CN114760205B - Self-calibration optimization method of distributed network - Google Patents
Self-calibration optimization method of distributed network Download PDFInfo
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- CN114760205B CN114760205B CN202210448265.2A CN202210448265A CN114760205B CN 114760205 B CN114760205 B CN 114760205B CN 202210448265 A CN202210448265 A CN 202210448265A CN 114760205 B CN114760205 B CN 114760205B
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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Abstract
The invention discloses a self-calibration optimization method of a distributed network, which simplifies a communication model of the distributed network, defines roles of nodes of the distributed network, selects calibration parameters and statistical targets of the distributed network, automatically detects the quality of a data transmission path of the distributed network and realizes an optimization strategy with transmission reliability/bandwidth utilization rate as priority. The invention can automatically calibrate the network communication parameters of the nodes in the distributed network, thereby achieving the purposes of network optimization and communication quality improvement. The operation and maintenance personnel can carry out self-calibration operation without manually adjusting and correcting network parameters of each node. The invention provides two optimization strategies of reliability and bandwidth utilization rate, and the scene adaptability is stronger.
Description
Technical Field
The invention belongs to the technical field of computer communication, and particularly relates to a self-calibration optimization method of a distributed network.
Background
Distributed networks are often formed by hybrid networking of different network media, such as wired local area networks, public networks, wireless WIFI, VPN virtual networks, and the like. The networking mode causes the network bandwidth of each data path in the distributed network to be inconsistent and the transmission quality of the link to be inconsistent. When the nodes of the current distributed network communicate with each other, the following problems exist: 1. the default communication parameters are used, so that the method cannot adapt to the bandwidth and link quality changes on different data links, and packet loss is caused; 2. no effective tool is available for calibrating the network, so that the network is difficult to tune; 3. the network-free tuning strategy can be selected by operation and maintenance personnel, and the scene adaptation is difficult.
The most similar technical scheme of the invention is from a patent CN201911126014.7, a method for optimizing network state in a DTU distribution automation remote terminal, and the patent proposes that a terminal node in a power network is added with a network state data forwarding and testing function, periodic data, random data and burst data passing through the node are counted, network analysis is completed by using a statistical algorithm, and the purpose of obtaining a proper communication data interval is finally achieved. However, the patent does not consider the packet loss rate and the size of single packet data as factors affecting the network communication quality, and only considers the data interval; the network state evaluation of the patent needs to modify the normal communication message of the node and occupies certain node resources; the statistical analysis algorithm used in the patent is complex, is difficult to realize and is not beneficial to batch implementation.
Disclosure of Invention
The invention aims to provide a self-calibration optimization method of a distributed network aiming at the defects of the prior art.
The purpose of the invention is realized by the following technical scheme: a method of self-calibration optimization of a distributed network, the method comprising the steps of:
s1, simplifying a communication model of a distributed network into a star network topology model, taking each distributed node as a central node, and carrying out point-to-point communication on the rest distributed nodes and the central node;
s2, defining the role of the distributed network nodes, deploying distributed network calibration software:
defining a central node as a calibration client on which calibration client software is deployed; defining distributed nodes except the central node as calibration service ends, and deploying calibration service end software on the calibration service ends;
the calibration client is used for sending a ping packet to the calibration server, designating the size of the packet, designating a packet sending period, calculating the packet return delay of the calibration server, and recording the packet return failure times;
the calibration server is used for responding to the ping packet sent by the calibration client and receiving a network parameter configuration command sent by the calibration client;
s3, selecting distributed network calibration parameters and a statistical target, wherein the calibration parameters comprise the size of a ping packet and the packet sending period, and the statistical target is the time and the packet loss quantity required by completing 1GB data transmission;
taking a plurality of steps for the size of the ping packet for calibration; recording the network delay from the central node to the distributed nodes as T1, and taking a plurality of file packet sending periods, wherein each file packet sending period is different multiples of T1; the sizes of the ping packets of different levels and the packet sending period form a group of calibration parameters;
s4, self-calibration of the distributed network, comprising:
the calibration client selects a group of calibration parameters, continuously sends out ping packets according to the calibration parameters, if response loss occurs in the midway, the number of lost packets is added by 1, when the number of bytes sent out by the accumulated ping packets reaches 1GB, the test of the group is completed, and the time T2 for completing 1GB data transmission of the group and the total number N of lost packets of the group are obtained; selecting another group of calibration parameters to repeat the test process to obtain T2 and N values corresponding to each group of calibration parameters; the group with the minimum T2 value is the group with the optimal network bandwidth utilization rate; the group with the minimum N value is the group with the optimal network reliability;
and the user selects a group of network parameters from the optimal group of the network bandwidth utilization rate and the optimal group of the network reliability through the calibration client and sends the network parameters to the calibration server, so that the self-calibration optimization of the distributed network is completed.
