CN106302656A - The Medical Data processing method of cloud storage platform - Google Patents
The Medical Data processing method of cloud storage platform Download PDFInfo
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- CN106302656A CN106302656A CN201610622378.4A CN201610622378A CN106302656A CN 106302656 A CN106302656 A CN 106302656A CN 201610622378 A CN201610622378 A CN 201610622378A CN 106302656 A CN106302656 A CN 106302656A
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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
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Abstract
The invention provides the Medical Data processing method of a kind of cloud storage platform, the method includes: determines memory node by storage load balancing strategy before write in data, redistributes copy memory node according to access frequency or node storage capacity after the data writing is finished.The present invention proposes the Medical Data processing method of a kind of cloud storage platform, simplifies server cluster deployment way, it is to avoid server cluster is directly operated by user, it is ensured that the reasonability of memory node and data stability.
Description
Technical field
The present invention relates to cloud storage, particularly to the Medical Data processing method of a kind of cloud storage platform.
Background technology
Cloud storage have employed the technology such as cloud computing, distributed file system and server cluster, deposits various in network
Storage resource is aggregating, common externally offer data storage and Operational Visit function, leads at medical scientific research, production and auto service
Territory extensive application.Current cloud storage is divided into publicly-owned service type to store, and i.e. provides storage service to enterprise or individual；One
Kind be privately owned architected cloud storage, i.e. enterprises build based on storage server cluster and distributed file system, dispose
In the node trustship place of enterprise data center or safety, provide for enterprise self and store service accordingly.Medicine cloud storage
Platform confidentiality is higher, and storing process is without too many I/O operation, therefore uses and builds private cloud storage system to preserve its medicine
Product data file is best selection.At present, private cloud storage building plan has a variety of: include key assignments type distributed field system
System, have employed the mode of packet, and server cluster is constituted by one or more groups, is the most standby relation with the service node in group.
The mode using packet storage can make storage server cluster more flexible, and controllability is the strongest.But, Hadoop makees
It is a distributed storage Computational frame increased income, the shortcoming also having its own.That is exactly system architecture design complexity, runs
Maintenance difficulties is bigger.Use to cloud storage platform not only needs many knowledge accumulation, and in terms of its operation maintenance
Also having a lot of technical ability to remove learning and mastering, the industry limiting medicine cloud storage platform to a certain extent is promoted and uses.Taking
Building in pharmaceutical information cloud storage platform, also two impacts are disposed and the problem of systematic function: first is in running, joint
Point is susceptible to fault.Once node failure occurs and can not prepare to process in time, will affect depositing of multiple node
In storage server cluster build process, each node has the operation of a lot of repetition so that build process is the most loaded down with trivial details and holds
Error-prone；Second is because node and is ordinary personal computers, rather than the private server of minicomputer or large scale computer etc,
Therefore data are in use, and by such as CPU, the impact such as internal memory and magnetic disc i/o is the most serious.
Summary of the invention
For solving the problem existing for above-mentioned prior art, the present invention proposes at the Medical Data of a kind of cloud storage platform
Reason method, including:
Memory node is determined by storage load balancing strategy before write, after the data writing is finished according to accessing frequency in data
Rate or node storage capacity redistribute copy memory node.
