KR20120102971A - Energy saving routing apparatus and method for setting up transmission route using the same and table managing method of routing apparatus - Google Patents

Abstract

PURPOSE: A routing apparatus, transmission route setting method, and table management method thereof are provided to set a transmission route of a service packet based on acquired information by acquiring a pheromone of artificial ants according to each neighbor node-destination. CONSTITUTION: A logic unit(100) includes a delay statistics model(102), a traffic concentration model(104), and a pheromone table(106). The delay statistics model stores the statistics value of delay quality calculated on the basis of information collected by artificial ants applied to a network. The pheromone table stores pheromone values for neighbor nodes on the basis of the collected information. An update unit(110) updates the delay statistics model and the pheromone table using the information included in the artificial ants. [Reference numerals] (100) A-ESR logic; (102) Delay statistics model; (104) Traffic concentration model; (106) Pheromone table; (110) A-ESR update unit; (112) Delay statistics model update unit; (114) Traffic concentration model update unit; (116) Pheromone table update unit; (120) Ant processing unit; (130) A-ESR based path setup unit; (AA) Packet

Description

ENERGY SAVING ROUTING APPARATUS AND METHOD FOR SETTING UP TRANSMISSION ROUTE USING THE SAME AND TABLE MANAGING METHOD OF ROUTING APPARATUS}

The present invention relates to an energy-saving routing device for future green network configuration, and more particularly, it is possible to effectively reduce energy consumed in an IP network by using traffic centrality and to expand in terms of network. The present invention relates to an energy saving routing device capable of improving performance and robustness, a transmission path setting method using the same, and a table management method technology of the energy saving routing device.

As the Internet grows rapidly, energy-saving technology is emerging as a key technology. As the movement to reduce carbon dioxide emissions soars around the world, efforts are needed to reduce carbon dioxide emissions by saving energy in the network sector as well.

On the other hand, a way to reduce carbon dioxide emissions from the network is to save energy, but the rapid development of the Internet has led to the activation of multimedia services that require high bandwidth and low latency, which leads to a surge in traffic and serious waste of energy in the network. Caused. Therefore, in order to reduce energy waste and achieve energy saving in the network, research on a new routing technique that can recognize the energy consumption state in the network and can reduce it in-depth.

To meet these requirements, new routing devices for energy savings must be able to directly or indirectly measure the energy consumption of equipment in the first network. Furthermore, it should be possible to minimize energy consumption. Therefore, the routing device for saving energy in the future will be developed into a technique that can recognize the consumption state of energy consumed in the network, and select a path that minimizes energy consumption based on this.

However, the OSPF (Open Shortest Path Protocol) protocol, which is widely used in the current network, establishes a path using only link weights and network topology information, so it is not considered at all. Lack of ability Moreover, since the operation algorithm is to find the shortest distance based on the end-to-end, the route has a limit that does not satisfy the required quality of the service when the amount of traffic applied to the network is large, resulting in a waste of energy. Cause.

Recently, heuristic measures for minimizing the energy consumption of the network and the Green-OSPF protocol have been proposed as a routing technique for energy saving. Green-OSPF protocol aims to minimize the number of active links, and it has the advantage that it can be easily implemented in the network by modifying existing OSPF protocol. This algorithm uses a method of selecting a representative router (Exporter router) and setting up routing based on OSPF protocol by sharing neighboring nodes with the SPT (Shortest Path Tree) calculated by the node.

However, the algorithm using the Green-OSPF protocol selects the most representative node as the representative node without selecting the network traffic information or QoS information when selecting the representative node used for the SPT calculation. There is a limit that it is difficult to achieve minimization of energy consumption due to failure to elect.

In addition, even if neighboring nodes share the same SPT as the representative node, the energy saving effect is insufficient if this algorithm is introduced from all nodes. In other words, adding up the SPTs at each node deactivates up to 10% of the links.

In addition, since the Green-OSPF protocol is a modified version of the OSPF protocol, the recalculation of the SPT is performed only by the link status update message. This message is sent at least 5 minutes apart, which not only accommodates traffic load changes and QoS state changes that have changed during that time, but also wastes energy.

On the other hand, in the case of heuristic measures for minimizing energy consumption, it is assumed that the network topology information and the traffic information to be applied are known in advance, and the link to be activated and the link to be deactivated are calculated by calculating in a centralized manner. do. Therefore, since this algorithm needs to know all the information for calculation, it is not feasible in the actual network and the collected information must be accurate. Therefore, it is difficult to consider it as an optimized algorithm for minimizing energy consumption. In addition, it is time-consuming because it decides whether to activate the link because it is concentrated, and it is difficult to be optimized in terms of energy consumption reduction because it is difficult to accept the actual network state change due to the static method.

Therefore, the routing scheme for energy saving proposed by the conventional papers has the following problems.

First, it is difficult to cope with the change of network state due to the centralized static method. Since the Green-OSPF and the heuristic schemes also use static method calculation, it is difficult to accommodate network state change by using the established routing table before the recalculation of the routing path and link activation is completed, resulting in waste of energy. The disadvantage is that.

