software definition task of effect unloads scheme.[Hongzhi G, Jiajia L, the Jie Z.Computation of document 2 Offloading for Multi-Access Mobile Edge Computing in Ultra-Dense Networks[J] .IEEE Communications Magazine, 2018,56 (8): 14-19.] the analyzing intensive MEC deployment scenario of the task unloads Load problem.The heuristic greedy unloading scheme of one kind is proposed as unloading problem prioritization scheme, simulation result is calculated and shows macro base It stands and the collaboration use of the MEC server of small base station can be with lifting system performance.Document 3 [Zhang J, Xia W, Cheng Z, et al.An evolutionary game for joint wireless and cloud resource allocation in mobile edge computing[C]//20179th International Conference on Wireless Communications and Signal Processing (WCSP) .IEEE, 2017:1-6.] it mainly studies and is won based on evolving The joint cloud and radio resource allocation algorithm played chess consider the energy consumption and time delay and mobile edge calculations environment of mobile terminal In monetary cost.The stability of Evolutionary Game is analyzed, and balance of evolving is obtained by duplicating dynamics method.4 [Zhao of document Power is strong, Lu Xiaodi, Liang Kai, Yang Jian, and Song Fengfei is a kind of to provide the network system and method for servicing of mobile edge calculations service: in State .108494612 [P] .2018.09.04] a kind of network system grade service side of mobile edge calculations service of offer is provided Method, solve mobile communications network on MEC flexible deployment the technical issues of, in C-RAN framework, dispose based on SDN MEC control Device disposes MEC server in BBU.Local MEC server calculating, multiple MEC server consolidation meters are set forth in text Calculation, specific non-local MEC server calculates and four kinds of modes of cloud center calculation, and controller is quick according to the calculation amount of task and delay For sensitivity as judgement foundation, it is more reasonable using which kind of calculation to judge.Document 5 [Zhang Weizhe, Fang Binxing, He Hui, Liu Chuan Meaning, Yu Xiangzhan, Liu Yawei, the mobile edge calculations task discharging method under a kind of multi-user scene of Liu Guoqiang: China .108920279 [P] .2018.11.30] in order to reduce the reaction time delay and energy consumption of mobile device.The multi-user scene is Multiple mobile devices are connected with MEC server, and each mobile device may be selected a plurality of between the mobile device and MEC server One in channel is communicated, and MEC server is connected by backbone network with center cloud.More particularly to two stages: the first rank Section is that decision task is to execute or be unloaded to the execution of MEC server in local mobile device；Second stage is to work as server When inadequate resource, judgement task, which is to continue with, to be waited or is unloaded to distal end cloud center on MEC server and executed.
The inventors discovered that the MEC server resource distribution of the more MEC deployment scenarios of multi-user is deposited under heterogeneous network environment In following problem: first, in multi-user's list MEC server access scene, user equipment energy consumption and delay optimization problem It is related to wireless bandwidth and computing resource co-allocation.A part of document only accounts for computational resource allocation and ignores bandwidth resources Distribute influence to optimum results, another part document is although it is contemplated that bandwidth resources and computing resource co-allocation, but only Only bandwidth resources are given to each user by subcarrier form proportional allocations.This seems the fairness that ensure that each user, but It can seem that bandwidth resources are not enough for being the task big for input data amount, and for the task small to transmission data, band Wide resource has more than needed again.Resource allocation un-reasonable phenomenon is caused, so needing a kind of more fine-grained wireless bandwidth resource point Method of completing the square provides wireless bandwidth resource according to task particular community differentiation.Second, one under super-intensive isomery MEC deployment scenario A user equipment can be associated with multiple MEC servers simultaneously, and a MEC server can provide service simultaneously for multiple UE, greatly Amount UE random selection MEC server will cause load uneven phenomenon.
Summary of the invention
To solve the above-mentioned problems, the invention proposes wireless bandwidth in mobile edge calculations network and computing resource joints Allocation plan.The technical solution covers wireless communication field and mobile edge calculations field.In super-intensive isomery MEC server In deployed environment, different types of base station is densely deployed, MEC server is disposed in each base station.Realize that a user can To be associated with multiple MEC servers simultaneously, a MEC server also provides calculating service simultaneously for multiple UE.The program specifically walks It is rapid:
Step S1: mobile subscriber generates new calculating task, and sends task unloading request to macro base station controller.
Step S2: macro base station controller collects the calculating unloading solicited message that all user terminals are sent in this time slot, and leads to Know all MEC server report resource residue situations in its compass of competency.
Step S3: user's calculating task and MEC server resource are carried out first fit by macro base station controller, are constituted initial Matching strategy collection.
Step S4: wireless bandwidth and computational resource allocation rule in MEC server are formulated on the basis of step S3.
Step S5: establishing Cooperative reference, and using user as game participant, user selects different MEC servers Mode unloads cost using user task and system always unloads cost as cost function as game strategies.
Step S6: macro base station controller successively chooses participant from participant's set, and participant's task is matched again To other MEC servers, user task unloading cost and the system under new matching way of calculating always unload cost.
Step S7: judging whether to meet jump condition, if it is going to step S6, if not going to step S8.
Step S8: judging whether to reach balance coalition structure, if it is going to step S9, if not going to step S6.
Step S9: judge whether otherwise all meeting calculating unloading condition will be discontented with if so, continuing to execute step S10 The mobile subscriber of foot unloading decision rejects from participant's set, return step S6.
Step S10: final matching strategy collection is exported.
The beneficial effects of the present invention are: the present invention has taken into account the characteristic of each user, significantly reduces calculating unloading Cost overhead, saved mobile client energy consumption.In identical quantity MEC server disposition, the present invention program can Receive more calculating unloading tasks.The computational load between server is balanced, the task execution efficiency of system is improved.
Detailed description of the invention
MEC server disposition scene in Fig. 1 heterogeneous network
Fig. 2 wireless bandwidth and computing resource combined optimization protocol procedures show
Specific embodiment
Principles and features of the present invention are described in conjunction with attached drawing, are further made by specific implementation example to of the invention It illustrates out.
As shown in Figure 1, heterogeneous network is by a macro base station (Macro Base Station, MBS) and the small base station (Small of k Base Station, SBS) composition, there are completely overlapped covering and juxtaposition overlay area between each base station, user equipment with Machine is dispersed in whole region.Scheduling of resource controller, all MEC service under unified management macro base station covering are disposed on MBS Device, MEC server, which has certain cloud computing ability and establishes wireless channel with mobile device by base station, to be connect.The present invention The set expression that MEC server is formed be M={ 1,2,3 ..., m ..., M }, UE composition set expression be N=1,2, 3,...,n,...,N}.As shown in Fig. 2, a kind of mobile edge calculations resources in network distribution and calculating unloading combined optimization side Case, comprising the following steps:
Step S1 can be sent out when mobile subscriber generates new calculating task by Quality Initiative road direction macro base station controller Send calculating unloading request.The relevant information that unloading request message includes amount calculating task is calculated, ξ (L, T are represented byd,W,C)。L It is task input data size, TdThe deadline that expression task is completed, W indicates task computation intensity, with cpu cycle number table Show, C indicates that user's calculating task handles budget, and the value of C is related with task self attributes and mobile device performance.
Step S2, each time slot of macro base station controller will collect the calculating unloading request letter that a user terminal is sent Breath.And by send inquiry message notify macro base station compass of competency in all MEC server current radio channel bandwidth resources and Computing resource residue situation.It collects and finishes when all information, start to carry out resource allocation.
Step S3, before executing resource allocation, the present invention disposes network scenarios according to mobile edge calculations space, establishes Wireless transmission model, mobile device local computing model and Edge Server computation model, have formulated maximal benefit of system Problem model.For macro base station controller according to the mathematical model established, calculating, which is gone out on missions, is unloaded to the cost of each server, The MEC server of unloading cost minimization is chosen as unloading target.All calculating tasks are matched to corresponding MEC server, Constitute initial matching set of strategies.
Step S3 specifically includes the following steps:
Step S31, establishes traffic model.
If bnmThe bandwidth of UEn subchannel, h are distributed to for MEC server mnmFor subchannel gains, pnIt is UEn in subchannel s Transmission power, the present invention does not consider power distribution problems, by pnIt is considered as a fixed constant.n0For white Gaussian noise, data hair Transmission rate can obtain:
R in formula (1)nmIndicate wireless channel transmission rate,Indicate that the channel from other UE is dry It disturbs.
Step S32 establishes local computing model.
If mobile device CPU calculates capacityN indicates n-th of mobile device, and l indicates processing locality task.UEn appoints Business postpones in processing localityAnd energy consumption
Coefficient k in formula (3)nIndicate chip hardware framework related system, ε0Indicate energy consumed by each cpu cycle unit Amount.According to formula (2), (3) can find out and spend caused by local computingBudget C is handled with the calculating task of UEnn:
In formula (5), Q (n)=F (f1,f2,...,fn), f1,f2,...,fnIndicate calculating task except delay and energy consumption it Outer other attribute specifications, such as safety, reliability and the requirement of task relative importance etc..Point The weight that calculating task consumes delay and energy consumption and other attributes Biao Shi not be executed at UEn.
Step S33 establishes server apparatus computation model.
It mainly includes that transmission delay and server execute delay two that UEn task, which is unloaded to time delay caused by MEC server m, Part, respectively by symbolWithIt indicates, can be obtained in conjunction with formula (1):
In formula (7)It is the computing resource that MEC server m distributes to UEn.
In calculating uninstall process, transmission energy consumption is also that cannot be neglected cost, according to formula 6) transmission energy can be calculated ConsumptionFormula expression are as follows:
α is used respectivelymAnd βmIt indicates MEC server m unit bandwidth price and unit computing resource price, defines UEn in MEC The cost of bandwidth resources and computing resource is rented at server m are as follows:
Calculating task is unloaded to the time that MEC server m needs to undertake by simultaneous formula (6), (7), (8) and (9), UEn Delay, energy consumption and currency totle drilling cost are as follows:
In formulaRefer to the delayed impact factor,Indicate the energy consumption factor,Indicate the monetary impact factor.MeetAnd all impact factor value ranges are between [0,1].
Step S34, the problem of establishing wireless bandwidth and computing resource co-allocation based on maximizing the benefits model.
All user utilities are maximized and are used as optimization aim by the present invention, are based on discussed above, UEn utility function UnIt can table It is shown as:
In formula (11)AndCombined calculation unloading, wireless bandwidth and calculating Resource optimization problem can be stated are as follows:
System benefit function U and unloading decision A, channel width B and computing resource F have it can be seen from formula (12) Relationship.
Step S35, subproblem divide.Problem (12) are divided into two sub-problems by the present invention: be respectively wireless bandwidth and Computing resource combined optimization subproblem and calculating unloading optimization subproblem, pass through the mutual iteration of the two and obtain final result.
The present invention formulates in the problem of wireless bandwidth and computing resource co-allocation model, it is assumed that all UE calculating tasks are all The execution of MEC server will be unloaded to.Obtain wireless bandwidth and the description of computing resource combined optimization problem:
Due to UEn task once generating, task execution budget CnAlso it can be determined according to formula (5).So in system Total task execution invariable constant at last in advance.Therefore formula (13) can be further depicted as minimizing unloading Cost problem, as shown in formula (14):
Calculating unloading optimization problem can be described as:
As shown in formula (14) (15), problem (12) is decomposed into two sub-problems by the present invention, and the problem of reducing is asked Solve difficulty.
Step S36, user's calculating task and MEC server resource carry out first fit.
In step S33, UE calculates the cost for offloading tasks to each server according to formula (10), chooses unloading The MEC server of cost minimization is as first unloading target.
Step S4 formulates wireless bandwidth and computational resource allocation principle on MEC server.Two steps can be divided into:
Step S41 formulates wireless bandwidth distribution principle.
When MEC server m simultaneously have received k UE task unloading request, be denoted as set K=1,2 ..., K ..., K }, andBmAnd FmIndicate MEC server total bandwidth resources and computing resource.MEC server is k-th The wireless bandwidth resource and computing resource of UE distribution are expressed as bkmAnd fkm, meet ∑k∈Kbkm≤Bm,∑k∈Kfkm≤Fm.For Maximization transmission rate, the task input data amount l of each user is assigned to as bandwidth allocation foundation, k-th of UE Amount of bandwidth may be expressed as:
Communications cost is further obtained according to formula (16):
Step S42 formulates computational resource allocation principle.
MEC computational resource allocation mode then presses the distribution of task execution budget accounting situation, and task execution budget is by task Reason budget cuts communication and spends gained.The computing resource size that so UEk is assigned to may be expressed as:
Step S5, establishes Cooperative reference.
