CN108304414A - Big data teaching management architecture system - Google Patents
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Abstract
The present invention provides a kind of big data teaching management architecture system, is related to big data architecture technology field.The big data teaching management architecture system, including server cluster, interchanger and virtual desktop controller, the server cluster is connect by storing interchanger with disk array, the server cluster is connect with virtual machine management platform, the server cluster is connect with registrar server and virtual desktop controller respectively by interchanger, and the virtual desktop controller is connect with intelligent terminal, notebook, PC machine and thin client respectively by LAN.The big data teaching management architecture system can provide safe and reliable experimental situation, significantly promote big data technical ability；It uses desktop virtualization platform architecture, makes it have perfect complete series virtualization scheme, remarkable user experience, more fully data safety guarantee, centralization WEB management modes, professional Local Service pattern.
Description
Technical field
The present invention relates to big data architecture technology field, specially a kind of big data teaching management architecture system.
Background technology
Big data refers to the data that can not be captured, managed and be handled with conventional software tool within certain time
Set is to need new tupe that could have stronger decision edge, see clearly magnanimity, the Gao Zeng for finding power and process optimization ability
Long rate and diversified information assets.
The strategic importance of big data technology, which is not lain in, grasps huge data information, and is to these containing significant number
According to progress specialized process.In other words, if big data is compared to a kind of industry, this industry realizes the pass of profit
Key is to improve " working ability " to data, be realized by " processing " " increment " of data.
In existing society, usual high school meeting attended by all faculty and students uses big data teaching management architecture system.However, existing big data religion
Management framework system is learned, safe and reliable experimental situation can not be provided, to cannot significantly promote big data technical ability；Secondly,
Existing big data teaching management architecture system does not have perfect complete series virtualization scheme, without remarkable user's body
It tests, the Local Service without more fully data safety guarantee, without centralized WEB management modes, without profession
Pattern；In addition, existing big data teaching management architecture system, can not increase colleges and universities' Hard Power and influence power.
Invention content
(1) the technical issues of solving
In view of the deficiencies of the prior art, it the present invention provides a kind of big data teaching management architecture system, solves existing
Big data teaching management architecture system, safe and reliable experimental situation can not be provided, to cannot significantly promote big data
Technical ability；Secondly, existing big data teaching management architecture system does not have perfect complete series virtualization scheme, does not have Zhuo
User experience more, without more fully data safety guarantee, without centralized WEB management modes, without profession
Local Service pattern；In addition, existing big data teaching management architecture system, colleges and universities' Hard Power and influence power can not be increased
Problem.
(2) technical solution
In order to achieve the above object, the present invention is achieved by the following technical programs：A kind of big data teaching management framework
System, including server cluster, interchanger and virtual desktop controller, the server cluster is by storing interchanger and magnetic
Disk array connects, and the server cluster connect with virtual machine management platform, the server cluster by interchanger respectively with pipe
Reason person's server and virtual desktop controller connection, the virtual desktop controller by LAN respectively with intelligent terminal, notes
Originally, PC machine and thin client connection.
Preferably, the intelligent terminal, notebook, PC machine and thin client integrate cloud terminal.
Preferably, the server cluster includes cProc cloud processing softwares, which includes double successively
To the application layer of connection, operation layer, process layer, management level, accumulation layer and virtual resource layer, and cProc clouds processing software is also
Including monitoring cooperation layer.
Preferably, the process layer includes JobKeeper softwares and MapReduce softwares, and management level include that data are vertical
Method, system and Hbase distributed memory systems, accumulation layer include eStor components and HDFS systems.
Preferably, the monitoring cooperation layer includes Zookeeper components and Chukwa components.
Preferably, the server cluster uses the distributed structure/architecture system of Hadoop, and big data processing engine to the greatest extent may be used
Can close storage, Hadoop system include MapReduce components, distributed data base, distributed storage mechanism,
ZooKeeper components and general utility functions module, and MapReduce components respectively with distributed data base and distributed storage
Mechanism is connected, and distributed data base is connected with distributed storage mechanism and ZooKeeper components respectively, the distributed storage
Mechanism is also connected with ZooKeeper components.
Preferably, the MapReduce components include M segmentation input, M Map task, R Reduce task and R
It is a as a result, and M segmentation input connect respectively with M Map task, and each Map tasks respectively with R Reduce tasks company
It connects, and each Reduce tasks export corresponding result.
(3) advantageous effect
The present invention provides a kind of big data teaching management architecture systems.Has following advantageous effect：
1, big data teaching management architecture system, it is possible to provide safe and reliable experimental situation significantly promotes big data
Technical ability.
