CN106095565B - Cloud computing system resource distribution backward inference system and configuration method based on backward stochastic differential equation - Google Patents
Cloud computing system resource distribution backward inference system and configuration method based on backward stochastic differential equation Download PDFInfo
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- 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
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- 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
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Abstract
The invention belongs to cloud computing system resource distribution fields, and in particular to cloud computing system resource distribution backward inference system and configuration method based on backward stochastic differential equation.Cloud computing resources based on backward stochastic differential equation configure system, request module, historical data processing module, backward inference module and user interactive module is handled by user to form, user's request processing module receives user to the access request of cloud computing server first, in accordance with specified format, and the constraint condition to resource distribution, and according to the configuration of cloud computing system calculate node and network condition, aforementioned access is requested and is constrained condition resolution be central processing unit demand, bandwidth demand, memory requirements parameter.The computing resource currently needed can be determined, and guarantee that the computing resource currently prepared is " most saving " according to the following computational resource requirements situation for determining the moment；The stochastic volatility that Future configuration can be responded actively, improves the stability and availability of cloud computing system.
Description
Technical field
The invention belongs to cloud computing system resource distribution fields, and in particular to the cloud computing based on backward stochastic differential equation
System resource configures backward inference system and configuration method.
Background technique
Cloud computing is that a large amount of computing resources (including narrow sense computing resource, storage resource and Internet resources etc.) are virtually turned to
One shared resource pond, service needed for user is obtained by rent mode.The maximum feature of cloud computing is to use as needed, i.e. client
It can apply for cloud resource or expansion/reduction resource at any time as needed, and operator completes related resource according to customer demand and matches
It sets.The allowable resource that service provider provides is more, and the service experience and availability of user is higher, and reply pop-up mission is asked
The ability asked is stronger.But assignable cloud computing resources are more, put into also more, expense is also bigger.Both how to balance
It is faced with huge challenge.
Currently, the resource distribution of cloud computing system is faced with both sides challenge.On the one hand, the important spy of cloud computing system
Property be randomness, either service request is initiated or resource distribution deployment, is filled with uncertain and chance phenomenon everywhere, not
The resource distribution come often has stochastic volatility.This randomness and fluctuation must be thus considered during resource distribution
Property.On the other hand, existing cloud computing system predicts that can future time point meet use only according to current computing resource
The demand at family, and the problem of cloud computing system is increased sharply there may be pop-up mission and user at any time, do not ensure that user and clothes
The service-level agreement SLA (Serice Level Agreement) that business provider signs is certain to be met just, does not generate resource
Waste.It is then desired to which a kind of reverse inference method, determines that current time is least according to the resources requirement of future time instance
Resource distribution amount.It is existing in terms of the method for cloud computing system resource distribution does not all also consider the problems of this.
" backward stochastic differential equation " theory is exactly the target according to future time instance, by the formulation of strategy gradually random
The uncertain counteracting that fluctuation introduces, thus a kind of method that risk averse is fallen.It has set up between " random " and " determination "
Bridge, allow people go to solve the problems, such as with determining strategy, method it is random uncertain, or random uncertain
Thing carries out optimization processing.Backward stochastic differential equation at present " theory is mainly used for solving finance, economics and engineering science
The practical problem in equal fields, does not apply to this field.
Summary of the invention
It pushes away the purpose of the present invention is to provide a kind of randomness feature for having fully considered cloud computing resources configuration and inversely
The demand of reason, resource allocation proposal are more reasonable；The waste of cloud computing system resource distribution is advantageously reduced, resource distribution is promoted
Efficiency saves the cloud computing system resource distribution backward inference system based on backward stochastic differential equation of cost.Of the invention
Purpose, which also resides in, provides a kind of cloud computing system resource distribution backward inference method based on backward stochastic differential equation.
