CN108965024A - A kind of virtual network function dispatching method of the 5G network slice based on prediction - Google Patents
A kind of virtual network function dispatching method of the 5G network slice based on prediction Download PDFInfo
- Publication number
- CN108965024A CN108965024A CN201810863512.9A CN201810863512A CN108965024A CN 108965024 A CN108965024 A CN 108965024A CN 201810863512 A CN201810863512 A CN 201810863512A CN 108965024 A CN108965024 A CN 108965024A
- Authority
- CN
- China
- Prior art keywords
- network
- service
- slice
- queue
- prediction
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000012549 training Methods 0.000 claims abstract description 6
- 230000008859 change Effects 0.000 claims abstract description 5
- 230000006870 function Effects 0.000 claims description 117
- 239000003016 pheromone Substances 0.000 claims description 25
- 230000008569 process Effects 0.000 claims description 17
- 238000012545 processing Methods 0.000 claims description 13
- 238000005457 optimization Methods 0.000 claims description 10
- 238000013468 resource allocation Methods 0.000 claims description 10
- 230000007704 transition Effects 0.000 claims description 9
- 210000002569 neuron Anatomy 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 4
- 230000004913 activation Effects 0.000 claims description 3
- 230000005540 biological transmission Effects 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims description 3
- 230000007774 longterm Effects 0.000 claims description 3
- 239000013598 vector Substances 0.000 claims description 3
- 230000006399 behavior Effects 0.000 claims description 2
- 238000012804 iterative process Methods 0.000 claims description 2
- 238000007781 pre-processing Methods 0.000 claims description 2
- 238000013507 mapping Methods 0.000 abstract description 2
- 238000010295 mobile communication Methods 0.000 abstract description 2
- 238000012544 monitoring process Methods 0.000 description 6
- 238000004458 analytical method Methods 0.000 description 5
- 238000013528 artificial neural network Methods 0.000 description 5
- 238000007726 management method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000009975 flexible effect Effects 0.000 description 4
- 230000007246 mechanism Effects 0.000 description 3
- 239000000203 mixture Substances 0.000 description 3
- 238000009825 accumulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007787 long-term memory Effects 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 230000001537 neural effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000005574 cross-species transmission Effects 0.000 description 1
- 125000004122 cyclic group Chemical group 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000000644 propagated effect Effects 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/08—Configuration management of networks or network elements
- H04L41/0803—Configuration setting
- H04L41/0823—Configuration setting characterised by the purposes of a change of settings, e.g. optimising configuration for enhancing reliability
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/12—Discovery or management of network topologies
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
- H04L41/147—Network analysis or design for predicting network behaviour
Abstract
The present invention relates to a kind of 5G networks to be sliced the virtual network function dispatching method based on prediction, belongs to mobile communication field.This method specifically includes: for the service function chain feature of service traffics dynamic change, establishing the service function queue chain model based on time delay；More queue cache models are established, in different moments according to slice service queue size, the lowest service rate that determines the priority of slice request and should provide；It is a series of continuous time windows by time discrete, and using the queuing message in time window as training dataset sample, establishes the traffic aware model based on prediction；According to the every kind of slice service queue size predicted and corresponding lowest service rate, the dispatching method for meeting optimal service function chain VNF under the resource constraint that slice service queue caching is not spilt over is found.The present invention realizes the online mapping of network slice, reduces the ensemble average scheduling delay of multiple network slices, improves the performance of network service.
Description
Technical field
The invention belongs to mobile communication technology fields, are related to a kind of virtual network function tune of the 5G network slice based on prediction
Degree method.
Background technique
It is hard based on generic server that network function virtualizes (Network Function Virtualization, NFV)
Part, and disparate networks function is provided in the form of software implementation, it is the flexible deployment and network structure of operator's future network business
It is quick adjustment provide great enabling capabilities, especially relevant service function chain (Service Function
Chaining, SFC) technology, it is different from traditional network function realization, more flexible and more dynamical network service is created, it is full
The more diversified demand of foot.Service function chain be an orderly virtual network function (Virtual Network Function,
VNF) to gather, service traffics pass sequentially through multiple VNF according to specified strategy to realize processing by demand for network service, due to
VNF in SFC is operated in virtual machine (VM) environment of generic server, and this business processing framework ensure that VNF's is flexible
Property and adjustability, while bringing a series of researchs challenge, such as the scheduling problem of VNF and corresponding resource point in SFC
With problem.With the increase of terminal device and network application quantity, network flow is acutely increasing, the guarantor of service quality (QoS)
Barrier has become service provider's major issue urgently to be resolved, and wherein end-to-end time delay and bandwidth are two kinds of basic QoS attributes.
The scheduling mode of different VNF provides identical service for user.However, different scheduling modes may cause service function chain
The resource distribution of VNF changes, to influence the end-to-end time delay of service function chain, therefore, how by the machine of status monitoring
System is integrated to the network environment of variation, to reasonably realize that the scheduling of VNF, the configuration of virtual resource, resource supply and demand balance are closed
The optimization of system reduces the scheduling delay of virtual network function while guaranteed qos, improves resource utilization, is 5G network slice
One of middle resource management and scheduling mechanism critical issue to be solved.
At present in the invention in research SFC deployment about end-to-end time delay, it is single that most of work all only rest on solution
Resource scheduling on dispatching cycle, and have ignored the service request changed in time-domain and cause the accumulation of data and generate
Queue overstock, this processing mode in actual scene excessively simplify, distribute to VNF computing resource and be used for transmission number
According to link bandwidth resource should be adjusted according to the variation of service request quantity dynamic.Meanwhile in order to solve VNF scheduling and virtual
Possible hysteresis quality problem during resource distribution can realize the monitoring of network state using the mechanism of resource requirement prediction.
Incidence relation between resource characteristic and resource requirement can be predicted well by having invention and demonstrating nerual network technique, but very
The combination of resource requirement prediction and virtual network function scheduling is solved the problems, such as with this method less.And shot and long term memory network
(LSTM) as one of classical way of deep learning, LSTM is that one kind is improved by RNN and can be used to carry out time sequence
The deep learning model of column forecast analysis.This method have powerful data characteristics capability of fitting, by mass data from
The dynamic feature for extracting resource requirement is trained, the most essential feature of mining data, therefore the precision of prediction is caused to be higher than biography
System statistical models.This method is improved by RNN simultaneously, is more suitable and is handled long-distance dependence problem.
Based on above-mentioned advantage, the present invention predicts service function chain to resource most in real time using shot and long term memory network
Low demand.It is based on prediction as a result, propose scheduling and the Resource Allocation Formula of a kind of dynamic service function chain VNF, draw
The Dynamical Deployment that a kind of minimax ant group algorithm realizes a plurality of service function chain is entered.
Summary of the invention
In view of this, the virtual network function scheduling the purpose of the present invention is to provide a kind of 5G network slice based on prediction
Method, can realize the monitoring of network state by the mechanism of prediction, predict clothes according to the queuing message feature that network is sliced
Function chain of being engaged in proposes the scheduling and resource of a kind of dynamic service function chain VNF based on the result to the Minimum requirements of resource
Allocation plan is scheduled underlying resource on time dimension, protects resource in not reserved bottom-layer network, finds virtual network
It can get the communication path of maximum resource, realize the online mapping of network slice, reduce the ensemble average scheduling of multiple network slices
Time delay.
In order to achieve the above objectives, the invention provides the following technical scheme:
A kind of virtual network function dispatching method of the 5G network slice based on prediction, specifically includes the following steps:
S1: it under the application scenarios of 5G network slice, for the service function chain feature of service traffics dynamic change, establishes
The Time Delay Model of the network model of service function queue chain based on time delay, service function queue chain model and more queues；
S2: establishing more queue cache models, when spatial cache is limited, to prevent queuing data from losing, in different moments
According to slice service queue size, the lowest service rate that determines the priority of slice request and should provide；
S3: being a series of continuous time windows by time discrete, and using the queuing message in time window as training data
Collect sample, establishes the traffic aware model based on prediction；
S4: it according to the every kind of slice service queue size predicted and corresponding lowest service rate, finds and meets slice
The dispatching method of optimal service function chain VNF under the resource constraint that service queue caching is not spilt over.
Further, in step S1, the network model of the service function queue chain based on time delay are as follows:
Virtual network topology is indicated that wherein V indicates the set of dummy node by weighted-graph G=(V, E), and E indicates empty
The set of quasi- link；BmThe total output link bandwidth for indicating node m, is shared by the virtual link connecting with the node, for net
Network is sliced Si, the virtual network function set expression of processing business request is Fi={ fi1,fij,...fiJ, i ∈ [1, | S |]z,j
∈[1,|Fi|]z, wherein S indicates the set of all-network slice, and J indicates FiThe number of middle VNF；For forming service function chain
VNF, indicated with f, wherein fijIndicate that network is sliced SiJ-th of VNF for needing to dispatch；It enablesExpression is able to carry out virtualization
Network function fijDummy node set, wherein
Further, in step S1, the service function queue chain model are as follows:
Enable Γ={ 1 ..., t..., T } indicate the network operation time slot sets, wherein define each time slot t it is lasting when
Between be Ts；Therefore in time slot t, and f is performedijThe bandwidth resources distributed of the connected the l articles virtual link of node useIt indicates；It enablesIndicate slice SiF is executed in time slot t interior nodesijThe service speed actually provided；Qi(t) it indicates
S is sliced in time slot tiQueue length, that is, indicate etc. number-of-packet to be transmitted；
Assuming that each slice rents the cache resources of respective numbers for caching its corresponding business datum, for every
A queue enables Ai(t) arrival process for indicating data packet, since the application of virtual network user aperiodicity generates the random of data
Property, it is assumed that packet arrival process Ai(t) obeying parameter is λiPoisson distribution, the packet arrival process of all users is in different scheduling
Time slot is independently distributed, i.e., mutually independent λ is obeyed at successive arrival time intervaliQuantum condition entropy；Enable Mi(t) number is indicated
According to packet size, it is assumed that data package size obeys average value and isExponential distribution, then the average treatment rate of data packet beTherefore the length renewal process of queue indicates are as follows:
Wherein,Indicate the processed number of data packets in time slot t.
Further, in step S1, the Time Delay Model of more queues are as follows:
The time delay includes queuing delay, processing delay and propagation delay time；It enablesIt respectively indicates and cuts
Piece SiThe data packet queue of arrival is in whole network by the average queuing delay, accordingly empty in whole network before each node processing
Average treatment time delay on quasi- node and the mean transit delay in the transmission of whole network corresponding link；One network is cut
The mean difference at time point and network slice request arrival time point that the data flow of piece has been handled on the last one node
It is defined as average scheduling delay, is indicated with τ, and is met:
Processing delay XiThe processing delay of VNF is executed by multiple dummy nodesComposition, and Because packet size obeys average valueExponential distribution, soParameter is obeyed respectively
ForExponential distribution and mutually indepedent, as Ireland distribution:Data can be obtained by Irish distribution property
The average treatment time delay of packet are as follows:
The similarly mean transit delay of data packet are as follows:
Average queuing delay are as follows:
WhereinIt indicates to execute f in service function chainijNode waiting
Annual distribution function.
So network is sliced SiData packet overall average scheduling delay are as follows:
Wherein, data package size obedience average value isExponential distribution；Wi(t) it indicates to execute f in service function chainij
Node serving-time distribution function, specially Wi(t)=P (Wi1+Wij+...+WiJ≤t).Optimization aim of the invention is
The ensemble average scheduling delay for minimizing the service function chain VNF of multiple network slice requests in network, indicates are as follows: min τ,
Middle τ=max { τ1,τ2,...,τi}。
Further, in step S2, more queue cache models are as follows:
Usual dynamic resource scheduling and queue buffer status (such as: remaining cache size and current queue size), data
Packet arrival rate etc. is related.More long then its data cached delay of the queue length of virtual network in systems is bigger, therefore, passes through
The scheduling of dynamic adjustresources can directly affect its delay performance and reduce the overflow probability of the queue caching of virtual network.At this
Invention only considers to be sliced service queue overflow situation, because queue underflows mean to distribute to the resource for handling the slice business
Be it is sufficient, not will cause loss of data, and queue overflow mean to distribute to the resource for handling the slice business be it is inadequate,
It will cause the loss of bit when queue length reaches the slice caching upper limit.Indicate that i-th of slice queue is permitted
Perhaps largest buffered length, in the present invention because queue length can dynamically become with the variation of the arrival rate of data packet
Change, therefore every by TsThe deployment way of a service function chain and the distribution of resource will be optimized.If in current TsInterior i-th
A queue length is greater than corresponding at this timeIllustrate there is bit spilling, bit drop-out occurs.Optimization problem can retouch as a result,
It states to provide a service rate appropriate and going to ensure that queue length is less than
In order to reduce the average bit Loss Rate of slice, realize that effective distribution of resource, present invention calculating prevent slice team
Column service speed minimum needed for overflowing, in any t, the increment of i-th of slice queue length can be indicated are as follows:
Ii(t)=Ai(t)-Di(t)
When starting for any t+1 time slot, the length of i-th of slice queue be may be expressed as:
Qi(t+1)=Qi(t)+Ii(t)
Slice queue does not spill over and needs to meet:
It can should meet in the hope of service speed:
Further, in step S3, the traffic aware model based on prediction are as follows:
The arrival rate of data packet is determined that service rate depends on service function by type of service and user to the request of business
The strategy of deployment and the resource distribution of chain, when arrival process and service process, are mutually independent, and it is an object of the present invention to protect
It demonstrate,proves under the premise of each slice queue is not spilt over and maximizes service speed, so that system performance reaches between handling capacity, fairness
To a relative equilibrium, throughput of system is effectively improved while guaranteeing fairness, when minimizing the scheduling of network ensemble average
Prolong.Due to AiIt (t) is to determine there is certain randomness, therefore the present invention is by using base by data packet arrival in time slot t
In the prediction technique of LSTM, look-ahead goes out to guarantee the lowest serve rate that slice queue is not spilt overAccording to prediction
As a result the deployment way of optimization service function chain and the allocation strategy of resource are formulated in advance, to improve network efficiency.
Due to preventing the demand of the minimum resources of queue overflow from being requested the data packet of the business to reach by user in caching
Rate AiAnd the queue length Q of last momentiInfluence, can by the current cache queue length observing or monitor with
And user requests the historical data of the data packet arrival rate of the business as slice feature.Specially at virtual network G=(V, E)
In, for service function chain j-th of VNF of i, enableExpression prevents minimum resources (cpu resource of such as VNF, storage of queue overflow
Resource etc.) demand, in order to simplify problem, the present invention only considers the use of cpu resource.So slice SiCharacter representation are as follows: xi=
[Ai,Qi], wherein AiIndicate data packet arrival rate, QiIndicate the queue length of last moment；Define a length be ε it is discrete when
Between window, using the data in the time window as a historical data sample, therefore, in the range of historical juncture t- ε to t, net
The dataset representation of network mode input are as follows:
The sample of each sample set is different, after being pre-processed to sample data construct LSTM model carry out before to
It calculates, is calculated comprising state computation and output；Then reverse train weight is carried out again to improve the performance of prediction.
Further, the forward calculation in the traffic aware model based on prediction specifically refers to: by using with it is each
Related σ (W) (sigmoid activation primitive) is sliced to carry out the calculating that the process of iterating realizes each slice state, state
The result of calculating is calculated for exporting, so that it is determined that resource requirement predicted value；Specifically includes the following steps:
(1) observe user's requested service data packet arrival rate and record a certain amount of data packet it is processed after team
Column length；
(2) using obtained slice state successively calculate network hiding layer state and long-term location mode；
(3) the resource requirement value of prediction is determined using the result of upper two step.
In order to realize that VNF resource requirement is accurately predicted, weighting function needs training repeatedly, this process need using
Data, the trained targets such as input x and target the output ξ to neural network are to make to punish secondary cost function minimization:
The first item for wherein punishing secondary cost function is standard error item,For predicted value,For true value；Second
Item is penalty, and β ' is constant term；Trained target is to find optimal weight W (feature of fitting data) to make cost letter
Number minimizes, and training algorithm is based on gradient optimization algorithm.
Further, in the traffic aware model based on prediction reverse train specifically includes the following steps:
(1) when the number of iterations κ=0, weight W, the output valve of each neuron of forward calculation, i.e. f are initializedt,it,ct,
ot,htThe value of five vectors, ft,it,ct,ot,htIt respectively indicates forgetting door, input gate, be location mode, out gate, hidden layer.
(2) the error term δ value of each neuron of retrospectively calculate；The backpropagation of LSTM error term includes both direction: one
A is the backpropagation along the time, i.e., since current t moment, calculates the error term at each moment；The other is by error term
Upper layer is propagated；
(3) according to corresponding error term, using back-propagation algorithm (Back Propagation Trough Time,
BPTT), the gradient of each weight is calculated；Weight is updated to be shown below:
Wherein,Indicate learning rate, GwIt indicates to punish secondary cost function.
Further, in step S4, the dispatching method of the service function chain VNF refers to: being asked using ant group algorithm modeling
The optimal path of solution VNF scheduling is to realize the deployment issue of service function chain；Described problem is based on logical described in step S3
Cross the lowest serve rate that the guarantee slice queue that prediction obtains is not spilt overIn the premise for meeting minimum resources demand
Under, optimal service function chain deployment path is found to obtain maximum resource allocation plan by minimax ant group algorithm, thus
Minimize whole VNF scheduling delay；Integrated scheduling time delay is calculated by the Time Delay Model of more queues described in step S1.
Further, the specific steps of a plurality of service function chain dispositions method based on minimax ant group algorithm are as follows:
(1) usually to ant scale, information prime factor, the heuristic function significance level factor, pheromones volatilization factor, information
The parameters such as several and maximum number of iterations are initialized；
(2) the dummy node set that service function chain VNFs has been accessed in taboo list is updated；
(3) node set that next VNF can be selected is determined according to taboo list；
It is true in a manner of roulette method according to state transition probability under the premise of the VNF can be handled by meeting dummy node
Surely the next node of VNF module is handled；The same of the higher VNF scheduling strategy of pheromones is followed in a part of ant of guarantee in this way
When, moreover it is possible to new locally optimal solution is found by the strategy of random schedule；Wherein state transition probability is defined as:
Wherein, c indicates fij-1Dummy node, k indicate next node,Expression is able to carry out fijDummy node collection
It closes, α indicates that information prime factor, value reflection ant motor behavior during scanning for receive pheromone concentration influence
Degree；β is the heuristic function significance level factor, and effect is the relative importance for reacting heuristic function in state transition probability, β
Bigger, state transition probability, η can be determined with the rule for approaching greed by representing antkRepresent heuristic function；
(4) after the scheduling strategy more than all VNF in an ant are pressed completes scheduling strategy, fair with ratio
Mode by virtual machine computing resource and output link bandwidth resource allocation give corresponding VNF and link, while in order to guarantee
Data packet is weighted the resource being assigned in the continuity of each node processing, finally obtains each VNF and link
The respective resources distributed；
(5) update of pheromones, specific renewal process are as follows: 1) all pheromone concentrations are reduced into p%；2) to each iteration
Every ant of process is realized by converting corresponding the sum of the resource being assigned to of its Path selection to the variation of pheromones
The update of pheromones；Because every ant difference Path selection causes the resource being assigned to have differences, updated letter
It is different to cease result of the plain concentration matrix on the different node of correspondence.
(6) by minimax section to pheromone concentration volatility coefficient, information prime factor, heuristic function significance level because
The parameters such as son and pheromone concentration are updated；It repeats the above steps, after completing successive ignition, finds optimal service function
The scheduling solution of chain VNFs.
The beneficial effects of the present invention are: the present invention is for the service request changed in time-domain under 5G network slice scene
Problem is overstock in the queue for causing the accumulation of data and generating, and establishes service function queue chain model and caching mould based on time delay
Type, rather than work is only rested on the resource scheduling solved on single dispatching cycle；One kind is established on this basis
Following resource Minimum requirements situation of traffic aware model prediction business slice based on LSTM neural network.It is tied according to prediction
Fruit proposes scheduling and the Resource Allocation Formula of a kind of dynamic service function chain VNF, establishes a kind of based on minimax ant colony
Algorithm realizes the Dynamical Deployment model of a plurality of service function chain.Prediction technique of the invention not only has good prediction effect, and
And dynamic dispatching is carried out to virtual network resource in time series, actual network condition of more coincideing optimizes slice industry
The time delay of business improves the performance of network service.
Detailed description of the invention
In order to keep the purpose of the present invention, technical scheme and beneficial effects clearer, the present invention provides following attached drawing and carries out
Illustrate:
Fig. 1 is the scene example schematic diagram that can apply the embodiment of the present invention；
Fig. 2 is the virtual network function scheduling flow figure in the present invention；
Fig. 3 is the queue system illustraton of model in the present invention；
Fig. 4 is the resource requirement prediction model schematic diagram based on LSTM neural network in the present invention；
Fig. 5 is LSTM neuronal structure figure in the present invention；
Fig. 6 is to dispose flow chart based on a plurality of service function chain of minimax ant group algorithm in the present invention.
Specific embodiment
Below in conjunction with attached drawing, a preferred embodiment of the present invention will be described in detail.
Fig. 1 is can be using the schematic diagram of the scene example of the embodiment of the present invention.As shown in Figure 1, different types of slice table
Show different types of service, is followed successively by unlimited virtual network user, virtual network management platform, virtual network function from left to right
Dispatch layer, physical resource pool.Within the system, Cloud Server in physical resource pool provide comprising computing resource, cache resources,
A plurality of types of physical network resources such as link bandwidth resource, virtual network manage platform then according to the business of virtual network user
State, QoS demand etc. realize the flexible allocation of the scheduling of virtual network function module, physical network resource.In order to more efficient
Ground distributes physical resource, efficiently utilizes with realizing physical resource, and the virtual network management platform that the present invention designs is by service request
The part such as unit, load analysis module, resource management entity, network status monitoring entity, virtual network scheduler forms,
In, service request unit newly reaches ground data packet for caching each slice user, and load analysis module is for analyzing each slice
Load characteristic is cached, and predicts next cyclic loading state and the money provided needed for queue overflow caching lowest serve rate is provided
Source, virtual network scheduler then determine the deployment scheme of every service function chain according to the assessment result of load analysis module, money
Source control entity distributes the optimum physical stock number that each virtual network function module obtains after service function chain completes deployment,
To ensure the QoS demand of each network slice.The effect of network status monitoring entity is to observe the real-time status of each physical resource.
Target of the invention is exactly to request the data packet of slice business to arrive by real-time monitoring caching load condition and user
Up to rate, next cycle service function chain is predicted to the Minimum requirements of resource, being based on should be as a result, by virtual network scheduler and resource
Management entity realizes the offer of business according to the scheduling of the service function chain VNF of formulation and Resource Allocation Formula.
Fig. 2 is in the present invention based on virtual network function scheduling flow figure, and the data packet that network is sliced service request is big
Small, quantity is all traffic characteristic and the next cycle service function chain of caching load characteristic prediction that be random, being sliced according to network
The dynamic allocation to virtual network function scheduling and resource are realized in minimum resource requirement on time dimension.As shown in Fig. 2,
Steps are as follows:
Step 201: generating full connecting-type virtual network topology, the virtual network function module that dummy node can be handled
Type, different types of network slice and the service function chain composition for realizing the slice business；
Step 202: establishing service function queue chain model and cache model based on time delay；
Step 203: collection network is sliced historical data packet arrival data and history buffer queue length (caches negative
It carries)；
Step 204: minimum to service function chain resource using the neural network model based on LSTM for the data of collection
Demand is predicted, wherein trained method uses gradient optimization algorithm；
Step 205: judging whether the secondary cost function of punishment is more than thresholding, if so, returning to 203；Otherwise step is continued to execute
Rapid 205；
Step 206: executing a plurality of service function chain deployment operation based on minimax ant group algorithm, realize to VNF tune
Degree and dynamic resource allocation；
Step 207: the whole tune of network slice after the more queue Time Delay Model computational resource allocations established based on step 202
Spend time delay；Return step 203 carries out next period resource requirement prediction；
Fig. 3 is the queue system illustraton of model in the present invention, is sliced S in time slot tiQueue length Qi(t) it indicates, the ginseng
Number such as also illustrates that at the number-of-packet to be transmitted, it is assumed that each slice rent the cache resources of respective numbers for cache its corresponding one
A business datum enables A for each queuei(t) arrival process for indicating data packet, since virtual network user aperiodicity is answered
With the randomness for generating data, queue length can dynamically change with the variation of the arrival rate of data packet, therefore every warp
Cross TsIt will be according to the deployment way of service function chain of queue length dynamic optimization and the distribution of resource.Adjusted by dynamic
Whole service rate Di(t) to guarantee to maximize service speed under the premise of current queue caching is not spilt over, so that system performance exists
Handling capacity reaches a relative equilibrium between fairness, throughput of system is effectively improved while guaranteeing fairness, and minimizes
Network ensemble average scheduling delay.
Fig. 4 is the resource requirement prediction model schematic diagram based on LSTM neural network in the present invention, in the prediction model,
Due to preventing the demand of the minimum resources of queue overflow from being requested by user the data packet arrival rate A of the business in cachingiAnd
The queue length Q of last momentiInfluence, therefore first by load analysis module queue in the current cache monitored is long
Degree and user request the historical data of the data packet arrival rate of the business as slice feature, i.e., the feature of each slice i can
To indicate are as follows: xi=[Ai,Qi], in order to indicate the slice state and data packet history arrival rate of history, define herein one long
Degree is the discrete time window of ε may be expressed as: x={ x (t- using the data in the time window as a historical data sample
ε) ..., x (t) }, the sample of each sample set is different, and after pre-processing to sample data, is passed throughct=f ⊙ ct-1+it⊙gt、ht=ot⊙tanh(ct) building LSTM network carry out service function
The prediction of energy chain minimum resources demand.Wherein σ and ⊙ respectively indicates activation primitive sigmoid and Element-Level product.The mistake of prediction
Journey includes two step of forward calculation and reverse train, and training algorithm uses gradient optimization algorithm.
Fig. 5 is LSTM neuronal structure figure in the present invention, which indicates each to cut using a kind of state h (hidden layer)
The input feature vector of piece load, c is location mode, and for saving long-term state, x indicates the input of neural network, i.e. history number
According to sample.It can be seen that there are three neuron inputs: the input value x of current time network in t momentt, last moment nerve
The output valve h of membert-1And the location mode c of last momentt-1, there are two neuron outputs: the output of current time neuron
Value ht, current time location mode ct。
Fig. 6 disposes flow chart to be based on a plurality of service function chain of minimax ant group algorithm in the present invention, as shown in fig. 6,
Steps are as follows:
Step 601: the prediction result of input service function chain VNF resource Minimum requirements；
Step 602: initialization ant scale, information prime factor, the heuristic function significance level factor, pheromones volatilization because
The parameters such as son, pheromones constant, maximum number of iterations；
Step 603: state transition probability being calculated to every ant and carries out the scheduling of VNF in the form of roulette method；
Step 604: to the service function chain deployment result of every ant, fair mode calculates and distributes to VNF's in proportion
The bandwidth resources of computing resource and link；
Step 605: update Pheromone Matrix, wherein include two small steps, 1) by all pheromone concentrations reduce p%, 2) it is right
Every ant of each iterative process, by the variation for converting corresponding the sum of the resource being assigned to of its Path selection to pheromones
Amount, realizes the update of pheromones；
Step 606: judging whether to fall into locally optimal solution, if so, continuing to execute step 607；It is carried out if it is not, returning to 603
Next iteration；
Step 607: updating pheromone concentration volatility coefficient ρ, pheromones factor-alpha, heuristic function significance level factor-beta, letter
Plain concentration τ is ceased, and returns to 603 and carries out next iteration.
Finally, it is stated that preferred embodiment above is only used to illustrate the technical scheme of the present invention and not to limit it, although logical
It crosses above preferred embodiment the present invention is described in detail, however, those skilled in the art should understand 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 (10)
1. a kind of 5G network is sliced the virtual network function dispatching method based on prediction, which is characterized in that this method specifically includes
Following steps:
S1: under the application scenarios of 5G network slice, for the service function chain feature of service traffics dynamic change, foundation is based on
The Time Delay Model of the network model of the service function queue chain of time delay, service function queue chain model and more queues；
S2: establishing more queue cache models, when spatial cache is limited, to prevent queuing data from losing, different moments according to
It is sliced service queue size, the lowest service rate that determines the priority of slice request and should provide；
S3: being a series of continuous time windows by time discrete, and using the queuing message in time window as training dataset sample
This, establishes the traffic aware model based on prediction；
S4: according to the every kind of slice service queue size predicted and corresponding lowest service rate, searching meets slice business
The dispatching method of optimal service function chain VNF under the resource constraint that queue caching is not spilt over.
2. a kind of 5G network according to claim 1 is sliced the virtual network function dispatching method based on prediction, feature
It is, in step S1, the network model of the service function queue chain based on time delay are as follows:
Virtual network topology is indicated that wherein V indicates the set of dummy node by weighted-graph G=(V, E), and E indicates virtual chain
The set on road；BmThe total output link bandwidth for indicating node m, is shared by the virtual link connecting with the node, network is cut
Piece Si, the virtual network function set expression of processing business request is Fi={ fi1,fij,...fiJ, i ∈ [1, | S |]z,j∈[1,
|Fi|]z, wherein S indicates the set of all-network slice, and J indicates FiThe number of middle VNF, for forming the VNF of service function chain,
It is indicated with f, wherein fijIndicate that network is sliced SiJ-th of VNF for needing to dispatch；It enablesExpression is able to carry out virtualization network function
It can fijDummy node set, wherein
3. a kind of 5G network according to claim 2 is sliced the virtual network function dispatching method based on prediction, feature
It is, in step S1, the service function queue chain model are as follows:
Γ={ 1 ..., t..., T } is enabled to indicate the time slot sets of the network operation, wherein the duration for defining each time slot t is
Ts；Therefore in time slot t, and f is performedijThe bandwidth resources distributed of the connected the l articles virtual link of node use
It indicates；It enablesIndicate slice SiF is executed in time slot t interior nodesijThe service speed actually provided；Qi(t) it indicates in time slot t
Interior slice SiQueue length, that is, indicate etc. number-of-packet to be transmitted；
Assuming that each slice rents the cache resources of respective numbers for caching its corresponding business datum, for each team
Column enable Ai(t) arrival process for indicating data packet, it is false since the application of virtual network user aperiodicity generates the randomness of data
If packet arrival process Ai(t) obeying parameter is λiPoisson distribution, the packet arrival process of all users is in different time slot schedulings
It is independently distributed, i.e., mutually independent λ is obeyed at successive arrival time intervaliQuantum condition entropy；Enable Mi(t) indicate that data packet is big
It is small, it is assumed that data package size obeys average value and isExponential distribution, then the average treatment rate of data packet beTherefore the length renewal process of queue indicates are as follows:
Wherein,Indicate the processed number of data packets in time slot t.
4. a kind of 5G network according to claim 3 is sliced the virtual network function dispatching method based on prediction, feature
It is, in step S1, the Time Delay Model of more queues are as follows:
The time delay includes queuing delay, processing delay and propagation delay time；It enablesRespectively indicate slice Si
The data packet queue of arrival whole network by before each node processing average queuing delay, in whole network respective virtual section
Average treatment time delay on point and the mean transit delay in the transmission of whole network corresponding link；One network is sliced
The mean difference at time point and network slice request arrival time point that data flow has been handled on the last one node defines
It for average scheduling delay, is indicated, and met with τ:
Network is sliced SiData packet overall average scheduling delay are as follows:
Wherein, data package size obedience average value isExponential distribution；Wi(t) it indicates to execute f in service function chainijNode
Serving-time distribution function；Therefore, optimization aim is the service function chain VNF for minimizing multiple network slice requests in network
Ensemble average scheduling delay, indicate are as follows: min τ, wherein τ=max { τ1,τ2,...,τi}。
5. a kind of 5G network according to claim 4 is sliced the virtual network function dispatching method based on prediction, feature
It is, in step S2, more queue cache models are as follows: service speed minimum needed for preventing slice queue overflow is calculated,
Middle service speed meets:
Wherein, Ri(t) lowest serve rate that business i should be provided is indicated,Indicate i-th of permitted maximum of slice queue
Buffer storage length.
6. a kind of 5G network according to claim 5 is sliced the virtual network function dispatching method based on prediction, feature
It is, in step S3, the traffic aware model based on prediction are as follows:
By using the prediction technique based on LSTM, look-ahead goes out to guarantee the lowest serve rate that slice queue is not spilt overThe deployment way of optimization service function chain and the allocation strategy of resource are formulated in advance according to the result of prediction, to mention
High network efficiency；It is sliced SiCharacter representation are as follows: xi=[Ai,Qi], wherein AiIndicate data packet arrival rate, QiIndicate last moment
Queue length；Defining a length is ε discrete time window, using the data in the time window as a historical data sample,
Therefore, in the range of historical juncture t- ε to t, the dataset representation of network model input are as follows:
The sample of each sample set is different, and is constructed before LSTM model carries out after pre-processing to sample data to meter
It calculates, is calculated comprising state computation and output；Then reverse train weight is carried out again to improve the performance of prediction.
7. a kind of 5G network according to claim 6 is sliced the virtual network function dispatching method based on prediction, feature
It is, the forward calculation in the traffic aware model based on prediction specifically refers to: by using related with each slice
Sigmoid activation primitive σ (W) carries out the calculating that the process of iterating realizes each slice state, and the result of state computation uses
It is calculated in output, so that it is determined that resource requirement predicted value；Specifically includes the following steps:
(1) observe user's requested service data packet arrival rate and record a certain amount of data packet it is processed after queue it is long
Degree；
(2) using obtained slice state successively calculate network hiding layer state and long-term location mode；
(3) the resource requirement value of prediction is determined using the result of upper two step.
8. a kind of 5G network according to claim 6 is sliced the virtual network function dispatching method based on prediction, feature
Be, reverse train in the traffic aware model based on prediction specifically includes the following steps:
(1) when the number of iterations κ=0, weight W, the output valve of each neuron of forward calculation, i.e. f are initializedt,it,ct,ot,htFive
The value of a vector, wherein ft,it,ct,ot,htIt respectively indicates forgetting door, input gate, be location mode, out gate and hidden layer；
(2) the error term δ value of each neuron of retrospectively calculate；The backpropagation of LSTM error term includes both direction: one is
Along the backpropagation of time, i.e., since current t moment, calculate the error term at each moment；The other is error term is upward
One Es-region propagations；
(3) according to corresponding error term, using back-propagation algorithm (Back Propagation Trough Time, BPTT),
Calculate the gradient of each weight；Weight is updated to be shown below:
Wherein,Indicate learning rate, GwIt indicates to punish secondary cost function, expression formula are as follows:
The first item for wherein punishing secondary cost function is standard error item,For predicted value,For true value；Section 2 is
Penalty, β ' are constant term；Trained target is to find optimal weight W to make cost function minimization.
9. a kind of 5G network according to claim 6 is sliced the virtual network function dispatching method based on prediction, feature
It is, in step S4, the dispatching method of the service function chain VNF refers to: using ant group algorithm model solution VNF scheduling
Optimal path is to realize the deployment issue of service function chain；Described problem by predicting based on being obtained described in step S3
Guarantee the lowest serve rate that slice queue is not spilt overUnder the premise of meeting minimum resources demand, most by maximum
Small ant group algorithm finds optimal service function chain deployment path to obtain maximum resource allocation plan, to minimize whole
VNF scheduling delay；Integrated scheduling time delay is calculated by the Time Delay Model of more queues described in step S1.
10. a kind of 5G network according to claim 9 is sliced the virtual network function dispatching method based on prediction, feature
It is, the specific steps of a plurality of service function chain dispositions method based on minimax ant group algorithm are as follows:
(1) to ant scale, information prime factor, the heuristic function significance level factor, pheromones volatilization factor, pheromones constant and
Maximum number of iterations is initialized；
(2) the dummy node set that service function chain VNFs has been accessed in taboo list is updated；
(3) node set that next VNF can be selected is determined according to taboo list；
Under the premise of the VNF can be handled by meeting dummy node, according to state transition probability in a manner of roulette method determine at
Manage the next node of VNF module；Wherein state transition probability is defined as:
Wherein, c indicates fij-1Dummy node, k indicate next node,Expression is able to carry out fijDummy node set, α
Indicate that information prime factor, value reflection ant motor behavior during scanning for receive the journey of pheromone concentration influence
Degree；β is the heuristic function significance level factor, and effect is the relative importance for reacting heuristic function in state transition probability, and β is got over
Greatly, state transition probability, η can be determined with the rule for approaching greed by representing antkRepresent heuristic function；
(4) after the scheduling strategy more than all VNF in an ant are pressed completes scheduling strategy, in such a way that ratio is fair
By on virtual machine computing resource and output link bandwidth resource allocation give corresponding VNF and link, while in order to guarantee data
The continuity in each node processing is wrapped, the resource being assigned to is weighted, each VNF is finally obtained and link divides
The respective resources matched；
(5) update of pheromones, specific renewal process are as follows: 1) all pheromone concentrations are reduced into p%；2) to each iterative process
Every ant realize information by converting corresponding the sum of the resource being assigned to of its Path selection to the variation of pheromones
The update of element；
(6) by minimax section to pheromone concentration volatility coefficient, information prime factor, the heuristic function significance level factor and
Pheromone concentration is updated；It repeats the above steps, after completing successive ignition, finds the tune of optimal service function chain VNFs
Degree solution.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810863512.9A CN108965024B (en) | 2018-08-01 | 2018-08-01 | Virtual network function scheduling method based on prediction for 5G network slice |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810863512.9A CN108965024B (en) | 2018-08-01 | 2018-08-01 | Virtual network function scheduling method based on prediction for 5G network slice |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108965024A true CN108965024A (en) | 2018-12-07 |
| CN108965024B CN108965024B (en) | 2021-08-13 |
Family
ID=64466590
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810863512.9A Active CN108965024B (en) | 2018-08-01 | 2018-08-01 | Virtual network function scheduling method based on prediction for 5G network slice |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108965024B (en) |
Cited By (51)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109361431A (en) * | 2018-12-13 | 2019-02-19 | 中国科学院计算技术研究所 | A kind of dispatching method and system of slice |
| CN109547263A (en) * | 2018-12-15 | 2019-03-29 | 华南理工大学 | Network-on-chip optimization method based on approximate calculation |
| CN109862442A (en) * | 2019-02-22 | 2019-06-07 | 伟乐视讯科技股份有限公司 | A kind of input method for stream processing and processing unit based on IP transmission |
| CN109889366A (en) * | 2019-01-04 | 2019-06-14 | 烽火通信科技股份有限公司 | Network flow accrual accounting, analysis method and system |
| CN109905330A (en) * | 2019-03-22 | 2019-06-18 | 西南交通大学 | A kind of dynamic weighting Fair Queue train network dispatching method based on queue length |
| CN110222379A (en) * | 2019-05-17 | 2019-09-10 | 井冈山大学 | Manufacture the optimization method and system of network service quality |
| CN110381541A (en) * | 2019-05-28 | 2019-10-25 | 中国电力科学研究院有限公司 | A kind of smart grid slice distribution method and device based on intensified learning |
| CN110519783A (en) * | 2019-09-26 | 2019-11-29 | 东华大学 | 5G network based on enhancing study is sliced resource allocation methods |
| CN110545204A (en) * | 2019-08-30 | 2019-12-06 | 海南电网有限责任公司 | resource allocation method and server based on external penalty function and fruit fly optimization |
| CN110618879A (en) * | 2019-08-15 | 2019-12-27 | 北京三快在线科技有限公司 | Message processing method and device, electronic equipment and computer readable medium |
| CN110971451A (en) * | 2019-11-13 | 2020-04-07 | 国网河北省电力有限公司雄安新区供电公司 | NFV resource allocation method |
| CN111314235A (en) * | 2020-02-19 | 2020-06-19 | 广东技术师范大学 | Network delay optimization method based on virtual network function resource demand prediction |
| CN111464371A (en) * | 2019-09-25 | 2020-07-28 | 南昌航空大学 | PSN (packet switched network) time slicing method based on network change degree |
| CN111526070A (en) * | 2020-04-29 | 2020-08-11 | 重庆邮电大学 | Service function chain fault detection method based on prediction |
| CN111538570A (en) * | 2020-05-12 | 2020-08-14 | 广东电网有限责任公司电力调度控制中心 | VNF deployment method and device for energy conservation and QoS guarantee |
| CN111629443A (en) * | 2020-06-10 | 2020-09-04 | 中南大学 | Optimization method and system for dynamic spectrum slicing frame in super 5G vehicle networking |
| CN111669787A (en) * | 2020-06-05 | 2020-09-15 | 国网上海市电力公司 | Resource allocation method and device based on time delay sensitive network slice |
| CN112306719A (en) * | 2020-11-23 | 2021-02-02 | 中国科学院计算机网络信息中心 | Task scheduling method and device |
| CN112311578A (en) * | 2019-07-31 | 2021-02-02 | 中国移动通信集团浙江有限公司 | VNF scheduling method and device based on deep reinforcement learning |
| CN112543119A (en) * | 2020-11-27 | 2021-03-23 | 西安交通大学 | Service function chain reliability deployment method based on deep reinforcement learning |
| CN112653580A (en) * | 2020-12-16 | 2021-04-13 | 国网河南省电力公司信息通信公司 | Virtual network resource allocation method based on active detection under network slice |
| CN112737823A (en) * | 2020-12-22 | 2021-04-30 | 国网北京市电力公司 | Resource slice allocation method and device and computer equipment |
| CN112732409A (en) * | 2021-01-21 | 2021-04-30 | 上海交通大学 | Method and device for enabling zero-time-consumption network flow load balancing under VNF architecture |
| CN112887142A (en) * | 2021-01-26 | 2021-06-01 | 贵州大学 | Method for generating web application slice in virtualized wireless access edge cloud |
| CN112929187A (en) * | 2019-12-05 | 2021-06-08 | 中国电信股份有限公司 | Network slice management method, device and system |
| CN112954742A (en) * | 2021-02-08 | 2021-06-11 | 中国科学院计算技术研究所 | Resource allocation method for mobile communication network slice |
| CN113098707A (en) * | 2021-03-16 | 2021-07-09 | 重庆邮电大学 | Virtual network function demand prediction method in edge network |
| CN113207048A (en) * | 2021-03-15 | 2021-08-03 | 广东工业大学 | Uplink bandwidth allocation method based on neural network prediction in 50G-PON (Passive optical network) |
| CN113259145A (en) * | 2020-02-13 | 2021-08-13 | 中国移动通信集团浙江有限公司 | End-to-end networking method of network slice, networking device and network slice equipment |
| CN113411223A (en) * | 2021-08-03 | 2021-09-17 | 上海交通大学 | Industrial software defined network slicing method based on edge cooperation |
| CN113411207A (en) * | 2021-05-28 | 2021-09-17 | 中国人民解放军战略支援部队信息工程大学 | Service function circulation arrangement basic platform and method of intelligent network service function chain |
| CN113543160A (en) * | 2020-04-14 | 2021-10-22 | 中国移动通信集团浙江有限公司 | 5G slice resource allocation method and device, computing equipment and computer storage medium |
| CN113676909A (en) * | 2021-07-20 | 2021-11-19 | 东北大学 | Virtual network function universal scheduling method under 5G/B5G environment |
| CN113766523A (en) * | 2020-06-02 | 2021-12-07 | 中国移动通信集团河南有限公司 | Network resource utilization rate prediction method and device for serving cell and electronic equipment |
| CN113784395A (en) * | 2021-08-26 | 2021-12-10 | 南京邮电大学 | 5G network slice resource allocation method and system |
| CN113810939A (en) * | 2021-08-17 | 2021-12-17 | 中国人民解放军战略支援部队信息工程大学 | User-insensitive network slice dynamic mapping device and method |
| CN113875192A (en) * | 2019-05-24 | 2021-12-31 | 瑞典爱立信有限公司 | First entity, second entity, third entity and method performed thereby for providing a service in a communication network |
| CN113923129A (en) * | 2021-09-08 | 2022-01-11 | 中国人民解放军战略支援部队信息工程大学 | VNF demand prediction method and system based on data driving |
| CN114070708A (en) * | 2021-11-18 | 2022-02-18 | 重庆邮电大学 | Virtual network function resource consumption prediction method based on flow characteristic extraction |
| CN114268548A (en) * | 2021-12-24 | 2022-04-01 | 国网河南省电力公司信息通信公司 | Network slice resource arranging and mapping method based on 5G |
| CN114390489A (en) * | 2022-03-04 | 2022-04-22 | 重庆邮电大学 | Service deployment method for end-to-end network slice |
| CN114500560A (en) * | 2022-01-06 | 2022-05-13 | 浙江鼎峰科技股份有限公司 | Edge node service deployment and load balancing method for minimizing network delay |
| CN114585024A (en) * | 2022-02-10 | 2022-06-03 | 电子科技大学 | Slice access control method of 5G/B5G network |
| CN115080263A (en) * | 2022-05-12 | 2022-09-20 | 吉林省吉林祥云信息技术有限公司 | Batch processing scale method in real-time GPU service |
| CN115134288A (en) * | 2021-03-10 | 2022-09-30 | 中国移动通信集团广东有限公司 | Communication network routing scheduling method and system |
| CN115209431A (en) * | 2021-04-13 | 2022-10-18 | 中移(成都)信息通信科技有限公司 | Triggering method, device, equipment and computer storage medium |
| CN115242630A (en) * | 2021-04-23 | 2022-10-25 | 中国移动通信集团四川有限公司 | Arranging method and device for 5G network slices and electronic equipment |
| US11575582B2 (en) | 2021-05-18 | 2023-02-07 | International Business Machines Corporation | Service chain based network slicing |
| CN115914039A (en) * | 2022-11-22 | 2023-04-04 | 贵州电网有限责任公司 | Network performance monitoring device and monitoring method thereof |
| CN117082009A (en) * | 2023-10-16 | 2023-11-17 | 泰雷兹数字科技(北京)有限公司 | Cloud resource management method and management system based on software definition security |
| CN117082008A (en) * | 2023-10-17 | 2023-11-17 | 深圳云天畅想信息科技有限公司 | Virtual elastic network data transmission scheduling method, computer device and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108063830A (en) * | 2018-01-26 | 2018-05-22 | 重庆邮电大学 | A kind of network section dynamic resource allocation method based on MDP |
-
2018
- 2018-08-01 CN CN201810863512.9A patent/CN108965024B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108063830A (en) * | 2018-01-26 | 2018-05-22 | 重庆邮电大学 | A kind of network section dynamic resource allocation method based on MDP |
Non-Patent Citations (1)
| Title |
|---|
| 唐伦等: "基于可靠性的5G网络切片在线映射算法", 《电子与信息学报》 * |
Cited By (91)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109361431B (en) * | 2018-12-13 | 2020-10-27 | 中国科学院计算技术研究所 | Slice scheduling method and system |
| CN109361431A (en) * | 2018-12-13 | 2019-02-19 | 中国科学院计算技术研究所 | A kind of dispatching method and system of slice |
| CN109547263A (en) * | 2018-12-15 | 2019-03-29 | 华南理工大学 | Network-on-chip optimization method based on approximate calculation |
| CN109547263B (en) * | 2018-12-15 | 2021-08-20 | 华南理工大学 | Network-on-chip optimization method based on approximate calculation |
| CN109889366B (en) * | 2019-01-04 | 2020-06-16 | 烽火通信科技股份有限公司 | Network traffic increment counting and analyzing method and system |
| CN109889366A (en) * | 2019-01-04 | 2019-06-14 | 烽火通信科技股份有限公司 | Network flow accrual accounting, analysis method and system |
| CN109862442A (en) * | 2019-02-22 | 2019-06-07 | 伟乐视讯科技股份有限公司 | A kind of input method for stream processing and processing unit based on IP transmission |
| CN109862442B (en) * | 2019-02-22 | 2022-05-17 | 伟乐视讯科技股份有限公司 | Input stream processing method and processing device based on IP transmission |
| CN109905330B (en) * | 2019-03-22 | 2020-10-16 | 西南交通大学 | Dynamic weighted fair queue train network scheduling method based on queue length |
| CN109905330A (en) * | 2019-03-22 | 2019-06-18 | 西南交通大学 | A kind of dynamic weighting Fair Queue train network dispatching method based on queue length |
| CN110222379A (en) * | 2019-05-17 | 2019-09-10 | 井冈山大学 | Manufacture the optimization method and system of network service quality |
| CN113875192A (en) * | 2019-05-24 | 2021-12-31 | 瑞典爱立信有限公司 | First entity, second entity, third entity and method performed thereby for providing a service in a communication network |
| CN113875192B (en) * | 2019-05-24 | 2024-04-26 | 瑞典爱立信有限公司 | First entity, second entity, third entity and method performed thereby for providing services in a communication network |
| CN110381541A (en) * | 2019-05-28 | 2019-10-25 | 中国电力科学研究院有限公司 | A kind of smart grid slice distribution method and device based on intensified learning |
| CN110381541B (en) * | 2019-05-28 | 2023-12-26 | 中国电力科学研究院有限公司 | Smart grid slice distribution method and device based on reinforcement learning |
| CN112311578A (en) * | 2019-07-31 | 2021-02-02 | 中国移动通信集团浙江有限公司 | VNF scheduling method and device based on deep reinforcement learning |
| CN112311578B (en) * | 2019-07-31 | 2023-04-07 | 中国移动通信集团浙江有限公司 | VNF scheduling method and device based on deep reinforcement learning |
| CN110618879A (en) * | 2019-08-15 | 2019-12-27 | 北京三快在线科技有限公司 | Message processing method and device, electronic equipment and computer readable medium |
| CN110545204B (en) * | 2019-08-30 | 2022-09-23 | 海南电网有限责任公司 | Resource allocation method and server based on external penalty function and fruit fly optimization |
| CN110545204A (en) * | 2019-08-30 | 2019-12-06 | 海南电网有限责任公司 | resource allocation method and server based on external penalty function and fruit fly optimization |
| CN111464371B (en) * | 2019-09-25 | 2022-09-02 | 南昌航空大学 | Time slicing method of PSN (packet switched network) based on network change degree |
| CN111464371A (en) * | 2019-09-25 | 2020-07-28 | 南昌航空大学 | PSN (packet switched network) time slicing method based on network change degree |
| CN110519783A (en) * | 2019-09-26 | 2019-11-29 | 东华大学 | 5G network based on enhancing study is sliced resource allocation methods |
| CN110519783B (en) * | 2019-09-26 | 2021-11-16 | 东华大学 | 5G network slice resource allocation method based on reinforcement learning |
| CN110971451B (en) * | 2019-11-13 | 2022-07-26 | 国网河北省电力有限公司雄安新区供电公司 | NFV resource allocation method |
| CN110971451A (en) * | 2019-11-13 | 2020-04-07 | 国网河北省电力有限公司雄安新区供电公司 | NFV resource allocation method |
| CN112929187B (en) * | 2019-12-05 | 2023-04-07 | 中国电信股份有限公司 | Network slice management method, device and system |
| CN112929187A (en) * | 2019-12-05 | 2021-06-08 | 中国电信股份有限公司 | Network slice management method, device and system |
| CN113259145B (en) * | 2020-02-13 | 2022-06-03 | 中国移动通信集团浙江有限公司 | End-to-end networking method and device for network slicing and network slicing equipment |
| CN113259145A (en) * | 2020-02-13 | 2021-08-13 | 中国移动通信集团浙江有限公司 | End-to-end networking method of network slice, networking device and network slice equipment |
| CN111314235B (en) * | 2020-02-19 | 2023-08-11 | 广东技术师范大学 | Network delay optimization method based on virtual network function resource demand prediction |
| CN111314235A (en) * | 2020-02-19 | 2020-06-19 | 广东技术师范大学 | Network delay optimization method based on virtual network function resource demand prediction |
| CN113543160A (en) * | 2020-04-14 | 2021-10-22 | 中国移动通信集团浙江有限公司 | 5G slice resource allocation method and device, computing equipment and computer storage medium |
| CN113543160B (en) * | 2020-04-14 | 2024-03-12 | 中国移动通信集团浙江有限公司 | 5G slice resource allocation method, device, computing equipment and computer storage medium |
| CN111526070B (en) * | 2020-04-29 | 2022-06-03 | 重庆邮电大学 | Service function chain fault detection method based on prediction |
| CN111526070A (en) * | 2020-04-29 | 2020-08-11 | 重庆邮电大学 | Service function chain fault detection method based on prediction |
| CN111538570B (en) * | 2020-05-12 | 2024-03-19 | 广东电网有限责任公司电力调度控制中心 | Energy-saving and QoS guarantee-oriented VNF deployment method and device |
| CN111538570A (en) * | 2020-05-12 | 2020-08-14 | 广东电网有限责任公司电力调度控制中心 | VNF deployment method and device for energy conservation and QoS guarantee |
| CN113766523B (en) * | 2020-06-02 | 2023-08-01 | 中国移动通信集团河南有限公司 | Method and device for predicting network resource utilization rate of serving cell and electronic equipment |
| CN113766523A (en) * | 2020-06-02 | 2021-12-07 | 中国移动通信集团河南有限公司 | Network resource utilization rate prediction method and device for serving cell and electronic equipment |
| CN111669787B (en) * | 2020-06-05 | 2024-02-23 | 国网上海市电力公司 | Resource allocation method and device based on time delay sensitive network slice |
| CN111669787A (en) * | 2020-06-05 | 2020-09-15 | 国网上海市电力公司 | Resource allocation method and device based on time delay sensitive network slice |
| CN111629443B (en) * | 2020-06-10 | 2022-07-26 | 中南大学 | Optimization method and system for dynamic spectrum slicing frame in super 5G Internet of vehicles |
| CN111629443A (en) * | 2020-06-10 | 2020-09-04 | 中南大学 | Optimization method and system for dynamic spectrum slicing frame in super 5G vehicle networking |
| CN112306719A (en) * | 2020-11-23 | 2021-02-02 | 中国科学院计算机网络信息中心 | Task scheduling method and device |
| CN112543119B (en) * | 2020-11-27 | 2022-02-18 | 西安交通大学 | Service function chain reliability deployment method based on deep reinforcement learning |
| CN112543119A (en) * | 2020-11-27 | 2021-03-23 | 西安交通大学 | Service function chain reliability deployment method based on deep reinforcement learning |
| CN112653580B (en) * | 2020-12-16 | 2022-11-08 | 国网河南省电力公司信息通信公司 | Virtual network resource allocation method based on active detection under network slice |
| CN112653580A (en) * | 2020-12-16 | 2021-04-13 | 国网河南省电力公司信息通信公司 | Virtual network resource allocation method based on active detection under network slice |
| CN112737823A (en) * | 2020-12-22 | 2021-04-30 | 国网北京市电力公司 | Resource slice allocation method and device and computer equipment |
| CN112732409B (en) * | 2021-01-21 | 2022-07-22 | 上海交通大学 | Method and device for enabling zero-time-consumption network flow load balancing under VNF architecture |
| CN112732409A (en) * | 2021-01-21 | 2021-04-30 | 上海交通大学 | Method and device for enabling zero-time-consumption network flow load balancing under VNF architecture |
| CN112887142B (en) * | 2021-01-26 | 2022-01-21 | 贵州大学 | Method for generating web application slice in virtualized wireless access edge cloud |
| CN112887142A (en) * | 2021-01-26 | 2021-06-01 | 贵州大学 | Method for generating web application slice in virtualized wireless access edge cloud |
| CN112954742A (en) * | 2021-02-08 | 2021-06-11 | 中国科学院计算技术研究所 | Resource allocation method for mobile communication network slice |
| CN112954742B (en) * | 2021-02-08 | 2023-03-24 | 中国科学院计算技术研究所 | Resource allocation method for mobile communication network slice |
| CN115134288A (en) * | 2021-03-10 | 2022-09-30 | 中国移动通信集团广东有限公司 | Communication network routing scheduling method and system |
| CN115134288B (en) * | 2021-03-10 | 2023-08-15 | 中国移动通信集团广东有限公司 | Communication network route scheduling method and system |
| CN113207048B (en) * | 2021-03-15 | 2022-08-05 | 广东工业大学 | Neural network prediction-based uplink bandwidth allocation method in 50G-PON (Passive optical network) |
| CN113207048A (en) * | 2021-03-15 | 2021-08-03 | 广东工业大学 | Uplink bandwidth allocation method based on neural network prediction in 50G-PON (Passive optical network) |
| CN113098707A (en) * | 2021-03-16 | 2021-07-09 | 重庆邮电大学 | Virtual network function demand prediction method in edge network |
| CN115209431B (en) * | 2021-04-13 | 2023-10-27 | 中移(成都)信息通信科技有限公司 | Triggering method, device, equipment and computer storage medium |
| CN115209431A (en) * | 2021-04-13 | 2022-10-18 | 中移(成都)信息通信科技有限公司 | Triggering method, device, equipment and computer storage medium |
| CN115242630A (en) * | 2021-04-23 | 2022-10-25 | 中国移动通信集团四川有限公司 | Arranging method and device for 5G network slices and electronic equipment |
| CN115242630B (en) * | 2021-04-23 | 2023-10-27 | 中国移动通信集团四川有限公司 | 5G network slice arrangement method and device and electronic equipment |
| US11575582B2 (en) | 2021-05-18 | 2023-02-07 | International Business Machines Corporation | Service chain based network slicing |
| CN113411207B (en) * | 2021-05-28 | 2022-09-20 | 中国人民解放军战略支援部队信息工程大学 | Service function circulation arrangement basic platform and method of intelligent network service function chain |
| CN113411207A (en) * | 2021-05-28 | 2021-09-17 | 中国人民解放军战略支援部队信息工程大学 | Service function circulation arrangement basic platform and method of intelligent network service function chain |
| CN113676909A (en) * | 2021-07-20 | 2021-11-19 | 东北大学 | Virtual network function universal scheduling method under 5G/B5G environment |
| CN113411223B (en) * | 2021-08-03 | 2022-03-11 | 上海交通大学 | Industrial software defined network slicing method based on edge cooperation |
| CN113411223A (en) * | 2021-08-03 | 2021-09-17 | 上海交通大学 | Industrial software defined network slicing method based on edge cooperation |
| CN113810939B (en) * | 2021-08-17 | 2023-07-18 | 中国人民解放军战略支援部队信息工程大学 | User-noninductive network slice dynamic mapping device and method |
| CN113810939A (en) * | 2021-08-17 | 2021-12-17 | 中国人民解放军战略支援部队信息工程大学 | User-insensitive network slice dynamic mapping device and method |
| CN113784395A (en) * | 2021-08-26 | 2021-12-10 | 南京邮电大学 | 5G network slice resource allocation method and system |
| CN113784395B (en) * | 2021-08-26 | 2023-08-15 | 南京邮电大学 | 5G network slice resource allocation method and system |
| CN113923129B (en) * | 2021-09-08 | 2023-08-29 | 中国人民解放军战略支援部队信息工程大学 | VNF demand prediction method and system based on data driving |
| CN113923129A (en) * | 2021-09-08 | 2022-01-11 | 中国人民解放军战略支援部队信息工程大学 | VNF demand prediction method and system based on data driving |
| CN114070708B (en) * | 2021-11-18 | 2023-08-29 | 重庆邮电大学 | Virtual network function resource consumption prediction method based on flow characteristic extraction |
| CN114070708A (en) * | 2021-11-18 | 2022-02-18 | 重庆邮电大学 | Virtual network function resource consumption prediction method based on flow characteristic extraction |
| CN114268548A (en) * | 2021-12-24 | 2022-04-01 | 国网河南省电力公司信息通信公司 | Network slice resource arranging and mapping method based on 5G |
| CN114500560B (en) * | 2022-01-06 | 2024-04-26 | 浙江鼎峰科技股份有限公司 | Edge node service deployment and load balancing method for minimizing network delay |
| CN114500560A (en) * | 2022-01-06 | 2022-05-13 | 浙江鼎峰科技股份有限公司 | Edge node service deployment and load balancing method for minimizing network delay |
| CN114585024A (en) * | 2022-02-10 | 2022-06-03 | 电子科技大学 | Slice access control method of 5G/B5G network |
| CN114390489A (en) * | 2022-03-04 | 2022-04-22 | 重庆邮电大学 | Service deployment method for end-to-end network slice |
| CN115080263B (en) * | 2022-05-12 | 2023-10-27 | 吉林省吉林祥云信息技术有限公司 | Batch processing scale selection method in real-time GPU service |
| CN115080263A (en) * | 2022-05-12 | 2022-09-20 | 吉林省吉林祥云信息技术有限公司 | Batch processing scale method in real-time GPU service |
| CN115914039A (en) * | 2022-11-22 | 2023-04-04 | 贵州电网有限责任公司 | Network performance monitoring device and monitoring method thereof |
| CN117082009B (en) * | 2023-10-16 | 2024-02-27 | 天翼安全科技有限公司 | Cloud resource management method and management system based on software definition security |
| CN117082009A (en) * | 2023-10-16 | 2023-11-17 | 泰雷兹数字科技(北京)有限公司 | Cloud resource management method and management system based on software definition security |
| CN117082008B (en) * | 2023-10-17 | 2023-12-15 | 深圳云天畅想信息科技有限公司 | Virtual elastic network data transmission scheduling method, computer device and storage medium |
| CN117082008A (en) * | 2023-10-17 | 2023-11-17 | 深圳云天畅想信息科技有限公司 | Virtual elastic network data transmission scheduling method, computer device and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108965024B (en) | 2021-08-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108965024A (en) | A kind of virtual network function dispatching method of the 5G network slice based on prediction | |
| CN110096349A (en) | A kind of job scheduling method based on the prediction of clustered node load condition | |
| CN110275758A (en) | A kind of virtual network function intelligence moving method | |
| CN107172166A (en) | The cloud and mist computing system serviced towards industrial intelligentization | |
| Djigal et al. | Machine and deep learning for resource allocation in multi-access edge computing: A survey | |
| CN107404409A (en) | Towards the container cloud elastic supply number of containers Forecasting Methodology and system of mutation load | |
| CN108288115A (en) | A kind of daily short-term express delivery amount prediction technique of loglstics enterprise | |
| CN112486690A (en) | Edge computing resource allocation method suitable for industrial Internet of things | |
| CN111611062B (en) | Cloud-edge collaborative hierarchical computing method and cloud-edge collaborative hierarchical computing system | |
| CN108170531B (en) | Cloud data center request flow scheduling method based on deep belief network | |
| Swarup et al. | Task scheduling in cloud using deep reinforcement learning | |
| CN113037877B (en) | Optimization method for time-space data and resource scheduling under cloud edge architecture | |
| Yan-hua et al. | Optimization of cloud database route scheduling based on combination of genetic algorithm and ant colony algorithm | |
| Tong et al. | DDQN-TS: A novel bi-objective intelligent scheduling algorithm in the cloud environment | |
| CN113011678A (en) | Virtual operation platform operation control method based on edge calculation | |
| CN112084035B (en) | Task scheduling method and system based on ant colony algorithm | |
| Markowitz et al. | Heavy traffic analysis of dynamic cyclic policies: a unified treatment of the single machine scheduling problem | |
| CN108845886A (en) | Cloud computing energy consumption optimization method and system based on phase space | |
| Ouyang et al. | Band-area resource management platform and accelerated particle swarm optimization algorithm for container deployment in Internet-of-Things cloud | |
| CN111176784A (en) | Virtual machine integration method based on extreme learning machine and ant colony system | |
| CN109951875A (en) | Caching method, device, equipment and medium based on content popularit prediction | |
| Song et al. | Dram: A drl-based resource allocation scheme for mar in mec | |
| Larrañaga | Dynamic control of stochastic and fluid resource-sharing systems | |
| Faraji-Mehmandar et al. | A self-learning approach for proactive resource and service provisioning in fog environment | |
| CN113485803B (en) | Self-adaptive packaging and collaborative reasoning method under task flow field scene with time delay constraint |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20231106 Address after: 117000 No. 130, Guangyu Road, Pingshan District, Benxi City, Liaoning Province Patentee after: BENXI STEEL (GROUP) INFORMATION AUTOMATION CO.,LTD. Address before: 1003, Building A, Zhiyun Industrial Park, No. 13 Huaxing Road, Henglang Community, Dalang Street, Longhua District, Shenzhen City, Guangdong Province, 518000 Patentee before: Shenzhen Wanzhida Technology Transfer Center Co.,Ltd. Effective date of registration: 20231106 Address after: 1003, Building A, Zhiyun Industrial Park, No. 13 Huaxing Road, Henglang Community, Dalang Street, Longhua District, Shenzhen City, Guangdong Province, 518000 Patentee after: Shenzhen Wanzhida Technology Transfer Center Co.,Ltd. Address before: 400065 Chongqing Nan'an District huangjuezhen pass Chongwen Road No. 2 Patentee before: CHONGQING University OF POSTS AND TELECOMMUNICATIONS |
|
| TR01 | Transfer of patent right |