CN106059942A - Traffic control method based on load prediction in SDN network - Google Patents
Traffic control method based on load prediction in SDN network Download PDFInfo
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- CN106059942A CN106059942A CN201610622523.9A CN201610622523A CN106059942A CN 106059942 A CN106059942 A CN 106059942A CN 201610622523 A CN201610622523 A CN 201610622523A CN 106059942 A CN106059942 A CN 106059942A
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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
- H04L47/10—Flow control; Congestion control
- H04L47/12—Avoiding congestion; Recovering from congestion
- H04L47/127—Avoiding congestion; Recovering from congestion by using congestion prediction
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Abstract
The invention discloses the flow control methods based on load estimation in a kind of SDN network, belong to field of communication technology. Flow control methods in SDN network of the invention based on load estimation are the following steps are included: S1: interchanger is to SDN controller feedback each of the links in time slot
Interior flow information; S2:SDN controller counts link flow period changing rule according to the link information that interchanger is fed back; S3: when reaching predicted time point, SDN controller counts the flow load range in each of the links, obtains
Average flow rate parameter in period
; The real-time link flow information that S4:SDN controller is fed back according to interchanger, obtains
Short-term averaging flow parameter in period
. By link flow predicted value and real-time link state, dynamic regulation flow forwarding strategy effectively mitigates link load and solves network congestion problem, has good application value flow control methods in the SDN network of the invention based on load estimation.
Description
Technical field
The present invention relates to communication technical field, flow-control side based on load estimation in a kind of SDN of concrete offer
Method.
Background technology
Along with developing rapidly of computer technology, user proposes the highest requirement to network performance, is used for running use
The data traffic of family business is the most increasing.Control of network flow quantity mainly has three kinds of methods, i.e. source control, destination control and
Data transmission procedure controls.Traditional network architecture to realize above control to be needed add a large amount of agreement and configure complexity, and
Software defined network SDN is a kind of novel network architecture, and it separates and controls plane and Forwarding plane, reduces network complexity,
The operational efficiency of network is improved by control algolithm flexibly.SDN detects opening up of whole network by OpenFlow agreement
Flutter structure, the load state of the change in topology of perception bottom-layer network, and link simultaneously, set up with this in control plane and safeguard
The topological view of one whole net and load table.SDN controller is the network control software according to OpenFlow Protocol Design, uses
In management data stream, the configuration network equipment, formulate stream table (Flow Table), undertake the logical of Network and LA Management Room
News.The state of the whole network of SDN monitoring control devices, and formulate forwarding flow table according to protocol requirement and be issued to respective switch
Place.
Change over time, the link flow load of network typically exhibits three rules: the periodicity of link load becomes
The load long-term growth that the peak load that law, disposable task or accident cause changes, causes due to business growth
Trend.Certain link transmission overload or certain burst flow are forcibly occupied and all can be caused network congestion, service performance fall according to service
Low, make troubles to client.Under prior art, also do not have to adjust flow in SDN in real time according to the change of load specially
The technology of distribution, when network traffics fluctuation is bigger, the problem easily causing network congestion not yet has been resolved.
Summary of the invention
The technical assignment of the present invention is the problem for above-mentioned existence, it is provided that a kind of link flow predictive value that passes through is with in real time
Link State, dynamically regulation flow forwarding strategy, thus effectively alleviate link load and solve the SDN of network congestion problem
In flow control methods based on load estimation.
For achieving the above object, the technical scheme is that
Flow control methods based on load estimation in a kind of SDN, comprises the following steps:
S1:OpenFlow switch is the flow letter in time slot to SDN controller feedback current network information and each of the links
Breath；
The link information statistics link flow period Changing Pattern that S2:SDN controller feeds back according to OpenFlow switch；
S3: set predicted time point, when arriving predicted time point, the flow load scope in SDN controller statistics each of the links,
ObtainAverage discharge parameter in period, according to average discharge parameterFormulate the forwarding flow table of load balancing, and will turn
Send out stream table and be issued to the switch ports themselves of correspondence；
Flow transmitting procedure monitored in real time by S4:SDN controller, according to the real-time link flow information of switch feedback, obtains
Short-term averaging flow parameter in period, with this dynamic regulating link traffic load average discharge parameter and regulate forwarding
Stream table.