Second, minimization of energy consumption is difficult to achieve. Conventional methods are not considered to be an algorithm for optimizing energy consumption in terms of complexity, implementation, and energy saving in information and algorithms used in core algorithm calculation.

Therefore, in order to achieve the minimum energy consumption, the router must be able to detect the current energy consumption of the network. Based on this, the routing table must be configured with a path that can minimize the energy consumption.

In view of the above, the object of the present invention is to provide an energy-saving routing device for recognizing the energy consumption state of a network through routing in a distributed manner and minimizing the energy consumption of the network.

In addition, the present invention measures the delay quality, which is network status information by applying artificial ant capable of accumulating pheromone to the network, and accumulates pheromone at each node based on the measured value to update the table of the routing device. An object of the present invention is to provide a table management method of a low-cost routing device.

The present invention provides a transmission path setting method using an energy-saving routing device that can set a transmission path of a service packet based on a pheromone value and a traffic concentration value of each neighbor node-destination of a pheromone table.

The objects of the present invention are not limited to the above-mentioned objects, and other objects not mentioned can be clearly understood by those skilled in the art from the following description.

According to an aspect of the present invention, the energy-saving routing device according to an embodiment of the present invention is a delay statistics model that stores the statistical value of the delay quality calculated based on the information collected by the artificial ants applied on the network The pheromone values of the neighbor nodes are stored based on the traffic concentration model in which the traffic volume is stored for each link connected to the neighbor node in the node and the information collected by the artificial ants applied to the network. An A-ESR logic unit managing a pheromone table and an A-ESR updater updating the delay statistics model and the pheromone table using information included in an artificial ant applied to the network.

An energy-saving routing device according to an embodiment of the present invention applies a forward artificial ant on the network, and by the forward artificial ant as any forward artificial ant arrives through the network as its destination node. And generating an backward artificial ant using the collected information and applying the same to the network.

In the energy-saving routing device according to an embodiment of the present invention, the A-ESR updater may update the traffic concentration model by measuring the amount of traffic for each link connected to the neighboring node in the own node.

In the energy-saving routing device according to an embodiment of the present invention, the A-ESR update unit includes the node (i) of the node (i) at the destination node as a backward artificial ant having a destination node of "n" among the artificial ants arrives. Calculates the number of hops to, set the expected arrival node of the backward artificial ant to the sum of its own node and a predetermined number of past hops, and calculates a difference value of the delay quality from the own node to the set scheduled arrival node The apparatus may further include a delay statistics model updater configured to update the delay quality statistics value, which is the arrival node, in the delay statistics model based on the calculation.

In the energy-saving routing device according to an embodiment of the present invention, the A-ESR update unit selects a pheromone value, which is the expected arrival node, from the pheromone table through any neighbor node in the own node, and selects the selected pheromone value. And a pheromone table updating unit for updating the calculated delay quality using the difference between the calculated delay quality and the maximum support delay quality value calculated using the delay quality statistics of the arrival node updated in the delay statistics model. It is done.

In the energy-saving routing device according to an embodiment of the present invention, the maximum supportable delay quality value uses a predetermined confidence interval setting constant, a sample window size, an average value of delay quality corresponding to the arrival node, and a standard deviation value. It is characterized by.

In the energy saving routing apparatus according to the embodiment of the present invention, the pheromone table updating unit may update the pheromone table at a predetermined period by reflecting a preset evaporation constant.

According to an embodiment of the present invention, an energy saving routing device may calculate a packet based on a traffic concentration value calculated based on the traffic concentration model and a phermon value for neighbor nodes stored in the phermon table as packets arrive from the outside. And an A-ESR path setting unit configured to transmit the packet based on the set transmission path after setting the transmission path of the.

In the energy-saving routing device according to an embodiment of the present invention, the A-ESR path setting unit extracts a data structure having an arrival node of the packet from the pheromone table, and then links all neighbor links connected to its own node in the extracted data structure. After calculating the routing probability for each neighbor node based on the pheromone value of the and the amount of traffic to all the neighbor nodes connected to the node, and using this to set the transmission path of the packet.

According to another aspect of the present invention, the table management method of the energy-saving routing device according to an embodiment of the present invention, the delay statistics model that stores the statistics of the delay quality, the amount of traffic for each link connected to the neighbor node in its node A table management method of a routing device having a stored traffic concentration model and a pheromone table storing pheromone values for its neighbor nodes, wherein the backward artificial ant arrives through the network. Calculating an estimated arrival node of the node, calculating a difference value of delay quality from the node to the expected arrival node, selecting a statistical value of the expected arrival node in the delay statistical model, and The delay statistics by reflecting the difference value of the calculated delay quality to the selected statistics value Updating the model, and updating the pheromone table by using the calculated difference value of delay quality.

In the method of managing a table of an energy-saving routing device according to an embodiment of the present invention, the calculating of the expected arrival node may include a target node of the backward artificial ant arriving at the destination node of the backward artificial ant as it arrives through the network. Calculating the number of hops to the node, and calculating the arrival node by the sum of its own node and the preset number of last hops, wherein the arrival node is less than or equal to the total number of nodes on the network. In the case of proceeding to calculate the difference value of the delay quality to update the delay statistics model and the pheromone table, increasing the value of the last hop number, and proceeds to calculating the expected arrival node of the backward artificial ant It is characterized by.

,

는 페로몬 테이블에서 자신의 노드 i에서 이웃 노드 k를 통해 도착 예정 노드 j에 대한 페로몬 값,

는 지연 통계 모델의 도착 예정 노드 j에서의 지연 품질 통계 값을 이용하여 계산된 최대 지원 가능 지연 품질 값)을 통해 상기 페로몬 테이블을 업데이트하는 것을 특징으로 한다.The updating of the pheromone table in the table management method of the energy saving routing apparatus according to the embodiment of the present invention,

,

Is the pheromone value for node j, which is expected to arrive through its neighbor node k from its node i in the pheromone table,

Is characterized by updating the pheromone table with the maximum supportable delay quality value calculated using the delay quality statistical value at the expected arrival node j of the delay statistics model.

,

는 현재 노드 i에서 도착 예정 노드가 j인 지연 통계 모델의 지연 품질의 평균 값,

는 현재 노드 i에서 도착 예정 노드가 j인 지연 통계 모델의 지연 품질의 표준 편차 값, 신뢰 구간 설정 상수 v와 샘플 윈도우 사이즈 w)을 산출되는 것을 특징으로 한다.In the table management method of the energy-saving routing device according to an embodiment of the present invention, the maximum supportable delay quality value is represented by an equation (

,

Is the average value of the delay quality of the delay statistics model for which j is expected to arrive at node i,

Is calculated from the current node i, the standard deviation value of the delay quality, the confidence interval setting constant v, and the sample window size w) of the delay statistics model whose arrival node is j.

Table management method of the energy-saving routing device according to an embodiment of the present invention further comprises the step of updating the pheromone table by using a predetermined evaporation constant.

The table management method of the energy-saving routing device according to an embodiment of the present invention comprises the steps of measuring the traffic volume of all neighboring links connected to the own node, and updating the traffic concentration model based on the measured traffic volume Characterized in that it comprises a step.

According to another aspect of the present invention, a transmission path setting method using an energy-saving routing device according to an embodiment of the present invention, a delay statistics model that stores the statistical value of the delay quality, a link connected to the neighbor node in its own node A method of establishing a transmission path using a traffic concentration model in which traffic volume is stored for each node and a pheromone table in which pheromone values of neighboring nodes are stored, the method comprising arbitrarily setting a weight of the traffic concentration as packets arrive. And selecting a pheromone data structure having a destination corresponding to an arrival node of the packet in the pheromone table, extracting the traffic amount of neighboring nodes in the selected data structure from the traffic concentration model, and then To calculate the probability associated with traffic concentration Extracting a pheromone value for neighboring nodes in the selected data structure from the pheromone table and calculating a probability related to the pheromone value based on the pheromone value; Calculating a routing probability for each of the neighboring nodes using an associated probability and setting a neighboring node having the largest value among the calculated probability values as a transmission path of the packet.

In the transmission path setting method using the energy-saving routing device according to an embodiment of the present invention, calculating the probability related to the traffic concentration level, the traffic concentration model for all the neighboring links connected to the node of the traffic concentration model And extracting from and calculating a probability related to the traffic concentration by using the sum of the extracted traffic amounts and the traffic concentration weight.

Computing a probability related to the pheromone value in a transmission path setting method using an energy-saving routing device according to an embodiment of the present invention, and the pheromone value for all neighboring links connected to the node in the selected data structure Extracting the pheromone from the pheromone table, and calculating a probability related to the pheromone value by using the sum of the extracted pheromone values and the traffic weight.

In the transmission path setting method using the energy-saving routing device according to an embodiment of the present invention, the calculating of the routing probability may include calculating the routing probability by calculating a probability value related to the traffic concentration and a probability value related to the pheromone value. It is characterized by calculating,

,

는 트래픽 집중도의 가중치,

는 현재 노드 i에 연결된 이웃 링크 k의 트래픽 량,

는 현재 노드 i에 연결된 모든 이웃 링크의 트래픽 량의 합,

는 페로몬 테이블에서 현재 노드 i에서 이웃 노드 k를 통해 도착 예정 노드 j에 대한 페로몬 값,

는 페로몬 테이블에서 도착 노드가 j인 데이터 구조(

)에서 현재 노드 i에 연결된 모든 이웃 링크의 페로몬 양의 합)을 통해 상기 라우팅 확률을 계산하는 것을 특징으로 한다.
In the transmission path setting method using the energy-saving routing device according to an embodiment of the present invention, the calculating of the routing probability may be performed by the following equation:

,

Is the weight of traffic concentration,

Is the amount of traffic on neighbor link k that is currently connected to node i,

Is the sum of the traffic volume of all neighboring links currently connected to node i,

Is the pheromone value for node j in the pheromone table,

Is a data structure whose arrival node is j in the pheromone table (

The routing probability is calculated by the sum of the pheromone amounts of all neighboring links currently connected to node i).

The present invention applies an artificial ant capable of accumulating pheromones to the network, acquires the pheromone amount for each neighboring node-destination, and then sets the transmission path of the service packet based on the obtained information, thereby minimizing the energy consumption of the network. In addition to achieving energy savings, it is possible to efficiently meet the bandwidth quality required by the service, while improving scalability and robustness in terms of network.

1 is a block diagram showing an ACO-based energy saving routing device for a future green network configuration according to an embodiment of the present invention;
2 is a view illustrating the inside of an A-ESR logic unit according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a process of updating network statistics information by a ant and delay statistics model and pheromone table, which are data structures of an A-ESR logic unit, according to an embodiment of the present invention;
4 is a flowchart specifically illustrating a process of applying a local search and updating a pheromone table through a backward artificial ant during a process of collecting network state information of the A-ESR and updating a data structure according to an embodiment of the present invention;
5 is a flowchart illustrating a process in which a routing apparatus sets a transmission path for transmitting a service packet based on pheromone information and a traffic concentration value according to an embodiment of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention and the manner of achieving them will become apparent with reference to the embodiments described in detail below with reference to the accompanying drawings. The present invention may, however, be embodied in many different forms and should not be construed as being limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. Is provided to fully convey the scope of the invention to those skilled in the art, and the invention is only defined by the scope of the claims. Like reference numerals refer to like elements throughout.

In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear. The following terms are defined in consideration of the functions in the embodiments of the present invention, which may vary depending on the intention of the user, the intention or the custom of the operator. Therefore, the definition should be based on the contents throughout this specification.

Each block of the accompanying block diagrams and combinations of steps of the flowchart may be performed by computer program instructions. These computer program instructions may be loaded into a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus so that the instructions, which may be executed by a processor of a computer or other programmable data processing apparatus, And means for performing the functions described in each step are created. These computer program instructions may be stored in a computer usable or computer readable memory that can be directed to a computer or other programmable data processing equipment to implement functionality in a particular manner, and thus the computer usable or computer readable memory. It is also possible for the instructions stored in to produce an article of manufacture containing instruction means for performing the functions described in each block or flowchart of each step of the block diagram. Computer program instructions may also be mounted on a computer or other programmable data processing equipment, such that a series of operating steps may be performed on the computer or other programmable data processing equipment to create a computer-implemented process to create a computer or other programmable data. Instructions that perform processing equipment may also provide steps for performing the functions described in each block of the block diagram and in each step of the flowchart.

In addition, each block or step may represent a portion of a module, segment or code that includes one or more executable instructions for executing a specified logical function (s). It should also be noted that in some alternative embodiments, the functions mentioned in the blocks or steps may occur out of order. For example, the two blocks or steps shown in succession may in fact be executed substantially concurrently or the blocks or steps may sometimes be performed in the reverse order, depending on the functionality involved.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a block diagram illustrating an energy-saving routing device based on ACO (Ant Colony Optimization) for constructing a future green network according to an embodiment of the present invention.

As shown in FIG. 1, the energy-saving routing device according to an embodiment of the present invention includes an A-ESR logic unit 100, an A-ESR update unit 110, an ant processor 120, and an A-ESR based path. The setting unit 130 may be included.

The A-ESR logic unit 100 may include a delay statistics model 102, a traffic concentration model 104, a pheromone table 106, and the like.

The delay statistical model 102 may manage statistical information of delay quality measured by artificial ants. That is, the delay quality statistical information may be generated based on statistical information of the delay quality measured by artificial ants, as shown in FIG. 2, and examples thereof include an average and a standard deviation.

The traffic concentration model 104 may manage information about the traffic amount per link connected to the neighbor node in the current node, that is, information about the traffic amount per link connected to each neighbor node in the current node as shown in FIG. 2. Is saving.

The pheromone table 106 may manage pheromone information for each path updated by artificial ants, that is, as shown in FIG. 2, the pheromone table may store and manage pheromone values for each neighboring node-destination.

As such, the information stored in the A-ESR logic unit 100 may be updated by an artificial ant. Here, the artificial ant may be generated by the ant processing unit 120.

The ant processing unit 120 generates artificial ants (forward artificial ants) to update the information stored in the delay statistical model 102 and the pheromone table 106 at arbitrary time intervals, and the forward artificial ants arrive from other nodes. Accordingly, after extracting the information collected by the forward artificial ants, using the extracted information to generate a backward artificial ant and removes the forward artificial ants. The generated backward artificial ants are applied to the network and moved to the opposite path of the forward artificial ant.

The information stored in the backward artificial ant is provided to the A-ESR updater 110, and the A-ESR updater 110 uses the information included in the backward artificial ant to determine the delay statistics model 102 and the pheromone table ( 106, a delay statistic model updater 112 for updating delay quality statistic values in the delay statistic model 102 based on information included in the backward artificial ant, and connected to a neighbor node at the current node. The traffic concentration model update unit 114, which updates the traffic volume in the traffic concentration model 104 by measuring the traffic volume per link, updates the pheromone value in the pheromone table 106 based on information included in the backward artificial ant. The pheromone table updater 116 may be included.

A process of updating the delay statistics model 102 and the pheromone table 106 by the A-ESR updater 110 having the above configuration will be described below with reference to FIG. 3.

FIG. 3 is a flowchart illustrating a process of updating network delay information and pheromone tables, which are data structures of an A-ESR logic unit, by collecting network state information by artificial ants according to an embodiment of the present invention.

Referring to FIG. 3, the ant processor 120 of the router, which is an arbitrary node, generates an artificial ant, that is, a forward artificial ant (S300). At this time, the generated artificial artificial ant is applied to the network, receives the delay quality value of the intermediate router (node) in the transmission path and moves while collecting network state information (S302).

Subsequently, the ant processing unit 120 of the arrival node that the forward artificial ant arrives extinguishes the forward artificial ant and generates a new artificial ant, that is, a backward artificial ant (S304), wherein the information of the forward artificial ant is a backward artificial ant. Is moved to. Here, the backward artificial ants move to the opposite path of the forward artificial ant's path, and every intermediate router in the path through which the forward artificial ant is sent is based on the information in the delayed ant model (102). In step S306, the statistic values stored in the control table and the pheromone values of the pheromone table 106 are updated.

4 is a flowchart specifically illustrating a process of applying a local search and updating a pheromone table through a backward artificial ant during a process of collecting network state information of the A-ESR and updating a data structure according to an embodiment of the present invention.

)를 계산한다(S400). 그 후 지난 홉수(h)를 기 설정된 값, 예컨대 1로 설정한 후(S402), 도착 예정 노드인 j를 현재 노드(i)와 지난 홉수(h)의 합으로 설정한다(S404). 즉, 도착 예정 노드 j는 현재 노드(i)와 지난 홉수(h)의 합에 의해 설정될 수 있다.As shown in FIG. 4, first, the ant processing unit 120 transmits the information stored in the backward artificial ant to the A-ESR update unit 110 as the backward artificial ant arrives at the routing device of its node, for example, the i node. The A-ESR updater 110 provides the number of hops from the destination node n to its current node i.

) Is calculated (S400). After that, the last hop number h is set to a preset value, for example, 1 (S402), and j, which is a node expected to arrive, is set as the sum of the current node i and the last hop number h (S404). That is, the expected arrival node j may be set by the sum of the current node i and the last hop number h.

를 계산한다(S408). 이때, 지연 품질 차이 값은 아래의 수학식 1을 통해 계산한다. Then, comparing the arrival node j with the total number of nodes n in the network (S406), and if the expected arrival node j is greater than the total number n of nodes in step S406, the backward artificial ant is selected from the current node. End local search application and pheromone table update, otherwise, the difference in delay quality from the current node i to the expected arrival node j,

To calculate (S408). At this time, the delay quality difference value is calculated through Equation 1 below.

는 출발 노드에서 도착 예정 노드 j까지의 지연 품질 값을 의미한다.In Equation 1 above, Is the delay quality value up to the departure node i,

Denotes a delay quality value from the departure node to the expected arrival node j.

)을 토대로 지연 통계 모델(102)에서 도착 예정 노드가 j인 통계 값을 선택하며, 선택된 통계 값에 지연 품질의 차이 값(

)값을 반영하여 도착 예정 노드가 j인 지연 품질 통계 값을 아래의 수학식 2를 통해 업데이트한다(S410). Thereafter, the A-ESR updater 110 calculates the calculated difference value (

), Select the statistical value whose arrival node is j in the delay statistics model 102, and the difference value of the delay quality (

In step S410, the delay quality statistic value of which the expected arrival node is j is updated by reflecting the) value.

는 현재 노드 i에서 도착 예정 노드가 j인 지연 통계 모델의 지연 품질의 평균 값,

는 현재 노드 i에서 도착 예정 노드가 j인 지연 통계 모델의 지연 품질의 분산 값,

는 가중치 상수로써 움직임 평균(Moving average) 사용 시 현재 샘플 값 개수의 가중치를 의미한다.In Equation 2 above,

Is the average value of the delay quality of the delay statistics model for which j is expected to arrive at node i,

Is the variance value of the delay quality of the delay statistics model for which node j is expected to arrive at node i,

Denotes the weight of the current number of sample values when using a moving average as a weight constant.

값을 반영하여 아래의 수학식 3을 통해 페로몬 테이블(106)을 업데이트할 수 있다(S412).Next, in order to update the information in the pheromone table 106,

The pheromone table 106 may be updated by using Equation 3 below to reflect the value (S412).

는 페로몬 테이블(106)에서 현재 노드 i에서 이웃 노드 k를 통해 도착 예정 노드 j에 대한 페로몬 값,

는 지연 통계 모델(102)의 도착 예정 노드 j에서의 지연 품질 통계 값을 이용하여 계산된 최대 지원 가능 지연 품질 값을 의미한다. 이때, 신뢰 구간 설정 상수 v와 샘플 윈도우 사이즈 w의 설정을 통해 아래의 수학식 4를 통해

을 구할 수 있다. In Equation 3 above,

Is the pheromone value for node j expected to arrive from neighbor node k from current node i in pheromone table 106,

Denotes the maximum supportable delay quality value calculated using the delay quality statistical value at the expected arrival node j of the delay statistics model 102. At this time, by setting the confidence interval setting constant v and the sample window size w through the following equation (4)

Can be obtained.

는 현재 노드 i에서 도착 예정 노드가 j인 지연 통계 모델의 지연 품질의 평균 값,

는 현재 노드 i에서 도착 예정 노드가 j인 지연 통계 모델의 지연 품질의 표준 편차 값을 의미한다.In Equation 4 above,

Is the average value of the delay quality of the delay statistics model for which j is expected to arrive at node i,

Denotes the standard deviation value of the delay quality of the delay statistics model whose current node i is expected to arrive at node j.

On the other hand, the pheromone value stored in the pheromone table 106 has a characteristic that evaporates every predetermined period, in order to reflect this, the pheromone table updating unit 116 according to an embodiment of the present invention through the equation (5) below Update periodically.

는 증발 상수를 나타내며 0과 1사이의 값을 가진다.In Equation 5 above,

Represents the evaporation constant and has a value between 0 and 1.

)을 측정하고(S414), 측정된 값을 통해 트래픽 집중도 모델(104)의 현재 노드 i에 연결된 이웃 링크 k의 트래픽 량을

값으로 저장한다(S416). 마지막으로, 지난 홉수(h)의 값을 하나 증가시키고 난 후(S416), 도착 예상 노드 j를 계산하는 단계 S404부터 재 수행한다.After that, the amount of traffic on all neighboring links k currently connected to node i (

(S414), and calculates the traffic volume of the neighbor link k connected to the current node i of the traffic concentration model 104 through the measured value.

Save as a value (S416). Finally, after increasing the value of the last hop number (h) by one (S416), the process is performed again from step S404 of calculating the expected arrival node j.

The A-ESR based route setting unit 130 determines the transmission path of the service packet based on the pheromone value stored in the pheromone table 106 and the traffic amount stored in the traffic concentration model 104 as the packet arrives at the routing device. The service packet is transmitted to the neighbor node based on the determined transmission path. In other words, when the packet arrives, the A-ESR-based path setting unit 130 sets one of the neighbor nodes as the transmission path of the packet based on the pheromone value and the traffic concentration value of the neighbor nodes as the packet arrives, and then sets the transmission path. Packet can be sent.

A process of transmitting the service packet based on the pheromone value and the traffic concentration value by the A-ESR based path setting unit 130 will be described with reference to FIG. 5.

5 is a flowchart illustrating a process in which a routing apparatus sets a transmission path for transmitting a service packet based on pheromone information and a traffic concentration value according to an embodiment of the present invention.

의 값을 임의로 설정한다(S500). 이때,

값은 트래픽 집중도의 가중치로써 얼마나 에너지를 절감할지에 대한 가중치를 나타내며, 0에서 1사이의 값을 갖는다. 특히,

값이 0.5 보다 작을 경우 에너지 절감이 많이 되지만 서비스 품질 보장이 어려우며,

값이 0.5 보다 클 경우 에너지 절감은 조금 줄어들어도 서비스 품질 보장이 가능해진다. 따라서,

값에 따라서 에너지 절감 정도와 서비스 품질 보장 사이에 양자간의 관계가 성립되므로 효과적인

값을 설정하는 것이 중요하다.Referring to FIG. 5, in order to determine a service packet transmission path based on pheromone information and traffic concentration, the A-ESR based path setting unit 130 is a weight of traffic concentration.

The value of is set arbitrarily (S500). At this time,

The value represents a weight of how much energy is saved as a weight of traffic concentration, and has a value between 0 and 1. Especially,

If the value is less than 0.5, the energy savings are large, but the quality of service is difficult.

If the value is greater than 0.5, the energy savings can be slightly reduced, ensuring service quality. therefore,

Depending on the value, the relationship between energy savings and quality of service is established.

It is important to set a value.

)를 선택하고(S502), 도착 노드 j에서 임의의 이웃 노드 k를 선택한다(S504). Then, the A-ESR based routing unit 130 uses the arrival node j of the packet to determine the data structure (j) in which the arrival node is j in the pheromone table 106.

) Is selected (S502), and any neighbor node k is selected from the arrival node j (S504).

에 속하는지 여부를 판단한다(S506).Thereafter, the A-ESR based routing unit 130 determines that any neighbor node k is the neighbor node set of the current node i.

It is determined whether to belong to (S506).

If it does not belong to the result of the determination in S506, the A-ESR based path setting unit 130 sets the neighbor node k having the largest value among the routing probability values for each neighbor node calculated so far as the transmission path of the packet (S518), After setting the transmission path for transmitting the service packet, the packet is transmitted through the set transmission path. That is, the A-ESR based path setting unit 130 transmits the packet through the configured neighbor node k.

)(S508), 이웃 노드 k에서의 트래픽 집중도에 관련된 확률

을 아래 수학식 6을 통해 계산한다(S510).If the result of the determination in S506, the A-ESR based path setting unit 130 extracts the traffic amount of all neighboring links currently connected to the node i from the traffic concentration model 104, and adds the extracted traffic amounts (=

(S508), the probability related to the traffic concentration at neighbor node k

It is calculated through Equation 6 below (S510).

는 트래픽 집중도의 가중치,

는 현재 노드 i에 연결된 이웃 링크 k의 트래픽 량,

는 현재 노드 i에 연결된 모든 이웃 링크의 트래픽 량의 합을 의미한다.In Equation 6 above,

Is the weight of traffic concentration,

Is the amount of traffic on neighbor link k that is currently connected to node i,

Is the sum of the traffic volumes of all neighboring links currently connected to node i.

)에서 현재 노드 i에 연결된 모든 이웃 링크의 페로몬 양을 합치고 (=

)(S512), 이웃 노드 k에서의 페로몬 양에 관련된 확률

를 아래 수학식 7을 통해 계산한다(S514).Subsequently, the A-ESR based path setting unit 130 uses a data structure whose destination node is j in the pheromone table 106 (

) Sums the pheromone amounts of all neighboring links currently connected to node i and (=

(S512), the probability related to the amount of pheromone at neighbor node k

Is calculated through the following equation (7) (S514).

는 트래픽 집중도의 가중치,

는 페로몬 테이블(106)에서 현재 노드 i에서 이웃 노드 k를 통해 도착 예정 노드 j에 대한 페로몬 값을 의미한다. In Equation 7,

Is the weight of traffic concentration,

Denotes the pheromone value for the node j expected to arrive from the current node i through the neighbor node k in the pheromone table 106.

을 아래 수학식 8을 통해 계산한다(S516). Next, routing probability at neighbor node k

It is calculated through the equation (8) (S516).

Thereafter, the process proceeds to S504 in which the destination node j selects any neighbor node k, and performs a subsequent step.

According to an embodiment of the present invention, an artificial ant capable of accumulating pheromones is applied to a network to obtain a pheromone amount for each neighboring node-destination, and then a service packet transmission path is set based on the obtained information. In addition to achieving energy savings by minimizing energy consumption, the band quality required by the service can be efficiently satisfied.

While the present invention has been described in connection with what is presently considered to be practical exemplary embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, You will understand. For example, those skilled in the art can change the material, size, etc. of each component according to the application field, or combine or replace the disclosed embodiments in a form that is not clearly disclosed in the embodiments of the present invention, but this also It does not depart from the scope of the invention. Therefore, it should be understood that the above-described embodiments are to be considered in all respects as illustrative and not restrictive, and that these modified embodiments are included in the technical idea described in the claims of the present invention.

100: A-ESR logic section
110: A-ESR update unit
120: ant processing unit
130: A-ESR based path setting unit

Claims (20)

A delay statistics model in which statistical values of delay quality calculated based on information collected by artificial ants applied on a network are stored, a traffic concentration model in which a traffic amount is stored in each node connected to a neighbor node, and the traffic concentration model An A-ESR logic unit that manages a pheromone table storing pheromone values for its neighbor nodes based on information collected by artificial ants applied to a network;
And an A-ESR updater for updating the delay statistics model and the pheromone table by using information included in an artificial ant applied to the network.
Energy-saving routing device.
The method of claim 1,
Applying a forward artificial ant on the network and generating a backward artificial ant using information collected by the forward artificial ant as any forward artificial ant arriving as its destination node through the network; And then an ant processing unit for applying it on the network.
Energy-saving routing device.
The method of claim 1,
The A-ESR update unit,
The traffic concentration model is updated by measuring the amount of traffic for each link connected to the neighbor node in the own node.
Energy-saving routing device.
The method of claim 1,
The A-ESR update unit,
The number of hops from the destination node to its own node I is calculated as a backward artificial ant having a destination node of "n" among the artificial ants arrives, and the expected arrival node of the backward artificial ant is determined as its own. The delay quality statistical value, which is the expected arrival node in the delay statistics model, is calculated based on the difference value of the delay quality from the node to the predetermined arrival node, which is set to the sum of the node and the preset number of past hops. Further comprising the delay statistics model updating unit for updating the
Energy-saving routing device.
The method of claim 4, wherein
The A-ESR update unit,
The pheromone value, which is the expected arrival node, is selected from the pheromone table through any neighbor node in the own node, and the selected pheromone value is updated based on the difference between the calculated delay quality and the delay statistics model. And a pheromone table updater for updating using the maximum supportable delay quality value calculated using the delay quality statistical value of the node.
Energy-saving routing device.
The method of claim 5, wherein
The maximum supportable delay quality value is calculated using a predetermined confidence interval setting constant and a sample window size, an average value of delay quality corresponding to the arrival node, and a standard deviation value.
Energy-saving routing device.
The method of claim 5, wherein
The pheromone table updating unit,
The pheromone table is updated at a predetermined cycle by reflecting a preset evaporation constant.
Energy-saving routing device.
The method of claim 1,
As the packet arrives from the outside, the packet transmission path is set based on the traffic concentration value calculated based on the traffic concentration model and the pheromone values for neighbor nodes stored in the phermon table, and then based on the set transmission path. A-ESR path setting unit for transmitting the packet, characterized in that
Energy-saving routing device.
The method of claim 8,
The A-ESR based route setting unit
After extracting the data structure having the arrival node of the packet from the pheromone table, based on the pheromone values of all neighbor links connected to the node and the traffic amount of all neighbor nodes connected to the node in the extracted data structure After calculating the routing probability for each neighbor node to set the transmission path of the packet using this
Energy-saving routing device.
A delay statistics model that stores statistical values of delay quality, a traffic concentration model in which traffic volume is stored for each link connected to a neighbor node in its own node, and a pheromone table in which pheromone values of its neighbor nodes are stored. A table management method of a routing device,
Calculating an expected arrival node of the backward artificial ant as the backward artificial ant arrives through the network;
Calculating a difference value of delay quality from the own node to the expected arrival node;
Selecting a statistical value of the expected arrival node in the delay statistical model;
Updating the delay statistical model by reflecting the difference value of the calculated delay quality in the selected statistical value;
And updating the pheromone table by using the calculated difference value of delay quality.
How to manage tables in energy-saving routing devices.
11. The method of claim 10,
Computing the expected arrival node,
Calculating a hop number from the destination node of the backward artificial ant to its own node as the backward artificial ant arrives through the network;
Calculating the expected arrival node based on the sum of the node and the preset number of last hops;
If the expected arrival node is less than or equal to the total number of nodes on the network, calculating the difference value of the delay quality to update the delay statistics model and the pheromone table, and then increase the value of the last hop number. Calculating the expected arrival node of the backward artificial ant
How to manage tables in energy-saving routing devices.
11. The method of claim 10,
The updating of the pheromone table may include updating the pheromone table through the following equation.
How to manage tables in energy-saving routing devices.

(

Is the pheromone value for node j, which is expected to arrive through its neighbor node k from its node i in the pheromone table,

Is the maximum supportable delay quality value calculated using the delay quality statistics value at the expected arrival node j of the delay statistics model.
The method of claim 12,
The maximum supportable delay quality value is calculated by the following equation
How to manage tables in energy-saving routing devices.

(

Is the average value of the delay quality of the delay statistics model for which j is expected to arrive at node i,

Is the standard deviation value of the delay quality of the delay statistics model of the expected arrival node at current node i, the confidence interval setting constant v and the sample window size w)
11. The method of claim 10,
And updating the pheromone table by using a preset evaporation constant.
How to manage tables in energy-saving routing devices.
11. The method of claim 10,
Measuring a traffic amount of all neighbor links connected to the own node;
And updating the traffic concentration model based on the measured traffic volume.
How to manage tables in energy-saving routing devices.
Using the delay statistics model that stores the statistics of delay quality, the traffic concentration model that stores the traffic volume for each link connected to the neighbor node in its node, and the phermon table that stores the pheromone values for its neighbor nodes As a method of setting up a transmission path,
Arbitrarily setting a weight of the traffic concentration as the packet arrives,
Selecting a pheromone data structure having a destination corresponding to an arrival node of the packet in the pheromone table;
Extracting a traffic amount of neighboring nodes in the selected data structure from the traffic concentration model and calculating a probability related to the traffic concentration based on the traffic concentration model;
Extracting pheromone values for neighboring nodes within the selected data structure from the pheromone table and calculating a probability related to the pheromone values based on the extracted pheromone values;
Calculating a routing probability for each of the neighboring nodes using the probability related to the calculated traffic concentration and the probability related to the pheromone value;
And setting a neighbor node having the largest value among the calculated probability values as a transmission path of the packet.
How to set up transmission paths for energy-saving routing devices.
17. The method of claim 16,
Calculating the probability related to the traffic concentration,
Extracting, from the traffic concentration model, the amount of traffic for all neighboring links connected to the own node;
And calculating a probability related to the traffic concentration using the sum of the extracted traffic amounts and the traffic concentration weight.
How to set up transmission paths for energy-saving routing devices.
17. The method of claim 16,
Calculating a probability related to the pheromone value;
Extracting pheromone values for all neighboring links connected to the node in the selected data structure from the pheromone table;
And calculating a probability related to the pheromone value by using the sum of the extracted pheromone values and the traffic weight.
How to set up transmission paths for energy-saving routing devices.
The method of claim 15,
Computing the routing probability,
The routing probability is calculated by calculating a probability value related to the traffic concentration and a probability value related to the pheromone value.
How to set up transmission paths for energy-saving routing devices.
The method of claim 19,
The calculating of the routing probability may be calculated by the following equation.
How to set up transmission paths for energy-saving routing devices.

(

Is the weight of traffic concentration,

Is the amount of traffic on neighbor link k that is currently connected to node i,

Is the sum of the traffic volume of all neighboring links currently connected to node i,

Is the pheromone value for node j in the pheromone table,

Is a data structure whose arrival node is j in the pheromone table (

Sum of the pheromone amounts of all neighboring links currently connected to node i)

Images