The object of the present invention is achieved like this:
Cloud computing resources based on backward stochastic differential equation configure system, handle request module, historical data by user
Processing module, backward inference module and user interactive module composition, user's request processing module are received first, in accordance with specified format
User is to the access request of cloud computing server, and to the constraint condition of resource distribution, and is calculated and is saved according to cloud computing system
The configuration of point and network condition, aforementioned access is requested and constrained condition resolution is central processing unit demand, bandwidth demand, memory
The parameter of demand, form are a triple ζ=(CPU (T), MEM (T), BW (T)), and wherein T is user and cloud service provider
The future arranged in service-level agreement SLA determines the moment, and CPU (T) is the central processing unit demand at T moment, and MEM (T) is T
The bandwidth demand at moment, BW (T) are the memory requirements at T moment, and triple ζ is passed to backward inference module；At historical data
Manage on the one hand resource distribution historical data that module collects cloud computing system operation；Another aspect historical data processing module is collected
Cloud computing system operation resource distribution historical data, determine the generating function G (*) of backward stochastic differential equation；History number
According to processing module tool, there are two Boolean type flag bits: G (*) pre-sets flag bit PRECONFIG_G and updates flag bit
UPDATE_FLAG determines G (*) analytic expression according to the generating function setting method of backward stochastic differential equation；By generating function G
(*) passes to backward inference module；Backward inference module receives the parameter ζ from user's request processing module, and comes from
In the generating function G (*) of historical data processing module；Using ζ as terminal condition, is established using generating function G (*) and be based on swinging to
The resource distribution model of stochastic differential equation；According to the aforementioned resource distribution model based on backward stochastic differential equation, by swinging to
The numerical solution of stochastic differential equation obtains one group of (Y (0), Z (0)) uniquely determined, and then show that cloud computing system initially provides
Source configuration；The resource distribution condition (Y (0), Z (0)) of initial time is sent to user interactive module；Wherein Y (0), Z (0) points
Not Wei initial time normal resource configuration and cope with the anticipating risk since resource random fluctuation is influenced and generated by environment
Resource distribution；User interactive module receives the resource distribution condition of the initial time from backward inference module, and resource
Allocation plan feeds back to user.
Cloud computing resources configuration method based on backward stochastic differential equation, includes the following steps:
(1) user's request processing module receives user to the access request of cloud computing server first, in accordance with specified format,
And the constraint condition to resource distribution；And according to the configuration of cloud computing system calculate node and network condition, by aforementioned access
Request and constraint condition resolution be central processing unit demand, bandwidth demand, memory requirements parameter, form be a triple ζ
=(CPU (T), MEM (T), BW (T)), wherein T is that user and cloud service provider are arranged not in service-level agreement SLA
Determine the moment, CPU (T) is the central processing unit demand at T moment, MEM (T) is the bandwidth demand at T moment, and BW (T) is the T moment
Memory requirements；And ζ is passed to backward inference module；
(2) on the one hand historical data processing module collects the resource distribution historical data of cloud computing system operation；Another party
The resource distribution historical data that face historical data processing module is run according to industry experience value or the cloud computing system of collection, really
Determine the generating function G (*) of backward stochastic differential equation；There are two Boolean type flag bits for historical data processing module tool: G (*)
It pre-sets flag bit PRECONFIG_G and updates flag bit UPDATE_FLAG, according to the generating function of backward stochastic differential equation
Setting method determines G (*) analytic expression；And generating function G (*) is passed into backward inference module；
The generating function setting method of backward stochastic differential equation above-mentioned is specific further include:
(2.1) historical data processing module first checks for G (*) and pre-sets flag bit PRECONFIG_G, generates if 1
Function G (*) is set by industry experience value, is turned (2.7), is otherwise turned (2.2)；
(2.2) it checks and updates whether flag bit UPDATE_FLAG is 0, if UPDATE_FLAG is 0, turns (2.3), otherwise turn
(2.4)；
(2.3) the resource distribution historical data that historical data processing module is run according to the cloud computing system of collection utilizes
The generating function G (*) of one preset function set fitting backward stochastic differential equation, and UPDATE_FLAG is set as 1,
The time Lasttime of record at this time simultaneously turns (2.7)；
(2.4) UPDATE_FLAG is 1 at this time, current time system time Nowtime is read, if Nowtime-
Lasttime >=Interval, wherein Interval > 0 is default update cycle constant, turns (2.5), otherwise turns (2.6)；
(2.5) generating function G (*) is fitted again at this time, completes the update of G (*)；The time of record at this time simultaneously
Lasttime turns (2.7)；
(2.6) at this point, Nowtime-Lasttime < Interval, records time Lasttime at this time, turn (2.7)；
(2.7) generating function G (*) is passed to backward inference module by historical data processing module；
(3) backward inference module receives the parameter ζ from user's request processing module, and at historical data
Manage the generating function G (*) of module；And using ζ as terminal condition, is established using generating function G (*) and be based on swinging to stochastic differential side
The resource distribution model of journey；And according to the aforementioned resource distribution model based on backward stochastic differential equation, by swinging to stochastic differential
The numerical solution of equation obtains one group of (Y (0), Z (0)) uniquely determined, and then show that cloud computing system initial resource configures；And
The resource distribution condition (Y (0), Z (0)) of initial time is sent to user interactive module；Wherein Y (0), Z (0) are respectively initial
The normal resource at moment configures and copes with the anticipating risk resource distribution since resource random fluctuation is influenced and generated by environment；
Resource distribution model above-mentioned based on backward stochastic differential equation has the feature that
(3.1) the resource distribution model based on backward stochastic differential equation established meets following equation
Wherein, [0, T] t ∈, t is the time, and at the time of T is following determines, W is the Brownian movement of d dimension；
(3.2) resource configuration amount can be expressed as the processing capacity CPU (t), interior of central processing unit within [0, the T] period
The capacity BW (t) of the capacity MEM (t), bandwidth that deposit；
(3.3) Y (t) is t moment resource distribution, i.e. Y (t)=(CPU (t), MEM (t), BW (t))；
(3.4) Z (t) is set as coping with since the resource for being influenced by environment and generating the anticipating risk of resource random fluctuation is matched
It sets (the hereinafter referred to as resource distribution of anticipating risk), and wherein environment influences master to Z (t)=(CPU (t) ', MEM (t) ', BW (t) ')
Refer to the factors such as pop-up mission request, the temperature surge of calculate node, main board power supply deficiency；Z (t) can be random with coping resources
Disturbance, provides redundancy for resource distribution；
(3.5) generating function G (*) is the relation function of Y (t), Z (t), t, is matched according to the resource of each node of cloud computing system
Historical data processing result or the setting of industry experience value are set, can be provided by historical data processing module；
(3.6) ζ is the terminal condition of backward stochastic differential equation, is one group of measurable stochastic variable, ζ is by user's request
Reason module provides；
(4) user interactive module receives the resource distribution condition of the initial time from backward inference module, and money
Source allocation plan feeds back to user.
The beneficial effects of the present invention are: (1) can according to the following computational resource requirements situation that determines moment, determination it be worked as
The computing resource of preceding needs, and guarantee that the computing resource currently prepared is " most saving "；(2) Future can be responded actively
The stochastic volatility of configuration improves the stability and availability of cloud computing system.
Detailed description of the invention
Fig. 1 is the module map of the cloud computing system resource distribution backward inference system based on backward stochastic differential equation；
Fig. 2 is the generating function setting method flow chart that the present invention implements the backward stochastic differential equation provided.
Specific embodiment
The present invention is described further with reference to the accompanying drawing.
The existing method about cloud computing system resource distribution can only configure the possibility shape for calculating future according to Current resource
State, and present resource deployment demand cannot be calculated according to the random fluctuation in future with swinging to, this makes at analysis, calculating and place
When managing cloud computing resources allocation problem, it not can guarantee the computing resource currently prepared and meet just.Base disclosed by the invention
In the cloud computing system resource distribution backward inference system and method for backward stochastic differential equation, target is configured according to Future
And random fluctuation, current resource allocation proposal is calculated by backward inference, advantageously reduces cloud computing system resource distribution
Waste, promoted Allocation Efficiency, save cost.
Cloud computing resources configuration system of the present invention based on backward stochastic differential equation includes that user handles request
4 module compositions such as module, historical data processing module, backward inference module and user interactive module.
1, user's request processing module receives user to the access request of cloud computing server first, in accordance with specified format, with
And the constraint condition to resource distribution.And according to the configuration of cloud computing system calculate node and network condition, aforementioned access is asked
Summation constraint condition resolve to central processing unit demand, bandwidth demand, memory requirements parameter, form be a triple ζ=
(CPU (T), MEM (T), BW (T)), wherein T is user and the future that cloud service provider is arranged in service-level agreement SLA
Determine the moment, CPU (T) is the central processing unit demand at T moment, and MEM (T) is the bandwidth demand at T moment, and BW (T) is the T moment
Memory requirements.And ζ is passed to backward inference module.
2, on the one hand historical data processing module collects the resource distribution historical data of cloud computing system operation；On the other hand
The resource distribution historical data that historical data processing module is run according to industry experience value or the cloud computing system of collection, determines
The generating function G (*) of backward stochastic differential equation.There are two Boolean type flag bits for historical data processing module tool: G (*) is pre-
Flag bit PRECONFIG_G is set and updates flag bit UPDATE_FLAG, is set according to the generating function of backward stochastic differential equation
Determine method, determines G (*) analytic expression.And generating function G (*) is passed into backward inference module.
The generating function setting method of backward stochastic differential equation above-mentioned is specific further include:
(1) historical data processing module first checks for G (*) and pre-sets flag bit PRECONFIG_G, generates letter if 1
Number G (*) is set by industry experience value, is turned (7), is otherwise turned (2).
(2) it checks and updates whether flag bit UPDATE_FLAG is 0, if UPDATE_FLAG is 0, turns (3), otherwise turn (4)；
(3) the resource distribution historical data that historical data processing module is run according to the cloud computing system of collection, utilizes one
The generating function G (*) of a preset function set fitting backward stochastic differential equation, and UPDATE_FLAG is set as 1, together
The time Lasttime of Shi Jilu at this time turns (7).
(4) UPDATE_FLAG is 1 at this time, reads current time system time Nowtime, if Nowtime-Lasttime >
=Interval, wherein Interval > 0 is default update cycle constant, turns (5), otherwise turns (6).
(5) generating function G (*) is fitted again at this time, completes the update of G (*).The time of record at this time simultaneously
Lasttime turns (7).
(6) at this point, Nowtime-Lasttime < Interval, records time Lasttime at this time, turn (7).
(7) generating function G (*) is passed to backward inference module by historical data processing module.
3, backward inference module receives the parameter ζ from user's request processing module, and at historical data
Manage the generating function G (*) of module.And using ζ as terminal condition, is established using generating function G (*) and be based on swinging to stochastic differential side
The resource distribution model of journey.And according to the aforementioned resource distribution model based on backward stochastic differential equation, by swinging to stochastic differential
The numerical solution of equation obtains one group of (Y (0), Z (0)) uniquely determined, and then show that cloud computing system initial resource configures.And
The resource distribution condition (Y (0), Z (0)) of initial time is sent to user interactive module.Wherein Y (0), Z (0) are respectively initial
The normal resource at moment configures and copes with the anticipating risk resource distribution since resource random fluctuation is influenced and generated by environment.
Resource distribution model above-mentioned based on backward stochastic differential equation has the feature that
(1) the resource distribution model based on backward stochastic differential equation established meets following equation
Wherein, [0, T] t ∈, t are the time, and at the time of T is following determines, W is that (present invention is set as the Brownian movement of d dimension
3)。
(2) resource configuration amount can be expressed as the processing capacity CPU (t) of central processing unit, memory within [0, the T] period
Capacity MEM (t), bandwidth capacity BW (t).
(3) Y (t) is t moment resource distribution, i.e. Y (t)=(CPU (t), MEM (t), BW (t)).
(4) Z (t) is set as coping with the resource distribution since the anticipating risk of resource random fluctuation is influenced and generated by environment
(the hereinafter referred to as resource distribution of anticipating risk), and wherein environment influences mainly Z (t)=(CPU (t) ', MEM (t) ', BW (t) ')
Refer to the factors such as pop-up mission request, the temperature surge of calculate node, main board power supply deficiency.Z (t) can be disturbed at random with coping resources
It is dynamic, redundancy is provided for resource distribution.
(5) generating function G (*) is the relation function of Y (t), Z (t), t, according to the resource distribution of each node of cloud computing system
Historical data processing result or the setting of industry experience value, can be provided by historical data processing module.
(6) ζ is the terminal condition of backward stochastic differential equation, is one group of measurable stochastic variable, and ζ is handled by user's request
Module provides.
4, user interactive module receives the resource distribution condition of the initial time from backward inference module, and resource
Allocation plan feeds back to user.
The example of this method be a simple cloud computing server system, service can occupy resource have CPU, bandwidth,
Memory.
Cloud computing resources configuration system of the present invention based on backward stochastic differential equation includes that user handles request
4 module compositions such as module, historical data processing module, backward inference module and user interactive module.
1, user's request processing module receives user to the access request of cloud computing server first, in accordance with specified format, with
And the constraint condition to resource distribution.And according to the configuration of cloud computing system calculate node and network condition, aforementioned access is asked
Summation constraint condition resolve to central processing unit demand, bandwidth demand, memory requirements parameter, form be a triple ζ=
(CPU (T), MEM (T), BW (T)), wherein T is user and the future that cloud service provider is arranged in service-level agreement SLA
Determine the moment, CPU (T) is the central processing unit demand at T moment, and MEM (T) is the bandwidth demand at T moment, and BW (T) is the T moment
Memory requirements.And ζ is passed to backward inference module.
2, on the one hand historical data processing module collects the resource distribution historical data of cloud computing system operation；On the other hand
The resource distribution historical data that historical data processing module is run according to industry experience value or the cloud computing system of collection, determines
The generating function G (*) of backward stochastic differential equation.There are two Boolean type flag bits for historical data processing module tool: G (*) is pre-
Flag bit PRECONFIG_G is set and updates flag bit UPDATE_FLAG, is set according to the generating function of backward stochastic differential equation
Determine method, determines G (*) analytic expression.And generating function G (*) is passed into backward inference module.
The generating function setting method of backward stochastic differential equation above-mentioned is specific further include:
(1) historical data processing module first checks for G (*) and pre-sets flag bit PRECONFIG_G, generates letter if 1
Number G (*) is set by industry experience value, is turned (7), is otherwise turned (2).
(2) it checks and updates whether flag bit UPDATE_FLAG is 0, if UPDATE_FLAG is 0, turns (3), otherwise turn (4)；
(3) the resource distribution historical data that historical data processing module is run according to the cloud computing system of collection, utilizes one
The generating function G (*) of a preset function set fitting backward stochastic differential equation, and UPDATE_FLAG is set as 1, together
The time Lasttime of Shi Jilu at this time turns (7).
(4) UPDATE_FLAG is 1 at this time, reads current time system time Nowtime, if Nowtime-Lasttime >
=Interval, wherein Interval > 0 is default update cycle constant, turns (5), otherwise turns (6).
(5) generating function G (*) is fitted again at this time, completes the update of G (*).The time of record at this time simultaneously
Lasttime turns (7).
(6) at this point, Nowtime-Lasttime < Interval, records time Lasttime at this time, turn (7).
(7) generating function G (*) is passed to backward inference module by historical data processing module.
3, backward inference module receives the parameter ζ from user's request processing module, and at historical data
Manage the generating function G (*) of module.And using ζ as terminal condition, is established using generating function G (*) and be based on swinging to stochastic differential side
The resource distribution model of journey.And according to the aforementioned resource distribution model based on backward stochastic differential equation, by swinging to stochastic differential
The numerical solution of equation obtains one group of (Y (0), Z (0)) uniquely determined, and then show that cloud computing system initial resource configures.And
The resource distribution condition (Y (0), Z (0)) of initial time is sent to user interactive module.Wherein Y (0), Z (0) are respectively initial
The normal resource at moment configures and copes with the anticipating risk resource distribution since resource random fluctuation is influenced and generated by environment.
Resource distribution model above-mentioned based on backward stochastic differential equation has the feature that
(1) the resource distribution model based on backward stochastic differential equation established meets following equation
Wherein, [0, T] t ∈, t are the time, and at the time of T is following determines, W is the Brownian movement of 3 dimensions.
(2) resource configuration amount can be expressed as the processing capacity CPU (t) of central processing unit, memory within [0, the T] period
Capacity MEM (t), bandwidth capacity BW (t).
(3) Y (t) is t moment resource distribution, i.e. Y (t)=(CPU (t), MEM (t), BW (t)).
(4) Z (t) is set as coping with the resource distribution since the anticipating risk of resource random fluctuation is influenced and generated by environment
(the hereinafter referred to as resource distribution of anticipating risk), and wherein environment influences mainly Z (t)=(CPU (t) ', MEM (t) ', BW (t) ')
Refer to the factors such as pop-up mission request, the temperature surge of calculate node, main board power supply deficiency.Z (t) can be disturbed at random with coping resources
It is dynamic, redundancy is provided for resource distribution.
(5) generating function G (*) is the relation function of Y (t), Z (t), t, according to the resource distribution of each node of cloud computing system
Historical data processing result or the setting of industry experience value, can be provided by historical data processing module.
(6) ζ is the terminal condition of backward stochastic differential equation, is one group of measurable stochastic variable, and ζ is handled by user's request
Module provides.
4, user interactive module receives the resource distribution condition of the initial time from backward inference module, and resource
Allocation plan feeds back to user.
This example can cover the present invention.By the description to this example it can be found that need to only input the following a certain determination
Time cloud computing system resource distribution demand to be achieved, the cloud computing system resource that can reversely release initial time are matched
It sets.It is provided by the invention to have the beneficial effect that: (1) can be determined according to the following computational resource requirements situation for determining the moment
The computing resource currently needed, and guarantee that the computing resource currently prepared is " most saving "；(2) the following money can be responded actively
The stochastic volatility of source configuration, improves the stability and availability of cloud computing system.
Claims (2)
1. the cloud computing resources based on backward stochastic differential equation configure system, request module, historical data are handled by user
Manage module, backward inference module and user interactive module composition, it is characterised in that: user handles request module first, in accordance with specified
Reception of beacons user is to the access request of cloud computing server, and to the constraint condition of resource distribution, and according to cloud computing system
Configuration and the network condition for counting operator node, aforementioned access is requested and constrained condition resolution is central processing unit demand, bandwidth
Demand, the parameter of memory requirements, form are a triple ζ=(CPU (T), MEM (T), BW (T)), and wherein T is user and cloud
The future that service provider arranges in service-level agreement SLA determines the moment, and CPU (T) is that the central processing unit at T moment needs
It asks, MEM (t) is the capacity of memory, the capacity that BW (t) is bandwidth, and triple ζ is passed to backward inference module；Historical data
On the one hand processing module collects the resource distribution historical data of cloud computing system operation；Another aspect historical data processing module root
According to the resource distribution historical data that the cloud computing system of collection is run, the generating function G (*) of backward stochastic differential equation is determined；
There are two Boolean type flag bits for historical data processing module tool: G (*) pre-sets flag bit PRECONFIG_G and updates mark
Position UPDATE_FLAG determines G (*) analytic expression according to the generating function setting method of backward stochastic differential equation；Generating function G
(*) is the relation function of Y (t), Z (t), t, according to the resource distribution historical data processing result or row of each node of cloud computing system
The setting of industry empirical value, can be provided by historical data processing module；Y (t) be t moment resource distribution, i.e. Y (t)=(CPU (t),
MEM(t),BW(t))；Z (t) is set as coping with the resource since the anticipating risk of resource random fluctuation is influenced and generated by environment
Configuration；And wherein environment influence is primarily referred to as pop-up mission request, calculates section Z (t)=(CPU (t) ', MEM (t) ', BW (t) ')
The temperature of point is increased sharply, main board power supply is insufficient, and Z (t) can provide redundancy with coping resources random perturbation for resource distribution；
Generating function G (*) is passed into backward inference module；Backward inference module, which is received, handles request module from user
Parameter ζ, and the generating function G (*) from historical data processing module；Using ζ as terminal condition, using generating function G
(*) establishes the resource distribution model based on backward stochastic differential equation；According to the aforementioned resource based on backward stochastic differential equation
Allocation models obtains one group of (Y (0), Z (0)) uniquely determined by the numerical solution of backward stochastic differential equation, and then obtains cloud
The configuration of computing system initial resource；The resource distribution condition (Y (0), Z (0)) of initial time is sent to user interactive module；Its
Middle Y (0), Z (0) are respectively the normal resource configuration of initial time and cope with and due to being influenced by environment generate resource random wave
Dynamic anticipating risk resource distribution；User interactive module receives the resource distribution item of the initial time from backward inference module
Part, and resource allocation proposal is fed back to user.
2. the cloud computing resources configuration method based on backward stochastic differential equation, which comprises the steps of:
(1) user handles request module and receives user to the access request of cloud computing server first, in accordance with specified format, and
To the constraint condition of resource distribution；And according to the configuration of cloud computing system calculate node and network condition, aforementioned access is requested
With constraint condition resolution be central processing unit demand, bandwidth demand, memory requirements parameter, form be a triple ζ=
(CPU (T), MEM (T), BW (T)), wherein T is user and the future that cloud service provider is arranged in service-level agreement SLA
It determines the moment, and ζ is passed to backward inference module；
(2) on the one hand historical data processing module collects the resource distribution historical data of cloud computing system operation；On the other hand it goes through
The resource distribution historical data that history data processing module is run according to industry experience value or the cloud computing system of collection, determines
To the generating function G (*) of stochastic differential equation；There are two Boolean type flag bits for historical data processing module tool: G (*) is default
It sets flag bit PRECONFIG_G and updates flag bit UPDATE_FLAG, set according to the generating function of backward stochastic differential equation
Method determines G (*) analytic expression；And generating function G (*) is passed into backward inference module；
The generating function setting method of backward stochastic differential equation above-mentioned is specific further include:
(2.1) historical data processing module first checks for G (*) and pre-sets flag bit PRECONFIG_G, if 1 generating function G
(*) is set by industry experience value, is turned (2.7), is otherwise turned (2.2)；
(2.2) it checks and updates whether flag bit UPDATE_FLAG is 0, if UPDATE_FLAG is 0, turns (2.3), otherwise turn
(2.4)；
(2.3) the resource distribution historical data that historical data processing module is run according to the cloud computing system of collection, utilizes one
The generating function G (*) of preset function set fitting backward stochastic differential equation, and UPDATE_FLAG is set as 1, simultaneously
The time Lasttime of record at this time, turns (2.7)；
(2.4) UPDATE_FLAG is 1 at this time, reads current time system time Nowtime, if Nowtime-Lasttime >=
Interval, wherein Interval > 0 is default update cycle constant, turns (2.5), otherwise turns (2.6)；
(2.5) generating function G (*) is fitted again at this time, completes the update of G (*)；The time of record at this time simultaneously
Lasttime turns (2.7)；
(2.6) at this point, Nowtime-Lasttime < Interval, records time Lasttime at this time, turn (2.7)；
(2.7) generating function G (*) is passed to backward inference module by historical data processing module；
(3) backward inference module receives the parameter ζ that request module is handled from user, and handles mould from historical data
The generating function G (*) of block；And using ζ as terminal condition, established using generating function G (*) based on backward stochastic differential equation
Resource distribution model；And according to the aforementioned resource distribution model based on backward stochastic differential equation, by backward stochastic differential equation
Numerical solution obtain one group of (Y (0), Z (0)) uniquely determined, and then obtain cloud computing system initial resource configure；And at the beginning of
The resource distribution condition (Y (0), Z (0)) at moment beginning is sent to user interactive module；Wherein Y (0), Z (0) are respectively initial time
Normal resource configuration and cope with the anticipating risk resource distribution since resource random fluctuation is influenced and generated by environment；
Resource distribution model above-mentioned based on backward stochastic differential equation has the feature that
(3.1) the resource distribution model based on backward stochastic differential equation established meets following equation
Wherein, [0, T] t ∈, t is the time, and at the time of T is following determines, W is the Brownian movement of d dimension；
(3.2) resource configuration amount can be expressed as the processing capacity CPU (t) of central processing unit, memory within [0, the T] period
The capacity BW (t) of capacity MEM (t), bandwidth；
(3.3) Y (t) is t moment resource distribution, i.e. Y (t)=(CPU (t), MEM (t), BW (t))；
(3.4) Z (t) is set as coping with the resource distribution since the anticipating risk of resource random fluctuation is influenced and generated by environment,
And wherein environment influences to refer to that pop-up mission request, the temperature of calculate node are sharp Z (t)=(CPU (t) ', MEM (t) ', BW (t) ')
Increase, main board power supply is insufficient；Z (t) can provide redundancy with coping resources random perturbation for resource distribution；
(3.5) generating function G (*) is the relation function of Y (t), Z (t), t, is gone through according to the resource distribution of each node of cloud computing system
History data processed result or the setting of industry experience value, can be provided by historical data processing module；
(3.6) ζ is the terminal condition of backward stochastic differential equation, is one group of measurable stochastic variable, and ζ asks modulus by user's processing
Block provides；
(4) user interactive module receives the resource distribution condition of the initial time from backward inference module, and resource is matched
The scheme of setting feeds back to user.
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