CN115914111A - Node resource allocation method and system based on Internet - Google Patents
Node resource allocation method and system based on Internet Download PDFInfo
- Publication number
- CN115914111A CN115914111A CN202211327930.9A CN202211327930A CN115914111A CN 115914111 A CN115914111 A CN 115914111A CN 202211327930 A CN202211327930 A CN 202211327930A CN 115914111 A CN115914111 A CN 115914111A
- Authority
- CN
- China
- Prior art keywords
- module
- data
- network
- server
- base station
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a node resource distribution method and a node resource distribution system based on the Internet, which comprise the node resource distribution method and the node resource distribution system of the Internet, wherein the node resource distribution system comprises a network receiving module, a database module, a network state measuring and calculating module and a resource distribution module, the network receiving module is used for receiving a data request command sent by a client, the database module is used for storing the number of network surfing personnel in a region, caching resource data and classifying network data requests, the network state measuring and calculating module is used for measuring and calculating the congestion degree of a network channel, the resource distribution module is used for enhancing data transmission, and when the link load of the network channel is overlarge, the direct cliff-breaking type reduction of the network transmission performance can be caused, so that the node resource distribution system is used in the region, the congestion state of the network channel can be analyzed in time, and the network transmission performance is greatly improved.
Description
Technical Field
The invention relates to the technical field of internet resource adjustment, in particular to a node resource allocation method and system based on the internet.
Background
With the gradual digitization and visualization of network data, the application requirements of the network resource data gradually shift from integrity to accuracy, the accuracy of the network resource has decisive influence on the planning, design, maintenance and management of a communication network and the subsequent service fulfillment, and by constructing a network resource accuracy evaluation model, a unified criterion is provided for the integrity and accuracy of regional resources comprehensively from different dimensions, the availability of data information is maintained and improved, the construction expense is saved, the maintenance expense is reduced, and the management efficiency of the communication network is improved.
However, when the network traffic used in an area is too large, the network channel is blocked, the transmission of the network channel is not linearly increased, the network use tends to be smooth due to the increase of the number of network users, but the cliff type decline occurs, and the network resource deadlock occurs under severe conditions, so that the reasonable distribution of the network node resources is beneficial to the smoothness of the whole network, and therefore, it is necessary to design a node resource distribution method and a system based on the internet, which are reasonable in resource distribution and smooth in network.
Disclosure of Invention
The present invention aims to provide a node resource allocation method and system based on the internet, so as to solve the problems proposed in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: the node resource allocation system comprises a network receiving module, a database module, a network state measuring and calculating module and a resource allocation module, wherein the network receiving module is used for searching network base station information and receiving a data request command sent by a client, the database module is used for storing the number of network surfing personnel in a region, caching resource data and classifying network data requests, the network state measuring and calculating module is used for measuring and calculating the congestion degree of a network channel according to the condition of the network channel, the resource allocation module is used for transmitting the network data to a mobile phone and enhancing the data transmission, the network receiving module is in data connection with the database module, and the database module is in data connection with the network state measuring and calculating module.
According to the technical scheme, the network receiving module comprises a network base station searching module and a request command receiving module, the network base station searching module is used for searching base station information in a search area, the request command receiving module is used for grabbing a command received by a base station through an open interface, and the network base station searching module is in data connection with the request command receiving module;
the database module comprises a network load counting module, a background data classifying module and a base station resource storage module, wherein the network load counting module is used for counting the demand of a network in a region, the background data classifying module is used for classifying commands captured by a base station interface, the base station resource storage module is used for caching partial data, the request command receiving module is in data connection with the background data classifying module, and the background data classifying module is in data connection with the base station resource storage module;
the network state measuring and calculating module comprises a data model analysis module and a network congestion optimization module, the data model analysis module is used for establishing a congestion state model according to time and data throughput and analyzing the congestion state model, the network congestion optimization module is used for relieving network congestion when a network channel is in a congestion state, and the data model analysis module is in data connection with the network congestion optimization module;
the resource allocation module comprises a data guiding module and a data enhancing module, the data guiding module is used for guiding base station data into regional user client equipment, the data enhancing module is used for enhancing smoothness of a server and a network of a client according to reallocation of resource nodes, and the data guiding module is in data connection with the data enhancing module.
