CN102006235A - Flow control method and device in cognitive packet network - Google Patents
Flow control method and device in cognitive packet network Download PDFInfo
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- CN102006235A CN102006235A CN2010105825318A CN201010582531A CN102006235A CN 102006235 A CN102006235 A CN 102006235A CN 2010105825318 A CN2010105825318 A CN 2010105825318A CN 201010582531 A CN201010582531 A CN 201010582531A CN 102006235 A CN102006235 A CN 102006235A
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Abstract
The invention discloses a flow control method in a cognitive packet network. Before a data stream is transmitted, a source node transmits smart packets to all adjacent nodes. From each node that adjacent to the source node, each node that the smart packets pass through makes a routing selection in accordance with the RNN algorithm; the source node receives multiple acknowledgements from destination nodes, and the acknowledgements carry flow forecasting information of the corresponding path; and the source node selects a path to transmit the data stream according to the flow forecasting information. The embodiment of the invention also provides a node in the cognitive packet network. The method in the embodiment of the invention provides a manner of realizing flow control in the cognitive packet network. Because the source node transmits the smart packets to all adjacent nodes, multiple paths from the source node to the destination nodes can be obtained for selection, better routing, load sharing and the like can be realized, and better services can be provided to users.
Description
Technical field
The present invention relates to network communications technology field, relate in particular to flow control methods and device in the cognitive packet network of grouping.
Background technology
Along with developing rapidly of computer networking technology, network size is huge day by day, and the QoS parameter excursion of network carrying business is bigger.In order to deal with complicated day by day network environment, realize the efficient utilization of frequency spectrum resource, Internet resources, for the user provides better service quality, cognitive packet network (CPN, Cognitive Packet Networks) arises at the historic moment.
In cognitive packet network, three types packet is arranged, be respectively: smart packets (cognitive packet) is used for seeking by the path of source node to destination node; Dumb packets (clear data bag) is used for carrying the data message that needs transmission; And acknowledgements (reply data bag), return the routing information that above-mentioned smart packets determines with the cause destination node to source node.The operation principle of cognitive packet network is as follows: source node sends smart packets to certain adjacent node, smart packets is by seeking route and collecting the information relevant with link-quality in the process of transmitting in network, routing iinformation and link quality information leave CM (Cognitive map) field among the smart packets in; After smart packets arrives destination node, destination node generates corresponding acknowledgements, the source of this acknowledgements, destination address are opposite with corresponding smart packets, and carry CM field and information among the corresponding smart packets among this acknowledgements; Acknowledgements returns to source node according to the routing iinformation among the CM, and when each hop node of acknowledgements process, node is preserved routing iinformation in the CM field and link quality information get off; Source node is that data flow to be sent is set up route according to the routing iinformation among the CM after receiving this acknowledgements, and by the dumbpackets transmitting data stream.In cognitive packet network, router does not need maintaining routing list, but carries out routing decision according to stochastic neural net (RNN, Random Neural Network) algorithm.
In the prior art, also how not realize the specific implementation of flow control at cognitive packet network.
Summary of the invention
The embodiment of the invention provides flow control methods and the device in a kind of cognitive packet network, thereby realizes load balancing and flow control preferably.
For solving the problems of the technologies described above, the method that the embodiment of the invention provides and install as follows:
Flow control methods in a kind of cognitive packet network before transmitting data stream, comprising:
Source node sends cognitive packet smart packets to all adjacent nodes;
From each adjacent node of described source node, each node of each smart packets process all carries out Route Selection according to stochastic neural net RNN algorithm;
Described source node receives a plurality of reply data bag acknowledgements that destination node is returned, and carries the traffic prediction information in corresponding path among each acknowledgements;
Described source node is selected a paths transmitting data stream according to described traffic prediction information.
Node in a kind of cognitive packet network comprises:
The packet sending module is used for sending smart packets or acknowledgements to next-hop node, if described node is a source node, described packet sending module sends smart packets to all adjacent nodes;
The packet receiver module is used to receive smart packets or the acknowledgements that a hop node sends, and carries the traffic prediction information in corresponding path among the described acknowledgements;
If described node is a source node, described node also comprises path selection module, is used for selecting a paths transmitting data stream according to the traffic prediction information that receives.
The method of the embodiment of the invention provides a kind of specific implementation of flow control in cognitive packet network, because source node all sends smart packets to all adjacent nodes, therefore can access source node for you to choose to the mulitpath of destination node, thereby realize better route, load balancing etc., and then provide better service quality for the user.
Description of drawings
The cognitive packet network architecture schematic diagram that Fig. 1 provides for the embodiment of the invention;
Node structure schematic diagram in the cognitive packet network that Fig. 2 provides for the embodiment of the invention.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the invention, the technical scheme in the embodiment of the invention is clearly and completely described, obviously, described embodiment only is the present invention's part embodiment, rather than whole embodiment.Based on the embodiment among the present invention, those of ordinary skills belong to the scope of protection of the invention not making the every other embodiment that is obtained under the creative work prerequisite.
The embodiment of the invention provides the flow control methods in a kind of cognitive packet network, and this method comprises following operation:
Before transmitting data stream, source node sends smart packets to all adjacent nodes;
From each adjacent node of this source node, each node of each smart packets process all carries out Route Selection according to existing RNN algorithm;
Each smart packets finally arrives destination node, destination node generates corresponding acknowledgements respectively according to each smart packets that receives, and these acknowledgements return above-mentioned source node according to the CM field information among the corresponding smart packets;
This source node receives a plurality of acknowledgements that destination node is returned, and carries the traffic prediction information in corresponding path among each acknowledgements;
This source node is selected a paths transmitting data stream according to above-mentioned traffic prediction information.
The embodiment of the invention realizes flow control by above-mentioned processing procedure.The method of the embodiment of the invention provides a kind of specific implementation of flow control in cognitive packet network, because source node all sends smartpackets to all adjacent nodes, therefore can access source node for you to choose to the mulitpath of destination node, thereby realize better route, load balancing etc., and then make QoS parameters such as packet loss, average delay be improved, for the user provides better service quality.
The method that the embodiment of the invention provides comprises that also each node between source node and the destination node generates the operation of traffic prediction information, its specific implementation can but be not limited only to following two kinds:
(1) each node of each smart packets process periodically must calculate the traffic prediction information on connection chain road; Described each node is kept at above-mentioned traffic prediction information among the described smartpackets when receiving described smart packets; Accordingly, the above-mentioned purpose node is saved in the traffic prediction information of carrying among each smart packets among the corresponding acknowledgements.Wherein, connect each bar link that link is connected with node, promptly node periodically calculates the traffic prediction information of connected each link.
(2) each node of each smart packets process periodically calculates the traffic prediction information that connects link; Each acknowledgements is when arriving node corresponding, and corresponding node is kept at the traffic prediction information that calculates among the acknowledgements.
Wherein, the traffic prediction information in path is the link flow information of forecasting of the maximum on this path.
Node specifically can obtain the traffic prediction information of link according to BP (Back Propagation, error back propagation neural net) algorithm.
In order further to improve the speed of flow control, described source node can also pick up counting according to default timing time when all adjacent nodes send smartpackets; Then, above-mentioned source node selects a paths transmitting data stream specifically to comprise according to described traffic prediction information: when timing finished, described source node was selected the path transmitting data stream of load minimum according to the traffic prediction information of carrying among the acknowledgements that obtains before timing finishes.Wherein, specifically can adopt the mode of countdown to carry out timing, also can adopt the mode of reverse countdown to carry out timing.
In order further to improve the speed of flow control, above-mentioned source node selects the specific implementation of a paths transmitting data stream to be according to described traffic prediction information: the traffic prediction information and the preset flow rate threshold value in the path of carrying among each acknowledgements that described source node will receive compare, if the traffic prediction information that the path is arranged is less than described Stream threshold value, then determine the path transmitting data stream of described traffic prediction information correspondence less than described Stream threshold value.Specifically can be, source node be whenever received an acknowledgements, and just traffic prediction information and the preset flow rate threshold value with the path of carrying among this acknowledgements compares; Can also be periodically the traffic prediction information and the preset flow rate threshold value in the path of carrying among the acknowledgements that receives in this cycle to be compared.
To describe in detail to the implementation of the embodiment of the invention in concrete application scenarios below.
Embodiment one
The embodiment of the invention one will be described the BP algorithm application in detail in cognitive packet network, the method for calculated flow rate information of forecasting.