Current network information described in step S1 is the topological structure of current network.
In step S4, if real-time link flow is in rising trend, then link prediction average discharge parameter isOtherwise,
On a declining curve, prediction average discharge parameter is, and regulate forwarding flow table with this.
As preferably, described in step S1, each of the links is at time slotInterior flow information includes the residue width of each of the links
Band, the transfer rate of every business data flow, the forwarding rate of each switch ports themselves, transmission average delay.
As preferably, in step S3, SDN controller is according to the target MAC (Media Access Control) address of business data packet, joins according to average discharge
NumberShortest path first is used to formulate the forwarding flow table of load balancing.
Described MAC Address is physical address or hardware address.If business data packet is not forwarded to target MAC (Media Access Control) address, then
Feeding back an error message to SDN controller, SDN controller detects the overload whether having burst on this transmission link, and
Reformulate forwarding flow table, alleviate link flow pressure.
Compared with prior art, the present invention has beneficial effect highlighted below: based on negative in SDN of the present invention
Carry the flow control methods of prediction, the average discharge parameter in SDN controller statistics each of the links, and SDN controller is real-time
Monitoring flow transmitting procedure, according to the real-time link flow information of switch feedback, obtainsShort-term averaging flow in period
ParameterIf real-time link flow is in rising trend, then link prediction average discharge parameter isOtherwise, become in declining
Gesture then predicts that average discharge parameter is, regulate forwarding flow table with this, can dynamically regulate flow forwarding strategy, in time
Find network congestion problem, effectively alleviate link load, thereby may be ensured that the operation of network-efficient.
Accompanying drawing explanation
Fig. 1 is the process schematic of flow control methods based on load estimation in SDN of the present invention.
Detailed description of the invention
Below in conjunction with drawings and Examples, to flow control methods based on load estimation in the SDN of the present invention
It is described in further detail.
Embodiment
As it is shown in figure 1, flow control methods based on load estimation in the SDN of the present invention, comprise the following steps:
S1:OpenFlow switch feeds back current network information and each of the links at time slot to SDN controllerInterior flow
Information.
OpenFlow switch is by the network information, i.e. bottom-layer network before LLDP agreement (Link Layer Discovery Protocol) perception
Topological structure.The flow information of each of the links includes remaining bandwidth, the transfer rate of every business data flow, each switch ports themselves
Forwarding rate, transmission average delay etc..
The link information statistics link flow period change rule that S2:SDN controller feeds back according to OpenFlow switch
Rule.
S3: set predicted time point, flow load when arriving predicted time point, in SDN controller statistics each of the links
Scope, obtainsAverage discharge parameter in period, according to average discharge parameterFormulate the forwarding flow table of load balancing, and
Forwarding flow table is issued to the switch ports themselves of correspondence.
SDN controller is according to the target MAC (Media Access Control) address i.e. physical address of business data packet or hardware address, according to average discharge
ParameterShortest path first is used to formulate the forwarding flow table of load balancing.If business data packet is not forwarded to purpose MAC ground
Location, then one error message of feedback is to SDN controller, and SDN controller detects the flow mistake whether having burst on this transmission link
Carry, and reformulate forwarding flow table, alleviate link flow pressure.
Flow transmitting procedure monitored in real time by S4:SDN controller, according to the real-time link flow information of switch feedback,
ArriveShort-term averaging flow parameter in period, the average discharge parameter with the load of this dynamic regulating link traffic is the most suitable
Regulation forwarding flow table.
If real-time link flow is in rising trend, then link prediction average discharge parameter isOtherwise, become in declining
Gesture then predicts that average discharge parameter is, and regulate forwarding flow table with this.
