CN107743100A - A kind of online adaptive network section virtual resource allocation method based on traffic forecast - Google Patents
A kind of online adaptive network section virtual resource allocation method based on traffic forecast Download PDFInfo
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- CN107743100A CN107743100A CN201710941231.6A CN201710941231A CN107743100A CN 107743100 A CN107743100 A CN 107743100A CN 201710941231 A CN201710941231 A CN 201710941231A CN 107743100 A CN107743100 A CN 107743100A
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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
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/83—Admission control; Resource allocation based on usage prediction
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/80—Actions related to the user profile or the type of traffic
- H04L47/805—QOS or priority aware
Abstract
The present invention relates to a kind of online adaptive virtual resource allocation algorithm based on traffic forecast, belong to moving communicating field.The algorithm is under the premise of the QoS of virtual network section is ensured, to minimize the online dynamic resource scheduling that is carried out as target of network section resource impact cost.The business cut into slices in big time scale using autoregressive moving-average model to each network is predicted, and the optimal spatial cache size that should be reserved according to the maximum probability load value of prediction selection next cycle；The queue overflow probability or remaining time value cut into slices in small time scale according to each network adjust the priority that each network is cut into slices, and premised on ensureing the queue overflow probability of each network section, appropriate Physical Resource Block is distributed for the section of each network to ensure the service rate of each network section.Online dynamic self-adapting virtual resource allocation method proposed by the present invention can each network section of effective guarantee QoS, while minimize the resource impact cost of network section.
Description
Technical field
The invention belongs to moving communicating field, is related to a kind of virtual resource allocation of the online adaptive based on traffic forecast
Method.
Background technology
With the swift and violent growth of mobile subscriber's quantity, intelligent terminal it is widely available, next generation network technology needs to provide
Higher power system capacity, the availability of frequency spectrum and lower time delay etc., thus, wireless network virtualization technology arises at the historic moment.
In wireless network virtualization scene, Incumbent is decoupled into two independent roles：Infrastructure device supplier
(Infrastructure Provider, InP) and service provider (ServiceProvider, SP).InP is by the thing in network
Manage resource (including frequency spectrum, power, antenna, passback etc.) to be abstracted, cut into slices, to be supplied to the virtual network of SP structure different demands；
SP can lease physical resource at different InP, provide the user and service end to end.Due to the scarcity of physical resource, net
The randomness of network business, SP diversified QoS demand, how to be efficiently SP (alternatively referred to as section, virtual network) allotment
It is most important with lifting system performance to manage resource.
Inventor has found that it has the disadvantage that during prior art is studied：
Existing Virtualization Study primarily focuses on to be shared to frequency spectrum resource, and less consideration network section is to caching
The demand of resource, in actual applications, the method for salary distribution of cache resources will have a strong impact on the service quality of network section, if network
The cache resources that section is rented can cause serious data packetloss more at least, and correspondingly, the user experience quality of network section will
Drastically decline, if the cache resources that network section is rented are excessive, the waste of resource can be caused.Therefore, it is necessary to virtualizing
The method of salary distribution of frequency spectrum resource and cache resources is considered in scene simultaneously.In addition, existing research is mainly static resource allocation
Strategy or network state based on current dispatching cycle carry out real-time scheduling of resource, however, sudden due to network load
Change, it is most likely that cause the situation of network over loading or resource provisioning surplus, it is therefore desirable to a kind of variation characteristic according to load
Proactive property, dynamically adjust the allocation strategy of resource.
The content of the invention
In view of this, it is an object of the invention to provide a kind of virtual resource of the online adaptive based on traffic forecast point
Method of completing the square, be advantageous to the QoS demand of Logistics networks section using the technical scheme while reduce the mapping cost of network section.
To reach above-mentioned purpose, the present invention provides following technical scheme：
A kind of online adaptive network section virtual resource allocation method based on traffic forecast, comprises the following steps：
S1：Calculate the queue growth of each network section；
S2：Determine the priority of each network section；
S3：Perform scheduling of resource；
S4：Judge whether resource also has residue；If there is residue, into step S5；If without residue, terminate to flow
Journey；
S5：Construct new network set of slices；
S6：Perform scheduling of resource；
Under the premise of the QoS of virtual network section is ensured, according to the traffic forecast result of each network section, to minimize net
Network section resource impact cost is target, and co-allocation is carried out to spatial cache and Physical Resource Block on different time scales.
Further, in the process, it is necessary to the QoS demand of Logistics networks section, i.e., the queue overflow of each network section
Probability is no more than set threshold value, and ensures that the packet loss of each network section is unsuitable excessive.
Further, the queue overflow probability of each network section is：The cache business of each network section exceedes current adjust
Spend the probability of its reserved spatial cache size cycle Nei；The service condition cut into slices on each dispatching cycle according to each network
Carry out dynamic resource scheduling.
Further, the dynamic resource scheduling is to minimize network section mapping cost as target, wherein described mapping
Cost isρkThe unit price of spatial cache, B are rented for network section kkAdjusted for network section k currently
Spend the size for the spatial cache reserved in the cycle, αkThe unit price of Physical Resource Block, x are rented for network section kn,kFor current scheduling
The allocation strategy of Physical Resource Block, x in cyclen,k=1 represents that Physical Resource Block n distributes to network section k, on the contrary, xn,k=0
Then represent that Physical Resource Block n is not yet assigned to network section k.
Further, the different time scales include big time scale and small time scale, are performed in big time scale
The reservation policy of spatial cache, and the allocation strategy of Physical Resource Block is performed in small time scale.
Further, the reservation policy specific method of the execution spatial cache in big time scale is：First by business
State discrete is melted into multiple sections, and counts the number that each network section is in each state interval within the big time scale cycle,
And record the state interval that maximum times are within each dispatching cycle；Secondly using autoregressive moving-average model to each net
The maximum frequency state interval in network section next cycle is modeled and predicted；Last each network section is dispatched in next large scale
The reserved value of the spatial cache in cycle is the prediction result value.
Further, the allocation strategy of execution Physical Resource Block is specially in the small time scale：
First, the priority of each network section is ranked up, specific method is：Queue average growth rate is more than tolerable
Queue increment network section priority higher than queue average growth rate be less than tolerable queue increment network cut into slices；
In the network set of slices that queue average growth rate is more than tolerable queue increment, the remaining time of each network section is calculated
Value, time value is smaller, and the priority of network section is higher；It is less than the network of tolerable queue increment in queue average growth rate
In set of slices, each network section queue overflow probability is calculated, probable value is bigger, and the priority of network section is higher；Its squadron
Column average growth rate is the ratio of the summation of the queue growth of each dispatching cycle and the length of observation window in observation window；Can
The queue increment of tolerance be network section current cache space size and the length of buffer queue of previous dispatching cycle difference with
The ratio of the length of observation window；
Secondly, the priority orders cut into slices according to the network of determination perform the allocation strategy of Physical Resource Block successively, specifically
Method is：The priority cut into slices first according to each network, it is followed successively by each network section and distributes enough physical resource numbers of blocks,
Make the service speed that network is cut into slices not less than the data packet number that network section reaches within dispatching cycle；If all-network is cut into slices
Physical Resource Block has all been obtained, and has also had remaining Physical Resource Block in system, then has selected newly arrived packet and upper one week
The network that the queue length sum of phase is more than service speed is cut into slices, and constructs new set of slices, and cut into slices according to network and rent thing
The size of reason resource block unit price is ranked up to the network section in the set, the smaller section of unit price, and its priority is higher；
The network section increase physical resource number of blocks is followed successively by according to fixed priority, until the service speed of network section
Rate is not less than its newly arrived packet and the queue length sum in a upper cycle
Further, the traffic forecast of each network section is specially：According to functional expressionCalculate each network section
The service condition in next cycle, whereinCut into slices service condition predicted values of the k in dispatching cycle n for network,Cut into slices k service condition values in dispatching cycle (n-e), e=1 ... p respectively for network,For the autoregressive coefficient corresponding to each observation cycle, ε (n-e), e=0 ..., q are white noise sequence, its average
For 0, variance isθe, e=1 ..., q is that parameter is slided corresponding to noise sequence；
Forecast model modeling method is：
First, in order to ensure that observation sequence is stable sequence, consider to carry out logarithm process to observation sequence, utilize processing
Sequential value afterwards is predicted；
Secondly, the exponent number of initialization model, the parameter of model is estimated using observation sequence, including the autoregression
Coefficient and slip parameter；
Method for parameter estimation therein is：The value of the parameter is estimated using invertible function method, first with observation sequence from
The inverse function of correlation function Matrix Estimation forecast model, the slip parameter of forecast model is estimated according to obtained inverse function value, then
Auto-regressive parameter is estimated using the system of linear equations corresponding to the backward shift operator of autoregressive moving-average model；
Finally, the exponent number of forecast model is determined using AIC order selection criterias.