According to the technical scheme, the method comprises the following steps:
step S1: the node resource allocation system works to search the number W of clients within Q kilometers of the coverage area of the server base station n Sending the data to a database module for storage;
step S2: the server base station starts command port scanning, classifies the received data according to the content of the command, and counts the cache content in the current server database;
and step S3: the network load module in the server calculates the real-time required communication traffic according to the number of the clients and the sent request;
and step S4: establishing a data analysis model according to the real-time communication volume and the moment, analyzing the network condition and performing certain prediction processing;
step S5: counting the data content of the temporary cache resources in the server database and the TTL value of the survival time of the temporary cache resources;
step S6: the server feeds back data according to the request command of the client, and if the server has cache of the data, the TTL value is prolonged.
According to the above technical solution, the step S2 further comprises the steps of:
step S21: capturing data of a monitoring port through a request command receiving module;
step S22: searching whether the data cache exists in the server according to the request content of the command, if so, directly sending the data cache, otherwise, requesting the transmission of the data from a higher-layer server by the server;
step S23: after the connection between the server and the client is established, the commands are stored and the request frequency E of the commands is counted.
According to the above technical solution, the step S3 further includes the steps of:
step S31: calling the number W of clients in the region at the time n ；
Step S32: estimating the flow Y of the required network channel at the moment and recording the time T;
step S33: and transmitting Y and T even to a network state measuring and calculating module.
According to the above technical solution, the step S32 further includes the following steps:
step S321: the client R sending the request at the moment is counted n Establishing N transmission nodes;
step S322: read the number of clients W n Comparing the command content with the existing cache data in the database;
step S323: if the server has a local cache, counting the node P which can be optimized;
step S324: and (3) estimating the channel flow Y, Y = S x (N-P) x 50% by measurement and calculation.
According to the above technical solution, the step S4 further includes the steps of:
step S41: establishing a network congestion condition analysis coordinate chart, setting a horizontal axis as time and a vertical axis as network usage;
step S42: transmitting the received parameters into a network congestion analysis table, and drawing a smooth curve graph;
step S43: and checking whether a point with an inflection point exceeding a threshold value U exists in the curve, if so, determining that the network is congested, otherwise, determining that the network is smooth.
According to the above technical solution, the step S5 further includes the steps of:
step S51: when the server receives a client request, checking whether a cache exists in a server base station database;
step S52: if the cache exists, prolonging the TTL value of the cache according to the classified request command frequency E, if the cache does not exist, carrying out data request to a higher-layer server by the base station server, transmitting the data to the client, caching the data in a local server, and setting an initial TTL value;
step S53: and when the TTL value is zero, the local cache of the data is cleared.
Compared with the prior art, the invention has the following beneficial effects: the invention enables limited internet resources in an area to complete more excellent network service by configuring the node resource distribution system, and in a base station area, if the number of users is relatively small, the network channel is more smooth, if the number of the users is too large, the network channel can not bear the bandwidth required by data transmission, and the link load is too large, so that the transmission performance in the network is directly reduced in a cliff-breaking manner, therefore, the congestion condition of the network channel can be analyzed in time by using the node resource distribution system in the area, internet transmission nodes are saved, the saved resource nodes are distributed to new transmission requests, and the network transmission performance is greatly improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of the system module composition of 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.
Referring to fig. 1, the present invention provides a technical solution: the node resource allocation system comprises a network receiving module, a database module, a network state measuring module and a resource allocation module, wherein the network receiving module is used for searching network base station information and receiving a data request command sent by a client, the database module is used for storing the number of people on the internet in a region, caching resource data and classifying network data requests, the network state measuring module is used for measuring and calculating the congestion degree of a network channel according to the condition of the network channel, the resource allocation module is used for transmitting the network data to a mobile phone and enhancing the data transmission, the network receiving module is in data connection with the database module, the data connection between the database module and the network state measuring module is realized, the internet resources are limited in one region, if the number of users is relatively small, the network channel is relatively large, if the users use too much, the network channel cannot bear the bandwidth required by the data transmission, the link load is too large, the transmission performance in the network is directly reduced, the node resource allocation system is used in the region, the congestion condition of the network channel can be analyzed, the new node transmission performance can be timely saved, and the new node transmission performance can be greatly improved.