UE all in system are formed into a group, such a group is called enterprises union, is indicated with D by the present invention, Define cooperative alliances game form:
G(D,(Sn)n∈N,(πn)n∈N) (19)
In formula (19), the present invention indicates that participant gathers using UE as the participant of game, with D, SnIndicate participant UEn The set of available policies, strategy here refer to selection of the UE to MEC server in system in alliance.πnIndicate participant's cost letter Number, specifically includes energy consumption, time delay and monetary cost.According to formula (10) it can be concluded that
Step S6, macro base station controller successively choose participant and carry out unloading transfer.According to acquired in each UE of step S4 Wireless and computing resource, calculate each UE in conjunction with formula (10) and calculate unloading costWith the total executory cost Z of systemr.System Determine shown in jump condition such as formula (20) (21):
Zr(S'n)≤Zr(Sn) (21)
Formula (20) indicates that the unloading cost after UEn transfer is less than the cost before transfer, after (21) indicate transfer System synthesis sheet be less than transfer before system synthesis sheet.Only simultaneously meet condition (20) (21), UE could alliance it Between shifted.
Step S7 judges whether to meet jump condition (20) (21), if so, UE is shifted, after update UE transfer Calculating unload costUpdate this Z of system synthesisr, update the calculating unloading of all UE in the preceding alliance with after transfer of transfer Then cost goes to step S6, otherwise goes to step S8.
Step S8 judges whether to reach balance coalition structure.The condition for reaching balance coalition structure is that all UE of traversal are equal It is unsatisfactory for jump condition (20) (21).
Judge whether all participants meet task unloading condition, task unloading condition such as formula (22) institute in step S9 Show:
In formula (22),Expression task unloads executory cost,Indicate local task execution cost.If met public Formula (22), exports final resource allocation result.If being unsatisfactory for formula (22), that is, indicate that the UE is performed locally calculating task, UE is rejected from game participant set, unloads costIt is updated to the machine executory costThen S6 is gone to step.
Final matching strategy collection is obtained in step S10, macro base station controller is informed resource allocation result with the forms of broadcasting All transmission computing resources request user.If mobile subscriber rejects from participant's set, declare this resource request Failure only waits next time slot to retransmit and calculates unloading request.The calculating for notifying MEC to prepare to receive UE unloading simultaneously is appointed Business.

Claims (11)

1. mobile edge calculations resources in network distribution and calculating unloading combined optimization scheme, which is characterized in that mainly include with Lower step:
Step S1: mobile subscriber generates new calculating task, and sends task unloading request to macro base station controller.
Step S2: macro base station controller collects the calculating unloading solicited message that all user terminals are sent in this time slot, and notifies it All MEC servers report Current resource residue situation in compass of competency.
Step S3: according to information acquired in step S2, macro base station controller is by user's calculating task and MEC server resource First fit is carried out, initial matching set of strategies is constituted.
Step S4: wireless bandwidth and computational resource allocation rule in MEC server are formulated on the basis of step S3.
Step S5: establishing Cooperative reference, and using user as game participant, user is to different MEC server selection modes As game strategies, cost is unloaded using user task and system always unloads cost as cost function.
Step S6: macro base station controller successively chooses participant from participant's set, and participant's task is matched to it again His MEC server, user task unloading cost and the system under new matching way of calculating always unload cost.
Step S7: judging whether to meet jump condition, if it is going to step S6, if not going to step S8.
Step S8: judging whether to reach balance coalition structure, if it is going to step S9, if not going to step S6.
Step S9: judge whether otherwise all meeting calculating unloading condition will be unsatisfactory for unloading if so, continuing to execute step S10 The mobile subscriber for carrying decision rejects from participant's set, return step S6.
Step S10: final matching strategy collection is exported.
2. a kind of mobile edge calculations resources in network distribution according to claim 1 and calculating unloading combined optimization side Case, which is characterized in that the mobile subscriber in the step S1 is randomly distributed in super-intensive heterogeneous wireless network, and the network is by one A macro base station and Multiple Small Cell Sites form, mobile to use there are completely overlapped covering and juxtaposition overlay area between each base station Family can be associated with multiple base stations simultaneously.Scheduling of resource controller is disposed on macro base station, it is all under unified management macro base station covering MEC server, MEC server, which has certain cloud computing ability and establishes wireless channel by base station and mobile subscriber, to be connected It connects.When mobile subscriber generates new calculating task, it can send to calculate to unload by Quality Initiative road direction macro base station controller and ask It asks.