Preferably, the method farther includes: arranges node in the file system of cloud storage platform and selects and scheduling prison
Visual organ, its interior joint selection strategy is implemented among namenode, namenode call when selecting service node, scheduling prison
Visual organ is used for monitoring server cluster operation conditions, including access frequency and the memory capacity of service node of data block, is
System is when idle state, and management node, according to data access frequency and power system capacity, dispatches copy deposit position；
Before files passe, service node sends write data requests to namenode, and namenode calls node and selects mould
Type, by dispatch monitor, obtains server cluster operation information, calculates the stored ratio of node, calculate each frame node
Stored ratio, and according to backup factor number, prioritizing selection stored ratio the highest node composition node queue be sent to visitor
Data to be stored are divided into multiple data block by client by family end, are stored on different service nodes；
After All Files has been stored in server cluster, collects server cluster by dispatch monitor and run letter
Breath, gets data access frequency and the memory capacity of all nodes of all nodes, if the access frequency of data exceedes predetermined
Justice threshold value, then by Replica placement on the minimum node of access frequency；If system spare capacity is less than threshold value, then by Replica placement
At the node that stored ratio is the highest；
Server cluster operation information is measured by cloud storage platform operation information and display frame monitors, described frame
The task that frame monitoring is relevant with scheduling cloud storage platform；Client selects to be divided into two ways at node: client is at server
Selection strategy on clustered node and client selection mode outside server cluster node, specific implementation is as follows:
Each frame at n rack server cluster arranges TR platform service node, and number of copies is r；If client is at clothes
On business device group service node, then
A) client sends write data requests to management node；
B) management node is according to file content and system configuration scenarios, calculates all service nodes of client place frame
Stored ratio, process is as follows:
If client is i-th frame, initialize selected node set SDN for sky；
The residual capacity of this frame jth node is CLij, the block number of storage is BLij, the storage preferred proportion RS of nodeij
=CLij/BLij, storage is preferably put into selected node set than two the highest nodes, i.e. SDN={DNia、DNib}；Wherein
DNia、DNibRepresent a and the b service node in i-th frame,
C) from remaining the node calculating r-2 storage each frame than maximum, r-2 node of maximum after sequence, is selected
Put into selected node set SDN, altogether r node, be used for depositing data block and copy thereof；
D) the node distribution service node during SDN is gathered by management node, to client, is write by client；
When client is not on service node, then the stored ratio of all nodes, choosing in direct calculation server cluster
Select front r maximum node, be data memory node；FromIn individual node, according to RSij=CLij/BLijSelect storage
R the node that ratio is the highest, puts in SDN list, is the optimum node chosen.
The present invention compared to existing technology, has the advantage that
The present invention proposes the Medical Data processing method of a kind of cloud storage platform, simplifies server cluster deployment side
Formula, it is to avoid server cluster is directly operated by user, it is ensured that the reasonability of memory node and data stability.
Accompanying drawing explanation
Fig. 1 is the flow chart of the Medical Data processing method of cloud storage platform according to embodiments of the present invention.
Detailed description of the invention
Hereafter provide retouching in detail one or more embodiment of the present invention together with the accompanying drawing of the diagram principle of the invention
State.Describe the present invention in conjunction with such embodiment, but the invention is not restricted to any embodiment.The scope of the present invention is only by right
Claim limits, and the present invention contains many replacements, amendment and equivalent.Illustrate in the following description many details with
Thorough understanding of the present invention is just provided.These details are provided for exemplary purposes, and without in these details
Some or all details can also realize the present invention according to claims.
An aspect of of the present present invention provides the Medical Data processing method of a kind of cloud storage platform.Fig. 1 is according to the present invention
The Medical Data process flow figure of the cloud storage platform of embodiment.
In order to preferably manage server cluster, the present invention whole life to cloud storage platform Distributed Architecture running
The life cycle carries out automated management, including installing, builds and monitors, it is provided that visualization interface, improves the efficiency of manager.With
Time storage resource control system carry out fault alarm and process.Except the operation maintenance of server cluster is operated, in addition it is also necessary to right
The performance of server cluster is optimized.In server cluster after newly-increased node, re-optimization performance of server cluster.For cloud
Storage server cluster variety of problems during deployment, operation maintenance and use, the present invention is directed to the server set built
Group, utilizes the node scheduling Optimized model in reading and writing data stage, realizes convenient management and the optimization of server cluster.The present invention
The aspect of deployment framework, node administration and server Optimized Operation for server cluster is described in detail.
The present invention uses host-guest architecture, comprises a management node and multiple service node.Management node is used for and business
Node is mutual, the heartbeat request that node of accepting business sends, and completes centralized management watchdog logic, and each service node is responsible for
The state acquisition of place node and maintenance work.Management node deployment is at single node, as server cluster deployment framework
Management node, its responsibility be receive user send order performs request, with rear to service node send order, employing JSON
Mode sends order, and these JSON data include installation, start, stop the configuration information of service.