The network receiving module comprises a network base station searching module and a request command receiving module, wherein the network base station searching module is used for searching base station information in a search area, the request command receiving module is used for capturing a command received by a base station through an open interface, and the network base station searching module is in data connection with the request command receiving module;
the database module comprises a network load counting module, a background data classifying module and a base station resource storage module, wherein the network load counting module is used for counting the demand of a network in a region, the background data classifying module is used for classifying commands captured by a base station interface, the base station resource storage module is used for caching partial data, the request command receiving module is in data connection with the background data classifying module, and the background data classifying module is in data connection with the base station resource storage module;
the network state measuring and calculating module comprises a data model analysis module and a network congestion optimization module, the data model analysis module is used for establishing a congestion state model according to time and data throughput and analyzing the congestion state model, the network congestion optimization module is used for relieving network congestion when a network channel is in a congestion state, and the data model analysis module is in data connection with the network congestion optimization module;
the resource allocation module comprises a data guiding module and a data enhancing module, the data guiding module is used for guiding base station data into regional user client equipment, the data enhancing module is used for enhancing smoothness of a server and a network of a client according to reallocation of resource nodes, and the data guiding module is in data connection with the data enhancing module.
The method comprises the following steps:
step S1: node resource partitioningThe matching system works to search the number of clients W within Q kilometers of the coverage area of the server base station n Sending the data to a database module for storage, wherein Q is a signal coverage range of a network database base station;
step S2: the server base station starts command port scanning, classifies the received data according to the content of the command, counts the cache content in the current server database, classifies the command, records the occurrence frequency of the command, and can acquire the reasonable cache time of the data;
and step S3: the network load module in the server calculates the real-time required communication traffic according to the number of the clients and the sent requests, the network channel is smooth and has great influence on data transmission, the transmission performance of the network data is not linear in the initial stage and stable in the saturation stage, but the cliff descends after the saturation state is reached, so that the detection of the congestion of the network channel is important;
and step S4: establishing a data analysis model according to the real-time communication volume and the moment, analyzing the network condition and performing certain prediction processing;
step S5: the data content and the TTL value of the temporary cache resources in the server database are counted, the cache resources in the server can save the transmission expense of data to a certain extent, but the resources are occupied if the resources which are not commonly used exist in the server, so the TTL value is set, the cache of the local server is dynamically updated, and the transmission efficiency is improved;
step S6: the server feeds back data according to the request command of the client, if the server has a cache of the data, the TTL value is prolonged according to the data use frequency, the use frequency is high, the time stored in the cache is long, the data does not need to be requested to a superior server, and if the use frequency is low, the data can be automatically cleared away in short time after being reused, so that a space is reserved for new resources.
Step S2 further comprises the steps of:
step S21: the data of the monitoring port is captured by the request command receiving module, the working mechanism of the server is in a port monitoring mode, and the content of the command can be checked by capturing the command of the port by the module;
step S22: searching whether the data cache exists in the server according to the request content of the command, if so, directly sending the data, otherwise, requesting the transmission of the data from a higher-layer server by the server, and requesting the upper-layer server by the local data if not;
step S23: after the connection between the server and the client is established, the command is stored and the request frequency E of the command is counted, and the request frequency is counted to prepare for adjusting the TTL value of the cache resource.
Step S3 further comprises the steps of:
step S31: calling the number W of clients in the region at the time n ；
Step S32: estimating the flow Y of the required network channel at the moment and recording the time T;
step S33: and transmitting Y and T even to a network state measuring and calculating module.
Step S32 further comprises the steps of:
step S321: the client R sending the request at the moment is counted n Establishing N transmission nodes;
step S322: read the number of clients W n Comparing the command content with existing cache data in a database;
step S323: if the server has local cache, counting the optimizable nodes P, retrieving a resource cache module in a server database, if cache data exists, saving transmission node resources in the transmission process, and directly transmitting the data to a plurality of request nodes;
step S324: the estimated channel flow Y is measured and calculated, Y = S x (N-P) x 50%, S is bandwidth required by a transmission node, the condition that a plurality of clients use the same data needs to be considered when the network channel flow is calculated, secondly, data transmission of network videos is intermittent, the data transmission is usually stopped after a section of cache is buffered, and the cache processing is carried out again when the playing is close to the end of the cache, so the channel flow is discontinuous.