Embodiment described above, the simply present invention more preferably detailed description of the invention, those skilled in the art is at this
The usual variations and alternatives carried out in inventive technique aspects all should comprise within the scope of the present invention.
Claims (3)
1. flow control methods based on load estimation in a SDN, it is characterised in that: comprise the following steps:
S1:OpenFlow switch feeds back current network information and each of the links at time slot to SDN controllerInterior flow letter
Breath；
The link information statistics link flow period Changing Pattern that S2:SDN controller feeds back according to OpenFlow switch；
S3: set predicted time point, when arriving predicted time point, the flow load scope in SDN controller statistics each of the links,
ObtainAverage discharge parameter in period, according to average discharge parameterFormulate the forwarding flow table of load balancing, and will turn
Send out stream table and be issued to the switch ports themselves of correspondence；
Flow transmitting procedure monitored in real time by S4:SDN controller, according to the real-time link flow information of switch feedback, obtains
Short-term averaging flow parameter in period, with this dynamic regulating link traffic load average discharge parameter and regulate forwarding
Stream table.
Flow control methods based on load estimation in SDN the most according to claim 1, it is characterised in that: step
Described in S1, each of the links is at time slotInterior flow information includes residue broadband, the transmission of every business data flow of each of the links
Speed, the forwarding rate of each switch ports themselves, transmission average delay.
Flow control methods based on load estimation in SDN the most according to claim 1 and 2, it is characterised in that: step
In rapid S3, SDN controller is according to the target MAC (Media Access Control) address of business data packet, according to average discharge parameterUse shortest path first
Formulate the forwarding flow table of load balancing.
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| CN201610622523.9A CN106059942A (en) | 2016-08-02 | 2016-08-02 | Traffic control method based on load prediction in SDN network |
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| CN201610622523.9A CN106059942A (en) | 2016-08-02 | 2016-08-02 | Traffic control method based on load prediction in SDN network |
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| WO2018095300A1 (en) * | 2016-11-23 | 2018-05-31 | 华为技术有限公司 | Network control method, apparatus and system, storage medium |
| CN110011926A (en) * | 2019-03-07 | 2019-07-12 | 新华三技术有限公司 | A kind of method, apparatus, equipment and storage medium adjusting message sending time |
| CN110445671A (en) * | 2019-06-27 | 2019-11-12 | 浪潮思科网络科技有限公司 | A kind of network flow monitoring method based on SDN |
| CN110768916A (en) * | 2019-10-22 | 2020-02-07 | 新华三信息安全技术有限公司 | Port statistical parameter notification method and device |
| CN111600804A (en) * | 2020-05-19 | 2020-08-28 | 北京思特奇信息技术股份有限公司 | System and method for dynamically scheduling network traffic based on application load |
| CN108600055B (en) * | 2018-07-26 | 2020-10-16 | 新华三技术有限公司合肥分公司 | Data forwarding method and device and routing equipment |
| CN112866131A (en) * | 2020-12-30 | 2021-05-28 | 神州绿盟成都科技有限公司 | Traffic load balancing method, device, equipment and medium |
| CN113114582A (en) * | 2021-05-25 | 2021-07-13 | 电子科技大学 | Link congestion fault prediction and network autonomous control method based on machine learning |
| WO2022213817A1 (en) * | 2021-04-08 | 2022-10-13 | 华为技术有限公司 | Routing method and routing apparatus |
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| WO2022213817A1 (en) * | 2021-04-08 | 2022-10-13 | 华为技术有限公司 | Routing method and routing apparatus |
| CN113114582A (en) * | 2021-05-25 | 2021-07-13 | 电子科技大学 | Link congestion fault prediction and network autonomous control method based on machine learning |
| CN113114582B (en) * | 2021-05-25 | 2022-05-17 | 电子科技大学 | Link congestion fault prediction and network autonomous control method based on machine learning |
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