CN107220118A - Resource pricing is calculated in mobile cloud computing to study with task load migration strategy - Google Patents
Resource pricing is calculated in mobile cloud computing to study with task load migration strategy Download PDFInfo
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- CN107220118A CN107220118A CN201710402476.1A CN201710402476A CN107220118A CN 107220118 A CN107220118 A CN 107220118A CN 201710402476 A CN201710402476 A CN 201710402476A CN 107220118 A CN107220118 A CN 107220118A
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- 230000005012 migration Effects 0.000 title claims description 12
- 238000006243 chemical reaction Methods 0.000 claims description 8
- 238000005457 optimization Methods 0.000 claims description 7
- 210000004209 Hair Anatomy 0.000 claims 1
- 238000005265 energy consumption Methods 0.000 abstract description 3
- 230000015572 biosynthetic process Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 3
- 238000000034 method Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 238000005755 formation reaction Methods 0.000 description 2
- HNQBPUIXFDQDRJ-UHFFFAOYSA-N N-ethyl-N-(2-hydroxyethyl)nitrous amide Chemical compound CCN(N=O)CCO HNQBPUIXFDQDRJ-UHFFFAOYSA-N 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 230000002194 synthesizing Effects 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; 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
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; 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
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
-
- 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
Abstract
Computing resource price and mobile task load transition strategy under a kind of mobile cloud computing environment of present invention design.The strategy use thought of game theory.The expense that mobile task buys mobile resources can be effectively reduced, equipment energy consumption is reduced, and increase task completion rate.The strategy is comprised the steps of：First, local mobile resources price is configured to a Stackelberg problem of game and solved, the use price of local computing resource is determined according to the effectiveness of resource provider and user both sides.When both sides' game is after reaching that Nash is balanced, mobile resources price and the benefit of maximized both sides.Second, and after local resource price is tried to achieve, using set forth herein improvement chemically react optimized algorithm, being performed locally for task is assigned in different equipment and performed, realizes that mobile resources provide the load balancing of equipment as far as possible.
Description
Technical field
The invention belongs to mobile cloud computing task load migration research field, and in particular to mobile computing resource pricing strategy
With the research of many migration mesh decision strategies.
Background technology
With developing rapidly for cloud computing and mobile cloud computing research, mobile cloud computing correlation technique be considered as solve by
The effective way that mobile device computing capability is limited the problem of cause.By using mobile device in itself beyond resource carry
Donor carrys out the execution of trustship Mobile solution, to solve the intrinsic problem of mobile device.By by local cloud and other mobile devices
(rather than home server) is combined, it would be preferable to support mobility, without other infrastructure.In addition, in mobile cloud
Among calculating, some an open questions are also there are, for example some potential technical matters, contribute to be appointed first
The cost-benefit model of business load migration decision-making, this is related to so that mobile device has enthusiasm to share resource to set for other
It is standby to use, and maximize benefit of the mobile resources user using service.On the other hand, the migrating technology of task how is carried out
With the selection of task immigration destination, rational task immigration selection can improve the performance of mobile cloud environment and save energy consumption,
This is a np hard problem, be especially considering that it is other some it is related the problem of, for example network disconnects and changeability, data
Privacy and security, the load change of mobile cloud environment etc., these problems all increasingly cause the extensive concern of domestic and foreign scholars
And research.
The content of the invention
The present invention fixes a price on the basis of existing achievement in research to the mobile resources among task immigration under mobile cloud computing
The main research work carried out with task immigration strategy is as follows:
1. the present invention employs a kind of three layers of mobile cloud computing task scheduling framework first, it is set by local movement respectively
The mobile cloud environment of standby composition, explorer, the publicly-owned cloud service provider in distal end is constituted.Among local mobile cloud environment,
Computing resource can be shared between mobile device, the abundant equipment of computing resource can be jointly formed a shared resource pond,
Resource, which is supplied to, needs the neighbor devices of resource, and resource pool can sell part according to certain price to local mobile device and calculate
Resource.When local resource can not meet the demand of local task, or compared to local load migration, buying publicly-owned cloud service is
During a kind of higher mode of benefit, task can be migrated to public cloud execution, and transmission energy consumption and the purchase of task are reduced with this
The synthesis expense of computing resource.
2. secondly, the present invention proposes a kind of pricing method of local mobile computing resource.Mobile resources are considered
User and supplier both sides effectiveness, be resource user reduce resource buyer buy local resource cost, together
When add the income of resource provider.The present invention solves this problem as Stackelberg problem of game is solved.
And propose heuritic approach fast searching best price.This part is also task immigration decision making algorithm proposed by the invention
Basis.
3. last, after the price of local mobile cloud computing resources is tried to achieve, task can primarily determine that local or remote
End cloud carries into execution a plan.And the deadline strategy of each task in itself.The present invention chemical reaction Optimization Framework on the basis of,
And the framework is improved, with reference to mobile cloud computing task load migration scheduling particular problem proposed by the invention, if
Meter finds OPTIMAL TASK load migration strategy, to realize the load balancing of local resource supplier.It is demonstrated experimentally that the present invention is carried
The algorithm gone out, same to GACO, EENA, other a variety of traditional algorithms such as ARD are contrasted, and innovatory algorithm proposed by the invention is very big
Ground accelerates the convergence rate of chemical reaction optimization, while adding the solution degree of accuracy of optimal solution.
Brief description of the drawings
The present invention is further described below in conjunction with the accompanying drawings.
Fig. 1 is mobile cloud computing system structural model schematic diagram.
Fig. 2 is mobile cloud computing environment formation figure.
Fig. 3 is improvement chemical reaction optimized algorithm ICRO flow charts.
Fig. 4 is that mobile resources price solves simulated experiment result figure
Fig. 5 is that application ICRO compares other algorithm local device load balancing simulated experiment result figures
Embodiment
Fig. 1 gives the mobile cloud computing system structural model that inventive algorithm is used, and system architecture has network sense
The characteristics of knowing, is divided into three layers from top to bottom, mainly comprising following structure：Local mobile cloud, centralized resource task dispatcher
CRB, cloud computing service provider.One group of mobile device in mobile cloud environment is to resource request j is submitted, and another part calculates money
The abundant equipment m in source adds local cloud computing resource pool, needs the equipment of computing resource to provide resource to other, and to offer
Resource collects certain expense；Between mobile device and their cloud computing service facility, middle resource tasks scheduler
CRB, is made up of some calculate nodes, is not provided as local device provides computing resource, is responsible for the calculating of local device
Task, finds suitable computing resource.In resource pricing stage CRB information exchange module, in resource provider and resource
Between buyer, help to coordinate the resource pricing strategy of resource provider and resource buyer's task deadline strategy.CRB portions
Administration is supplied to mobile device, to strengthen the performance of mobile cloud service near network access point as a kind of local service.
Fig. 2 describes the formation in mobile cloud local resource pond.The forming process of local cloud computing environment is imagined as one
Resource market.Each user i are in the case of the computing resource required for oneself equipment can not meet current task, it is desirable to
Local cloud computing resource pool buys certain resource.User i have a utility function U.Meanwhile, there is another type of use
Family provider j, are used as computing resource more abundant mobile device, it is desirable to as the mobile device of surrounding provide a part from
The computing resource that oneself leaves unused.The abundant mobile device user of all computing resources, with utility function P, puts a part of resource
Enter to enter resource pool resource pool.Entered with the user i task task i submitted and appoint queue job queue, wait quilt
Performed in certain equipment or remote mobile cloud being dispatched in local resource pond.
The process of mobile resources price can be described below：
(1) Stackelberg problem of game is constructed.Optimal mobile computing resource price p*Can be by solving as follows
Optimization problem is obtained:
As local resource price plocalAfter given, user i is according to the task j of itselfi, compare task load sharing to distal end
Cloud computing provider benefit function value Uremote(ji,premote) or local certain the mobile device m moved in cloud environment
The upper benefit function value U handledlocal(ji,plocal) so that number one U*(ji) maximize.U*(ji) be defined as follows：
As locally executing for a task jiLearning local resource price, in that case it can be decided that when optimal maximum tolerance is responded
BetweenAnd then the calculating energy input of purchase is determined, to cause number one to maximize.WhereinIt can convert
Solved for following optimization problem：
p*For the computing resource commercial value of resource provider interests maximum, j can be caused*For that resource can be caused to buy
The maximum computing resource quantity purchase of person's benefit.Therefore (p*,j*) it is the Stackelberg problem of game feasible solutions that the present invention is defined.
And meet following condition：
U(j*,p*)≥U(j,p*)
P(p*,J*)≥P(p,J*)
(2) Optimal calculation resource purchase strategy.As a given resource price plocalAfterwards, all tasks are for local meter
Calculate resource purchase volume be：
(3) Optimal calculation resource pricing strategy.Optimal calculation resource pricing is：
Fig. 3 gives the flow chart of improved chemical reaction optimization, and main corrective measure is：When each round is reacted into beginning
Individual molecule and multiple molecule selection algorithms, are set up on the idea basis of roulette, and each molecule M is selected to be reacted
Probability and its target function value f (M) it is relevant, participate in reaction molecule select probability can be calculated by formula below：
And do not have secondary iteration to terminate, improve many worst molecules of wheel：Improve its kinetic energy KE
KEi+1(m)=ωkKEi(m)+c1r1|KE(Mpbst)-KEi(m)|+c2r2|KE(Mgbst)-KEi(m)|ωk
=ωmax-(ωmax-ωmin)k/kmax
It is local to move in the case of 10,20,30 three kinds of task quantity in the case that Fig. 4 assumes local 10 resource providing devices
Dynamic resource price is with the increase of both sides' game iterations, price gradually convergent simulated experiment result figure.
Fig. 5, compared to the CRO and GACO of script, is locally provided using the improvement chemical reaction optimized algorithm ICRO of the present invention
Source provides the experimental result picture of the non-load balanced case of equipment.
Claims (4)
- Resource pricing and task load migration strategy are calculated in cloud computing 1. moving, it is characterised in that consider mobile resources at the same time On the premise of the maximization of utility of supplier and the double hairs of user, it is proposed that the pricing strategy of local mobile resources, task is completed Public cloud and the preliminary screening locally executed, a part of task are performed locally, and another part is performed in public cloud, is then made With ICRO algorithms by the task immigration locally executed to destination equipment.
- 2. resource pricing and task load migration strategy are calculated in the mobile cloud computing according to right 1, it is characterised in that comprehensive On the premise of closing the maximization of utility for considering resource provider and resource buyer both sides, local resource pricing problem is defined Solved for a Stackelberg problem.
- 3. resource pricing and task load migration strategy are calculated in the mobile cloud computing according to right 1, it is characterised in that examine Consider the load balancing of the equipment in local mobile cloud environment, for the multiple tasks locally executed, use improved chemistry Reaction optimization ICRO algorithms are solved.
- 4. resource pricing and task load migration strategy are calculated in the mobile cloud computing according to right 1, it is characterised in that right Original chemical reaction optimization CRO is improved, and increases its convergence rate.
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Cited By (7)
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|---|---|---|---|---|
| CN107911478A (en) * | 2017-12-06 | 2018-04-13 | 武汉理工大学 | Multi-user based on chemical reaction optimization algorithm calculates discharging method and device |
| CN108541071A (en) * | 2018-04-10 | 2018-09-14 | 清华大学 | Wireless communication system multi-user resource distribution system based on the double-deck game |
| CN109901050A (en) * | 2019-02-25 | 2019-06-18 | 哈尔滨师范大学 | A kind of three dimension system chip testing method for optimizing resources and system |
| CN110012044A (en) * | 2018-01-04 | 2019-07-12 | 财团法人工业技术研究院 | Dynamic duty transfer method and server |
| CN111400001A (en) * | 2020-03-09 | 2020-07-10 | 清华大学 | Online computing task unloading scheduling method facing edge computing environment |
| CN111414250A (en) * | 2020-02-24 | 2020-07-14 | 国际关系学院 | Cloud database load balancing method and system for space data |
| CN113238839A (en) * | 2021-04-26 | 2021-08-10 | 深圳微品致远信息科技有限公司 | Cloud computing based data management method and device |
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107911478B (en) * | 2017-12-06 | 2020-09-22 | 武汉理工大学 | Multi-user calculation unloading method and device based on chemical reaction optimization algorithm |
| CN107911478A (en) * | 2017-12-06 | 2018-04-13 | 武汉理工大学 | Multi-user based on chemical reaction optimization algorithm calculates discharging method and device |
| CN110012044A (en) * | 2018-01-04 | 2019-07-12 | 财团法人工业技术研究院 | Dynamic duty transfer method and server |
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| CN108541071A (en) * | 2018-04-10 | 2018-09-14 | 清华大学 | Wireless communication system multi-user resource distribution system based on the double-deck game |
| CN108541071B (en) * | 2018-04-10 | 2019-03-01 | 清华大学 | Wireless communication system multi-user resource distribution system based on the double-deck game |
| CN109901050A (en) * | 2019-02-25 | 2019-06-18 | 哈尔滨师范大学 | A kind of three dimension system chip testing method for optimizing resources and system |
| CN111414250A (en) * | 2020-02-24 | 2020-07-14 | 国际关系学院 | Cloud database load balancing method and system for space data |
| CN111414250B (en) * | 2020-02-24 | 2022-11-04 | 国际关系学院 | Cloud database load balancing method and system for space data |
| CN111400001A (en) * | 2020-03-09 | 2020-07-10 | 清华大学 | Online computing task unloading scheduling method facing edge computing environment |
| CN111400001B (en) * | 2020-03-09 | 2022-09-23 | 清华大学 | Online computing task unloading scheduling method facing edge computing environment |
| CN113238839A (en) * | 2021-04-26 | 2021-08-10 | 深圳微品致远信息科技有限公司 | Cloud computing based data management method and device |
| CN113238839B (en) * | 2021-04-26 | 2022-04-12 | 深圳微品致远信息科技有限公司 | Cloud computing based data management method and device |
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Application publication date: 20170929 |