CN116506507A - Data processing method based on client characteristics - Google Patents
Data processing method based on client characteristics Download PDFInfo
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- CN116506507A CN116506507A CN202310776910.8A CN202310776910A CN116506507A CN 116506507 A CN116506507 A CN 116506507A CN 202310776910 A CN202310776910 A CN 202310776910A CN 116506507 A CN116506507 A CN 116506507A
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- 238000003672 processing method Methods 0.000 title claims abstract description 12
- 230000005540 biological transmission Effects 0.000 claims abstract description 19
- 238000004891 communication Methods 0.000 claims abstract description 15
- 238000000034 method Methods 0.000 claims description 13
- 230000010365 information processing Effects 0.000 abstract description 4
- 238000012545 processing Methods 0.000 description 6
- 238000012544 monitoring process Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1004—Server selection for load balancing
- H04L67/1008—Server selection for load balancing based on parameters of servers, e.g. available memory or workload
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
- H04L67/1001—Protocols in which an application is distributed across nodes in the network for accessing one among a plurality of replicated servers
- H04L67/1004—Server selection for load balancing
- H04L67/1021—Server selection for load balancing based on client or server locations
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/52—Network services specially adapted for the location of the user terminal
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
- H04L67/61—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources taking into account QoS or priority requirements
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- 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
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- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Computer And Data Communications (AREA)
Abstract
The invention provides a data processing method based on client characteristics, which is applied to an information system comprising a cloud service layer, an edge service layer and an end user layer, and is used for calculating communication frequency and data transmission quantity according to historical communication records of clients installed by terminals in the end user layer and judging the type of the client; determining the priority service type of each edge server according to the resources of each edge server in the edge service layer; according to the client type of the terminal, the priority service type of the edge server, the load and the distance of the edge server, the weight of the edge server is calculated, and the edge server is matched with the terminal. The invention classifies the clients according to the communication frequency and the data transmission quantity and matches the clients with the corresponding edge servers, thereby improving the information processing efficiency and saving the computing resources.
Description
Technical Field
The invention belongs to the technical field of information, and particularly relates to a data processing method based on client characteristics.
Background
The Bian Yun collaborative computing system in the prior art comprises a cloud service layer, an edge service layer and an end user layer, and is interconnected and intercommunicated through the Internet.
The cloud service layer comprises a cloud service center and is composed of a plurality of isomorphic or heterogeneous hardware such as computing, storage, network and the like. The cloud service center provides high-performance, high-reliability and extensible resources by using virtualization, software defined networking, redundancy and other technologies so as to support various on-demand services for users. A Control Flow (CF) is generated between the cloud service layer and the edge service layer. The cloud service center predicts task requests received by each edge server from a local user side, and pushes resources required by task operation (including software required by task operation and software dependence, abbreviated as task resources) to the edge server through the CF in advance according to a prediction result. The cloud service center monitors task processing and resource use of the edge servers in real time, gathers the task processing and resource use conditions of the edge servers, and sends the task processing and resource use conditions to the edge servers through the CF.
The edge service layer is composed of a plurality of edge servers with limited resources and dispersed geographic positions, and provides real-time, rapid, various and flexible network application for the user end of the end user layer.
The Bian Yun collaborative computing system does not distinguish between the clients, but in fact, in some application fields, especially in the smart city field, the non-distinction between the clients results in low information processing efficiency and serious waste of computing resources.
Disclosure of Invention
The invention provides a data processing method based on client characteristics, which is used for distinguishing clients with large variability, improving information processing efficiency and saving computing resources.
In order to achieve the above purpose, the technical scheme of the invention is realized as follows:
a data processing method based on client characteristics is applied to an information system comprising a cloud service layer, an edge service layer and an end user layer, and comprises the following steps:
calculating communication frequency and data transmission quantity according to historical communication records of clients installed by all terminals in a terminal user layer, and judging the type of the client to which the terminals belong;
determining the priority service type of each edge server according to the resources of each edge server in the edge service layer;
according to the client type of the terminal, the priority service type of the edge server, the load and the distance of the edge server, the weight of the edge server is calculated, and the edge server is matched with the terminal.
Further, the client type includes type 1: high frequency low amount, type 2: high frequency high volume, type 3: low frequency high, type 4: low frequency low amount; the priority service type includes a priority service type 2 client type, a priority service type 3 client type, and priority service types 1 and 4 client types.
Further, the method for calculating the communication frequency and the data transmission quantity comprises the following steps:
step 11, obtaining the latest n records according to the communication records of the client and the cloud service layer;
step 12, calculating the time interval between the last time and the first time of the n times of records, dividing by n to obtain a frequency value, comparing the frequency value with a first threshold value, and marking as high frequency if the frequency value is smaller than the first threshold value; otherwise, marking as low frequency;
step 13, calculating the total quantity of the n recorded transmission data quantities, dividing the total quantity by n to obtain an average transmission data quantity, comparing the average transmission data quantity with a second threshold value, and marking the average transmission data quantity as high quantity if the average transmission data quantity is larger than the second threshold value; otherwise, marking as low;
and step 14, determining the type of the client according to the two marks.
Further, the specific method for matching the edge server comprises the following steps:
the method comprises the steps that edge servers within a certain distance are eliminated, the load of each edge server exceeds a preset threshold, the distance of each remaining edge server is set to be D, the load is set to be L, and the type is matched to be Q; weight = 1/(l×d×q); wherein, the client type of the terminal corresponds to the priority service type of the edge server, q=1, and q=2 if not;
and selecting the edge server with the largest weight to match the terminal.
Or further, the specific method for matching the edge server comprises the following steps:
edge servers within a certain distance exclude edge servers with load exceeding a preset threshold and edge servers with insufficient reservation of available resources; each of the rest edge servers is provided with a distance D, a load L and a type matching Q; then weight = 1/(L) 2 * D x Q); wherein, the client type of the terminal corresponds to the priority service type of the edge server, q=1, and q=2 if not;
and selecting the edge server with the largest weight to match the terminal.
Still further, the certain distance is:
inquiring a server closest to the client from a geographic position database, wherein the distance between the server and the client is m; and setting the distance which is a multiple of m to the client as the certain distance.
Compared with the prior art, the invention has the following beneficial effects:
the invention classifies the clients according to the communication frequency and the data transmission quantity and matches the clients with the corresponding edge servers, thereby improving the information processing efficiency and saving the computing resources.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of the invention.
Detailed Description
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
For the purpose of making the objects and features of the present invention more comprehensible, embodiments accompanied with figures are described in detail below.
In this embodiment, an information system in the smart city field is taken as an example, where the information system at least includes a cloud service layer, an edge service layer, and an end user layer, and are mutually connected through a network.
At the end user layer of the information system, there are a number of different client types for each terminal, including but not limited to the following:
1. smart phone client: smartphones are an indispensable tool in people's life, and many services of smart city information systems can be provided through smartphone clients.
2. Television set-top box client: the television set top box can directly present the service provided by the smart city information system on the television, and more convenient user experience is provided.
3. Tablet computer client: the tablet personal computer has portability and larger screen size, and is suitable for users to use the smart city information system outdoors or during traveling.
A pc client: PC clients are suitable for some scenarios that require complex operations or processing of large amounts of data, such as city planning, traffic management, etc.
5. Client of Internet of things equipment: the internet of things equipment is an important component of a smart city information system, and equipment such as smart homes, smart street lamps and the like can be used as clients to interact with the system.
For information systems in smart cities, clients are concerned in two aspects, the first is the frequency of transmission and the second is the amount of data transmitted. Since these two points directly determine the amount of resource data and the type of resource that needs to be prepared for the client. From these two dimensions, terminals are divided into 4 types:
type 1, high frequency low volume (internet of things device);
type 2, high frequency high volume (audio video monitoring class);
type 3, low frequency high volume (data processing class);
type 4 low frequency low quantity (other types).
Based on the foregoing, the main flow steps of the method proposed in this embodiment are shown in fig. 1, and include:
step 1, a cloud platform of a cloud service layer determines a terminal type according to a historical communication record of a client; the specific implementation method is as follows:
step 11, obtaining the latest n records according to the communication record of the client in the cloud platform;
step 12, calculating the time interval between the last time and the first time of the n times of records to be divided by n to obtain a frequency value, comparing the frequency value with a first threshold value, and marking the frequency value as a high-frequency client if the frequency value is smaller than the first threshold value; otherwise, marking as a low-frequency client;
step 13, calculating the total quantity of the n recorded transmission data quantities to divide by n to obtain an average data quantity, comparing the average data quantity with a second threshold value, and marking the average data quantity as a high-quantity client if the average data quantity is larger than the second threshold value; otherwise, marking as a low-volume client;
step 14, judging which type the client belongs to according to the two marking values, namely the client belongs to the type 1, namely the high-frequency low-volume (Internet of things equipment); type 2, high frequency high volume (audio video monitoring class); type 3, low frequency high volume (data processing class); type 4, which of low frequency and low amount (other types);
step 2, the cloud platform determines the priority service type of the edge server according to the resource type of the edge server; the method comprises the following specific steps:
setting an edge server with large memory capacity as a priority service type 2 client;
setting an edge server with large cup capacity as a priority service type 3 client;
servers other than the above two servers are set as clients of the priority service types 1 and 4.
Step 3, the cloud platform collects the geographic position information of all edge servers and stores the geographic position information in a geographic position database;
and 4, when the client requests access to the cloud platform, the cloud platform calculates the weight of the edge server according to the type of the client of the terminal, the priority service type of the edge server, the load and the distance of the edge server, and matches the edge server for the terminal.
The specific flow is as follows:
step 41: according to the position of the client and the allowable maximum distance between the server and the client preset by the system, all servers with the distance D smaller than the maximum distance from the client are inquired from geographic position data and used as alternative servers;
step 42: analyzing the alternative servers according to the monitored load (the load measuring method is that the ratio of the used computing resources to all the available computing resources is marked as L) of each server, and if the load exceeds a preset threshold, eliminating the range of the alternative servers;
step 43: according to the remaining alternative servers in step 62, weights are calculated according to the following three parameters: the distance D, load L and type match Q (e.g., type matching server assignment q=1, type non-matching server assignment q=2), all three of which are inversely related to the final weight, i.e., meet the spirit of the present invention without specific requirements. The preferred scheme is weight=1/(l×d×q).
If the situation that the consumption of certain internet of things terminals to server resources is overlarge is considered, and therefore the distribution of the edge servers is comprehensively considered, the specific flow is as follows:
step 51: the edge server periodically checks available resources and reports the resources to the cloud service;
step 52: and the cloud server determines the available resource ratio of each edge server according to the ratio of the available resources to the total resources of each edge server, and if the available resource ratio is lower than a certain threshold, the cloud server determines that the available resources of the edge server are insufficient in reservation. The insufficient reservation of available resources is not yet reached to the level of overload in step 42. For example, a residual resource of less than 5% is considered to be overloaded and a residual resource of less than 20% is considered to be an insufficient reservation of available resources.
Step 53: and according to the position of the client and the allowable maximum distance between the server and the client preset by the system, all servers with the distance D smaller than the maximum distance from the client are inquired from the geographic position data and used as alternative servers.
Step 54: the cloud server analyzes the value of the server resource required by the client according to the historical communication condition of the client, and if the value does not exceed the preset threshold, the cloud server processes the value according to the steps 42-43; otherwise, step 55 is performed.
Step 55: edge suit with insufficient reservation of available resourcesThe server excludes the candidate server and calculates the weights according to the following three parameters: distance D, load L, and type match Q (e.g., type match server assignment q=1, type no match server assignment q=2). Wherein the load is more demanding, i.e. the importance of the load is higher than the other two parameters. Preferably, the weight=1/(L) 2 * D x Q). Therefore, the edge server with more residual resources can be preferentially allocated to the terminals with large resource requirements.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.
Claims (6)
1. The data processing method based on the client characteristics is applied to an information system comprising a cloud service layer, an edge service layer and an end user layer, and is characterized by comprising the following steps:
calculating communication frequency and data transmission quantity according to historical communication records of clients installed by all terminals in a terminal user layer, and judging the type of the client to which the terminals belong;
determining the priority service type of each edge server according to the resources of each edge server in the edge service layer;
according to the client type of the terminal, the priority service type of the edge server, the load and the distance of the edge server, the weight of the edge server is calculated, and the edge server is matched with the terminal.
2. The data processing method based on the client characteristics according to claim 1, wherein the client type includes type 1: high frequency low amount, type 2: high frequency high volume, type 3: low frequency high, type 4: low frequency low amount; the priority service type includes a priority service type 2 client type, a priority service type 3 client type, and priority service types 1 and 4 client types.
3. The data processing method based on the client characteristics according to claim 1 or 2, wherein the method of calculating the communication frequency and the data transmission amount includes:
step 11, obtaining the latest n records according to the communication records of the client and the cloud service layer;
step 12, calculating the time interval between the last time and the first time of the n times of records, dividing by n to obtain a frequency value, comparing the frequency value with a first threshold value, and marking as high frequency if the frequency value is smaller than the first threshold value; otherwise, marking as low frequency;
step 13, calculating the total quantity of the n recorded transmission data quantities, dividing the total quantity by n to obtain an average transmission data quantity, comparing the average transmission data quantity with a second threshold value, and marking the average transmission data quantity as high quantity if the average transmission data quantity is larger than the second threshold value; otherwise, marking as low;
and step 14, determining the type of the client according to the two marks.
4. The data processing method based on the client characteristics according to claim 1, wherein the specific method for matching the edge server comprises:
the method comprises the steps that edge servers within a certain distance are eliminated, the load of each edge server exceeds a preset threshold, the distance of each remaining edge server is set to be D, the load is set to be L, and the type is matched to be Q; weight = 1/(l×d×q); wherein, the client type of the terminal corresponds to the priority service type of the edge server, q=1, and q=2 if not;
and selecting the edge server with the largest weight to match the terminal.
5. The data processing method based on the client characteristics according to claim 1, wherein the specific method for matching the edge server comprises:
edge servers within a certain distance exclude edge servers with load exceeding a preset threshold and edge servers with insufficient reservation of available resources; each of the rest edge servers is provided with a distance D, a load L and a type matching Q; then weight = 1/(L) 2 * D x Q); wherein, the client type of the terminal corresponds to the priority service type of the edge server, q=1, and q=2 if not;
and selecting the edge server with the largest weight to match the terminal.
6. The data processing method based on the client characteristics according to claim 4 or 5, wherein the certain distance is:
inquiring a server closest to the client from a geographic position database, wherein the distance between the server and the client is m; and setting the distance which is a multiple of m to the client as the certain distance.
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