Further, the method for parameter estimation is specially：
According to functional expressionThe inverse function of the forecast model is calculated, its
InFor contrafunctional value corresponding to each observation sequence,For the auto-correlation function of each observation sequence；
Bring the contrafunctional value of gained into functional expressionAnd to e=1 ..., p simultaneous equations
Group obtains the estimate of the slip parameter of forecast model：
The beneficial effects of the present invention are：The traffic forecast knot that the present invention cuts into slices on different time scales according to each network
Fruit dynamically adjusts the method for salary distribution of cache resources and frequency spectrum resource, can effectively avoid the network over loading feelings superfluous with resource provisioning
Condition.In addition, dynamically adjusting the priority of each network section within each dispatching cycle, the justice between each network section both ensure that
Property, while can realize and provide statistical QoS guarantee for the section of each network.
Brief description of the drawings
In order that the purpose of the present invention, technical scheme and beneficial effect are clearer, the present invention provides drawings described below and carried out
Explanation：
Fig. 1 is system framework figure；
Fig. 2 is resource allocation schematic diagram；
Fig. 3 is that big time scale cache resources reserve workflow diagram；
Fig. 4 is small time scale frequency spectrum resource allocation method flow chart.
Embodiment
Below in conjunction with accompanying drawing, the preferred embodiments of the present invention are described in detail.
Referring to Fig. 1, Fig. 1 is the system framework figure of the present invention.In an embodiment of the present invention, service request unit is used to delay
The newly arrived service request of each network section is deposited, load analysis unit is then responsible for dividing the business datum of each network section
Analyse, including the maximum frequency state interval on the service condition and big time scale in small time scale, and to small time scale
On queue overflow probability estimated and the state interval in big time scale be predicted.Resource management entity unit
Basic function is the resource that management is rented from physical network, and determines the optimal number for the frequency spectrum resource that each network section is rented
And the size of reserved spatial cache, so as to realize the QoS demand for minimizing resource impact cost and ensureing each network section.
Network status monitoring entity is used to perform to be monitored to the state of each rental resource.Slice scheduling device is responsible for each network section
Perform the scheduling decision of resource.
Referring to Fig. 2, Fig. 2 is resource allocation schematic diagram, and in embodiments of the present invention, each network is cut according to timeline expansion
The distribution of piece frequency spectrum resource and reserving for cache resources.On time scale, it is divided into big time scale cycle and small time scale
In the cycle, the business cut into slices on the big time scale cycle using autoregressive moving-average model to each network is predicted, and root
It is predicted that maximum probability load value select the optimal spatial cache size that next cycle should be reserved；In the small time scale cycle
On the queue overflow probability cut into slices according to each network or remaining time value adjust the priority that each network is cut into slices, and to ensure each net
Premised on the queue overflow probability of network section, appropriate Physical Resource Block is distributed for the section of each network to ensure each network section
Service rate.
Referring to Fig. 3, Fig. 3 is that big time scale cache resources reserve workflow diagram, is concretely comprised the following steps：
Step 301：History observation data are obtained, service condition are discretized into multiple sections first, and count each network
Cut into slices and the number of each state interval is within the big time scale cycle, and record and maximum times are within each dispatching cycle
State interval；Secondly, in order to ensure that observation sequence is stable sequence, to being in maximum times in each dispatching cycle
State interval carries out logarithm process, the historical data using the value after processing as forecast model.
Step 302：The exponent number of forecast model is initialized, makes p=1, q=1.Wherein described forecast model is：
According to functional expression
The service condition in each network section next cycle is calculated,
It is wherein describedFor：Network is cut into slices service condition predicted values of the k in dispatching cycle n,
It is describedFor：K is respectively at dispatching cycle (n-e) for network section, e=1 ... the business in p
State value,
It is describedFor：Autoregressive coefficient corresponding to service condition in each observation cycle,
The ε (n-e), e=0 ..., q is：White noise sequence, its average are 0, and variance is
The θe, e=1 ..., q is：Parameter is slided corresponding to noise sequence；
Step 303：Data are observed according to history to estimate the parameter of forecast model, including autoregressive coefficientWith slip parameter θe, e=1 ..., q.Specifically method of estimation is：
The value of the parameter is estimated using invertible function method,
First, using sliding parameter described in the estimate of autocorrelation of observation sequence,
According to functional expression：The inverse function of the forecast model is calculated,
It is wherein describedFor：Contrafunctional value corresponding to each observation sequence,
It is describedFor：The auto-correlation function of each observation sequence；
Bring the contrafunctional value of gained into functional expressionAnd to e=1 ..., p simultaneous equations
Group obtains the estimate of the slip parameter of the forecast model；
Secondly, using system of linear equations corresponding to the backward shift operator of the autoregressive moving-average model to the autoregression
Parameter estimated,
Wherein described backward shift operator equation group is
Step 304：The parameter value and the estimate of corresponding residual variance estimated according to above-mentioned steps calculate prediction mould
The AIC functions of type, if it is determined that model order under the values of AIC functions reach minimum value, perform step 305, otherwise increase
Add model order, continue executing with step 303.
Step 305：The model order and model parameter determined according to above-mentioned steps, is performed to the traffic value in next cycle
Prediction.Each network section is the prediction result value in the reserved value of the spatial cache of next large scale dispatching cycle.
Referring to Fig. 4, Fig. 4 is small time scale frequency spectrum resource allocation method flow chart, is concretely comprised the following steps：
Step 401：The queue growth of each section, including queue average growth rate and tolerable queue increment are calculated,
Wherein described queue average growth rate be equal in observation window the summation of the queue growth of each dispatching cycle with
The ratio of the length of observation window,
The tolerable queue increment is equal to network section current cache space size and caching team previous dispatching cycle
The ratio of the difference of row length and the length of observation window.
Step 402：The priority of each network section is determined, specific method is：
The priority that the network that queue average growth rate is more than tolerable queue increment is cut into slices is higher than queue balanced growth
The network that rate is less than tolerable queue increment is cut into slices.It is more than the net of tolerable queue increment in the queue average growth rate
In network set of slices, the remaining time value of each section is calculated, the time value is smaller, and the priority of section is higher.In the team
Column average growth rate is less than in the network set of slices of tolerable queue increment, calculates each network section queue overflow probability,
The probable value is bigger, and the priority of section is higher；
Step 403：The priority orders cut into slices according to each network of determination, perform the scheduling to frequency spectrum resource, each network
The physical resource number of blocks being assigned to of cutting into slices need to meet that the service speed of section is not less than its data reached within dispatching cycle
Bag quantity.
Step 404：If each network section has all obtained frequency spectrum resource, and now also has remaining frequency spectrum resource in system,
Step 405 is then performed, otherwise, is terminated.
Step 405：The queue length sum in newly arrived packet and a upper cycle was selected to be more than the network of service speed
Section constructs new set of slices, and rents Physical Resource Block monovalent size according to network section and the network of the set is cut
Piece is ranked up, and the smaller section priority of unit price is higher.
Step 406：The network section increase physical resource number of blocks is followed successively by according to fixed priority, until
The newly arrived packet of the network section and the queue length sum in a upper cycle are not more than service speed.
Finally illustrate, preferred embodiment above is merely illustrative of the technical solution of the present invention and unrestricted, although logical
Cross above preferred embodiment the present invention is described in detail, it is to be understood by those skilled in the art that can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (9)
- A kind of 1. online adaptive network section virtual resource allocation method based on traffic forecast, it is characterised in that：This method Comprise the following steps：S1：Calculate the queue growth of each network section；S2：Determine the priority of each network section；S3：Perform scheduling of resource；S4：Judge whether resource also has residue；If there is residue, into step S5；If without residue, terminate flow；S5：Construct new network set of slices；S6：Perform scheduling of resource；Under the premise of the QoS of virtual network section is ensured, according to the traffic forecast result of each network section, cut with minimizing network Piece resource impact cost is target, and co-allocation is carried out to spatial cache and Physical Resource Block on different time scales.
- A kind of 2. online adaptive network section virtual resource allocation side based on traffic forecast according to claim 1 Method, it is characterised in that：In the process, it is necessary to which the QoS demand of Logistics networks section, i.e., the queue overflow of each network section are general Rate is no more than set threshold value, and ensures that the packet loss of each network section is unsuitable excessive.
- A kind of 3. online adaptive network section virtual resource allocation side based on traffic forecast according to claim 2 Method, it is characterised in that：The queue overflow probability of each network section is：The cache business of each network section exceedes current scheduling The probability of its reserved spatial cache size in cycle；The service condition cut into slices on each dispatching cycle according to each network enters Mobile state scheduling of resource.
- A kind of 4. online adaptive network section virtual resource allocation side based on traffic forecast according to claim 3 Method, it is characterised in that：The dynamic resource scheduling is to minimize network section mapping cost as target, wherein described is mapped to Originally it isρkThe unit price of spatial cache, B are rented for network section kkAdjusted for network section k currently Spend the size for the spatial cache reserved in the cycle, αkThe unit price of Physical Resource Block, x are rented for network section kn,kFor current scheduling The allocation strategy of Physical Resource Block, x in cyclen,k=1 represents that Physical Resource Block n distributes to network section k, on the contrary, xn,k=0 Then represent that Physical Resource Block n is not yet assigned to network section k.
- A kind of 5. online adaptive network section virtual resource allocation side based on traffic forecast according to claim 1 Method, it is characterised in that：The different time scales include big time scale and small time scale, are performed in big time scale slow The reservation policy in space is deposited, and the allocation strategy of Physical Resource Block is performed in small time scale.
- A kind of 6. online adaptive network section virtual resource allocation side based on traffic forecast according to claim 5 Method, it is characterised in that：It is described in big time scale perform spatial cache reservation policy specific method be：First by business shape State is discretized into multiple sections, and counts the number that each network section is in each state interval within the big time scale cycle, and Record the state interval that maximum times are within each dispatching cycle；Secondly using autoregressive moving-average model to each net The maximum frequency state interval in network section next cycle is modeled and predicted；Last each network section is dispatched in next large scale The reserved value of the spatial cache in cycle is the prediction result value.
- A kind of 7. online adaptive network section virtual resource allocation side based on traffic forecast according to claim 5 Method, it is characterised in that：The allocation strategy of execution Physical Resource Block is specially in the small time scale：First, the priority of each network section is ranked up, specific method is：Queue average growth rate is more than tolerable team The network that the priority of the network section of row increment is less than tolerable queue increment higher than queue average growth rate is cut into slices；In team Column average growth rate is more than in the network set of slices of tolerable queue increment, calculates the remaining time value of each network section, Time value is smaller, and the priority of network section is higher；The network for being less than tolerable queue increment in queue average growth rate is cut In piece set, each network section queue overflow probability is calculated, probable value is bigger, and the priority of network section is higher；Wherein queue Average growth rate is the ratio of the summation of the queue growth of each dispatching cycle and the length of observation window in observation window；It can hold The queue increment born is the current cache space size of network section and the difference of the length of buffer queue of previous dispatching cycle and sight Survey the ratio of the length of window；Secondly, the priority orders cut into slices according to the network of determination perform the allocation strategy of Physical Resource Block, specific method successively For：The priority cut into slices first according to each network, it is followed successively by each network section and distributes enough physical resource numbers of blocks, make net The service speed of network section is not less than the data packet number that network section reaches within dispatching cycle；If all-network is cut into slices all Physical Resource Block is obtained, and also has remaining Physical Resource Block in system, then selected newly arrived packet and a upper cycle The network that queue length sum is more than service speed is cut into slices, and constructs new set of slices, and cut into slices according to network and rent physics money The size of source block unit price is ranked up to the network section in the set, the smaller section of unit price, and its priority is higher；According to Fixed priority is followed successively by the network section increase physical resource number of blocks, until the service speed of network section is not Less than its newly arrived packet and the queue length sum in a upper cycle.
- A kind of 8. online adaptive network section virtual resource allocation side based on traffic forecast according to claim 6 Method, it is characterised in that：The traffic forecast of each network section is specially：According to functional expressionCalculate each network section The service condition in next cycle, whereinCut into slices service condition predicted values of the k in dispatching cycle n for network,Cut into slices k service condition values in dispatching cycle (n-e), e=1 ... p respectively for network,For the autoregressive coefficient corresponding to each observation cycle, ε (n-e), e=0 ..., q are white noise sequence, its average For 0, variance isθe, e=1 ..., q is that parameter is slided corresponding to noise sequence；Forecast model modeling method is：First, in order to ensure that observation sequence is stable sequence, consider to carry out logarithm process to observation sequence, after processing Sequential value is predicted；Secondly, the exponent number of initialization model, the parameter of model is estimated using observation sequence, including the autoregressive coefficient With slip parameter；Method for parameter estimation therein is：The value of the parameter is estimated using invertible function method, first with the auto-correlation of observation sequence Jacobian matrix estimates the inverse function of forecast model, and the slip parameter of forecast model is estimated according to obtained inverse function value, recycles System of linear equations corresponding to the backward shift operator of autoregressive moving-average model is estimated auto-regressive parameter；Finally, the exponent number of forecast model is determined using AIC order selection criterias.
- A kind of 9. online adaptive network section virtual resource allocation side based on traffic forecast according to claim 8 Method, it is characterised in that：The method for parameter estimation is specially：According to functional expressionThe inverse function of the forecast model is calculated, whereinFor contrafunctional value corresponding to each observation sequence,For the auto-correlation function of each observation sequence；Bring the contrafunctional value of gained into functional expressionAnd to e=1 ..., p Simultaneous Equations obtain Obtain the estimate of the slip parameter of forecast model：
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