CN107659501B - Energy Efficiency Optimal Transmission Method and System Based on Complex Gradient Network - Google Patents

Abstract

The method for the efficiency optimization transmission based on complicated gradient network that the present invention provides a kind of, which comprises (A) assigns scalar field to each node on spacer screen；(B) the transfer function value that information is transmitted to its each neighbor node is carried out according to the scalar field computation present node assigned to each node on spacer screen；(C) the best next-hop node that present node carries out information transmission is selected according to the transfer function value of calculating.The present invention solves the problems, such as that network energy efficiency is low, energy consumption is high under the premise of ensuring service quality, while also complying with requirements of the times energy-saving and emission-reduction.

Description

Energy Efficiency Optimal Transmission Method and System Based on Complex Gradient Network

technical field

The present invention relates to the technical field of network energy efficiency, and more specifically, relates to a method and system for energy efficiency optimized transmission based on a complex gradient network.

Background technique

In recent years, the study of complex systems from the perspective of complex networks has aroused great interest among scientists. The main research content in this field includes: the static structure of the network, the dynamics of network evolution, the relationship between network structure and function, and the practical application of complex network theory. Among them, the relationship between network structure and function is the key to research in this field. Network functions are diverse, and different actual networks implement different functions. Transport function is an important function of many complex networks. The network transport characteristics are not only related to the network structure, but also related to the selected routing rules. In many real networks, the transport process is caused by the local gradient of some entities, and the transport of matter, energy or information on the network is carried out along the local gradient direction of these entities. A network whose transport dynamics is caused by a gradient-driven mechanism is called a complex gradient network, and the transport dynamics behavior depends on the base network structure and gradient properties. When people study the impact of complex network structure on transmission, the network design principles usually adopted are excess resource supply and redundant design, only considering how to ensure the quality of service and solve the problem of network congestion. For example, Zheng et al. studied the degree of congestion and the efficiency of complex transportation networks, and described the impact of congestion through a flow-based link cost function; Niu et al. proposed an optimal transmission strategy on complex gradient networks, analyzing the gradient field and The relationship between local structures; Bogdan et al. proposed congestion gradient-driven transmission based on local information routing on complex networks, and analyzed the transmission behavior under various congestion perceptions.

Most of the existing technologies focus on the research of network congestion, and do not involve energy efficiency issues, and as the scale of the network becomes larger and larger, the problems of high energy consumption and low efficiency of the network become increasingly prominent. At this stage, energy conservation and emission reduction have also become one of the national strategies, and ICT (Information Communication Technology) energy consumption accounts for 2% of global energy consumption. Therefore, improving energy utilization efficiency is an urgent task.

Contents of the invention

Aiming at the problems existing in the prior art, the present invention proposes an energy efficiency optimized transmission method and system based on a complex gradient network.

According to one aspect of the present invention, an energy efficiency optimization transmission method based on a complex gradient network is provided. The method includes: (A) assigning a scalar field to each node on the bottom network; (B) assigning a scalar field to each node on the bottom network; The scalar field calculates the transfer function value of the current node to transmit information to its neighbor nodes; (C) select the best next-hop node for the current node to transmit information according to the calculated transfer function value.

Preferably, step (B) includes: (B1) calculating the point potential of each node according to the scalar field of each node on the bottom net; (B2) calculating the potential difference between the current node and its neighbor nodes on the bottom net according to the point potential ; (B3) Calculate the probability that the current node transmits information to each of its neighbor nodes according to the potential difference; (B4) Calculate the transfer function value of the current node to transmit information to each of its neighbor nodes according to the probability.

Preferably, the point potential of a node x on the ground net is calculated by the following equation:

Among them, m represents the number of neighbor nodes of node x, m≥1, and is a positive integer, K _j represents the degree of the jth neighbor node of node x, and β is an adjustment factor.

Preferably, the potential difference in step (B2) is obtained by the equation R _y =E _i -E _y , wherein E _i is the point potential of the current node i on the bottom net, and E _y is the point of the neighbor node y of the current node i Potential, R _y is the potential difference between the current node i and its neighbor node y.

Preferably, the probability in step (B3) is P(i→y):

Among them, γ≥0, R _k is the potential difference between the current node i and its Kth neighbor node, and P(i→y) is the probability that the current node i transmits information to its neighbor node y.

Preferably, the value of the transfer function in step (B4) is L(i→y):

Among them, d _iy represents the spatial distance from the current node i to its neighbor node y, α is the adjustment factor, and L(i→y) represents the transfer function value of the information transmission from the current node i to its neighbor node y.

Preferably, step (C) includes: taking the neighbor node corresponding to the maximum value of the transfer function to calculate the congestion index, and when the congestion index is less than 1, determining the neighbor node as the optimal next-hop node for information transmission of the current node.

Another aspect of the present invention provides a system for energy-efficient transmission optimization based on a complex gradient network. The system includes: a calculation module for assigning a scalar field to each node on the bottom network, and according to each node on the bottom network The scalar field assigned by the node calculates the transfer function value of the current node for information transmission to each of its neighbor nodes; the optimization module is used to select the best next-hop node for the current node for information transmission according to the calculated transfer function value.

Preferably, the calculation module includes: a point potential calculation unit, which calculates the point potential of each node according to the scalar field of each node on the bottom net, and a potential difference calculation unit, which calculates the distance between the current node on the bottom net and its neighbor nodes according to the point potential. The potential difference between them, the probability calculation unit, calculates the probability that the current node transmits information to each of its neighbor nodes according to the potential difference, and the transfer function value calculation unit, calculates the transfer function of the current node to transmit information to each of its neighbor nodes according to the probability value.

Preferably, the point potential calculation unit calculates the point potential of the node x on the bottom net by the following equation:

Among them, m represents the number of neighbor nodes of node x, m≥1, and is a positive integer, K _j represents the degree of the jth neighbor node of node x, and β is an adjustment factor.

Preferably, the potential difference obtaining unit obtains the potential difference through the equation R _y =E _i -E _y , wherein E _i is the point potential of the current node i on the bottom net, and E _y is the point of the neighbor node y of the current node i Potential, R _y is the potential difference between the current node i and its neighbor node y.

Preferably, the probability obtaining unit calculates the probability P(i→y) through the following equation:

Among them, γ≥0, R _k is the potential difference between the current node i and its Kth neighbor node, and P(i→y) is the probability that the current node i transmits information to its neighbor node y.

Preferably, the transfer function value calculating unit calculates the transfer function value L(i→y) by the following equation:

Among them, d _iy represents the spatial distance from the current node i to its neighbor node y, α is the adjustment factor, and L(i→y) represents the transfer function value of the information transmission from the current node i to its neighbor node y.

Preferably, the optimization module calculates the congestion index corresponding to the neighbor node when the transfer function value is the largest, and when the congestion index is less than 1, determine the neighbor node as the optimal next-hop node for information transmission of the current node i

Another aspect of the present invention provides a computer-readable storage medium storing a program. When the computer program is run by a processor, the processor executes the energy-efficient transmission method based on a complex gradient network as described above.

Another aspect of the present invention provides a computer device, including a processor and a memory storing a computer program. When the computer program is run by the processor, the processor executes the energy-efficient transmission method based on the complex gradient network as described above.

In the present invention, the information flow network is regarded as an energy gradient network, the information flow is transmitted in the direction of energy decline, and energy is allocated on demand, so that the transmission process from the data center to the user through the CDN (Content Delivery Network) is mirrored to the user. Energy is the drive, so that each node can actively transmit information flow according to its own ability, and can also effectively select an energy-efficient path according to the "potential" of neighboring nodes. On the premise of ensuring service quality, it solves the problems of low network energy efficiency and high energy consumption, and also complies with the requirements of the times for energy conservation and emission reduction.

Description of drawings

The above and other aspects, features and advantages of exemplary embodiments of the present invention will become more apparent through the following description in conjunction with the accompanying drawings, in which:

FIG. 1 shows a flowchart of an energy-efficient transmission method based on a complex gradient network according to an exemplary embodiment of the present invention;

FIG. 2 shows a flow chart of calculating the transfer function value of the current node for information transmission to each of its neighbor nodes according to an exemplary embodiment of the present invention;

FIG. 3 shows a block diagram of an energy-efficient transmission system based on a complex gradient network according to an exemplary embodiment of the present invention;

Fig. 4 shows a block diagram of a computing module of an energy-efficiency optimized transmission system based on a complex gradient network according to an exemplary embodiment of the present invention.

Throughout the drawings, the same reference numerals will be understood to refer to the same elements, features and structures.

Detailed ways

The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the present invention as defined by the claims and their equivalents. The following description with reference to the accompanying drawings includes various specific details to assist in understanding, but the specific details are to be regarded as examples only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions may be omitted for clarity and conciseness.

The terms and words used in the following description and claims are not limited to the bibliographical meanings, but, are merely used by the inventor to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention as defined by the claims and their equivalents.

In the following, the relevant definitions of technical terms commonly used in the art related to the present invention are first described:

1. Complex gradient network model

A gradient network is a directed graph derived from the gradients of regions distributed over nodes. The model of the gradient network is defined as follows: First, select a bottom network S containing N nodes, assign a scalar field to each node x, and denote the value as h _x , h _x is used to represent the ability of node x to transmit data, from each A node x, draw a line, and the way of drawing the line: draw a line from each node x pointing to the point with the greatest potential among all neighbors connected to node x in the bottom network. If the potential of node x is the largest, it points to itself, which ensures that each node in the network has an outlet.

2. Network congestion

It refers to the situation that when the number of packets transmitted in the packet switching network is too large, the network transmission performance is degraded due to the limited resources of the store and forward nodes. When the network is congested, data loss will generally occur, the delay will increase, and the throughput will decrease. In severe cases, it will even lead to "congestion collapse". Typically, network congestion occurs when the load on the network increases so excessively that network performance degrades. The congestion index describes the degree of network congestion in the network, and the congestion index is calculated as follows:

Among them, N _receive and N _send represent the number of nodes with out-degree (the number of edges pointing to other nodes from this node) and in-degree (the number of edges pointing to this node from other nodes) in the gradient network respectively, <... > _network indicates that the congestion index is calculated under the formed gradient network, <...> _V indicates that the congestion index is calculated under the point potential distribution of the constructed network, and J indicates the ratio of the number of nodes without in-degree to the total nodes. J=1 means maximum blocking, J=0 means no blocking.

FIG. 1 is a flow chart illustrating an energy-efficient transmission method based on a complex gradient network according to an exemplary embodiment of the present invention.

First, in step S100, a scalar field is assigned to each node on the bottom net. Specifically, construct a bottom net, and assign a scalar field to each node on the bottom net. The scalar field refers to a definite numerical value or scalar function of a certain physical quantity corresponding to each node on the bottom net. For example, the bottom net The node degree of each node, a scalar field used to represent the ability of each node on the bottom net to transmit data. According to the embodiment of the present invention, for example, use the network tool in the Python language to construct a bottom network S containing N nodes, and perform scalar value assignment to each node on the bottom network S. It should be understood that the above-mentioned method of constructing the bottom net is only an example, and the applicable methods of the present invention are not limited thereto.

Next, in step S200, the transfer function for the current node to transmit information to its neighbor nodes is calculated according to the scalar field assigned to each node on the bottom net. Specifically, according to the embodiment of the present invention, firstly, the point potential of each node and the potential difference between the current node and its neighbor nodes are calculated according to the scalar field of each node on the bottom network, and then the current The probability that a node transmits information to its neighbor nodes, and finally calculates the transfer function value of the current node to transmit information to each of its neighbor nodes according to the probability.

The process of calculating the value of the transfer function for the current node to transmit information to its neighbor nodes will be described in detail below with reference to FIG. 2 .

Fig. 2 is a flow chart showing the calculation of the transfer function value for the current node to transmit information to its neighbor nodes according to an exemplary embodiment of the present invention.

In step S201, the point potential of each node is calculated according to the scalar field of each node on the bottom net. Specifically, according to an embodiment of the present invention, each node on the bottom net is assigned an energy factor, that is, a point potential. Assuming that the point potential of any node x on the bottom net S is represented by Ex, then Among them, m represents the number of neighbor nodes of node x, m≥1, and is a positive integer, K _j represents the degree of the jth neighbor node of node x, that is, the number of edges associated with node x , K _j can also be called the degree of correlation, β is an adjustment factor, and in general, β takes any value greater than 0. In the present invention, the value range of β can be limited between 0-10 according to the constructed bottom network S between.

In step S202, the potential difference between the current node and its neighbor nodes on the bottom net is calculated according to the point potential. According to the embodiment of the present invention, assuming that the current node is node i on the bottom network S, and the current node i has n neighbor nodes, then according to the above example, the point potential of the current node i is E _i , and the current node i and its neighbor nodes The potential difference of y can be expressed as R _y =E _i -E _y , where, 1≤y≤n, E _y is the point potential of the neighbor node y of the current node i.

In step S203, the probability that the current node transmits information to its neighbor nodes is calculated according to the potential difference. Specifically, according to the embodiment of the present invention, the probability that the current node i transmits information to its neighbor node y is Among them, γ≥0, R _k is the potential difference between the current node i and its Kth neighbor node, n represents the number of neighbor nodes of the current node i, n≥1, and is a positive integer, R _y is the current node i and its Kth neighbor node Potential difference of neighbor node y.

In step S204, the value of the transfer function for the current node to transmit information to its neighbor nodes is calculated according to the probability. Specifically, according to an embodiment of the present invention, the value of the transfer function for the current node i to transmit information to its neighbor node y can be expressed by the equation Calculated, where d _iy represents the spatial distance from the current node i to its neighbor node y, and the spatial distance refers to the distance between two nodes in the bottom network calculated according to Euclidean coordinates, that is, the physical distance between the two nodes Distance, α is an adjustment factor. Generally, α takes any value greater than 0. In the present invention, α can take a value greater than 10 as required.

Returning to FIG. 1, in step S300, the best next-hop node for information transmission of the current node is selected according to the calculated transfer function value. Specifically, the congestion index is calculated for the neighbor node corresponding to the maximum value of the transfer function, and when the congestion index is less than 1, the neighbor node is determined as the optimal next-hop node for information transmission of the current node. According to the embodiment of the present invention, for example, take the neighbor node y as the neighbor node corresponding to the maximum value of the transfer function, then calculate the congestion index J of the current node i for information transmission to the neighbor node y, if J<1, then determine The neighbor node y is the optimal next-hop node for information transmission of the current node i, where J=0 indicates that the information transmission of the current node i to the neighbor node y is not blocked, but if J=1, it means that the information transmission from the current node i The blockage of information transmission to neighbor node y is the largest. At this time, neighbor node y will be discarded, and the method described in the present invention will be repeated to judge and select the optimal next-hop node of other neighbor nodes.

FIG. 3 is a block diagram illustrating an energy-efficiency optimized transmission system based on a complex gradient network according to an embodiment of the present invention.

As shown in FIG. 3 , an energy efficiency optimization transmission system 400 based on a complex gradient network may include a calculation module 401 and an optimization module 402 . According to an embodiment of the present invention, the complex gradient network-based energy-efficiency optimized transmission system 400 can be implemented by various computing devices (eg, computers, servers, workstations, etc.). Specifically, the calculation module 401 is used to assign a scalar field to each node on the bottom network, and calculate the transfer function value of the current node for information transmission to its neighbor nodes according to the scalar field assigned to each node on the bottom network. The optimization module 402 is used to select the best next-hop node for the current node to transmit information according to the calculated transfer function value.

Calculation module 401 constructs the bottom net and assigns a scalar field to each node on the bottom net, then calculates the point potential of each node according to the scalar field of each node on the bottom net, and calculates the current The potential difference between the node and its neighboring nodes and the probability of the current node transmitting information to its neighboring nodes are calculated. Finally, the transfer function value of the current node transmitting information to its neighboring nodes is calculated according to the obtained probability. The calculation module 401 according to the embodiment of the present invention will be described in detail below with reference to FIG. 4 .

Fig. 4 is a block diagram showing the calculation modules of the complex gradient network-based energy-efficiency optimized transmission system according to the embodiment of the present invention.

As shown in Figure 4, the calculation module 401 includes a point potential calculation unit 501, a potential difference calculation unit 502, a probability calculation unit 503 and a transfer function value calculation unit 504, wherein the point potential calculation unit 501 is based on the The scalar field of each node calculates the point potential of each node, and the potential difference obtaining unit 502 calculates the potential difference between the current node and its neighbor nodes on the bottom net according to the point potential obtained by the point potential obtaining unit 501, and the probability obtaining unit 503 calculates the probability that the current node transmits information to each of its neighbor nodes according to the potential difference obtained by the potential difference obtaining unit 502, and the transfer function value obtaining unit 504 calculates the probability that the current node transmits information to each neighbor node according to the probability obtained by the probability obtaining unit 503 The transfer function value of each neighbor node.

Returning to Fig. 3, the optimization module 402 judges according to the transfer function value obtained by the calculation module 401 that the current node transmits information to its neighbor nodes, and calculates the congestion index corresponding to the neighbor node when the transfer function value is the largest, and when the congestion When the index is less than 1, the neighbor node is determined to be the optimal next-hop node for information transmission of the current node. Among them, if the calculated congestion index is 0, it means that the current node transmits information to the neighbor node without blocking, but if the calculated congestion index is 1, it means that the information transmission from the current node to the neighbor node is blocked most, At this time, the neighbor node is discarded to judge and select the best next-hop node for the remaining neighbor nodes.

According to the energy efficiency optimization transmission method and system based on the complex gradient network according to the embodiment of the present invention, the method regards the network of information flow as an energy gradient network, transmits the information flow in the direction of decreasing energy, and allocates energy on demand, so that each A node can actively transmit information flow according to its own ability, and can also effectively select a path with high energy efficiency according to the "potential" of neighboring nodes. On the premise of guaranteeing the quality of service, it solves the problems of low network energy efficiency and high energy consumption.

The energy-efficiency optimization transmission method based on a complex gradient network according to an embodiment of the present invention can be implemented as computer-readable codes on a computer-readable recording medium, or can be transmitted through a transmission medium. The computer readable recording medium is any data storage device that can store data which can be thereafter read by a computer system. Examples of the computer-readable recording medium include, but are not limited to, read-only memory (ROM), random-access memory (RAM), compact disk (CD)-ROM, digital versatile disk (DVD), magnetic tape, floppy disk, optical data storage device this. Transmission media may include carrier waves sent over a network or various types of communication channels. The computer readable recording medium can also be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion.

While the invention has been shown and described with reference to certain exemplary embodiments thereof, it will be understood by those skilled in the art that changes may be made without departing from the spirit and scope of the invention as defined by the claims and their equivalents. Various changes in form and detail.

Claims (14)

1. A method for energy efficiency optimization transmission based on a complex gradient network, characterized in that the method comprises: (A) assign a scalar field to each node on the bottom net; (B) According to the scalar field given to each node on the bottom network, calculate the transfer function value of the current node for information transmission to each of its neighbor nodes; (C) Select the best next-hop node for the current node to transmit information according to the calculated transfer function value, Wherein, described step (B) comprises: (B1) Calculate the point potential of each node according to the scalar field of each node on the bottom net; (B2) Calculate the potential difference between the current node on the bottom net and its neighbor nodes according to the point potential; (B3) Calculate the probability that the current node transmits information to its neighbor nodes according to the potential difference; (B4) Calculate the value of the transfer function for the current node to transmit information to its neighbor nodes according to the probability. 2. The method of claim 1, wherein the point potential of the node x on the bottom net is calculated by the following equation: Among them, m represents the number of neighbor nodes of node x, m≥1, and is a positive integer, K _j represents the degree of the jth neighbor node of node x, and β is an adjustment factor. 3. the method for claim 2 is characterized in that, in described step (B2), potential difference is obtained by equation _Ry =Ei- _{Ey ,} wherein, Ei is the point of current node _i on the bottom net Potential, E _y is the point potential of the neighbor node y of the current node i, R _y is the potential difference between the current node i and its neighbor node y. 4. The method according to claim 3, characterized in that, in the step (B3), the probability is P(i→y): Among them, n represents the number of neighbor nodes of the current node i, n≥1, and is a positive integer, γ≥0, R _k is the potential difference between the current node i and its Kth neighbor node, and R _y is the potential difference between the current node i and its Kth neighbor node. The potential difference of the neighbor node y, P(i→y) is the probability that the current node i transmits information to its neighbor node y. 5. the method for claim 4 is characterized in that, in the described step (B4), transfer function value is L (i → y): Among them, d _iy represents the spatial distance from the current node i to its neighbor node y, α is the adjustment factor, and L(i→y) represents the transfer function value of the information transmission from the current node i to its neighbor node y. 6. The method according to claim 1, wherein said step (c) comprises: When the transfer function value is the largest, the corresponding neighbor node is used to calculate the congestion index. When the congestion index is less than 1, the neighbor node is determined as the optimal next-hop node for information transmission of the current node i. 7. A system based on energy efficiency optimized transmission of a complex gradient network, characterized in that the system comprises: A calculation module, configured to assign a scalar field to each node on the bottom network, and calculate the transfer function value of the current node for information transmission to each of its neighbor nodes according to the scalar field assigned to each node on the bottom network; The optimization module is used to select the best next-hop node for the current node to transmit information according to the calculated transfer function value, Wherein, the calculation module includes: The point potential calculation unit calculates the point potential of each node according to the scalar field of each node on the bottom net; The potential difference calculation unit calculates the potential difference between the current node on the bottom net and its neighbor nodes according to the point potential; The probability calculation unit calculates the probability that the current node transmits information to its neighbor nodes according to the potential difference; The transfer function value calculation unit calculates the transfer function value of the current node for information transmission to each of its neighbor nodes according to the probability. 8. system as claimed in claim 7, is characterized in that, point potential obtaining unit calculates the point potential of the node x on the bottom net by following equation: Among them, m represents the number of neighbor nodes of node x, m≥1, and is a positive integer, K _j represents the degree of the jth neighbor node of node x, and β is an adjustment factor. 9. The system according to claim 8, wherein the potential difference obtaining unit obtains the potential difference through the equation R _y =E _i −E _y , wherein E _i is the point of the current node i on the bottom net Potential, E _y is the point potential of the neighbor node y of the current node i, R _y is the potential difference between the current node i and its neighbor node y. 10. The system according to claim 9, wherein the probability obtaining unit calculates the probability P(i→y) by the following equation: Among them, n represents the number of neighbor nodes of the current node i, n≥1, and is a positive integer, γ≥0, R _y is the potential difference between the current node i and its neighbor node y, R _k is the current node i and its Kth Potential difference of neighbor nodes, P(i→y) is the probability that current node i transmits information to its neighbor node y. 11. system as claimed in claim 10, is characterized in that, in described transfer function value calculating unit, transfer function value is L(i → y): Among them, d _iy represents the spatial distance from the current node i to its neighbor node y, α is the adjustment factor, and L(i→y) represents the transfer function value of the information transmission from the current node i to its neighbor node y. 12. The system of claim 7, wherein the optimization module is configured to: When the transfer function value is the largest, the corresponding neighbor node is used to calculate the congestion index. When the congestion index is less than 1, the neighbor node is determined as the optimal next-hop node for information transmission of the current node i. 13. A computer-readable storage medium storing a computer program, wherein when the computer program is run by a processor, the processor executes the method according to any one of claims 1-6. 14. A computer device, comprising a processor and a memory storing a computer program, wherein when the computer program is run by the processor, the processor executes the method according to any one of claims 1-6.