CN108021451A - A kind of adaptive container moving method under mist computing environment - Google Patents
A kind of adaptive container moving method under mist computing environment Download PDFInfo
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- G—PHYSICS
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- 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]
- G06F9/5083—Techniques for rebalancing the load in a distributed system
- G06F9/5088—Techniques for rebalancing the load in a distributed system involving task migration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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- 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
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
- G06F9/5016—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals the resource being the memory
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- 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
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Abstract
The present invention proposes the adaptive container moving method under a kind of mist computing environment, comprises the following steps：A mist Computational frame based on container is established, container is located on mist node, and mobile application is located at user, and the task of user performs in container；The target that container migrates under scene is calculated to mist to be modeled, the migration target includes time delay, power consumption and migration overhead；The setting of state space and motion space is carried out, Reward Program is defined and sets Q iteration functions；Dimensionality reduction is carried out to state space by deep neural network；By the optimization to acting selection, the dimensionality reduction to motion space is realized.Finally realize the prototype of a container adaptive-migration system and whole flow process is verified.Adaptive container moving method under mist computing environment proposed by the present invention, the resource in calculating mist are preferably planned, reduce the time delay between user and mist node, reduce the energy consumption expense of mist node.
Description
Technical field
The invention belongs to the mist calculating field in computer network, be related to mist calculate, mobile edge calculations, intensified learning,
The methods of deeply learns, and the adaptive container moving method under more particularly to a kind of mist computing environment.
Background technology
Mist calculates becomes promising calculation paradigm in recent years, it provides a flexible framework to support to have
The application of the distributed specific region, specific area of the service quality of similar cloud computing.Mist calculate will substantial amounts of light-duty calculating and
Storage infrastructure (being known as mist node) is deployed near mobile subscriber.So that mobile application can transfer to suitable mist
Node, to shorten access time delay of the user to application.In addition, mist node is flexible, have the scalability, can support mobile subscriber's
Mobility.
The technology that the existing method learnt using deeply carries out container migration under mist computing environment is actually rare.
Related Research Domain be mainly in data center to the scene of Virtual Machine Manager, main method be by the migration to virtual machine,
By dynamic migration of virtual machine to part of nodes, so as to close idle mist node, reach the effect for reducing power consumption.And obtain migration side
The method of case is mainly predicted resource requirement so as to obtain pre-distribution scheme, or based on historical information, by returning
Analysis is returned to obtain some heuritic approaches of resource requirement.
And the Mission Scheduling under scene is calculated for mist, the prior art mainly considers a two-dimentional simple Ma Er
Section husband decision process model, when considering unique user movement, distance between user and mist node is simultaneously modeled, and is obtained simple
State space, in state space, by the value function of computation migration to determine whether task is migrated.
The method of Virtual Machine Manager will be directly migrated under mist computing environment in data center, a series of ask can be brought
Topic, including the dimension disaster problem that high-dimensional state space and motion space are brought, and do not consider to move in modeling process
The mobility problem at family is employed, so as to cause the delay problem under mobile context to be solved well.
And the existing method for scheduling task calculated for mist, the situation of unique user is only considered when establishing state space,
Actual multi-user's situation is not considered.And the transition probability between hypothesis state is fixed, and shape under actual conditions
Transition probability between state is unknown.
In order to overcome the above problem, the present invention proposes a set of mist Computational frame based on container, application program is placed on
In container, and container is placed on mist node.For the container scheduling being optimal, the present invention regards container migration problem as
Stochastic optimization problems, and based on Q study and deep learning strategy, it is empty to design suitable huge markov decision process state
Between and motion space algorithm, solve the problems, such as dimension disaster.On this basis, the present invention realizes the original of a container migration
Type system.
The content of the invention
The present invention proposes the adaptive container moving method under a kind of mist computing environment, and the resource in calculating mist carries out more
Good planning, reduces the time delay between user and mist node, reduces the energy consumption expense of mist node.
In order to reach purpose mentioned above, issue noted above is solved, we, which first proposed, a set of is based on container
Mist Computational frame, time delay, power consumption and migration overhead under scene then are calculated to mist under this frame and are modeled, and is set
The adaptive container migration algorithm based on deeply study is counted, finally realizes the prototype of a container adaptive-migration system
And whole flow process is verified.