2, the big data teaching management architecture system makes it have perfect complete set using desktop virtualization platform architecture
Row virtualization scheme, remarkable user experience, more fully data safety guarantee, centralization WEB management modes, professional local
Change service mode.
3, the big data teaching management architecture system, can increase colleges and universities' Hard Power and influence power.Specifically, the big data is taught
Management framework system is learned, teaching level can be improved, student is promoted to improve big data knowledge hierarchy；It contributes to big data laboratory
It builds；It can also promote undergraduate employment in universities level.
Description of the drawings
Fig. 1 is the platform integrated stand composition of the present invention；
Fig. 2 is the cProc Organization Charts of the present invention；
Fig. 3 is the Hadoop framework figure of the present invention；
Fig. 4 is the HDFS Organization Charts of the present invention；
Fig. 5 is the MapReduce Organization Charts of the present invention；
Fig. 6 is the HBace Organization Charts of the present invention.
Specific implementation mode
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation describes, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
The embodiment of the present invention provides a kind of big data teaching management architecture system, as shown in figures 1 to 6.According to the practical feelings of project
Condition, it is proposed that colleges and universities' big data laboratory is divided into three steps and is built：
1, big data virtual platform is built -- and it is directed to the scattered no management status of desktop terminal, disposes cDesktop virtual clouds
Desktop system；
2, backstage cloud storage system is built -- it is directed to the unordered storage and multiplexing of magnanimity integrated data, deployment big data experiment
Integrated machine system；
3, application system is built -- and the big data teaching request based on basis disposes intelligent tutoring cloud disk system；
Platform overall architecture is as shown in Figure 1：The big data teaching management architecture system, including server cluster, interchanger
And virtual desktop controller, the server cluster are connect by storing interchanger with disk array, the server cluster
It is connect with virtual machine management platform, which is controlled with registrar server and virtual desktop respectively by interchanger
Device connects, and the virtual desktop controller is connect with intelligent terminal, notebook, PC machine and thin client respectively by LAN.
In the present invention：Intelligent terminal, notebook, PC machine and thin client integrate cloud terminal.
Whole desktop cloud scheme is by cloud terminal Thinclient, virtual desktop controller OVD, virtual machine management platform
The compositions such as OVP, server storage device realize that by the desk tops unified plan of enterprise staff, employee's is a on server
Personal data is also centrally stored, and personal desktop's system is quickly then consigned to employee, member by network (LAN or wide area network)
Work can whenever and wherever possible be accessed by various types of terminal device such as thin client, notebook, mobile phone and tablet etc. and be done
Public affairs make a kind of novel desktop office pattern.
OVD desktop cloud platforms consign to whenever and wherever possible in the form of exclusively enjoying desktop, using desktop as a kind of on-demand service appoints
What user, using unique VDX desktops transport protocol, all users delivery that OVD can be quickly and safely into enterprise is whole
A desktop, no matter they are fixed office employee or mobile office employee.Platform core component is as follows：
Virtual machine management platform OVP：Build hardware resource can dynamic dispatching server cluster environment, can by virtual machine
Windows and Linux desktop operating systems and application are carried, realizes unified management and the performance monitoring in desktop pond.
Exclusive symmetrical cluster framework can log on any one server and be managed to entire cluster, is not necessarily to
Separately installed central management server, to ensure that the high availability of management platform.
Virtual desktop controller OVD：It cooperating with OVP, built-in domain service provides the desktop user certification of centralization, from
The desktop management of dynamicization, control OVP are created, are updated, restoring the operations such as virtual desktop.In the network independent of virtual machine
In the case of by virtual desktop safety, quickly, be reliably delivered to Thinclient.
Cloud terminal Thinclient：Either compact, the low thin client of power or PC machine, notebook, intelligence
Terminal, can act as the software carrier of cloud terminal Thinclient, is connected to virtual desktop whenever and wherever possible and carries out office application.
Big data resource pool construction, the step in main be related to cProc cloud computings；CProc is a kind of processing magnanimity
The cloud processing platform of the efficient distributed software and hardware set of data, the platform can have been excavated from TB or even PB grades of data
Information, and quick, efficient processing is carried out to these magnanimity informations.CProc cloud processing platforms are built in cloud storage system
On system, the distributed data processing platform of external development interface and data transmission interface is directly provided operation layer.At cProc clouds
Platform is a kind of parallel programming model and Computational frame of processing mass data, based on parallel to large-scale dataset
It calculates.
CProc Organization Charts are as shown in Figure 2：Server cluster includes cProc cloud processing softwares, the cProc cloud processing softwares
Including application layer, operation layer, process layer, management level, accumulation layer and the virtual resource layer being bi-directionally connected successively, and cProc clouds
Processing software further includes monitoring cooperation layer.
In the present invention：Process layer includes JobKeeper softwares and MapReduce softwares, and management level include data cube
System and Hbase distributed memory systems, accumulation layer include eStor components and HDFS systems；Monitoring cooperation layer includes
Zookeeper components and Chukwa components.
CProc cloud processing softwares are supported simultaneously and relational database mixed mode, most mass datas are deposited in point
Cloth platform simultaneously carries out distributed treatment, and the very high data of a small amount of requirement of real-time deposit in relational database, to meet support
Various types of business demands.Support support inquiry, statistics, analysis business；Sustainable depth data excavates and business intelligence point
Analysis business.It is required that reaching 50% or more to stsndard SQL specification support.Attributions selection is provided, classification prediction, regression forecasting, is gathered
The data mining algorithms such as alanysis, association analysis, time series analysis.Food two-dimensional code scanning function is provided, it can be to all kinds of
Information realization is traced to the source.
Big data multi-internet integration scientific research architecture system platform, is related to Hadoop technologies.
Hadoop can big data processing application in extensive use, have benefited from its own data extraction, deformation and add
Carry the inherent advantage in (ETL) aspect.Big data is handled engine storage close as far as possible by the distributed structure/architecture of Hadoop,
It is relatively suitable to batch operation as such as ETL, because the batch processing result of of this sort operation can be moved towards directly
Storage.The MapReduce functions of Hadoop, which realize, smashes individual task, and fragment task (Map) is sent to multiple sections
On point, loaded in the form of individual data collection again later in (Reduce) to data warehouse.
Hadoop framework figure is as shown in figure 3, server cluster uses the distributed structure/architecture system of Hadoop, at big data
Engine is managed as far as possible close to storage, and Hadoop system includes MapReduce components, distributed data base, distributed storage machine
System, ZooKeeper components and general utility functions module, and MapReduce components respectively with distributed data base and distribution
Memory mechanism is connected, and distributed data base is connected with distributed storage mechanism and ZooKeeper components respectively, the distribution
Memory mechanism is also connected with ZooKeeper components.
As seen in Figure 3, Hadoop is made of many elements.Its bottommost is HadoopDistributed File
System (HDFS), it stores the file on all memory nodes in Hadoop clusters.The last layer of HDFS (for herein) is
MapReduce engines, the engine are made of JobTrackers and TaskTrackers.By flat to Hadoop Distributed Calculations
Most crucial distributed file system HDFS, the MapReduce processing procedure of platform and Tool for Data Warehouse Hive and distributed number
According to the introduction of library Hbase, all technological cores of Hadoop distributed platforms are covered substantially.
Hadoop Introduction of key techniques, below we will be discussed in detail from HDFS, MapReduce, HBase three parts
The key indices of Hadoop.
HDFS Organization Charts are as shown in Figure 4：For external client, HDFS is just as a traditional hierarchical file system.
It can create, delete, moving or Rename file, etc..But the framework of HDFS is built based on one group of specific node
(referring to Fig. 1), this is determined by the characteristics of own.These nodes include NameNode (only one), it is inside HDFS
Metadata Service is provided；DataNode, it provides memory block for HDFS.Since there is only a NameNode, this is
A disadvantage (single point failure) of HDFS.
The file being stored in HDFS is divided into block, then copies to these blocks in multiple computers (DataNode).
This differs widely with traditional RAID frameworks.The size (be usually 64MB) of block and the number of blocks of duplication are in establishment file by visitor
Family machine determines.NameNode can control All Files operation.All communications inside HDFS are all based on the TCP/IP associations of standard
View.
MapReduce Organization Charts are as shown in figure 5, MapReduce components include M segmentation input, M Map task, R a
Reduce tasks and R as a result, and M segmentation input connect respectively with M Map task, and each Map tasks respectively with R
A Reduce tasks connection, and each Reduce tasks export corresponding result.
MapReduce major functions are as follows：
(1), data divide and calculating task is dispatched：System automatically divides the pending big data of an operation (Job)
For many data blocks, each data block corresponds to a calculating task (Task), and Automatic dispatching calculate node handles phase
The data block answered.Operation and task scheduling function are mainly responsible for distribution and scheduling calculate node (Map nodes or Reduce nodes),
It is responsible for monitoring the execution state of these nodes simultaneously, and is responsible for the synchronous control that Map nodes execute.
(2), data/code mutually positions：In order to reduce data communication, a basic principle is localization data processing, i.e.,
One calculate node handles the data that storage is distributed on its local disk as far as possible, this realizes migration of the code to data；
When this localization data processing can not be carried out, then finds other enabled nodes and data are sent to the node from network
(data to code migration), but enabled node will be found as far as possible from the local rack where data to reduce communication delay.
(3), system optimization：In order to reduce data communication overhead, intermediate result data can carry out before entering Reduce nodes
Certain merging treatment；Data handled by one Reduce node may come from multiple Map nodes, in order to avoid Reduce
Data dependence occurs for calculation stages, and the intermediate result of Map nodes output need to use certain strategy to carry out at division appropriate
Reason, ensures that correlation data is sent to the same Reduce nodes；In addition, system also carries out some calculated performance optimization processings,
Most slow calculating task is executed using prepare more part such as, selects most fast complete winner as a result.
(4), fluffing check and recovery：In the extensive MapReduce computing clusters constituted with low side commercial server, section
Point hardware (host, disk, memory etc.) malfunctions and software faults are normalities, therefore MapReduce needs can detect and isolate out
Wrong node, and the new node of dispatching distribution takes over the calculating task of error node.Meanwhile system also by maintenance data storage can
By property, the reliability of data storage is improved with prepare more part redundant storage mechanism, and can detect and restore the data of error in time.
HBace Organization Charts are as shown in Figure 6：HBase, that is, Hadoop Database are high reliability, high-performance, a face
Nematic, telescopic distributed memory system can erect large-scale structure using HBase technologies on cheap PC Server
Change storage cluster.Attached drawing describes each layer system in Hadoop EcoSystem.Wherein, HBase is located at structured storage layer,
The bottom that Hadoop HDFS provide high reliability for HBase stores support, and Hadoop MapReduce provide for HBase
High performance computing capability, Zookeeper provides for HBase stablizes service and failover mechanism.
It is supported in addition, Pig and Hive also provide upper language for HBase so that locate according to statistics in the enterprising line numbers of HBase
Reason becomes very simple.Sqoop is then the RDBMS data import features that HBase is provided a convenient so that traditional database data
The very convenient of change is migrated into HBase.
In conclusion the big data teaching management architecture system has a series of advantageous effect：
(1), big data teaching management architecture system, it is possible to provide safe and reliable experimental situation significantly promotes big number
According to technical ability.
(1.1), it uses Docker container techniques：Big data teaching platform is based on Docker container techniques, and Docker can
With the rapid automatized application deployment inside container, and kernel virtualization technology (namespaces and cgroups can be passed through
Deng) come resource isolation and the safety guarantee etc. that provide container.Since Docker realizes isolation by the virtualization of operating system layer,
So Docker containers are at runtime, resource utilization need not be improved similar to the additional operating system overhead of virtual machine (VM),
And the performance for promoting such as IO etc., can create the experimental situation run at any time moment.
(1.2), it can be disposed on demand：In terms of software configuration, major colleges and universities can select according to concrete application in container cloud
Dispose Hadoop, HBase, Ambari, HDFS, YARN, MapReduce, ZooKeeper, Spark, Storm, Hive, Pig,
The different big data application component such as Oozie, Mahout, R language.
(1.3), it is completely isolated to test cluster for it：Using Mesos+ZooKeeper+Mrathon architecture management clusters, several
Machine can fictionalize many experiments cluster, and experiment cluster is completely isolated, and experimental situation is not interfere with each other, if experimental situation is broken
Bad, a key, which is restarted, can establish new cluster, facilitate students up to a hundred while using.
(2), the big data teaching management architecture system is made it have perfect complete using desktop virtualization platform architecture
Serial virtualization scheme, remarkable user experience, more fully data safety guarantee, centralization WEB management modes, professional sheet
Ground service mode.
(2.1), perfect complete series virtualization scheme：Cover thin client, virtual desktop controller OVD, virtual machine pipe
Tri- big links of software OVP are managed, industry scheme is most comprehensive, and preferably, cost performance highest provides one kind more to compatibility for enterprise IT
It simplifies and manages user with the method for safety and the quick desktop services that can be accessed on demand are provided.
(2.2) remarkable user experience：Performance tuning is carried out for various application scenarios, high efficiency of transmission agreement VDX reaches
The access experience consistent with traditional PC.Local especially for HD video and online broadcasting, reach the experience of clear and smooth
Effect.
(2.3) more fully data safety guarantee：OVP, OVD, Thinclient tripartite's certification guarantee user's access safety,
Safety is transmitted in comprehensive Encryption Algorithm guarantee, flexible access control carries out the unified storage of concentration authentication, data and backup guarantee
Personal data safety, high reliability HA designs ensure platform safety, finally realize end-to-end desktop virtualization safeguard protection.
(2.4) centralization WEB management modes：Building for package only needs two components (OVP and OVD), opposite industry
Its deployment component of other manufacturers is minimum, and can provide centralization, the novel maintenance pattern of unification, improves virtual desktop deployment
Ease for use and maintainability.
(2.5) professional Local Service pattern：Setting up multiple offices in the whole nation has Localization Technology to support and profession
Virtualization technology R＆D team, become the domestic manufacturer for uniquely having independent research the whole series virtualization product system, product
Property development ability is strong, can quickly solve product late problems.
(3), the big data teaching management architecture system, can increase colleges and universities' Hard Power and influence power.Specifically, the big number
According to teaching management architecture system, teaching level can be improved, student is promoted to improve big data knowledge hierarchy；It contributes to big data real
Test room construction；It can also promote undergraduate employment in universities level.
(3.1) teaching level is improved, student is promoted to improve big data knowledge hierarchy：Big data tests all-in-one machine solution
Using the theoretical talents cultivating mode being combined with experiment, it is based on true enterprise base real training experience, abundant project is provided
Real training case carries out industry data research in conjunction with each professional actual conditions of colleges and universities, cultivates the items ability of practical talent.
(3.2) contribute to big data lab construction：Big data rises to national strategy, and 13 states are clearly set up in the Committee of Development and Reform
Family's grade big data laboratory, the construction of colleges and universities' big data Platform of Experimental Teaching meet national strategy, play demonstration effect, improve
Method of College Education Informatization Level and experimental project Research Ability, enhancing colleges and universities hardware strength.
(3.3) it is horizontal to promote undergraduate employment in universities：Big data industry welcomes development golden period, and talent shortage is quite huge, culture
The ability of student's related fields helps to provide Students ' Employment level, and then increases colleges and universities' influence power.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
Understanding without departing from the principles and spirit of the present invention can carry out these embodiments a variety of variations, modification, replace
And modification, the scope of the present invention is defined by the appended.
Claims (7)
1. a kind of big data teaching management architecture system, including server cluster, interchanger and virtual desktop controller, special
Sign is：The server cluster is connect by storing interchanger with disk array, the server cluster and Virtual Machine Manager
Platform connects, which is connect with registrar server and virtual desktop controller respectively by interchanger, described
Virtual desktop controller is connect with intelligent terminal, notebook, PC machine and thin client respectively by LAN.
2. big data teaching management architecture system according to claim 1, it is characterised in that：The intelligent terminal, notes
Originally, PC machine and thin client integrate cloud terminal.
3. big data teaching management architecture system according to claim 1, it is characterised in that：The server cluster includes
CProc cloud processing softwares, which includes the application layer being bi-directionally connected successively, operation layer, process layer, management
Layer, accumulation layer and virtual resource layer, and cProc cloud processing softwares further include monitoring cooperation layer.
4. big data teaching management architecture system according to claim 3, it is characterised in that：The process layer includes
JobKeeper softwares and MapReduce softwares, and management level include data cube system and Hbase distributed memory systems,
Accumulation layer includes eStor components and HDFS systems.
5. big data teaching management architecture system according to claim 3, it is characterised in that：The monitoring cooperation layer includes
Zookeeper components and Chukwa components.
6. big data teaching management architecture system according to claim 1, it is characterised in that：The server cluster uses
The distributed structure/architecture system of Hadoop, by big data processing engine as far as possible close to storage, Hadoop system includes
MapReduce components, distributed data base, distributed storage mechanism, ZooKeeper components and general utility functions module, and
MapReduce components are connected with distributed data base and distributed storage mechanism respectively, and distributed data base respectively with point
Cloth memory mechanism and ZooKeeper components are connected, which is also connected with ZooKeeper components.
7. big data teaching management architecture system according to claim 6, it is characterised in that：The MapReduce components
Including M segmentation input, M Map task, R Reduce task and R it is a as a result, and M be segmented input respectively with M Map
Task connects, and each Map tasks are connect with R Reduce task respectively, and each Reduce tasks export corresponding knot
Fruit.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108958947A (en) * | 2018-09-17 | 2018-12-07 | 北京市计算中心 | A kind of big data all-in-one machine and its application method |
| CN109637278A (en) * | 2019-01-03 | 2019-04-16 | 青岛萨纳斯智能科技股份有限公司 | Big data teaching experiment training platform |
| CN113055462A (en) * | 2021-03-09 | 2021-06-29 | 中国人民解放军63660部队 | Cloud service architecture design method |
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| CN103888530A (en) * | 2014-03-18 | 2014-06-25 | 成都盛思睿信息技术有限公司 | Experiment teaching system based on cloud desktop |
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