Step S4 further comprises the steps of:
step S41: establishing a network congestion condition analysis coordinate chart, setting a horizontal axis as time and a vertical axis as network usage;
step S42: transmitting the received parameters into a network congestion analysis table, drawing a smooth curve graph, wherein when a network channel is transmitted and does not reach a threshold state, the network channel is generally a smooth curve model;
step S43: and checking whether a point with an inflection point exceeding a threshold value U exists in the curve, if the point with the inflection point exceeding the threshold value U exists, then network congestion occurs, otherwise, the network is judged to be smooth, and when the load of a network channel reaches the threshold value, the network performance is reduced in a cliff breaking manner, and at the moment, the inflection point value of the image is large.
Step S5 further comprises the steps of:
step S51: when the server receives a client request, checking whether a cache exists in a server base station database;
step S52: if the cache exists, according to the classified request command frequency E, prolonging the TTL value of the cache, if the cache does not exist, the base station server carries out data request to a higher-layer server, the data is transmitted to a client and cached in a local server at the same time, an initial TTL value is set, E is the repetition rate of the command received by the server base station, the TTL value is prolonged through the request frequency, the cache time in the server is increased, when the cache does not exist locally, the data is requested to the upper layer and stored in the local cache, a default TTL value is set, if the request probability and the frequency are very low, the TTL value is not prolonged, the TTL value is automatically deleted after being reset to zero, and if the frequency is higher, the TTL is prolonged, and the data existence time is prolonged;
step S53: when the TTL value is zero, the local cache of the data is cleared.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. The node resource distribution system based on the Internet comprises a node resource distribution method and a node resource distribution system of the Internet, and is characterized in that: the node resource allocation system comprises a network receiving module, a database module, a network state measuring module and a resource allocation module, wherein the network receiving module is used for searching network base station information and receiving a data request command sent by a client, the database module is used for storing the number of network surfing personnel in an area, caching resource data and classifying network data requests, the network state measuring module is used for measuring and calculating the congestion degree of a network channel according to the condition of the network channel, the resource allocation module is used for transmitting the network data to a mobile phone and enhancing the data transmission, the network receiving module is in data connection with the database module, and the database module is in data connection with the network state measuring module.
2. The internet-based node resource allocation system of claim 1, wherein: the network receiving module comprises a network base station searching module and a request command receiving module, the network base station searching module is used for searching base station information in a search area, the request command receiving module is used for grabbing a command received by a base station through an open interface, and the network base station searching module is in data connection with the request command receiving module;
the database module comprises a network load counting module, a background data classifying module and a base station resource storage module, wherein the network load counting module is used for counting the demand of a network in a region, the background data classifying module is used for classifying commands captured by a base station interface, the base station resource storage module is used for caching partial data, the request command receiving module is in data connection with the background data classifying module, and the background data classifying module is in data connection with the base station resource storage module;
the network state measuring and calculating module comprises a data model analysis module and a network congestion optimization module, the data model analysis module is used for establishing a congestion state model according to time and data throughput and analyzing the congestion state model, the network congestion optimization module is used for relieving network congestion when a network channel is in a congestion state, and the data model analysis module is in data connection with the network congestion optimization module;
the resource allocation module comprises a data drainage module and a data enhancement module, the data drainage module is used for introducing base station data into regional user client equipment, the data enhancement module is used for enhancing the smoothness of a server and a client network according to the reallocation of resource nodes, and the data drainage module is in data connection with the data enhancement module.
3. The internet-based node resource allocation method according to claims 1-2, wherein: the method comprises the following steps:
step S1: the node resource allocation system works to search the number W of clients within Q kilometers of the coverage area of the server base station n Sending the data to a database module for storage;
step S2: the server base station starts command port scanning, classifies the received data according to the content of the command, and counts the cache content in the current server database;
and step S3: the network load module in the server calculates the real-time required communication traffic according to the number of the clients and the sent request;
and step S4: establishing a data analysis model according to the real-time communication volume and the time, analyzing the network condition and performing certain prediction processing;
step S5: counting the data content of the temporary cache resources in the server database and the TTL value of the survival time of the temporary cache resources;
step S6: the server feeds back data according to the request command of the client, and if the server has cache of the data, the TTL value is prolonged.
4. The internet-based node resource allocation method of claim 3, wherein: the step S2 further includes the steps of:
step S21: capturing data of a monitoring port through a request command receiving module;
step S22: searching whether the data cache exists in the server according to the request content of the command, if so, directly sending the data cache, otherwise, requesting the transmission of the data from a higher-layer server by the server;
step S23: after the connection between the server and the client is established, the commands are stored and the request frequency E of the commands is counted.