Further, in step S3, the size of the ping packet used for calibration is taken from 6 stages of 32, 64, 128, 256, 512, 1024 bytes, and 3 stages of 1.5xt1,2.0xt1,2.5xt1, and 3 stages of the packet transmission period are taken, 18 sets of calibration parameters are constructed, and an evaluation table is formed with the corresponding time T2 for completing 1GB data transmission and the packet loss number N.
Further, in step S4, the calibration client initiates a calibration procedure, and selects a set of calibration parameters: the method comprises the steps that 32 bytes of ping packets and a packet sending period are 1.5xT1, a calibration client firstly uses the 32-byte ping packets, 1.5 times of network delay T1 is used as a time interval from last response receiving time to current ping packet sending, the time difference from the ping packet sending to response receiving is recorded as T1, T2 and T3.. Tm, if the response is lost midway, a packet loss count N is added with 1, when the number of bytes sent out by the accumulated ping packets reaches 1GB, the test of the group is completed, the time for completing 1GB data transmission of the group is T2= T1+ T2+ T3. + tm, and the total packet loss quantity of the group is N; the next set of calibration parameters is selected: and testing 64 bytes of ping packets and a packet sending period of 1.5xT1 until all the groups of calibration parameters are tested, and obtaining a complete evaluation table.
Further, in step S4, the group with the minimum T2 value indicates that 1GB data transmission is completed at the fastest speed, and is the group with the best network bandwidth utilization rate; and the group with the minimum N value represents that 1GB data transmission is completed with the minimum packet loss number, and the group with the minimum N value is the group with the optimal network reliability, so that two optional tuning strategies are set.
The invention has the beneficial effects that: the method simplifies the communication model of the distributed network, defines the roles of the nodes of the distributed network, selects the calibration parameters and the statistical targets of the distributed network, automatically detects the quality of the data transmission path of the distributed network, and realizes the tuning strategy with the transmission reliability/bandwidth utilization ratio as the priority. The invention can automatically calibrate the network communication parameters of the nodes in the distributed network, thereby achieving the purposes of network tuning and communication quality improvement. Operation and maintenance personnel can carry out self-calibration operation without manually adjusting and correcting network parameters of each node. The invention provides two optimization strategies of reliability and bandwidth utilization rate, and the scene adaptability is stronger.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a method for self-calibration optimization of a distributed network according to an exemplary embodiment of the present invention;
FIG. 2 is a diagram illustrating an exemplary topology of a distributed network according to an exemplary embodiment of the present invention;
FIG. 3 is a simplified model diagram of a distributed network provided by an exemplary embodiment of the present invention;
FIG. 4 is a single set of self-calibration flow diagrams provided by an exemplary embodiment of the present invention.
Detailed Description
For better understanding of the technical solutions of the present application, the following detailed descriptions of the embodiments of the present application are provided with reference to the accompanying drawings.
It should be understood that the embodiments described are only a few embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terminology used in the embodiments of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the examples of this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The invention provides a self-calibration optimization method of a distributed network, which comprises the following specific implementation steps as shown in fig. 1:
(1) Communication model for simplifying distributed network
As shown in fig. 2, the distributed network is formed by hybrid networking of different network media, such as a wired local area network, a public network, wireless WIFI, a VPN virtual network, and the like. From the perspective of each distributed node, the remaining nodes communicate point-to-point with it within the network, which is a feature of the star network topology. Therefore, as shown in fig. 3, the distributed network topology is simplified into a star network topology model, that is, for each distributed node, the distributed node itself serves as a central node, and the other distributed nodes perform point-to-point communication with the central node.
(2) Defining distributed network node roles, deploying distributed network calibration software
By analysis, the main factors affecting communication quality in a distributed network are the size of individual data packets and the time slot spacing between packets.
Therefore, before self-calibration of the distributed network, the role of the distributed network nodes is defined, the central node is defined as a calibration client, and calibration client software is deployed on the central node; and defining distributed nodes except the central node as calibration service terminals, and deploying calibration service terminal software on the calibration service terminals.
The calibration client can send a ping packet to the calibration server, specify the size of the packet, specify the packet sending period, calculate the packet returning delay of the calibration server, and record the packet returning failure times.
The calibration server may respond to the ping packet sent by the calibration client, and may receive a network parameter configuration command sent by the calibration client.
(3) Selecting distributed network calibration parameters and statistical targets
In particular, the distributed network calibration parameters include ping packet size and packet transmission period.
In combination with a common communication scenario of a distributed network, the size of a ping packet for calibration is selected from 6 files of 32, 64, 128, 256, 512 and 1024 bytes.
The determination of the packet sending period is related to the actual network condition, the network delay from the central node to the distributed nodes is recorded as T1, and 3 grades of packet sending periods of 1.5xT1,2.0xT1 and 2.5xT1 are taken.
The method is mainly divided into two dimensions of communication reliability and communication effective bandwidth by combining with the analysis of the communication quality by a distributed network. Therefore, the time T2_ X required for completing the 1GB data transmission and the packet loss number N _ X are selected as statistical targets. The following evaluation table was obtained in conclusion:
TABLE 1 distributed network calibration parameters and statistical target set
(4) Distributed network self-calibration
As shown in fig. 4, a calibration flow is initiated by the calibration client. According to the calibration parameters in table 1, the calibration client firstly uses a 32-byte ping packet, takes 1.5 times of network delay T1 as the time interval from the last response receiving time to the sending of the ping packet this time, and the time difference from the sending of the ping packet to the response receiving (the time of receiving the response information of the calibration server) is recorded as T1, T2, and T3. The time for completing 1GB data transmission of this group is T2= T1+ T2+ T3. + tm, and the total packet loss number of this group is N. By repeating the test procedure shown in fig. 4 in sequence according to the parameters in table 1, the T2 and N values of each set of parameters can be obtained.
The group with the minimum T2 value is used for finishing 1GB data transmission at the fastest speed and is the group with the optimal network bandwidth utilization rate; the group with the minimum value of N indicates that 1GB data transmission is completed by the minimum packet loss number, and is the group with the best network reliability.
The user can select one group of network parameters from the optimal group of the network bandwidth utilization rate and the optimal group of the network reliability to be issued to the distributed nodes (calibration server) through the central node (calibration client), namely, the issued optimized network parameters are completed, and the optimization of the distributed network is completed. The invention provides two optimization strategies of network reliability/bandwidth utilization rate for the selection of users, and the scene adaptability is stronger.
It should be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising one of 8230; \8230;" 8230; "does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises that element.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The terminology used in the description of the one or more embodiments is for the purpose of describing the particular embodiments only and is not intended to be limiting of the description of the one or more embodiments. As used in one or more embodiments of the present specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms first, second, third, etc. may be used in one or more embodiments of the present description to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of one or more embodiments herein. The word "if" as used herein may be interpreted as "at" \8230; "or" when 8230; \8230; "or" in response to a determination ", depending on the context.
The above description is intended only to be exemplary of the one or more embodiments of the present disclosure, and should not be taken as limiting the one or more embodiments of the present disclosure, as any modifications, equivalents, improvements, etc. that come within the spirit and scope of the one or more embodiments of the present disclosure are intended to be included within the scope of the one or more embodiments of the present disclosure.
Claims (4)
1. A self-calibration optimization method of a distributed network is characterized by comprising the following steps:
s1, simplifying a communication model of a distributed network into a star network topology model, taking each distributed node as a central node, and carrying out point-to-point communication on the rest distributed nodes and the central node;
s2, defining the role of the distributed network nodes, deploying distributed network calibration software:
defining a central node as a calibration client on which calibration client software is deployed; defining distributed nodes except the central node as calibration service ends, and deploying calibration service end software on the calibration service ends;
the calibration client is used for sending a ping packet to the calibration server, designating the size of the packet, designating a packet sending period, calculating the packet return delay of the calibration server, and recording the packet return failure times;
the calibration server is used for responding to the ping packet sent by the calibration client and receiving a network parameter configuration command sent by the calibration client;
s3, selecting distributed network calibration parameters and a statistical target, wherein the calibration parameters comprise the size of a ping packet and the packet sending period, and the statistical target is the time and the packet loss quantity required for completing 1GB data transmission;
selecting a plurality of levels of the size of the ping packet for calibration; recording the network delay from the central node to the distributed nodes as T1, and taking a plurality of file packet sending periods, wherein each file packet sending period is different multiples of T1; the sizes of the ping packets of different levels and the packet sending period form a group of calibration parameters;
s4, self-calibration of the distributed network, comprising:
the calibration client selects a group of calibration parameters, continuously sends out ping packets according to the calibration parameters, if response loss occurs in the midway, the number of lost packets is added by 1, when the number of bytes sent out by the accumulated ping packets reaches 1GB, the test of the group is completed, and the time T2 for completing 1GB data transmission of the group and the total number N of lost packets of the group are obtained; selecting another group of calibration parameters to repeat the test process to obtain T2 and N values corresponding to each group of calibration parameters; the group with the minimum T2 value is the group with the optimal network bandwidth utilization rate; the group with the minimum N value is the group with the optimal network reliability;
and the user selects a group of network parameters from the optimal group of the network bandwidth utilization rate and the optimal group of the network reliability through the calibration client and sends the network parameters to the calibration server, so that the self-calibration optimization of the distributed network is completed.