Each node (being router) of setting in the cognitive packet network is that each the bar link that is connected to it carries out the computing of BP algorithm with pre-set time interval t all.
For example, at t
0Constantly, the input value of calculating predicted flows value needs is as follows:
T on this link
0Be carved into t during-t
0Flow f (t constantly
0-t);
T on this link
0Be carved into t during-2t
0Flow f (the t of-t
0-2t);
Other links t that the router that is connected with this link connects
0Be carved into t during-t
0Flow f constantly
n(t
0-t), and n=1,2 ...
Output: t on this link
0The time be carved into t
0+ t predicted flow rate f (t constantly
0+ t).
Each node calculates the predicted flows value (being traffic prediction information) of connected every link in the manner described above respectively.
Wherein, can select suitable time interval t according to situations such as the network size of reality, types of service.
Embodiment two
It is example that the embodiment of the invention two will be planted the mode that obtains traffic prediction information with above-mentioned (two), is described in detail in the cognitive packet network as shown in Figure 1, realizes the method for flow control.This method specifically comprises following operation:
Step 101, source node A send smart packets to all nodes (B, C and D) adjacent with it, specifically can the forms of broadcasting send;
Step 102, Node B, C and D are after receiving smart packets, carry out routing decision according to the RNN algorithm respectively, smart packets changes next-hop node over to according to corresponding routing decision respectively, and (in the embodiment of the invention two, the smart packets on the Node B is forwarded to node E; Smart packets on the node C is forwarded to node F; Smart packets on the node D is forwarded to node G), and carry corresponding routing iinformation and link-state information on each smart packets;
S103, node D, F and G repeat the action of above-mentioned steps 102 after receiving smart packets;
S104, each smart packets are respectively via B-E-H, C-F-I, and D-G arrives destination node J;
S105, destination node generate corresponding acknowledgements respectively according to each the smart packets that receives, and have carried routing iinformation and link-state information among the corresponding smart packets among each acknowledgements respectively;
S106, each acknowledgements respectively according to the routing iinformation that carries respectively via H-E-B, I-F-C, and G-D returns source node A, and each each node of acknowledgements approach will be saved among the corresponding acknowledgements according to the traffic prediction information that embodiment one calculates respectively;
S107, source node A obtain three feasible path B-E-H, C-F-I respectively according to the routing iinformation and the link-state information of carrying among three acknowledgements that receive, and D-G, and obtain the traffic prediction information of each link on every paths;
S108, source node A choose in every paths maximum link flow information of forecasting as the traffic prediction information of this paths (for example among the B-E-H of path, the traffic prediction information maximum of link B-E, then with the traffic prediction information of link B-E traffic prediction information as path B-E-H), and select the path of traffic prediction information minimum to carry out the transmission of data flow.
Embodiment three
It is example that the embodiment of the invention three will be planted the mode that obtains traffic prediction information with above-mentioned (two), is described in detail in the cognitive packet network as shown in Figure 1, realizes the method for flow control.This method specifically comprises following operation:
Step 201, source node A send smart packets to all nodes adjacent with it (B, C and D, and among other Fig. 1 unshowned node), and the triggering timing device picks up counting simultaneously;
Step 202, Node B, C and D and other nodes adjacent with source node A are after receiving smart packets, carry out routing decision according to the RNN algorithm respectively, smart packets changes next-hop node over to according to corresponding routing decision respectively, and (in the embodiment of the invention three, the smart packets on the Node B is forwarded to node E; Smartpackets on the node C is forwarded to node F; Smart packets on the node D is forwarded to node G), and carry corresponding routing iinformation and link-state information on each smart packets;
S203, node D, F and G and not shown other nodes repeat the action of above-mentioned steps 202 after receiving smart packets;
S204, each smart packets arrive destination node J via B-E-H, C-F-I, D-G and other unshowned paths respectively;
S205, destination node generate corresponding acknowledgements respectively according to each the smart packets that receives, and have carried routing iinformation and link-state information among the corresponding smart packets among each acknowledgements respectively;
S206, each acknowledgements return source node A via H-E-B, I-F-C, G-D and other unshowned paths respectively according to the routing iinformation that carries respectively, and each each node of acknowledgements approach will be saved among the corresponding acknowledgements according to the traffic prediction information that embodiment one calculates respectively;
When S207, timer arrive predetermined timing time, source node A receives respectively via three acknowledgements that return via H-E-B, I-F-C, G-D, according to the routing iinformation and the link-state information of carrying among these three acknowledgements, obtain three feasible path B-E-H, C-F-I respectively, and D-G, and obtain the traffic prediction information of each link on every paths;
S208, source node A choose in every paths maximum link flow information of forecasting as the traffic prediction information of this paths (for example among the B-E-H of path in above-mentioned three paths, the traffic prediction information maximum of link B-E, then with the traffic prediction information of link B-E traffic prediction information as path B-E-H), and select the path of traffic prediction information minimum to carry out the transmission of data flow.
In the embodiment of the invention three, by at source node timer being set, and the traffic prediction information according to the link that has obtained is selected transmission path when timing finishes, and has further improved flow control efficient.
Embodiment four
It is example that the embodiment of the invention four will be planted the mode that obtains traffic prediction information with above-mentioned (two), is described in detail in the cognitive packet network as shown in Figure 1, realizes the method for flow control.This method specifically comprises following operation:
Step 301, source node A send smart packets to all nodes (B, C and D) adjacent with it;
Step 302, Node B, C and D are after receiving smart packets, carry out routing decision according to the RNN algorithm respectively, smart packets changes next-hop node over to according to corresponding routing decision respectively, and (in the embodiment of the invention three, the smart packets on the Node B is forwarded to node E; Smart packets on the node C is forwarded to node F; Smart packets on the node D is forwarded to node G), and carry corresponding routing iinformation and link-state information on each smart packets;
S303, node D, F and G repeat the action of above-mentioned steps 202 after receiving smart packets;
S304, each smart packets are respectively via B-E-H, C-F-I, and D-G arrives destination node J;
S305, destination node generate corresponding acknowledgements respectively according to each the smart packets that receives, and have carried routing iinformation and link-state information among the corresponding smart packets among each acknowledgements respectively;
S306, each acknowledgements respectively according to the routing iinformation that carries respectively via H-E-B, I-F-C, and G-D returns source node A, and each each node of acknowledgements approach will be saved among the corresponding acknowledgements according to the traffic prediction information that embodiment one calculates respectively;
In the embodiment of the invention four, suppose that first returns source node A via the acknowledgements of path G-D, second of acknowledgements via path H-E-B returns source node A, returns source node A via the 3rd of the acknowledgements of path I-F-C.Then in S307, source node A obtains the traffic prediction information of each link that carries among the acknowledgements that returns via path G-D of first arrival, and choose the traffic prediction information of wherein maximum link flow information of forecasting as this paths, the traffic prediction information and the preset flow rate threshold value in this path are compared, if the traffic prediction information in this path is less than Stream threshold value, then select path D-G to carry out the transmission of data flow, no longer the follow-up traffic prediction information of obtaining is judged, if the traffic prediction information in this path is more than or equal to Stream threshold value, then carry out S308, continuation is carried out the threshold value comparison to the traffic prediction information in the path of the acknowledgements correspondence of second arrival, and its specific implementation can be with reference to step S307.
Embodiment of the invention four-way is crossed Stream threshold value is set, and the traffic prediction information in the path that obtains and Stream threshold value compared selects transmission path, has further improved the efficient of flow control.
The embodiment of the invention also provides the node in a kind of cognitive packet network, its structure as shown in Figure 2, the specific implementation structure comprises:
Packet sending module 201 is used for sending smart packets or acknowledgements to next-hop node, if described node is a source node, then packet sending module 201 sends smart packets to all adjacent nodes;
Packet receiver module 202 is used to receive smart packets or the acknowledgements that a hop node sends, and carries the traffic prediction information in corresponding path among this acknowledgements;
If above-mentioned node is a source node, then this node also comprises path selection module 203, is used for selecting a paths transmitting data stream according to the traffic prediction information that receives.
If the node that the embodiment of the invention provides is the node of smart packets process, then this node also comprises volume forecasting module 204, is used for periodically calculating the traffic prediction information that connects link.