In order to achieve the above object, the present invention proposes the adaptive container moving method under a kind of mist computing environment, including
The following steps：
A mist Computational frame based on container is established, container is located on mist node, and mobile application is located at user, uses
The task at family performs in container；
The target that container migrates under scene is calculated to mist to be modeled, the migration target includes time delay, power consumption and moves
Remove pin；
The setting of state space and motion space is carried out, Reward Program is defined and sets Q iteration functions；
Dimensionality reduction is carried out to state space by deep neural network；
By the optimization to acting selection, the dimensionality reduction to motion space is realized.
Further, each mist node has position data, and computing resource total amount, and wherein computing resource includes
Cpu resource, memory source, storage resource and bandwidth resources.
Further, each container has a resource request amount and an actual sendout of resource, described every
A mobile application has a position data and the request data to container.
Further, time delay is calculated by the following formula in the migration target：
dtotal=dnet-k×dcomp,
Wherein, dnetBe data transmission in network produce expense, the distance dependent between user and container, passage path
Loss is defined；dcompIt is the calculation delay on mist node, is determined with the violation degree of the service-level agreement of mist node.
Further, the power consumption of the mist node is defined as follows：
Wherein pidleAnd pmaxRefer to power consumption when cpu busy percentage is 0 and 100%, ui(t) be mist node the utilization of resources
Rate.
Further, the container migration overhead is defined as follows：
Wherein mmigIt is container CiMigration overhead, include propagation delay time, 1 { 〃 } is Allen Iverson bracket.
Further, the dimensionality reduction of the motion space includes action utilization, after obtaining state every time, all from Q value lists
Select corresponding optimal Q values and corresponding action.
Further, the dimensionality reduction of the motion space includes action probe, and each intelligent body all randomly chooses a state,
And selection is restricted, definition return income, when income is just, encouragement migrates.
Further, the dimensionality reduction of the state space is to store all status informations in deep neural network, with
This reduces state space dimension.
Adaptive container moving method under mist computing environment proposed by the present invention, its advantage are as follows：
(1) user mobility is taken into account model by this method, by being modeled to the time delay between user and mist node,
Reduce the time delay of user task under mist computing environment well, preferably adapt to mist computing environment.
(2) this method does not make the assumption that any transition probability, by exempting from the autonomous learning algorithm of model, adaptive learning
Go out the action that should be taken under various different conditions, can be very good to adapt to different mist computing environment.
(3) this method is for one and its approximate by the Q matrix conversions in storage state space by using deep neural network
Three-layer neural network, reduce the dimension of state space under mist computing environment well, solve the problems, such as dimension disaster.
(4) this method sets out the return income letter for contributing to selection to act by the analysis to mist calculating concrete condition
Number, can effectively reduction action selection when choose situation about negatively acting, so as to effectively accelerate whole convergence speed of the algorithm
And preferably reduce unnecessary energy loss.
(5) this method uses container package application, rather than traditional virtual machine, can effectively reduce in mist computing environment
Expense caused by lower migration, is more suitable for various resources all than relatively limited mist computing environment.
Brief description of the drawings
Fig. 1 show mist Computational frame and user's movement figure.
Fig. 2 show the adaptive container moving method flow chart under the mist computing environment of present pre-ferred embodiments.
Fig. 3 show different omega1In the case of average delay comparison diagram.
Fig. 4 show different omega1In the case of average energy consumption comparison diagram.
Fig. 5 show different omega1In the case of overhead comparison diagram.
Fig. 6 show different omega2In the case of average delay comparison diagram.
Fig. 7 show different omega2In the case of average energy consumption comparison diagram.
Fig. 8 show different omega2In the case of overhead comparison diagram.
Fig. 9 show the CPU overhead comparison diagram of container and virtual machine in the case of different loads.
Figure 10 show the migration overhead comparison diagram of container and virtual machine in the case of different loads.
Embodiment
The embodiment of the present invention is provided below in conjunction with attached drawing, but the invention is not restricted to following embodiment.Root
According to following explanation and claims, advantages and features of the invention will become apparent from.It should be noted that attached drawing is using very simple
The form of change and non-accurate ratio is used, be only used for conveniently, lucidly aiding in illustrating the purpose of the embodiment of the present invention.
Fig. 1 show mist Computational frame and user's movement figure.Include five levels altogether in Fig. 1：Client layer, access net
Network layers, mist layer, core network layer and cloud layer.Client layer includes the shifting that mobile subscriber and mobile subscriber are currently running with it
Dynamic application.Mobile application accesses mist layer by accessing network layer, and produces certain time delay.Mist node is located at mist layer, container position
In on mist node, the resource of mist node is asked, and so that mist node produces the expenses such as energy consumption.Mist node passes through core network
Layer is connected with cloud layer.Mobile subscriber moves between mist node, result in the distance between mobile subscriber and the container asked
Increase, so that whether time delay increases, the problem of needing to follow mobile subscriber's migration just to need decision-making into one in container at this time.
If container is migrated, it is only necessary to migrate the necessary Runtime Library of application that container includes and using itself；And to void
Plan machine is migrated, then needs to include whole virtual machine system.
Please refer to Fig.2, Fig. 2 show the adaptive container moving method under the mist computing environment of present pre-ferred embodiments
Flow chart.The present invention proposes the adaptive container moving method under a kind of mist computing environment, comprises the following steps：
Step S100：A mist Computational frame based on container is established, container is located on mist node, and mobile application is positioned at use
With family, the task of user performs in container；
Step S200：The target that container migrates under scene is calculated to mist to be modeled, the migration target includes time delay, work(
Consumption and migration overhead；
Step S300：The setting of state space and motion space is carried out, Reward Program is defined and sets Q iteration functions；
Step S400：Dimensionality reduction is carried out to state space by deep neural network；
Step S500：By the optimization to acting selection, the dimensionality reduction to motion space is realized.
The present invention initially sets up a mist Computational frame based on container, allows F={ F1,F2,…,Fm, C={ C1,C2,…,
Cn, M={ M1,M2,…,MlSet, the set of container of mist node, and the set of mobile application are represented respectively.Container position
In on mist node, mobile application is located at user, and the task of user performs in container.Each mist node has position Fi.l,
And the total amount F of computing resourcei.c, computing resource mainly includes cpu resource, memory source, storage resource and bandwidth resources,
Because mainly considering computing capability when scheduling, cpu resource is mainly considered here, and thinks memory source, storage money
Source and bandwidth resources are sufficient.For container, each container has a position Ci.l (t), and each container has a resource please
The amount of asking Ci.r (t) and the actual sendout C of a resourcei.a(t).In addition, for mobile application, each mobile application has one
Position Mi.l (t) and the request M to containeri.r(t)。
Then we calculate the target that container migrates under scene to mist and are modeled.Migration target mainly includes following several sides
Face：
1. time delay.Time delay dtotalIncluding two aspects, dnetAnd dcomp。dnetIt is the expense that data transmission in network produces, it is main
The distance dependent between user and container is wanted, can be lost with passage path and define it：
Wherein f is signal frequency, di(t) it is the mobile application with mobile subscriber and the mist node where corresponding container
Between distance, hbIt is the height of mist node, cmIt is 3dB, ah under City scenariosmBy being defined as below：
ahm=3.20 (log10(11.75hr))2- 4.97, f ＞ 400MHz,
Wherein hrIt is user's height.
In addition, dcompIt is the calculation delay on mist node, clothes of the time delay mainly with mist node can be learnt by proof
The violation degree (SLAV) of level protocol of being engaged in (SLA) determines.And SLAV is defined as follows：
Obtained by above-mentioned, dcompIt can be defined as：
And dtotalIt can be defined as：
dtotal=dnet+k×dcomp。
2. power consumption.Power consumption ptotalRefer to the power consumption of all mist nodes.If mist node is in sleep pattern, then power consumption is near
Like being 0, in addition, the power consumption of mist node is defined as follows：
Wherein pidleAnd pmaxRefer to power consumption when cpu busy percentage is 0 and 100%.ui(t) be mist node the utilization of resources
Rate, is defined as follows：
3. container migration overhead.Container migration overhead is defined as follows：
Wherein mmigIt is CiMigration overhead, include propagation delay time.1 { 〃 } is Allen Iverson bracket.
4. problem model.So far the model of whole problem can be obtained
After being modeled to the above problem, the setting of state space and motion space is carried out.Due to testing mainly and Mi.l
(t) and Ci.r (t) is related, definition：
Wherein：
In addition, with reference to C.l (t) and C.a (t), the state space of system can be obtained：
According to actual conditions, obtaining corresponding motion space is：
Due to needing overhead minimum, defining Reward Program is：
Rτ=-(dtotal(τ)+ω1ptotal(τ)+ω2mtotal(τ))
Then Q iteration functions are set：
Obviously, huge state space can bring dimension disaster, then pass through deep neural network to this progress dimensionality reduction, and
By the optimization to acting selection, the dimensionality reduction to motion space is realized.Key step includes following three aspects：
1. action utilizes.Safeguard the Q value lists of an optimal value Q compositionWherein
Each single item all byComposition.This link is utilized in action, obtains shape every time
After state, corresponding optimal Q values and corresponding action are all selected from Q value lists.
2. action probe.In the action probe stage, each intelligent body all randomly chooses a state.In order not to allow selection
State too causes negative optimization at random, and certain limitation is made to selection.Definition return income：
WhenWhen, income is just, encouragement migrates.Migration probability：
Finally obtain action：
Wherein：
It is hereby achieved that random action selection algorithm：
3. deep neural network reduces state space.All status informations are stored in deep neural network, with this
Reduce state space dimension.The training objective of neutral net is defined as：
L(θτ)=E [(y (τ)-Q (Sτ, Aτ；θτ))2]
Wherein：
Y (τ)=E [(1- α) Q (Sτ-1, Aτ-1；θτ-1)+α[Rτ-1+γmaxQ(Sτ, Aτ；θτ-1)]|Sτ-1, Aτ-1].
In addition, by experience replay, the association between training every time is reduced.Training algorithm is as follows：
And final container adaptive-migration algorithm：
Finally, on this basis, the prototype system of sleeve containes migration is realized.
The present invention is programmed using Python, simulates mist node, container and user.At the beginning of mist node class includes position
Beginningization module, mist euclidean distance between node pair computing module, power consumption calculation module, cpu resource module, container list module, user list
Module and bandwidth module.Container class includes numbering maintenance module, cpu resource uses module, location updating module, position mould
Block, migration overhead module and big little module.User class includes position initialization module, location updating module, request initialization
Module, request update module, distance calculation module and time delay module with mist node.Three mist node, container and user classes
Form the environment of whole system.In addition, core learns class, including intellectual Agent class, deep neural network part for Q
Brain classes, and memory playback storage Memory classes.Intellectual Agent class includes obtaining optimal action module, obtains currently
The module, memory playback module, pretreatment module and neutral net instruction of (state, action, return value, next state) tuple
Practice module, deep neural network Brain classes include network structure module, and Memory classes include the memory module of memory, carry
Modulus block and storage form.
Experimental data comes from the true of San Francisco and hires out car data, the latitude of data set from 32.87 to
50.31, longitude from -127.08 to -122.0.Region is divided, disposes 7 mist nodes, considers more than 200 user at it
In situation of movement.All mobile subscribers are active, and represent whether they get on or off the bus with 0 and 1, and being represented with this should
With the switching of request.
Setting for parameter, the energy consumption of CPU are obtained by following table：
| CPU Utilization (%) | 0% | 10% | 20% | 30% | 40% | 50% |
| HP ProLiant G4 | 86 | 89.4 | 92.6 | 96 | 99.5 | 102 |
| CPU Utilization (%) | 60% | 70% | 80% | 90% | 100% | |
| HP ProLiant G4 | 106 | 108 | 112 | 114 | 117 |
1 cpu busy percentage of table and energy consumption relation table
For other parameters, if f=2.5MHz, hb=35m, hr=1m, cm=3dB, and
Di (t)=| Mi.l(t)-Fj.l(t)|
In addition, Xscale=3, alpha=0.1, gamma=0.9, epsilon=0.9.
For contrast experiment's effect, two benchmark algorithms are have chosen.The algorithm of the present invention is referred to as ODQL, in addition, will pass
System Q learning algorithms carry out discretization and obtain DBQL algorithms, also have the base that an approximation greedy algorithm Myopic is also the present invention
Quasi- algorithm.
By to different omega1Contrasted, obtain following result.With reference to shown in 3~Fig. 5 of figure, Fig. 3 is shown not
Same omega1In the case of average delay comparison diagram, Fig. 4 show the average energy consumption comparison diagram in the case of different omega1, Fig. 5
It show different omega1In the case of overhead comparison diagram.
In addition, the present invention is also to different omega2Contrasted, experimental result is as follows.With reference to 6~Fig. 8 of figure institutes
Show, Fig. 6 show different omega2In the case of average delay comparison diagram, Fig. 7 show different omega2In the case of be averaged
Energy consumption comparison figure, Fig. 8 show different omega2In the case of overhead comparison diagram.Experimental result illustrates proposed by the present invention
Algorithm will be good than the effect of other two algorithms.
In addition, the present invention has also built a container migratory system prototype.Using the CPU of E5-1650v2@3.5GHz,
The memory of 16.0 GB, the desktop computer of the operating system of 16.04 LTS of Ubuntu is as mist node, using i7-4600U@
The CPU of 2.1GHz, the memory of 8.0 GB, the laptop of the operating system of Windows 10, analog subscriber group.On desktop computer
Docker container engines are installed, Nginx Website servers are installed by Docker, WordPress websites and MySQL database,
Ghost websites and SQLite3 databases, and the Docker containers of a static page.Pass through Docker containers on notebook
The different Ubuntu containers of engine management, the interior installation Webbench analog subscribers request of each Ubuntu containers, and pass through tc works
Have to change time delay.At the same time, the present invention sets virtual machine (vm) migration environment under same hardware environment, and has carried out pair
Than.Comparing result is as follows.As shown in Figure 9 and Figure 10, Fig. 9 show the CPU overhead of container and virtual machine in the case of different loads
Comparison diagram, Figure 10 show the migration overhead comparison diagram of container and virtual machine in the case of different loads.From experimental result, together
Etc. under hardware case, the migration overhead of container is the migration overhead much smaller than virtual machine, so mist proposed by the present invention calculates
Container adaptive-migration system under environment is very effective.
Although the present invention is disclosed above with preferred embodiment, so it is not limited to the present invention.Skill belonging to the present invention
Has usually intellectual in art field, without departing from the spirit and scope of the present invention, when can be used for a variety of modifications and variations.Cause
This, the scope of protection of the present invention is defined by those of the claims.
Claims (9)
1. the adaptive container moving method under a kind of mist computing environment, it is characterised in that comprise the following steps：
A mist Computational frame based on container is established, container is located on mist node, and mobile application is located at user, user's
Task performs in container；
The target that container migrates under scene is calculated to mist to be modeled, the migration target includes time delay, power consumption and migration and opens
Pin；
The setting of state space and motion space is carried out, Reward Program is defined and sets Q iteration functions；
Dimensionality reduction is carried out to state space by deep neural network；
By the optimization to acting selection, the dimensionality reduction to motion space is realized.
2. the adaptive container moving method under mist computing environment according to claim 1, it is characterised in that described each
Mist node has position data, and a computing resource total amount, wherein computing resource include cpu resource, memory source, storage resource with
And bandwidth resources.
3. the adaptive container moving method under mist computing environment according to claim 1, it is characterised in that described each
Container has a resource request amount and an actual sendout of resource, and each mobile application has a position data
And the request data to container.
4. the adaptive container moving method under mist computing environment according to claim 1, it is characterised in that the migration
Time delay is calculated by the following formula in target：
dtotalt=dnet+k×dcomp,
Wherein, dnetBe data transmission in network produce expense, the distance dependent between user and container, passage path loss
It is defined；dcompIt is the calculation delay on mist node, is determined by the violation degree of the service-level agreement of mist node.
5. the adaptive container moving method under mist computing environment according to claim 1, it is characterised in that the mist section
The power consumption of point is defined as follows：
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6. the adaptive container moving method under mist computing environment according to claim 1, it is characterised in that the container
Migration overhead is defined as follows：
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7. the adaptive container moving method under mist computing environment according to claim 1, it is characterised in that the action
The dimensionality reduction in space includes action and utilizes, and after obtaining state every time, corresponding optimal Q values and phase are all selected from Q value lists
The action answered.
8. the adaptive container moving method under mist computing environment according to claim 1, it is characterised in that the action
The dimensionality reduction in space includes action probe, and each intelligent body all randomly chooses a state, and selection is restricted, definition
Income is returned, when income is just, encouragement migrates.
9. the adaptive container moving method under mist computing environment according to claim 1, it is characterised in that the state
The dimensionality reduction in space is by all status information storages to deep neural network, and state space dimension is reduced with this.
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