CN110958626A - Link selection method based on shortest response time - Google Patents
Link selection method based on shortest response time Download PDFInfo
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- CN110958626A CN110958626A CN201911239264.1A CN201911239264A CN110958626A CN 110958626 A CN110958626 A CN 110958626A CN 201911239264 A CN201911239264 A CN 201911239264A CN 110958626 A CN110958626 A CN 110958626A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/06—Testing, supervising or monitoring using simulated traffic
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/12—Communication route or path selection, e.g. power-based or shortest path routing based on transmission quality or channel quality
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Abstract
The invention discloses a link selection method based on shortest response time, and relates to the technical field of computers. The invention comprises the following steps: establishing a complete detection system for completing quality detection of the network full link to acquire data indexes on the physical network link and reporting the data to the data system; the data system is responsible for summarizing all the detection data and providing retrieval capability according to four factor classifications of time, nodes, packet loss rate and route; analyzing and providing a complete scheduling model through an intelligent analysis system according to data provided by a data system; the flow scheduling of the whole network is realized through the scheduling model provided by the intelligent analysis system and the scheduling system, and all data packets are guaranteed to reach the destination address in a short time. The invention combines human function intelligence and big data technology, can simulate a network flow model in a future period based on data, carries out scheduling based on the most direct factor of response time, and realizes optimal link selection.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a link selection method based on shortest response time.
Background
At present, 4G is widely applied and 5G networks are coming, technologies such as VR and AR are continuously developed, exploratory landing applications also appear, 4K video and even 8K video technologies are continuously developed, and under the environment of AIoT with prosperity, the requirement on network bandwidth will be doubled in the future, and the requirement on high-speed bandwidth of any application will be a hard index, and the guarantee on network quality will be a new challenge. Under the environment, it is a strong demand to provide network experience for users and shorten the response time of various network services.
From the perspective of user experience, the most direct sensory effect of accessing a network resource is response time, and when a webpage, a video, a game, a live broadcast or an internet of things accesses a terminal device through the internet, the user wants to browse the accessed content in the shortest time and control the terminal device to execute the desired operation in the shortest time. The response duration will be determined for the controllable traffic routing.
The shortest path access is the fastest choice in the traditional sense, but in the high-speed development of the internet at present, the flow is doubled and increased due to the development of various technologies, and the demand for bandwidth far exceeds the infrastructure construction. Thus, shortest path based access may not be able to fully bring the fastest experience if the infrastructure is not a perfect match to the traffic, bandwidth requirements. For example, in a path, the fastest response time should theoretically be obtained, but if the traffic transmitted over the path is too much, causing congestion in the network bandwidth, the response time may change. Just like building multiple roads from the same origin to the same destination, the shortest distance is the time-consuming choice, but if a large number of cars are rushed in and far exceed the transmission capacity of the road, traffic jam is caused, the most direct result is that the time for reaching the destination cannot be estimated, and if other roads which are relatively far away and not jammed are selected, the time for reaching the destination is less than that for selecting the shortest path. The time is the final index, and the distance is only a factor for guaranteeing the time index. Therefore, the shortest response time based on a full link is the most realistic way to best meet this requirement.
Disclosure of Invention
The present invention is directed to a link selection method based on the shortest response time, so as to solve the problems of the background art mentioned above.
In order to solve the technical problems, the invention is realized by the following technical scheme:
the invention relates to a link selection method based on shortest response time, which comprises the following steps:
s01, establishing a complete detection system to complete the quality detection of the network full link, and acquiring the data indexes of packet loss and delay on the physical network link in real time in an icmp mode; and reporting the data to a data system;
s02, collecting all the detection data through the data system, and providing retrieval capability according to four factor analogies of time, node, packet loss rate and route;
s03, analyzing and simulating real-time conditions on a network link and predicting flow conditions in a future period through an intelligent analysis system based on a neural network model according to data provided by a data system, and providing a complete scheduling model;
and S04, realizing the flow scheduling of the whole network through the scheduling model provided by the intelligent analysis system and the scheduling system, and ensuring that all data packets reach the destination address in a short time.
A link selection system comprising:
the system comprises a detection system, a data system, an intelligent analysis system and a scheduling system.
The invention has the following beneficial effects:
1. the invention combines human function intelligence and big data technology, can simulate a network flow model in a future period based on data, carries out scheduling based on the most direct factor of response time, and realizes optimal link selection.
2. The flow scheduling system combines the technologies of artificial intelligence, big data and the like, can perform relatively accurate flow scheduling according to the current network condition, can simulate a flow model in a future period, pre-judges the flow trend in the future period, can be laid out in advance, enables the flow to flow through each node according to the expected model, controls the bandwidth condition of a full link, performs flow control on the network flow in advance, and reduces the conditions that a single path or a plurality of paths on the link are overloaded and other paths are unloaded to the maximum extent.
Of course, it is not necessary for any product in which the invention is practiced to achieve all of the above-described advantages at the same time.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced 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 that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a block diagram of a link selection system according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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 invention.
The invention relates to a link selection method based on shortest response time, which comprises the following steps:
s01, establishing a complete detection system to complete the quality detection of the network full link, and acquiring the data indexes of packet loss and delay on the physical network link in real time in an icmp mode; and reporting the data to a data system;
s02, collecting all the detection data through the data system, and providing retrieval capability according to four factor analogies of time, node, packet loss rate and route;
s03, analyzing and simulating real-time conditions on a network link and predicting flow conditions in a future period through an intelligent analysis system based on a neural network model according to data provided by a data system, and providing a complete scheduling model;
and S04, realizing the flow scheduling of the whole network through the scheduling model provided by the intelligent analysis system and the scheduling system, and ensuring that all data packets reach the destination address in a short time.
Referring to fig. 1, a link selection system includes:
the system comprises a detection system, a data system, an intelligent analysis system and a scheduling system.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (2)
1. A method for link selection based on shortest response time, comprising the steps of:
s01, establishing a complete detection system to complete the quality detection of the network full link, and acquiring the data indexes of packet loss and delay on the physical network link in real time in an icmp mode; and reporting the data to a data system;
s02, collecting all the detection data through the data system, and providing retrieval capability according to four factor analogies of time, node, packet loss rate and route;
s03, analyzing and simulating real-time conditions on a network link and predicting flow conditions in a future period through an intelligent analysis system based on a neural network model according to data provided by a data system, and providing a complete scheduling model;
and S04, realizing the flow scheduling of the whole network through the scheduling model provided by the intelligent analysis system and the scheduling system, and ensuring that all data packets reach the destination address in a short time.
2. The method as claimed in claim 1, wherein the link selection system based on the link selection method comprises:
the system comprises a detection system, a data system, an intelligent analysis system and a scheduling system.
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| CN201911239264.1A CN110958626A (en) | 2019-12-06 | 2019-12-06 | Link selection method based on shortest response time |
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| CN201911239264.1A CN110958626A (en) | 2019-12-06 | 2019-12-06 | Link selection method based on shortest response time |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111935747A (en) * | 2020-08-17 | 2020-11-13 | 南昌航空大学 | Method for predicting link quality of wireless sensor network by adopting GRU (generalized regression Unit) |
| CN114221874A (en) * | 2021-12-14 | 2022-03-22 | 平安壹钱包电子商务有限公司 | Traffic analysis and scheduling method and device, computer equipment and readable storage medium |
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| CN103581974A (en) * | 2012-12-26 | 2014-02-12 | 华平信息技术股份有限公司 | Link quality assessment method and system |
| US20160112327A1 (en) * | 2014-10-17 | 2016-04-21 | Ciena Corporation | Optical and packet path computation and selection systems and methods |
| CN106411766A (en) * | 2016-09-30 | 2017-02-15 | 赛特斯信息科技股份有限公司 | Flow scheduling system and method based on SDN |
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2019
- 2019-12-06 CN CN201911239264.1A patent/CN110958626A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103581974A (en) * | 2012-12-26 | 2014-02-12 | 华平信息技术股份有限公司 | Link quality assessment method and system |
| US20160112327A1 (en) * | 2014-10-17 | 2016-04-21 | Ciena Corporation | Optical and packet path computation and selection systems and methods |
| CN106411766A (en) * | 2016-09-30 | 2017-02-15 | 赛特斯信息科技股份有限公司 | Flow scheduling system and method based on SDN |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111935747A (en) * | 2020-08-17 | 2020-11-13 | 南昌航空大学 | Method for predicting link quality of wireless sensor network by adopting GRU (generalized regression Unit) |
| CN111935747B (en) * | 2020-08-17 | 2021-04-27 | 南昌航空大学 | Method for predicting link quality of wireless sensor network by adopting GRU (generalized regression Unit) |
| CN114221874A (en) * | 2021-12-14 | 2022-03-22 | 平安壹钱包电子商务有限公司 | Traffic analysis and scheduling method and device, computer equipment and readable storage medium |
| CN114221874B (en) * | 2021-12-14 | 2023-11-14 | 平安壹钱包电子商务有限公司 | Traffic analysis and scheduling method and device, computer equipment and readable storage medium |
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Application publication date: 20200403 |