3. a kind of mobile edge calculations resources in network distribution according to claim 1 and calculating unloading combined optimization side Case, which is characterized in that macro base station controller will collect the meter of user terminal transmission every a time slot in the step S2 Unloading solicited message is calculated, and notifies all current moneys of MEC server report in macro base station compass of competency by sending inquiry message Source residue situation.It collects and finishes when all information, start to carry out resource allocation.
4. a kind of mobile edge calculations resources in network distribution according to claim 1 and calculating unloading combined optimization side Case, which is characterized in that in the step S3 before executing resource allocation, the present invention disposes net according to mobile edge calculations space Network scene establishes wireless transmission model, mobile device local computing model and Edge Server computation model, has formulated system Maximizing the benefits problem model.For macro base station controller according to the mathematical model established, calculating, which is gone out on missions, is unloaded to each clothes The cost of business device chooses the smallest MEC server of unloading cost as unloading target.All calculating tasks are matched to accordingly MEC server constitutes initial matching set of strategies.
5. a kind of mobile edge calculations resources in network distribution according to claim 1 and calculating unloading combined optimization side Case, which is characterized in that step S4 controller on the basis of step S3 is used the wireless bandwidth resource of MEC server as mobile Family task input data amount accounts for the mobile subscriber that general assignment input quantity scale distributes to each access this MEC server, with Overall task transmission delay is minimized, computing resource is distributed into each user by mobile subscriber's residual accounting size, it is simultaneous The fairness of each user is cared for.
6. a kind of mobile edge calculations resources in network distribution according to claim 1 and calculating unloading combined optimization side Case, which is characterized in that the step S5 establishes Cooperative reference.Participant of the mobile subscriber as cooperative game, matching are same As an enterprises union, participant can match different MEC servers, all for the mobile subscriber group of one MEC server Matching scheme constitutes set of strategies, and mobile subscriber unloads cost and system always unloads cost function of the cost as game.
7. a kind of mobile edge calculations resources in network distribution according to claim 1 and calculating unloading combined optimization side Case, which is characterized in that in the step S6, according to wireless and computing resource acquired in each mobile subscriber of step S4, calculate Each mobile subscriber calculates unloading cost and the total unloading cost of whole system out.Formulate the item that participant shifts in enterprises union Part: after transfer mobile subscriber unload cost be less than transfer before unloading cost and system synthesis sheet be less than transfer before at This when, allows mobile subscriber to shift, otherwise forbids shifting.
8. a kind of mobile edge calculations resources in network distribution according to claim 1 and calculating unloading combined optimization side Case, which is characterized in that judge whether all participants meet the jump condition of step S6 in the step S7, if it is satisfied, moving It employs family to be shifted, updates the calculating unloading cost after mobile subscriber's transfer, more new system always unloads cost, updates transfer The calculating of all mobile subscribers unloads cost in alliance after preceding and transfer, then goes to step S6, otherwise goes to step S8.
9. a kind of mobile edge calculations resources in network distribution according to claim 1 and calculating unloading combined optimization side Case, which is characterized in that judge whether to reach balance coalition structure in the step S8.The condition for reaching balance coalition structure is time It goes through all mobile subscribers and jump condition is not satisfied.
10. a kind of mobile edge calculations resources in network distribution according to claim 1 and calculating unloading combined optimization side Case, which is characterized in that judge whether all participants meet task unloading condition in the step S9, if it is satisfied, output is most Mobile subscriber is rejected from game participant set if being unsatisfactory for task unloading condition, unloads cost by whole matching strategy collection It is updated to the machine executory cost, then goes to step S6.
11. a kind of mobile edge calculations resources in network distribution according to claim 1 and calculating unloading combined optimization side Case, which is characterized in that final matching strategy collection is obtained in the step S10, macro base station controller is divided resource with the forms of broadcasting All transmission computing resource request users and MEC server are informed with result.If mobile subscriber rejects from participant's set, So declare that this resource request fails, only waits next time slot to retransmit and calculate unloading request.
CN201910199126.9A 2019-03-15 2019-03-15 Mobile edge calculations resources in network distribution and calculating unloading combined optimization scheme Pending CN110062026A (en)

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