2. The self-calibration optimization method of the distributed network according to claim 1, wherein in step S3, the ping packet size for calibration is taken to be 6 th 32, 64, 128, 256, 512, 1024 bytes, the packet sending period is taken to be 3 rd 1.5xt1,2.0xt1,2.5xt1, and 18 sets of calibration parameters are constructed, and an evaluation table is formed with the corresponding time T2 for completing 1GB data transmission and the packet loss number N.
3. The self-calibration optimization method of the distributed network according to claim 2, wherein in step S4, the calibration client initiates a calibration procedure, selects a set of calibration parameters: the method comprises the steps that 32 bytes of a ping packet and a packet sending period are 1.5xT1, a calibration client firstly uses a 32-byte ping packet, 1.5 times of network delay T1 is a time interval from last response receiving time to sending of the ping packet, the time difference from sending of the ping packet to response receiving is recorded as T1, T2 and T3.. Tm, if response loss occurs midway, a packet loss count N is added with 1, when the number of bytes sent out by the accumulated ping packet reaches 1GB, the test of the group is completed, the time for completing 1GB data transmission of the group is T2= T1+ T2+ T3. + tm, and the total packet loss number of the group is N; the next set of calibration parameters is selected: and testing 64 bytes of ping packets and a packet sending period of 1.5xT1 until all the groups of calibration parameters are tested, and obtaining a complete evaluation table.
4. A self-calibration optimization method for a distributed network according to any one of claims 1 to 3, wherein in step S4, the group with the smallest T2 value represents that 1GB data transmission is completed at the fastest speed, and is the group with the best utilization rate of network bandwidth; and the group with the minimum N value represents that 1GB data transmission is completed with the minimum packet loss number, and the group with the minimum N value is the group with the optimal network reliability, so that two optional tuning strategies are set.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106657997A (en) * | 2016-12-06 | 2017-05-10 | 广东九联科技股份有限公司 | Method for testing performance of network port of set-top box |
| US9705751B1 (en) * | 2016-03-31 | 2017-07-11 | Sas Institute Inc. | System for calibrating and validating parameters for optimization |
| CN110855524A (en) * | 2019-11-18 | 2020-02-28 | 南京富尔登科技发展有限公司 | Method for optimizing network state in DTU distribution automation remote terminal |
| CN111010294A (en) * | 2019-11-28 | 2020-04-14 | 国网甘肃省电力公司电力科学研究院 | Electric power communication network routing method based on deep reinforcement learning |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9946818B2 (en) * | 2013-07-30 | 2018-04-17 | University Of Florida Research Foundation, Inc. | System and method for automated model calibration, sensitivity analysis, and optimization |
| US10554482B2 (en) * | 2016-03-18 | 2020-02-04 | Plume Design, Inc. | Optimization of distributed Wi-Fi networks estimation and learning |
-
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- 2022-04-26 CN CN202210448265.2A patent/CN114760205B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9705751B1 (en) * | 2016-03-31 | 2017-07-11 | Sas Institute Inc. | System for calibrating and validating parameters for optimization |
| CN106657997A (en) * | 2016-12-06 | 2017-05-10 | 广东九联科技股份有限公司 | Method for testing performance of network port of set-top box |
| CN110855524A (en) * | 2019-11-18 | 2020-02-28 | 南京富尔登科技发展有限公司 | Method for optimizing network state in DTU distribution automation remote terminal |
| CN111010294A (en) * | 2019-11-28 | 2020-04-14 | 国网甘肃省电力公司电力科学研究院 | Electric power communication network routing method based on deep reinforcement learning |
Non-Patent Citations (1)
| Title |
|---|
| 基于自适应流量采集的网络可靠性分析系统实现;李达等;《微计算机信息》(第33期);第101-103页 * |
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