If the node that the embodiment of the invention provides is a source node, then this node also comprises timing module 205, is used at packet sending module 201 picking up counting according to default timing time when all adjacent nodes send smart packets; Accordingly, path selection module 203 specifically is used for, and when timing module 205 timing finish, selects the path transmitting data stream of load minimum according to the traffic prediction information of carrying among the acknowledgements that obtains before timing finishes.
In order further to improve the efficient of flow control, above-mentioned path selection module 203 specifically is used for: the traffic prediction information and the preset flow rate threshold value in the path that each acknowledgements that will receive carries compare, if the traffic prediction information that the path is arranged is less than described Stream threshold value, then determine the path transmitting data stream of described traffic prediction information correspondence less than described Stream threshold value.
The above; only for the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, and anyly is familiar with those skilled in the art in the technical scope that the present invention discloses; the variation that can expect easily or replacement all should be encompassed within protection scope of the present invention.Therefore, protection scope of the present invention should be as the criterion with the protection range of claims.
Claims (10)
1. the flow control methods in the cognitive packet network before transmitting data stream, is characterized in that, comprising:
Source node sends cognitive packet smart packets to all adjacent nodes;
From each adjacent node of described source node, each node of each smart packets process all carries out Route Selection according to stochastic neural net RNN algorithm;
Described source node receives a plurality of reply data bag acknowledgements that destination node is returned, and carries the traffic prediction information in corresponding path among each acknowledgements;
Described source node is selected a paths transmitting data stream according to described traffic prediction information.
2. method according to claim 1 is characterized in that, this method also comprises:
Each node of each smart packets process periodically calculates the traffic prediction information that connects link;
Described each node is kept at described traffic prediction information among the described smart packets when receiving described smart packets;
Described destination node is saved in the described traffic prediction information of carrying among each smart packets among the corresponding acknowledgements.
3. method according to claim 1 is characterized in that, this method also comprises:
Each node of each smart packets process periodically calculates the traffic prediction information that connects link;
Described acknowledgements is when arriving node corresponding, and corresponding node is kept at the traffic prediction information that calculates among the acknowledgements.
4. according to any described method of claim 1～3, it is characterized in that:
When described source node sent smart packets to all adjacent nodes, this method also comprised: pick up counting according to default timing time;
Described source node selects a paths transmitting data stream specifically to comprise according to described traffic prediction information: when timing finished, described source node was selected the path transmitting data stream of load minimum according to the traffic prediction information of carrying among the acknowledgements that obtains before timing finishes.
5. according to any described method of claim 1～3, it is characterized in that described source node selects a paths transmitting data stream specifically to comprise according to described traffic prediction information:
The traffic prediction information and the preset flow rate threshold value in the path of carrying among each acknowledgements that described source node will receive compare, if the traffic prediction information that the path is arranged is less than described Stream threshold value, then determine the path transmitting data stream of described traffic prediction information correspondence less than described Stream threshold value.
6. according to claim 2 or 3 described methods, it is characterized in that the traffic prediction information in described path is the traffic prediction information of the link of the maximum on the described path.
7. the node in the cognitive packet network is characterized in that, comprising:
The packet sending module is used for sending smart packets or acknowledgements to next-hop node, if described node is a source node, described packet sending module sends smart packets to all adjacent nodes;
The packet receiver module is used to receive smart packets or the acknowledgements that a hop node sends, and carries the traffic prediction information in corresponding path among the described acknowledgements;
If described node is a source node, described node also comprises path selection module, is used for selecting a paths transmitting data stream according to the traffic prediction information that receives.
8. node according to claim 7 is characterized in that described node also comprises the volume forecasting module, is used for periodically calculating the traffic prediction information that connects link.
9. node according to claim 7, it is characterized in that if described node is a source node, then described node also comprises timing module, be used at described packet sending module when all adjacent nodes send smart packets, picking up counting according to default timing time;
Described path selection module specifically is used for, and when described timing module timing finishes, selects the path transmitting data stream of load minimum according to the traffic prediction information of carrying among the acknowledgements that obtains before timing finishes.
10. node according to claim 7 is characterized in that, described path selection module specifically is used for:
The traffic prediction information and the preset flow rate threshold value in the path of carrying among each acknowledgements that receives are compared, if the traffic prediction information that the path is arranged is less than described Stream threshold value, then determine the path transmitting data stream of described traffic prediction information correspondence less than described Stream threshold value.
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