CN113919412A - Data distribution method, equipment and storage medium - Google Patents
Data distribution method, equipment and storage medium Download PDFInfo
- Publication number
- CN113919412A CN113919412A CN202111025736.0A CN202111025736A CN113919412A CN 113919412 A CN113919412 A CN 113919412A CN 202111025736 A CN202111025736 A CN 202111025736A CN 113919412 A CN113919412 A CN 113919412A
- Authority
- CN
- China
- Prior art keywords
- data
- node
- state
- basic
- nodes
- 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.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
-
- 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
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a data distribution method, equipment and a storage medium, wherein the method comprises the steps of receiving original data and extracting basic characteristics corresponding to the original data; matching the basic features with a basic feature library, and if the matching is successful, adding the original data into a cache queue corresponding to a node to which the matched basic feature library belongs; otherwise, adding the basic features into the basic feature library of the corresponding child nodes according to a preset configuration rule; and when the data meet the distribution requirement, distributing the buffer queue to the corresponding processing board. The method utilizes the basic characteristic representation data of the data as the data division basis of each distributed sub-node, thereby ensuring clear and reliable data division; the central node is used for making the working state of the stator node, and dynamically dividing the data, so that the data processing efficiency is improved; and the software running state is monitored in real time by using the state feedback of the software of each child node, so that the software is ensured to be in an effective processing capacity range, and the system running efficiency is further improved.
Description
Technical Field
The invention belongs to the technical field of data management and distribution, and particularly relates to a data distribution method, data distribution equipment and a storage medium.
Background
Under the background of current big data, each part of a large-scale information system gradually adopts a structured and easily-expanded assembly mode based on functions, distributed software deployment is beneficial to software development and maintenance, and the distributed software deployment is widely used in the computer fields of communication, big data development, artificial intelligence and the like.
The data distribution method has great influence on data transmission of distributed software deployment. In the traditional method, the point-to-point data communication protocol based on UDP and TCP/IP is a common data communication protocol, and is a bottom layer protocol in link communication. The point-to-point communication method is widely applied, but cannot give consideration to both transmission efficiency and reliability, and meanwhile, for transmission stability, a complex package is often designed, which is not beneficial to secondary development. Therefore, mature data transmission methods based on UDP and TCP/IP are gaining wide attention. Such as a Client-server (C/S) structure, can simultaneously meet the communication requirements of a plurality of clients and a unified server. The C/S structure communication mode is a request-response mode, is suitable for a data centralized communication framework such as a database and the like, but is not efficient and has time delay for the application mode of a plurality of information nodes. In a publish-subscribe (P/S) mode, information is only transmitted between publishers and subscribers, which does not have a C/S centralization feature, but still has the problem that a data subscriber receives valid data without considering the processing burden of the subscriber, thereby reducing the software operating efficiency and causing data accumulation, so that real-time data processing service cannot be achieved. At this time, the distribution completes the data transmission, but loses the operation efficiency of the distributed software as a whole.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides the invention name, dynamically plans data division based on the working states of different node processing software, obtains high-efficiency distribution of data, improves the transmission efficiency, ensures the transmission real-time performance and optimizes the processing performance of sub-nodes; furthermore, the running state of the child node is separated from the working state recorded by the central node, the software structure is clear, and frequent distribution switching caused by different state changes is avoided; and extracting basic data characteristics at the distribution node as a data division rule to avoid data redundant transmission.
The purpose of the invention is realized by the following technical scheme:
a method for data distribution, comprising the steps of:
receiving original data and extracting corresponding basic features of the original data;
matching the basic features with a basic feature library, and if the matching is successful, adding the original data into a cache queue corresponding to a node to which the matched basic feature library belongs; otherwise, adding the basic features into the basic feature library of the corresponding child nodes according to a preset configuration rule;
and when the data meet the distribution requirement, distributing the buffer queue to the corresponding processing board.
Further, the matching specifically includes distance measurement, similarity is calculated within a threshold preset in the basic feature library, and a child node closest to the basic feature is searched.
Furthermore, during data distribution, supervision decision is made on the data distribution state.
Further, the supervision decision comprises supervision decision for the operation state and the working state of each node.
Further, the operation state and working state supervision decision comprises the following steps:
receiving and reporting an operation state;
classifying the operation states, including an idle processing state, a normal processing state and a busy processing state;
and executing operation state conversion on the nodes in the idle processing state and the busy processing state, so that the nodes in the idle processing state and the busy processing state are converted into the nodes in the normal processing state.
Further, the classifying the operation state specifically includes classifying the operation state according to the current CPU, the memory usage rate, the number of input data, and the number of caches.
Further, the operation state conversion specifically includes:
if the nodes in the idle processing state have no data deletion or characteristic deletion, the nodes can insert the basic characteristics transferred by other nodes; if the data or the characteristic is deleted, the basic characteristic transferred by other nodes is not received, so that the quantity of data deletion is reduced or the deleted basic characteristic is retrieved;
if there is no available idle processing state node and there is no data deletion or feature deletion in the normal processing state node, the basic feature transferred by other nodes can be inserted;
if the characteristic insertion of other nodes does not exist, the basic characteristic is transferred to the node in an idle processing state or the node in a normal processing state; and if no node capable of receiving the basic characteristics exists, deleting the data.
Further, if any node does not report the running state within the preset time, the basic characteristics of the node are transferred to other nodes in an idle processing state or nodes in a normal processing state.
In another aspect, the present application provides a computer device comprising a processor and a memory, wherein the memory stores a computer program, and the computer program is loaded and executed by the processor to implement any one of the data distribution methods described above.
In another aspect, the present application provides a computer-readable storage medium having a computer program stored therein, the computer program being loaded and executed by a processor to implement any one of the data distribution methods described above.
The invention has the beneficial effects that:
(1) the data are divided by using the extracted basic characteristics, the data redundancy of the traditional distribution method is solved, the data in different ranges are processed by the sub-nodes, and the data division is clear and reliable; the state feedback of the child nodes is utilized to provide a state decision method, the running state of the nodes is monitored, the data distribution strategy can be dynamically adjusted, the problem that the traditional method only focuses on data transmission and does not consider the running pressure of software is solved, and the running capacity of the child nodes is in an effective processing range; the data distribution method and the state decision method are cooperatively operated, so that the data transmission efficiency is improved, and the operation efficiency of the whole distributed system is improved.
(2) Compared with the traditional bottom layer transmission methods (UDP, TCP/IP), the method has complex development, does not consider the bottom layer communication protocol, and realizes multipoint communication on the basis.
(3) Compared with a point-to-point transmission method, a Client-server (C/S), a publishing-subscribing (P/S) and other secondary development methods, the method only pays attention to data transmission and does not consider software pressure, and the method divides data by combining the operation states of the sub-nodes to realize efficient transmission.
(4) Compared with the traditional method which cannot predict software blockage and causes the system to have time delay, the method further enables the software to efficiently process data through data division, and ensures the real-time performance of the whole system.
Drawings
Fig. 1 is a block diagram of a data distribution step of a data distribution method according to an embodiment of the present invention;
fig. 2 is a block diagram of a flow of a status supervision decision step of a data distribution method according to an embodiment of the present invention.
Fig. 3 is a software framework diagram of a data distribution method according to an embodiment of the present invention.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
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.
In order to distribute data of a distributed system when the data amount is large, the data distribution method provided by the embodiment mainly includes two parts: data distribution management and state supervision decisions. The data distribution management is responsible for creating a basic feature library, adding new features and finishing data distribution; the state supervision decision is responsible for adjusting the base feature library based on the child node state.
Fig. 3 is a schematic diagram of a software framework of a data distribution method according to this embodiment. The data distribution system specifically comprises a data distribution center node and N data sub-nodes. The data distribution center node acquires original data through the link and distributes the data to the N child nodes through the link. When receiving data distributed by the data distribution center node, the child nodes also return the states of the respective nodes, and the state supervision decision component of the data distribution center node receives the states of the nodes and adjusts the basic characteristics of the nodes according to the state information of the child nodes, so that dynamic planning of the data is realized, and the data distribution process is influenced.
As shown in fig. 1, it is a flow chart diagram of a data distribution step of a data distribution method provided in this embodiment, and the method specifically includes the following steps:
the method comprises the following steps: receiving original data x and extracting corresponding basic features of the original data. For the input data x, corresponding basic features y are extracted, and the extraction mode of the basic features is not limited here. In one embodiment, for pulse data, frequency, PRI, etc. information may be extracted as the base features.
Step two: judging a basic feature library to which the basic feature y belongs, namely matching the basic feature y with each node feature library, if the matching is successful, obtaining a node to which the original data x belongs, and adding the node into a corresponding cache queue; otherwise, adding the basic characteristics y into the corresponding characteristic library according to the characteristic range of the child nodes set by the configuration rule. The configuration rule is a basic feature range to be processed by each node.
In a specific embodiment, the matching method of the basic feature y and the basic feature library is distance measurement, and within the threshold value thre, the child node most similar to the basic feature y is found, and the similarity calculation method is as follows.
Where simi is the similarity, and t represents the feature stored in the feature library.
Step three: and when the data meet the distribution requirement, issuing the data meeting the requirement, and distributing the cache queue to the corresponding processing board. If the data does not meet the distribution condition, the data still waits for distribution in the buffer queue.
In one embodiment, when data is distributed, the data distribution state is also supervised and decided. The state supervision decision comprises an operation state supervision decision and a working state supervision decision.
Fig. 2 is a block diagram of a flow of a status supervision decision step of a data distribution method according to an embodiment.
The state decision flow is specifically as follows:
firstly, receiving the running state reported by each child node.
And secondly, classifying the received operation states of the child nodes.
In one embodiment, the run states are divided into an idle processing state, a normal operating state, and a processing busy state. The idle processing state indicates that the node has a large margin of the current processing capacity, has less input data and can receive new data. The normal working state indicates that the node can process the input data, no data accumulation exists, the input data is moderate, and the data can be properly inserted. The busy state indicates that the node has more current cache data and cannot process the data in time, the input data needs to be divided again, and the input data needs to be reduced. In this embodiment, state identifiers are added to various states, an identifier of an idle processing state is RUN _ FREE, an identifier of a NORMAL operating state is RUN _ NORMAL, and an identifier of a BUSY processing state is RUN _ BUSY.
The node running state is judged by the CPU and the memory utilization rate of the current program, the number of input data, the number of caches and the like. When in RUN _ BUSY state, input needs to be processed to this node. The specific operation is selected according to the working state of the child nodes recorded by the central node. If the running state is not reported for a long time, the data distribution center node considers that the corresponding child node does not work, namely, is in a 'death state'.
The data distribution center node also records the working state of each child node, and in a specific embodiment, the working state includes a default state, a feature deletion state, an insertion state and a data deletion state. The default state indicates that the node only processes data divided by the configuration file in normal work and does not delete the data, and the node is in an initial state and works normally; the characteristic deletion state represents that the basic characteristic library of the child node deletes the configured basic characteristic and has basic characteristic deletion; the insertion state represents the basic characteristics of the configuration of other nodes inserted into the child node, and the basic characteristics of other nodes are inserted into the child node; the data deleting state means that only a certain amount of data is processed, other data is deleted, one beat of data cannot be completely processed, and redundant data is deleted.
For different operation states of the child nodes, different processing measures are adopted, including basic feature transfer (deletion, insertion), data deletion, basic feature restoration (deletion, insertion) and data restoration (non-deletion). When the basic characteristic operation is carried out, if the idle node and the normal node exist at the same time, the idle node is preferentially used. And when the idle state does not exist, the normal node is reused for operation.
In a specific embodiment, the processing measures taken for the child nodes in different operating states are as follows:
for the nodes in the idle processing state, if no data deletion or characteristic deletion exists, the basic characteristics transferred by other nodes can be inserted; if the data or the characteristics are deleted, the basic characteristics transferred by other nodes are not received, and the quantity of data deletion is reduced or the deleted basic characteristics are retrieved.
For a node in a normal processing state, if there is no available idle processing state node and there is no data deletion or feature deletion in the node in the normal processing state, the basic feature transferred by another node may be inserted.
For a node in a busy processing state, if feature insertion of other nodes exists, returning the basic features which do not belong to the node, and if feature insertion of other nodes does not exist, transferring the basic features to the node in an idle processing state or the node in a normal processing state; and if no node capable of receiving the basic characteristics exists, deleting the data.
According to the situation after each treatment, the working state recorded by the central node is changed: if the child node has data deletion, entering a data deletion state; if the characteristics of the user are deleted, judging that the working state is a characteristic deletion state; if the basic characteristics of other nodes are inserted, the working state is changed into an insertion state; none exist as default operating states. If the child node with the dead running state appears, the basic characteristics of the child node are transferred to other idle or normal child nodes.
According to the data distribution method provided by the embodiment, the extracted basic features are used for dividing data, so that the data redundancy of the traditional distribution method is solved, the sub-nodes process data in different ranges, and the data division is clear and reliable; the state feedback of the child nodes is utilized to provide a state decision method, the running state of the nodes is monitored, the data distribution strategy can be dynamically adjusted, the problem that the traditional method only focuses on data transmission and does not consider the running pressure of software is solved, and the running capacity of the child nodes is in an effective processing range; the data distribution method and the state decision method are cooperatively operated, so that the data transmission efficiency is improved, and the operation efficiency of the whole distributed system is improved.
Compared with the traditional bottom layer transmission method (UDP, TCP/IP), the data distribution method provided by the embodiment is complex in development, does not consider the bottom layer communication protocol, and realizes multipoint communication on the basis.
Compared with a point-to-point transmission method, a Client-server (C/S) and a 'publish-subscribe' (P/S) secondary development method only emphasizes data transmission and does not consider software pressure, the data distribution method provided by the embodiment combines the operation state of the child nodes to divide data, and efficient transmission is achieved.
Compared with the traditional method that software blockage cannot be predicted so that the system has time delay, the data distribution method provided by the embodiment further enables software to efficiently process data through data division, and the real-time performance of the whole system is guaranteed.
Example 2
The preferred embodiment provides a computer device, which can implement the steps in any embodiment of the data distribution method provided in the embodiment of the present application, and therefore, the beneficial effects of the data distribution method provided in the embodiment of the present application can be achieved, for details, see the foregoing embodiment, and are not described herein again.
Example 3
It will be understood by those skilled in the art that all or part of the steps of the methods of the above embodiments may be performed by instructions or by associated hardware controlled by the instructions, which may be stored in a computer readable storage medium and loaded and executed by a processor. To this end, the present invention provides a storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps of any embodiment of a data distribution method provided by the present invention.
Wherein the storage medium may include: read Only Memory (ROM), Random Access Memory (RAM), magnetic or optical disks, and the like.
Since the instructions stored in the storage medium can execute the steps in any data distribution method embodiment provided in the embodiments of the present invention, the beneficial effects that can be achieved by any data distribution method provided in the embodiments of the present invention can be achieved, for details, see the foregoing embodiments, and are not described herein again.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. A method for data distribution, comprising the steps of:
receiving original data and extracting corresponding basic features of the original data;
matching the basic features with a basic feature library, and if the matching is successful, adding the original data into a cache queue corresponding to a node to which the matched basic feature library belongs; otherwise, adding the basic features into the basic feature library of the corresponding child nodes according to a preset configuration rule;
and when the data meet the distribution requirement, distributing the buffer queue to the corresponding processing board.
2. The data distribution method according to claim 1, wherein the matching specifically includes distance measurement, similarity is calculated within a threshold preset in the basic feature library, and a child node closest to the basic feature is searched.
3. A data distribution method according to claim 1, characterized in that at the time of data distribution, a supervised decision is made on the data distribution status.
4. A data distribution method according to claim 3, wherein the supervision decision comprises a supervision decision on the operational state and operational state of each node.