5. The internet-based node resource allocation method of claim 4, wherein: the step S3 further includes the steps of:
step S31: calling the number W of clients in the region at the time n ；
Step S32: estimating the flow Y of the required network channel at the moment and recording the time T;
step S33: and transmitting Y and T even to a network state measuring and calculating module.
6. The internet-based node resource allocation method of claim 5, wherein: the step S32 further includes the steps of:
step S321: the client R sending the request at the moment is counted n Establishing N transmission nodes；
Step S322: read the number of clients W n Comparing the command content with the existing cache data in the database;
step S323: if the server has a local cache, counting the node P which can be optimized;
step S324: and (3) estimating the channel flow Y, Y = S x (N-P) x 50% by measurement and calculation.
7. The internet-based node resource allocation method of claim 6, wherein: the step S4 further includes the steps of:
step S41: establishing a network congestion condition analysis coordinate chart, setting a horizontal axis as time and a vertical axis as network usage;
step S42: transmitting the received parameters into a network congestion analysis table, and drawing a smooth curve graph;
step S43: and checking whether a point with an inflection point exceeding a threshold value U exists in the curve, if so, determining that the network is congested, otherwise, determining that the network is smooth.
8. The internet-based node resource allocation method of claim 7, wherein: the step S5 further includes the steps of:
step S51: when the server receives a client request, checking whether a cache exists in a server base station database;
step S52: if the cache exists, prolonging the TTL value of the cache according to the classified request command frequency E, if the cache does not exist, carrying out data request to a higher-layer server by the base station server, transmitting the data to the client, caching the data in a local server, and setting an initial TTL value;
step S53: and when the TTL value is zero, the local cache of the data is cleared.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211327930.9A CN115914111A (en) | 2022-10-27 | 2022-10-27 | Node resource allocation method and system based on Internet |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211327930.9A CN115914111A (en) | 2022-10-27 | 2022-10-27 | Node resource allocation method and system based on Internet |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115914111A true CN115914111A (en) | 2023-04-04 |
Family
ID=86471940
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211327930.9A Pending CN115914111A (en) | 2022-10-27 | 2022-10-27 | Node resource allocation method and system based on Internet |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115914111A (en) |
-
2022
- 2022-10-27 CN CN202211327930.9A patent/CN115914111A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US8270315B2 (en) | Method and apparatus for relating to quality of service in wireless | |
| US9065809B2 (en) | Method and node for distributing electronic content in a content distribution network | |
| CN103889006B (en) | QoS measurements and monitoring in the wireless network of Differentiated Services | |
| US8346758B2 (en) | Method and system for transmitting a query in a wireless network | |
| CN111756848B (en) | QoS optimization method based on federal learning and mobile perception under mobile edge environment | |
| CN109348264B (en) | Video resource sharing method and device, storage medium and electronic equipment | |
| US11863436B2 (en) | Intelligent internet traffic routing | |
| Balasubramanian et al. | Edge-boost: Enhancing multimedia delivery with mobile edge caching in 5G-D2D networks | |
| US20220321486A1 (en) | Controlling data rate based on domain and radio usage history | |
| US9621438B2 (en) | Network traffic management | |
| CN115914111A (en) | Node resource allocation method and system based on Internet | |
| CN111278039B (en) | User perception suppression identification method, device, equipment and medium | |
| CN114124661A (en) | Power grid Ka high-flux satellite network communication fault determination method and device | |
| CN112003921B (en) | Method for actively caching and replacing hot data in edge computing environment | |
| WO2020244283A1 (en) | Video service processing method, system, and device | |
| CN109121073B (en) | Method, device and equipment for monitoring quality of mobile communication service | |
| CN115460451B (en) | Video playing method and device, storage medium and electronic device | |
| CN115051996B (en) | Video cache management method based on local video utility value under multi-access edge calculation | |
| CN115633390B (en) | IAB base station-based mobile network access regulation and control method and system | |
| Liu et al. | Resource Pre-caching Strategy of Digital Twin System Based on Hierarchical MEC Architecture | |
| Adipat et al. | A real-time adaptive traffic monitoring approach for multimedia content delivery in wireless environment | |
| KR20150046566A (en) | Method and apparatus for servicing video based on network quality | |
| CN117478636A (en) | Domain name resolution scheduling method, device, network equipment and readable storage medium | |
| CN117914883A (en) | Cloud network comprehensive performance and performance evaluation system and method | |
| KR20150046663A (en) | System and method for caching web contents |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |