CN111753892A  Deep learningbased interpretation method of global visual field network system  Google Patents
Deep learningbased interpretation method of global visual field network system Download PDFInfo
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 CN111753892A CN111753892A CN202010532906.3A CN202010532906A CN111753892A CN 111753892 A CN111753892 A CN 111753892A CN 202010532906 A CN202010532906 A CN 202010532906A CN 111753892 A CN111753892 A CN 111753892A
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Abstract
The invention relates to the technical field of internet information, in particular to an interpretation method of a global visual field network system based on deep learning. The method carries out causal explanation and conversion on the decision of the computer network system based on deep learning under the condition of global visual field. Firstly, training an original network system by adopting a deep reinforcement learning method, modeling a generated global configuration result in a hypergraph mode after the training of the original system based on the deep learning is completed, analyzing key pointhyperedge connections in the hypergraph, and scoring the influence of each pointhyperedge connection on a final global configuration result so that a network administrator understands key components in decision making. The method greatly reduces the understanding difficulty of the original deep learningbased global visual field network system, and facilitates the network administrator to understand the decision process. When the interpretation method is deployed on an actual system, a network administrator is facilitated to understand and correct the decision process of the original global view network system.
Description
Technical Field
The invention relates to the technical field of internet information, in particular to an interpretation method of a global visual field network system based on deep learning.
Background
Computer network systems can be generally divided into systems with a local view and systems with a global view. The local view refers to that the network system is deployed on a server side, a client side or middleware, a switch (such as a congestion control system), and the system with the local view can only observe information of one point in the system and make a decision. A system with a global view, such as a network management controller, a traffic engineering scheduling system, a software defined network controller, etc., can observe and make decisions for multiple devices in the network. In decision logic, part of the existing global visual field network systems adopt a deep learning technology as a decision algorithm thereof, such as a software defined network route optimization algorithm based on a deep neural network.
In a decision strategy of a conventional network system, decision is often made through a concise strategy such as 'multiplication and subtraction'. The decision mode of the existing deep learningbased systems cannot be understood by a network administrator: neural networks often contain thousands of neurons, and the final conclusion is reached through a series of nonlinear calculations. Thus, while performing well in training, network administrators are not able to understand the logic of their decisions and therefore tend to have difficulty gaining trust.
Disclosure of Invention
The invention aims to provide an interpretation method of a deep learningbased global view network system, which is used for causally interpreting and converting decisions of the deep learningbased computer network system under the condition of a global view. Firstly, training an original network system by adopting a deep reinforcement learning method, after the training of the original system based on the deep learning is completed, modeling a global configuration result generated by the system in a hypergraph (hypergraph) mode, analyzing key pointedge (hyperedge) connections in the hypergraph, and scoring the influence of each pointedge connection on the final global configuration result so as to enable a network manager to understand key components in decision making.
The invention provides an interpretation method of a global visual field network system based on deep learning, which comprises the following steps:
(1) inputting resources and requests into a global view network system S to be interpreted, outputting to obtain a global configuration result set, and recording the global configuration result set as O;
(2) constructing a globalview computer network system hypergraph H comprising resources and requests, the globalview computer network system hypergraph H having the following four forms:
a. when the global visual field network system S is a software defined network routing optimization system, physical links in the hypergraph H are constructed into points, and routing paths are constructed into hyperedges;
b. when the global visual field network system S is used for placing an optimization system for a virtual network function, a physical server in the hypergraph H is constructed into a point, and the virtual network function is a hyperedge;
c. when the global visual field network system S is a superdense cellular network optimization system, mobile users in the hypergraph H are constructed into points, and a base station is constructed into a super edge;
d. when the global visual field network system S is a cluster task scheduling optimization system, the request tasks in the hypergraph H are constructed into points, and the dependency relationship among the tasks is constructed into a hypergraph;
representing a hypergraph H of the global visual field computer network system by using an incidence matrix I, wherein the incidence matrix I is a V multiplied by E dimensional 01 matrix, when the position (V, E) in the incidence matrix I is 1, the incidence matrix I represents that a point V and a hyperedge E have a connection relation, and when the position (V, E) is 0, the incidence matrix I represents that the point V and the hyperedge E have no connection relation;
(3) and (3) performing feature calculation on the hypergraph H of the globalview computer network system in the step (2), wherein the feature calculation comprises the following steps:
(31) constructing an evaluation matrix W for representing the importance of each nonzero element in the incidence matrix I;
(32) calculating the performance loss of the evaluation matrix W:
judging the global configuration result set O in the step (1), and if the output result of the global configuration result in the step (1) is a discrete variable, calculating KL divergence D (W, I) of the incidence matrix I and the evaluation matrix W by using the following formula; if the output result of the global configuration result in the step (1) is a continuous variable, calculating the mean square error D (W, I) of the global configuration result correlation matrix I and the evaluation matrix W by using the following formula:
in the above formula, f (W) is the evaluation result of the evaluation matrix W by the evaluation function of the global view network system S to be interpreted, and f (I) is the evaluation result of the correlation matrix I of the global view network system S to be interpreted;
(33) calculating the conciseness of the evaluation matrix W in the step (31) by using the following formula, and defining the conciseness of the evaluation matrix W as:
in the above formula, W_{ev}To evaluate the element value of the matrix W at the (e, v) position of the incidence matrix I;
(34) representing the certainty of the evaluation matrix W by adopting the entropy H (W) of the evaluation matrix W:
(35) establishing an optimization model for solving the evaluation matrix W, wherein an objective function of the optimization model is as follows:
minD(W,I)+λ_{1}W+λ_{2}H(W)
wherein D (W, I) is the mean square error in step (32),   W   is the simplicity in step (33), H (W) is the entropy of the evaluation matrix W in step (34), and λ_{1}And λ_{2}Respectively calculating parameters of simplicity and entropy;
(4) and (3) solving the optimization model in the step (35) by adopting a gradient descent method to obtain an evaluation matrix W, and obtaining the representation of the importance of each connection relation in the incidence matrix I according to the evaluation matrix W to realize the explanation of the global visual field network system based on deep learning.
The invention provides an interpretation method of a global visual field network system based on deep learning, which has the characteristics and advantages that:
according to the deep learningbased global visual field network system interpretation method, the output of the deep learningbased global visual field network system is converted into an equivalent hypergraph, and the importance of each connection relation in the hypergraph is quantitatively shown, so that the understanding difficulty of the original deep learningbased global visual field network system is greatly reduced, and a network administrator can conveniently understand the decision process of the result. When the interpretation method is deployed on an actual system, a network administrator is facilitated to understand and correct the decision process of the original global view network system.
Drawings
Fig. 1 shows a global routing result O of a network routing system S constructed in an embodiment of the method of the present invention.
FIG. 2 is a hypergraph H converted from a global routing result O in an embodiment of the present invention.
FIG. 3 shows the result of the important connection relationship obtained in the embodiment of the present invention.
Detailed Description
The invention provides an interpretation method of a global visual field network system based on deep learning, which comprises the following steps:
(1) inputting resources and requests into a global view network system S to be interpreted, outputting to obtain a global configuration result set, and recording the global configuration result set as O; for a softwaredefined network route optimization system based on deep learning, a set O of configuration results is what path traffic at any two points in the network should be forwarded along;
(2) a global view computer network system hypergraph H is constructed which includes resources and requests, and the network system with the global view is the allocation of the resources and requests, such as the allocation of link resources to traffic requests, the allocation of physical machine resources to virtual machine service requests, and the like. Thus, resources and requests can be represented as points and hyperedges in the hypergraph, respectively. The globalview computer network system hypergraph H has the following four forms:
a. when the global visual field network system S is a software defined network routing optimization system, physical links in the hypergraph H are constructed into points, and routing paths are constructed into hyperedges;
b. when the global visual field network system S is used for placing an optimization system for a virtual network function, a physical server in the hypergraph H is constructed into a point, and the virtual network function is a hyperedge;
c. when the global visual field network system S is a superdense cellular network optimization system, mobile users in the hypergraph H are constructed into points, and a base station is constructed into a super edge;
d. when the global visual field network system S is a cluster task scheduling optimization system, the request tasks in the hypergraph H are constructed into points, and the dependency relationship among the tasks is constructed into a hypergraph;
a correlation matrix I is used for representing a hypergraph H of the global visual field computer network system, the correlation matrix I is a V multiplied by E dimensional 01 matrix, when the position (V, E) in the correlation matrix I is 1, the point V and the hypergraph E are represented to have a connection relation, and when the position (V, E) is 0, the point V and the hypergraph E are represented to have no connection relation, as shown in FIG. 1;
(3) and (3) performing feature calculation on the hypergraph H of the globalview computer network system in the step (2), wherein the feature calculation comprises the following steps:
(31) constructing an evaluation matrix W for representing the importance of each nonzero element in the incidence matrix I;
(32) calculating the performance loss of the evaluation matrix W:
judging the global configuration result set O in the step (1), and if the output result of the global configuration result in the step (1) is a discrete variable, calculating KL divergence D (W, I) of the incidence matrix I and the evaluation matrix W by using the following formula; if the output result of the global configuration result in the step (1) is a continuous variable, calculating the mean square error D (W, I) of the global configuration result correlation matrix I and the evaluation matrix W by using the following formula:
in the above formula, f (W) is the evaluation result of the evaluation matrix W by the evaluation function of the global view network system S to be interpreted, and f (I) is the evaluation result of the correlation matrix I of the global view network system S to be interpreted;
(33) calculating the conciseness of the evaluation matrix W in the step (31) by using the following formula, and defining the conciseness of the evaluation matrix W as:
in the above formula, W_{ev}To evaluate the element value of the matrix W at the (e, v) position of the incidence matrix I;
(34) representing the certainty of the evaluation matrix W by adopting the entropy H (W) of the evaluation matrix W: at the same time, it is also expected that importance should be clustered towards 0 or 1 to indicate the dichotomous nature of importance: either important or not.
(35) establishing an optimization model for solving the evaluation matrix W, wherein an objective function of the optimization model is as follows:
minD(W,I)+λ_{1}W+λ_{2}H(W)
wherein D (W, I) is the mean square error in step (32),   W   is the simplicity in step (33), H (W) is the entropy of the evaluation matrix W in step (34), and λ_{1}And λ_{2}Respectively calculating parameters of simplicity and entropy; lambda [ alpha ]_{1}And λ_{2}Can be set according to the preference of a network administrator for the corresponding property, and in one embodiment of the invention, lambda_{1}And λ_{2}Is 0.5 and 0.5;
(4) and (3) solving the optimization model in the step (35) by adopting a gradient descent method to obtain an evaluation matrix W, and obtaining the representation of the importance of each connection relation in the incidence matrix I according to the evaluation matrix W to realize the explanation of the global visual field network system based on deep learning.
The content of the method of the invention is described in detail below with reference to the accompanying drawings:
setting a softwaredefined network routing system S based on deep learning, and generating a routing decision result as shown in FIG. 1, wherein in FIG. 1, a to g are routers, 1 to 8 are physical links, and blue and red are routing paths from a to e and from a to g, which are calculated by the network routing system S. Constructing a hypergraph H of a network routing system S, wherein physical links are constructed as points, and routing paths are constructed as hyperedges; as shown in fig. 2. And calculating the evaluation matrix W to obtain the key influence on the final optimization target in the pointhyperedge connection in the hypergraph. For example, the algorithm finds that the path selection link 6 has a large influence on the global optimization result, and may prompt the network administrator to: note that link 6 acts as a performance bottleneck. Similar operations can be done for larger scale topologies, with the final possible visualization shown in fig. 3.
Claims (1)
1. A deep learningbased interpretation method of a global visual field network system is characterized by comprising the following steps:
(1) inputting resources and requests into a global view network system S to be interpreted, outputting to obtain a global configuration result set, and recording the global configuration result set as 0;
(2) constructing a globalview computer network system hypergraph H comprising resources and requests, the globalview computer network system hypergraph H having the following four forms:
a. when the global visual field network system S is a software defined network routing optimization system, physical links in the hypergraph H are constructed into points, and routing paths are constructed into hyperedges;
b. when the global visual field network system S is used for placing an optimization system for a virtual network function, a physical server in the hypergraph H is constructed into a point, and the virtual network function is a hyperedge;
c. when the global visual field network system S is a superdense cellular network optimization system, mobile users in the hypergraph H are constructed into points, and a base station is constructed into a super edge;
d. when the global visual field network system S is a cluster task scheduling optimization system, the request tasks in the hypergraph H are constructed into points, and the dependency relationship among the tasks is constructed into a hypergraph;
representing a hypergraph H of the global visual field computer network system by using an incidence matrix I, wherein the incidence matrix I is a V multiplied by E dimensional 01 matrix, when the position (V, E) in the incidence matrix I is 1, the incidence matrix I represents that a point V and a hyperedge E have a connection relation, and when the position (V, E) is 0, the incidence matrix I represents that the point V and the hyperedge E have no connection relation;
(3) and (3) performing feature calculation on the hypergraph H of the globalview computer network system in the step (2), wherein the feature calculation comprises the following steps:
(31) constructing an evaluation matrix W for representing the importance of each nonzero element in the incidence matrix I;
(32) calculating the performance loss of the evaluation matrix W:
judging the global configuration result set 0 in the step (1), and if the output result of the global configuration result in the step (1) is a discrete variable, calculating KL divergence D (W, I) of the incidence matrix I and the evaluation matrix W by using the following formula; if the output result of the global configuration result in the step (1) is a continuous variable, calculating the mean square error D (W, I) of the global configuration result correlation matrix I and the evaluation matrix W by using the following formula:
in the above formula, f (W) is the evaluation result of the evaluation matrix W by the evaluation function of the global view network system S to be interpreted, and f (I) is the evaluation result of the correlation matrix I of the global view network system S to be interpreted;
(33) calculating the conciseness of the evaluation matrix W in the step (31) by using the following formula, and defining the conciseness of the evaluation matrix W as:
in the above formula, W_{ev}For evaluating the elements of the matrix W at the (e, v) positions of the correlation matrix IThe prime value;
(34) representing the certainty of the evaluation matrix W by adopting the entropy H (W) of the evaluation matrix W:
(35) establishing an optimization model for solving the evaluation matrix W, wherein an objective function of the optimization model is as follows:
minD(W，I)+λ_{1}W+λ_{2}H(W)
wherein D (W, I) is the mean square error in step (32),   W   is the simplicity in step (33), H (W) is the entropy of the evaluation matrix W in step (34), and λ_{1}And λ_{2}Respectively calculating parameters of simplicity and entropy;
(4) and (3) solving the optimization model in the step (35) by adopting a gradient descent method to obtain an evaluation matrix W, and obtaining the representation of the importance of each connection relation in the incidence matrix I according to the evaluation matrix W to realize the explanation of the global visual field network system based on deep learning.
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Citations (5)
Publication number  Priority date  Publication date  Assignee  Title 

US20170116520A1 (en) *  20151023  20170427  Nec Laboratories America, Inc.  Memory Efficient Scalable Deep Learning with Model Parallelization 
CN108171735A (en) *  20171227  20180615  清华大学  1,000,000,000 pixel video alignment schemes and system based on deep learning 
CN109190626A (en) *  20180727  20190111  国家新闻出版广电总局广播科学研究院  A kind of semantic segmentation method of the multipath Fusion Features based on deep learning 
CN109460501A (en) *  20181115  20190312  成都傅立叶电子科技有限公司  A kind of global search Battle Assistant Decisionmaking system and method 
CN109919319A (en) *  20181231  20190621  中国科学院软件研究所  Deeply learning method and equipment based on multiple history best Q networks 

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Patent Citations (5)
Publication number  Priority date  Publication date  Assignee  Title 

US20170116520A1 (en) *  20151023  20170427  Nec Laboratories America, Inc.  Memory Efficient Scalable Deep Learning with Model Parallelization 
CN108171735A (en) *  20171227  20180615  清华大学  1,000,000,000 pixel video alignment schemes and system based on deep learning 
CN109190626A (en) *  20180727  20190111  国家新闻出版广电总局广播科学研究院  A kind of semantic segmentation method of the multipath Fusion Features based on deep learning 
CN109460501A (en) *  20181115  20190312  成都傅立叶电子科技有限公司  A kind of global search Battle Assistant Decisionmaking system and method 
CN109919319A (en) *  20181231  20190621  中国科学院软件研究所  Deeply learning method and equipment based on multiple history best Q networks 
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