5. A data distribution method according to claim 4, wherein said operational state and working state supervision decisions comprise the steps of:
receiving and reporting an operation state;
classifying the operation states, including an idle processing state, a normal processing state and a busy processing state;
and executing operation state conversion on the nodes in the idle processing state and the busy processing state, so that the nodes in the idle processing state and the busy processing state are converted into the nodes in the normal processing state.
6. The data distribution method according to claim 5, wherein the classifying the operation state specifically comprises classifying the operation state according to a current CPU, a memory usage rate, a number of input data, and a number of caches.
7. The data distribution method of claim 5, wherein the running state transition specifically comprises:
if the nodes in the idle processing state have no data deletion or characteristic deletion, the nodes can insert the basic characteristics transferred by other nodes; if the data or the characteristic is deleted, the basic characteristic transferred by other nodes is not received, so that the quantity of data deletion is reduced or the deleted basic characteristic is retrieved;
if there is no available idle processing state node and there is no data deletion or feature deletion in the normal processing state node, the basic feature transferred by other nodes can be inserted;
if the characteristic insertion of other nodes does not exist, the basic characteristic is transferred to the node in an idle processing state or the node in a normal processing state; and if no node capable of receiving the basic characteristics exists, deleting the data.
8. The data distribution method of claim 5, wherein if any node does not report the operation state within a preset time, the basic feature of the node is transferred to other nodes in an idle processing state or nodes in a normal processing state.
9. A computer device comprising a processor and a memory, the memory having stored therein a computer program that is loaded and executed by the processor to implement a data distribution method as claimed in any one of claims 1 to 8.
10. A computer-readable storage medium, in which a computer program is stored, which is loaded and executed by a processor to implement a data distribution method as claimed in any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111025736.0A CN113919412B (en) | 2021-09-02 | 2021-09-02 | Data distribution method, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111025736.0A CN113919412B (en) | 2021-09-02 | 2021-09-02 | Data distribution method, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113919412A true CN113919412A (en) | 2022-01-11 |
| CN113919412B CN113919412B (en) | 2023-07-25 |
Family
ID=79233798
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111025736.0A Active CN113919412B (en) | 2021-09-02 | 2021-09-02 | Data distribution method, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113919412B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102843420A (en) * | 2012-07-02 | 2012-12-26 | 上海交通大学 | Fuzzy division based social network data distribution system |
| CN104184819A (en) * | 2014-08-29 | 2014-12-03 | 城云科技（杭州）有限公司 | Multi-hierarchy load balancing cloud resource monitoring method |
| CN105975345A (en) * | 2016-05-20 | 2016-09-28 | 江苏得得空间信息科技有限公司 | Video frame data dynamic equilibrium memory management method based on distributed memory |
| US9602617B1 (en) * | 2015-12-16 | 2017-03-21 | International Business Machines Corporation | High performance and scalable telematics message dispatching |
| WO2018103315A1 (en) * | 2016-12-09 | 2018-06-14 | 上海壹账通金融科技有限公司 | Monitoring data processing method, apparatus, server and storage equipment |
| CN111274288A (en) * | 2020-01-17 | 2020-06-12 | 腾讯云计算（北京）有限责任公司 | Distributed retrieval method, device, system, computer equipment and storage medium |
| US20200293916A1 (en) * | 2019-03-14 | 2020-09-17 | Yadong Li | Distributed system generating rule compiler engine apparatuses, methods, systems and media |
| CN111813573A (en) * | 2020-06-29 | 2020-10-23 | 中国平安人寿保险股份有限公司 | Communication method of management platform and robot software and related equipment thereof |
| WO2021047664A1 (en) * | 2019-09-12 | 2021-03-18 | 华为技术有限公司 | Biometric feature recognition method and related device |
| CN112749012A (en) * | 2021-01-15 | 2021-05-04 | 北京智芯微电子科技有限公司 | Data processing method, device and system of terminal equipment and storage medium |
-
2021
- 2021-09-02 CN CN202111025736.0A patent/CN113919412B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102843420A (en) * | 2012-07-02 | 2012-12-26 | 上海交通大学 | Fuzzy division based social network data distribution system |
| CN104184819A (en) * | 2014-08-29 | 2014-12-03 | 城云科技（杭州）有限公司 | Multi-hierarchy load balancing cloud resource monitoring method |
| US9602617B1 (en) * | 2015-12-16 | 2017-03-21 | International Business Machines Corporation | High performance and scalable telematics message dispatching |
| CN105975345A (en) * | 2016-05-20 | 2016-09-28 | 江苏得得空间信息科技有限公司 | Video frame data dynamic equilibrium memory management method based on distributed memory |
| WO2018103315A1 (en) * | 2016-12-09 | 2018-06-14 | 上海壹账通金融科技有限公司 | Monitoring data processing method, apparatus, server and storage equipment |
| US20200293916A1 (en) * | 2019-03-14 | 2020-09-17 | Yadong Li | Distributed system generating rule compiler engine apparatuses, methods, systems and media |
| WO2021047664A1 (en) * | 2019-09-12 | 2021-03-18 | 华为技术有限公司 | Biometric feature recognition method and related device |
| CN111274288A (en) * | 2020-01-17 | 2020-06-12 | 腾讯云计算（北京）有限责任公司 | Distributed retrieval method, device, system, computer equipment and storage medium |
| WO2021143351A1 (en) * | 2020-01-17 | 2021-07-22 | 腾讯科技（深圳）有限公司 | Distributed retrieval method, apparatus and system, computer device, and storage medium |
| CN111813573A (en) * | 2020-06-29 | 2020-10-23 | 中国平安人寿保险股份有限公司 | Communication method of management platform and robot software and related equipment thereof |
| CN112749012A (en) * | 2021-01-15 | 2021-05-04 | 北京智芯微电子科技有限公司 | Data processing method, device and system of terminal equipment and storage medium |
Non-Patent Citations (2)
| Title |
|---|
| MONTAZERI等: "Optimal Cache Budget Distribution for Hierarchical ICN Networks", 《INTERNATIONAL WORKSHOP ON QOE MANAGEMENT》 * |
| 杨深圳: "面向分布式计算的光互连节点设计与实现", 《中国优秀硕士学位论文全文数据库 信息科技辑》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113919412B (en) | 2023-07-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111522636B (en) | Application container adjusting method, application container adjusting system, computer readable medium and terminal device | |
| EP1587282B1 (en) | Data overlay, self-organized metadata overlay, and application level multicasting | |
| US7457835B2 (en) | Movement of data in a distributed database system to a storage location closest to a center of activity for the data | |
| US7389293B2 (en) | Remastering for asymmetric clusters in high-load scenarios | |
| CN102209041B (en) | Scheduling method, device and system | |
| CN104503826B (en) | The virtual machine mapping method and device of a kind of cloud computation data center | |
| CN111597043B (en) | Full scene edge calculation method, device and system | |
| CN113364850A (en) | Software-defined cloud-edge collaborative network energy consumption optimization method and system | |
| WO2022016969A1 (en) | Data processing method and device | |
| CN113900810A (en) | Distributed graph processing method, system and storage medium | |
| CN112685179A (en) | Resource deployment system and method based on cost on cloud | |
| Gupta et al. | A technique based on ant colony optimization for load balancing in cloud data center | |
| CN110233802A (en) | A method of the block chain framework of the building more side chains of one main chain | |
| CN111064591B (en) | Data aggregation method, device, equipment, storage medium and system | |
| CA2319918A1 (en) | High performance relational database management system | |
| Valetto et al. | A self-organized load-balancing algorithm for overlay-based decentralized service networks | |
| CN105975345A (en) | Video frame data dynamic equilibrium memory management method based on distributed memory | |
| CN114465945A (en) | Identification analysis network construction method based on SDN | |
| Yujie et al. | An effective controller placement algorithm based on clustering in SDN | |
| CN113919412A (en) | Data distribution method, equipment and storage medium | |
| CN108667920B (en) | Service flow acceleration system and method for fog computing environment | |
| CN114238481A (en) | Distributed real-time data importing device | |
| Kopras et al. | Latency-aware virtual network embedding using clusters for green fog computing | |
| CN105323320A (en) | Content distribution method and device | |
| CN116468124B (en) | Quantum task scheduling method and related device |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |