SDN network communication control method and device based on cloud computing

Abstract

The invention provides an SDN network communication control method and device based on cloud computing, and the method comprises the steps: receiving a network communication demand of an application layer, and generating a cloud task; dynamically allocating and scheduling cloud tasks and network resources according to a propagation loss expression, a multipath signal expression and a link balance control function of cloud task scheduling; and load balancing is realized by adjusting the data transmission sequence and the switch selection sequence. According to the invention, the inter-symbol interference in the transmission process is suppressed, and the stability of cloud task scheduling transmission is improved. Meanwhile, the network communication efficiency is ensured through load balancing.

Description

Technical Field

[0001] The present invention relates to the field of communication control technology, and in particular to a SDN network communication control method and device based on cloud computing. Background Art

[0002] SDN (Software Defined Network) network communication is a new type of network architecture and control method. Its core idea is to separate the network control plane from the data plane, making network control more centralized and intelligent. In SDN network communication, the control plane is responsible for global network control and management, and uniformly configures, manages and schedules network devices and resources through a centralized controller, which greatly improves the flexibility and programmability of the network, allowing the network to be dynamically adjusted and optimized according to business needs. At the same time, SDN network communication also introduces open interfaces and standardized protocols, making it easier for network devices from different manufacturers to achieve interoperability, reducing the complexity of network construction and maintenance.

[0003] The existing SDN network communication control method has the following defects:

[0004] In cloud computing network communications, a large number of processing tasks in the cloud need to be processed and scheduled. During the transmission and scheduling of cloud tasks, due to the influence of phase deviation and inter-symbol interference, the anti-interference ability of the scheduling is poor. Summary of the invention

[0005] In order to solve the above problems in the prior art, the present invention provides a SDN network communication control method and device based on cloud computing, which suppresses inter-code interference during transmission and improves the stability of cloud task scheduling transmission.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] In a first aspect, the present invention provides a SDN network communication control method based on cloud computing, comprising the steps of:

[0008] Receive network communication requirements from the application layer and generate cloud tasks;

[0009] Dynamic allocation and scheduling of cloud tasks and network resources are performed according to the propagation loss expression, multipath signal expression and link balance control function of cloud task scheduling. The link balance control function f(s) is:

[0010]

[0011] Among them, C and U represent the characteristic parameters of the cloud task sequence and the characteristic parameters of the task node, A is the signal amplitude of the cloud task scheduling, p is the frequency modulation slope of the response in the cloud task scheduling, v1 is the cloud phase offset, and v2 is the initial channel position;

[0012] Load balancing is achieved by adjusting the data transmission sequence and switch selection order.

[0013] The beneficial effects of the present invention are: by optimizing the scheduling of a large number of cloud tasks, by feedback balancing control of the network communication transmission channel, the propagation loss of the cloud task transmission scheduling is obtained, and the phase offset is removed, the inter-code interference in the transmission process is suppressed, and the stability of the cloud task scheduling transmission is improved. At the same time, by adjusting the data transmission order and selectively sorting the switches according to the information arrival rate, load balancing is achieved and the network communication efficiency is guaranteed.

[0014] Optionally, the propagation loss expression is:

[0015] U CC =f(α1,α2,β)

[0016] Among them, U cc is the propagation loss, α1 and α2 are the two time values ​​before and after the data transmission of the cloud task information flow, and β is the symbol rate.

[0017] Optionally, the multipath signal expression is:

[0018]

[0019] Among them, a is the signal received by the receiving end, b is the number of categories in the cloud task information table, N is the number of multipaths, and C n is the attenuation coefficient of the nth path.

[0020] Optionally, the method further comprises the steps of:

[0021] Reconstruct the traffic transmission process based on traffic characteristics and distribution space:

[0022]

[0023] Among them, y(t) is the network traffic time series, k is the sampling frequency, t is the transmission time, and h(t) is the channel data transmission function;

[0024] The transmission sequence in the traffic transmission process is sampled, and the obtained samples are sampled, trained and analyzed. The neural network algorithm is used to extract features to obtain traffic features under a high-dimensional spectrum. The traffic features are input into the traffic matrix X, and the eigenvector of the principal component is intercepted to perform anomaly detection on the traffic features.

[0025] According to the above description, by detecting anomalies in traffic characteristics, network attacks and illegal access behaviors can be discovered in a timely manner, thereby protecting the security of network communications.

[0026] Optionally, the method of intercepting the characteristic vector of the principal component to perform abnormality detection on the flow characteristics comprises the steps of:

[0027] The subspace s is formed by using the eigenvectors of the intercepted part. The calculation formula of the vector x in the traffic matrix X is:

[0028] x=x1+x2

[0029] Among them, x1 is the projection vector of x in the normal subspace, and x2 is the projection vector of x in the abnormal subspace;

[0030] It is determined whether the vector x is the same as the vector x1. If they are not the same, there is a flow anomaly.

[0031] Optionally, adjusting the data transmission order includes:

[0032] Data with effective information volume that meets the preset conditions is transmitted first, and data with effective information volume that does not meet the preset conditions is deleted or transmitted later.

[0033] Optionally, the switch selection sorting includes:

[0034] After adjusting the data transmission order, the switches are selected and sorted according to the information arrival rates at the sending and receiving ends.

[0035] Optionally, the method further comprises the steps of:

[0036] Initialize the channel parameters as follows:

[0037]

[0038] Among them, A(S) is the channel scheduling operation cycle, V1 and V0 are the input and output voltages of the SDN controller respectively, S is the data operation parameter, R1 and R2 correspond to V1 and V0, and are the resistances formed by the input and output voltages respectively, f(A) is the signal operation frequency, and A(H) is the controller's control ability over network communication.

[0039] Optionally, the SDN network adopts standardized interfaces and protocols.

[0040] In a second aspect, the present invention provides an SDN network communication control device based on cloud computing, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, a SDN network communication control method based on cloud computing of the first aspect is implemented.

[0041] Among them, the technical effect corresponding to the SDN network communication control device based on cloud computing provided in the second aspect refers to the relevant description of the SDN network communication control method based on cloud computing provided in the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of the main process of a cloud computing-based SDN network communication control method according to an embodiment of the present invention;

[0043] Figure 2 The present invention is a schematic diagram of the overall process of a cloud computing-based SDN network communication control method according to an embodiment of the present invention;

[0044] Figure 3 The present invention is a schematic diagram of a framework of a cloud computing-based SDN network communication control device according to an embodiment of the present invention.

[0045] Description of reference numerals:

[0046] 1. A SDN network communication control device based on cloud computing;

[0047] 2. Processor;

[0048] 3. Memory. DETAILED DESCRIPTION

[0049] In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0050] Embodiment 1

[0051] This embodiment is applicable to the application scenario of SDN network communication. In the prior art, there are problems such as poor anti-interference performance of scheduling, frequent abnormal network traffic, and low communication efficiency. This embodiment suppresses inter-symbol interference through dynamic allocation and scheduling, detects network traffic abnormalities through communication security detection, and solves the problem of low communication efficiency through load balancing strategy. See the following description for details.

[0052] Please refer to Figure 1 and Figure 2 , a SDN network communication control method based on cloud computing, comprising the steps of:

[0053] Step S1: Control of network resources of the entire network.

[0054] In this embodiment, a global view of the network is obtained through the cloud computing platform, including real-time status information of all network devices and links, and the SDN controller performs global traffic analysis, path calculation and policy formulation based on this information.

[0055] Step S2: collecting and analyzing application requirements.

[0056] In this embodiment, the SDN controller receives and analyzes the requirements from the application plane through the northbound interface, including requirements for network traffic, bandwidth, latency, etc., specifically:

[0057] Receive network communication requirements from the application layer and generate cloud tasks;

[0058] Step S3: Standardize interfaces and protocols.

[0059] In this embodiment, the cloud computing-based SDN network communication control method focuses on openness and interoperability. The SDN network adopts standardized interfaces and protocols so that different devices can be integrated into the SDN network.

[0060] Step S4: Dynamic allocation and scheduling.

[0061] In this embodiment, the propagation loss of the cloud task transmission scheduling is obtained and the phase offset is removed to suppress the inter-symbol interference. Therefore, step S4 is specifically as follows:

[0062] Dynamically allocate and schedule cloud tasks and network resources based on the propagation loss expression, multipath signal expression, and link balance control function of cloud task scheduling;

[0063] In this embodiment, in the cloud computing platform, cloud tasks are controlled first, and the task information table of cloud computing network communication is divided into class b. After IFFT (inverse fast Fourier transform), the multipath signal a of the network communication cloud task scheduling channel under the cloud computing background is obtained, and its expression is:

[0064]

[0065] Among them, a is the signal received by the receiving end, b is the number of categories in the cloud task information table, N is the number of multipaths, and C n is the attenuation coefficient of the nth path.

[0066] Therefore, by optimizing the cloud task scheduling, the inter-symbol interference during the transmission process is reduced, which helps to improve the overall performance of the network, thereby indirectly affecting the quality and efficiency of traffic transmission.

[0067] In this embodiment, the propagation loss expression is:

[0068] U CC=f(α1,α2,β)

[0069] Among them, U cc is the propagation loss, α1 and α2 are the two time values ​​before and after the data transmission of the cloud task information flow, and β is the symbol rate.

[0070] Therefore, calculating the propagation loss of cloud tasks is crucial for evaluating the effectiveness of task scheduling and formulating scheduling strategies, and also indirectly affects the processing of traffic characteristics and anomaly detection.

[0071] In this embodiment, the characteristic parameter decomposition method is used to automatically sort the cloud tasks and construct a priority sequence. At the same time, the phase offset is removed by phase weighting, and the link balance control function f(s) in the cloud task scheduling channel is obtained as follows:

[0072]

[0073] Among them, C and U represent the characteristic parameters of the cloud task sequence and the characteristic parameters of the task node, A is the signal amplitude of the cloud task scheduling, p is the frequency modulation slope of the response in the cloud task scheduling, v1 is the cloud phase offset, and v2 is the initial channel position.

[0074] Therefore, combined with SDN modulation and mediation technology, cross-balanced attenuation is performed on cloud task scheduling, and network communication cloud tasks in the context of cloud computing are dynamically allocated and scheduled to suppress inter-code interference in the transmission scheduling process.

[0075] Step S5: Communication security detection.

[0076] Among them, SDN network communication adopts a distributed control method. Under the influence of the diversification and complexity of data types, network attacks and illegal access behaviors often occur, which may cause network communication failures and lack of communication security detection for abnormal network traffic.

[0077] Therefore, in this embodiment, step S5 is used to perform abnormality detection on the flow characteristics, specifically:

[0078] S51. Reconstruct the traffic transmission process according to traffic characteristics and distribution space:

[0079]

[0080] Among them, y(t) is the network traffic time series, k is the sampling frequency, t is the transmission time, and h(t) is the channel data transmission function;

[0081] S52. Sample the transmission sequence in the traffic transmission process, perform sampling training and analysis on the obtained samples, use the neural network algorithm to extract features, obtain traffic features under the high-dimensional spectrum, input the traffic features into the traffic matrix X, and intercept the eigenvector of the main component to perform anomaly detection on the traffic features.

[0082] The process of extracting the characteristic vector of the principal component in step S52 to perform abnormality detection on the flow characteristics includes the following steps:

[0083] S521. Use the eigenvectors of the intercepted part to form a subspace s. The calculation formula of the vector x in the flow matrix X is:

[0084] x=x1+x2

[0085] Among them, x1 is the projection vector of x in the normal subspace, and x2 is the projection vector of x in the abnormal subspace;

[0086] S522. Determine whether vector x is the same as vector x1. If not, there is a flow anomaly.

[0087] Among them, if x2 is not a zero vector, that is, x has a projection in the abnormal subspace, which means that x contains abnormal components. When x and x1 are the same, that is, x2 is a zero vector, it means that x is completely in the normal subspace and has no abnormal components. When the length of x is greater than x1, it can be determined that there is an abnormality. Therefore, by detecting abnormalities in traffic characteristics, network attacks and illegal access behaviors can be discovered in a timely manner, thereby protecting network communication security.

[0088] Step S6: load balancing.

[0089] Among them, diverse network information types are prone to load imbalance problems in large-scale network data, resulting in low communication efficiency.

[0090] Therefore, in this embodiment, step S6 is specifically as follows:

[0091] Load balancing is achieved by adjusting the data transmission sequence and switch selection order.

[0092] That is, this embodiment designs the SDN controller to adjust the order of data transmission, give priority to transmitting data with large amounts of information, delete or delay the transmission of data containing less information or invalid information, and achieve reasonable distribution of communication channel traffic. First, the router traffic is regulated and the parameters are initialized, and then the interface parameters and masks are analyzed to distribute the traffic.

[0093] First, initialize the channel parameters as follows:

[0094]

[0095] Among them, A(S) is the channel scheduling operation cycle, V1 and V0 are the input and output voltages of the SDN controller respectively, S is the data operation parameter, R1 and R2 correspond to V1 and V0, and are the resistances formed by the input and output voltages respectively, f(A) is the signal operation frequency, and A(H) is the controller's control ability over network communication.

[0096] The initialization of channel parameters will affect subsequent data transmission and scheduling. For example, if the transmission quality of a channel is poor, such as low signal-to-noise ratio and high bit error rate, the scheduling may try to avoid transmitting critical or important data on this channel.

[0097] Secondly, adjust the data transmission order including:

[0098] Data with effective information volume that meets the preset conditions is transmitted first, and data with effective information volume that does not meet the preset conditions is deleted or transmitted later.

[0099] Finally, the switch selection order includes:

[0100] After adjusting the data transmission order, the switches are selected and sorted according to the information arrival rates at the sending and receiving ends.

[0101] For example, after adjusting the data transmission order, the sender can add a sequence number to each piece of data, and the receiver can rearrange the data in the order of the sequence number to ensure the order of the data. At the same time, the number of data packets successfully received by the receiver is counted, and compared with the total number of data packets sent by the sender to obtain the information arrival rate. Finally, the switch can forward data packets according to their priority or type. By setting different priorities, it can ensure that high-priority data packets are transmitted first, thereby achieving the adjustment of the data transmission order.

[0102] In summary, the present invention optimizes the scheduling of a large number of cloud tasks, obtains the propagation loss of cloud task transmission scheduling through feedback and equalization control of the network communication transmission channel, and removes the phase offset, thereby suppressing the inter-code interference during the transmission process and improving the stability of cloud task scheduling transmission. At the same time, through the abnormal detection of traffic characteristics, network attacks and illegal access behaviors can be discovered in time, thereby protecting the security of network communications. By adjusting the data transmission order and selectively sorting the switches according to the information arrival rate, load balancing is achieved and network communication efficiency is guaranteed.

[0103] Embodiment 2

[0104] Please refer to Figure 3A cloud computing-based SDN network communication control device 1 includes a memory 3, a processor 2, and a computer program stored in the memory 3 and executable on the processor 2. When the processor 2 executes the computer program, the steps in the above-mentioned embodiment 1 are implemented.

[0105] Since the system / device described in the above embodiments of the present invention is a system / device used to implement the method of the above embodiments of the present invention, a person skilled in the art can understand the specific structure and deformation of the system / device based on the method described in the above embodiments of the present invention, and thus will not be described in detail here. All systems / devices used in the method of the above embodiments of the present invention belong to the scope of protection of the present invention.

[0106] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, devices or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0107] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus) and computer program products according to the embodiments of the present invention. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions.

[0108] It should be noted that in the claims, any reference numerals placed between brackets shall not be construed as limiting the claims. The word "comprising" does not exclude the presence of components or steps not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The invention may be implemented by means of hardware comprising several different components and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by the same hardware. The use of the words first, second, third, etc., is for convenience of expression only and does not indicate any order. These words may be understood as part of the component name.

[0109] In addition, it should be noted that, in the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0110] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments after knowing the basic creative concept. Therefore, the claims should be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0111] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include these modifications and variations.

Claims

1. A cloud computing-based SDN network communication control method, characterized in that: Includes steps: Receive network communication requirements from the application layer and generate cloud tasks; Dynamic allocation and scheduling of cloud tasks and network resources are performed according to the propagation loss expression, multipath signal expression and link balance control function of cloud task scheduling. The link balance control function f(s) is: Among them, C and U represent the characteristic parameters of the cloud task sequence and the characteristic parameters of the task node, A is the signal amplitude of the cloud task scheduling, p is the frequency modulation slope of the response in the cloud task scheduling, v1 is the cloud phase offset, and v2 is the initial channel position; Load balancing is achieved by adjusting the data transmission sequence and switch selection order.

2. The SDN network communication control method based on cloud computing according to claim 1 is characterized in that: The propagation loss expression is: U CC =f(α1,α2,β) Among them, U cc is the propagation loss, α1 and α2 are the two time values ​​before and after the data transmission of the cloud task information flow, and β is the symbol rate.

3. The SDN network communication control method based on cloud computing according to claim 1 is characterized in that: The multipath signal expression is: Among them, a is the signal received by the receiving end, b is the number of categories in the cloud task information table, N is the number of multipaths, and C n is the attenuation coefficient of the nth path.

4. The SDN network communication control method based on cloud computing according to claim 1 is characterized in that: Also includes the steps: Reconstruct the traffic transmission process based on traffic characteristics and distribution space: Among them, y(t) is the network traffic time series, k is the sampling frequency, t is the transmission time, and h(t) is the channel data transmission function; The transmission sequence in the traffic transmission process is sampled, and the obtained samples are sampled, trained and analyzed. The neural network algorithm is used to extract features to obtain traffic features under a high-dimensional spectrum. The traffic features are input into the traffic matrix X, and the eigenvector of the principal component is intercepted to perform anomaly detection on the traffic features.

5. The SDN network communication control method based on cloud computing according to claim 4 is characterized in that: The method of intercepting the characteristic vector of the principal component to perform abnormality detection on the flow characteristics comprises the following steps: The subspace s is formed by using the eigenvectors of the intercepted part. The calculation formula of the vector x in the traffic matrix X is: x=x1+x2 Among them, x1 is the projection vector of x in the normal subspace, and x2 is the projection vector of x in the abnormal subspace; It is determined whether the vector x is the same as the vector x1. If they are not the same, there is a flow anomaly.

6. The SDN network communication control method based on cloud computing according to claim 1 is characterized in that: The adjusting the data transmission sequence comprises: Data with effective information volume that meets the preset conditions is transmitted first, and data with effective information volume that does not meet the preset conditions is deleted or transmitted later.

7. The SDN network communication control method based on cloud computing according to claim 1 is characterized in that: The switch selection sorting includes: After adjusting the data transmission order, the switches are selected and sorted according to the information arrival rates at the sending and receiving ends.

8. A cloud computing-based SDN network communication control method according to any one of claims 1 to 7, characterized in that: Also includes the steps: Initialize the channel parameters as follows: Among them, A(S) is the channel scheduling operation cycle, V1 and V0 are the input and output voltages of the SDN controller respectively, S is the data operation parameter, R1 and R2 correspond to V1 and V0, and are the resistances formed by the input and output voltages respectively, f(A) is the signal operation frequency, and A(H) is the controller's control ability over network communication.

9. A cloud computing-based SDN network communication control method according to any one of claims 1 to 7, characterized in that: The SDN network adopts standardized interfaces and protocols.

10. A cloud computing-based SDN network communication control device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the SDN network communication control method based on cloud computing as described in any one of claims 1 to 9 is implemented.

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