Service node is deployed on the node of all server clusters to be added, is used for performing by holding that management node sends
Row task requests, performed script be stored in management node on assigned catalogue under, this script by service node receive from
The content transformation of the command file of management node is dictionary format, it is simple to the use configured when script realizes disposing.Disposing
During state and behavior transmission all for by manage node be sent to service node, service node receives certain action row
For, by behavior perform thread perform correspondence method, and will perform after message by message queue feed back to management save
Point.
During server cluster is disposed, operator perform different behaviors by the page, and management node is by this row
For being sent to service node. performed thread by the behavior of service node again and perform the operation of correspondence, complete server cluster portion
Administration.During service node performs, send back to the status information in server cluster manage node, by having of management node
Limit state machine judges.
In server cluster node configures, the present invention gives tacit consent to all nodes and the most successfully installs operating system no matter
It is physical machine or virtual machine.Node joins two steps in server cluster, first is both sides' safety certifications, and second is
The configuration of node name.Both sides' safety certification uses Shell script to write, and system performs this script, by the PKI of management node
File distributing is to each service node, to reach the state without password login.
Configuration service device cluster service includes selecting service and selecting service place node.Current information on services writes on
In JSON data, when selecting service by reading this JSON data, obtain all of cloud storage platform service, optionally enter
Row is installed.After selecting service, to service being distributed at corresponding node, the node listing before at this moment reading, then will
Each service carries out node selection.
Service and node configuration information all oneself after setting completed, corresponding cloud can be deposited by system by performing Shell
Storage platform service installation kit is distributed on the node of correspondence, and installs.The service profile information of all nodes synchronizes.
After server cluster has been built, server cluster interior joint increases along with the increase of data volume, adding of node
Add and the complexity of process of malfunctioning node the most exponentially goes up.The present invention utilizes node administration, by a cloud storage
Platform service monitors thread, is polled the cloud storage platform service of management node, monitors in real time on server cluster each
The running status of node, realizes increasing node and deletion action simultaneously.
Node administration uses observer's pattern to realize cloud storage platform service monitor, and wherein node manager is observation
Person, cloud storage platform service monitor is the person of being observed.Supervision includes monitoring server cluster operation conditions, including all nodes
Operation conditions, file system service condition.Management includes server cluster node and the unlatching of service, closedown, the increase of node
With deletion etc..
After cloud storage Platform Server is built, management node can start server cluster and monitor, periodically initiates
Nodal information obtains server cluster node index.Selected node increase or delete by cloud storage platform nodes manager simultaneously
The operation removed.
If there being new node server cluster to be added, then cloud storage platform nodes manager is needed to obtain service to be added
The nodal information of device cluster, then send that information to cloud storage platform nodes monitor, by performing relevant Shell order,
Judge whether this node can join server cluster, and feedback-related information is to cloud storage platform nodes manager, by cloud
Storage platform nodes manager selects the concrete behavior of node.In like manner, knot removal is also required to first save from cloud storage platform
In some manager, the nodal information that will delete is sent to cloud storage platform nodes monitor, and monitor judges the shape of this node
State, performs node and removes operation.
The server optimization that the present invention proposes includes that node selects and storage scheduling.Node selects to refer to that data are before write
Memory node is strategically specified, it is possible to ensure storage load balance, after storage scheduling refers to data write, according to visit by some
Ask that frequency or node storage capacity are to redistribute copy memory node so that system can be run more efficiently.
File system is provided with node and selects and dispatch monitor.Its interior joint selection strategy is implemented among namenode,
Called when selecting service node by namenode.Dispatch monitor is used for monitoring server cluster operation conditions, including data
The access frequency of block and the memory capacity of service node, system is when idle state, and management node is according to data access frequency
And power system capacity, dispatch copy deposit position, improve running efficiency of system.
Node selects to specifically include, and before files passe, service node sends write data requests, name byte to namenode
Point calls node preference pattern, by dispatch monitor, obtains server cluster operation information, calculates the stored ratio of node,
Calculate the stored ratio of each frame node, and according to backup factor number, the node composition that prioritizing selection stored ratio is the highest
Node queue, is sent to client, client data to be stored are divided into multiple data block, is stored in different business
On node.
Storage scheduling specifically includes, and after All Files has been stored in server cluster, is received by dispatch monitor
Collection server cluster operation information, gets data access frequency and the memory capacity of all nodes of all nodes, in service
When device cluster is in idle state, management node, according to the data got, carries out storage scheduling.If the access frequency of data surpasses
Cross predefined threshold value, then by Replica placement on the minimum node of access frequency；If system spare capacity is less than threshold value, then by pair
Originally the node that stored ratio is the highest it is placed on.
Below for server optimization scheduling model, its monitor and optimisation strategy are described.
First, server cluster operation information needs to be measured by cloud storage platform operation information to supervise with display frame
Depending on, survey tool is extended by described framework, it is provided that can show in real time and the instrument of historical data, help monitoring for one
The task relevant with scheduling cloud storage platform.
Cloud storage scheduling in the present invention is divided into two stages.Wherein first stage is service node during file write
Select, be to be realized by optimization node selection strategy.Client selects to be divided into two ways at node: client is at server
Selection strategy on clustered node and client selection mode outside server cluster node.Implementation is as follows:
Assuming that server cluster has n frame, each frame to have TR platform service node, number of copies is r
If client is on server cluster service node, then
A) client sends write data requests to management node；
B) management node is according to file content and system configuration scenarios, calculates all service nodes of client place frame
Stored ratio, process is as follows
If client is i-th frame, initialize selected node set SDN for sky；
The residual capacity of this frame jth node is CLij, the block number of storage is BLij, the storage preferred proportion RS of nodeij
=CLij/BLij, storage is preferably put into selected node set than two the highest nodes, i.e. SDN={DNia、DNib}；Wherein
DNia、DNibRepresent a and the b service node in i-th frame,
C) from remaining the node calculating r-2 storage each frame than maximum, r-2 node of maximum after sequence, is selected
Put into selected node set SDN, altogether r node, be used for depositing data block and copy thereof；
D) the node distribution service node during SDN is gathered by management node, to client, is write by client.
When client is not on service node, then the stored ratio of all nodes, choosing in direct calculation server cluster
Select front r maximum node, be data memory node.FromIn individual node, according to RSij=CLij/BLijSelect storage
R the node that ratio is the highest, puts in SDN list, is the optimum node chosen.
In sum, the present invention proposes the Medical Data processing method of a kind of cloud storage platform, simplifies server set
Group's deployment way, it is to avoid server cluster is directly operated by user, it is ensured that the reasonability of memory node and data stability.
Obviously, it should be appreciated by those skilled in the art, each module of the above-mentioned present invention or each step can be with general
Calculating system realize, they can concentrate in single calculating system, or be distributed in multiple calculating system and formed
Network on, alternatively, they can realize with the executable program code of calculating system, it is thus possible to by they store
Performed by calculating system within the storage system.So, the present invention is not restricted to the combination of any specific hardware and software.
It should be appreciated that the above-mentioned detailed description of the invention of the present invention is used only for exemplary illustration or explains the present invention's
Principle, and be not construed as limiting the invention.Therefore, that is done in the case of without departing from the spirit and scope of the present invention is any
Amendment, equivalent, improvement etc., should be included within the scope of the present invention.Additionally, claims purport of the present invention
Whole within containing the equivalents falling into scope and border or this scope and border change and repair
Change example.
Claims (2)
1. the Medical Data processing method of a cloud storage platform, it is characterised in that including:
Determine memory node by storage load balancing strategy before write in data, after the data writing is finished according to access frequency or
Node storage capacity redistributes copy memory node.
Method the most according to claim 1, it is characterised in that the method farther includes: in the file system of cloud storage platform
In arrange node select and dispatch monitor, its interior joint selection strategy is implemented among namenode, by namenode choosing
Call when selecting service node, dispatch monitor be used for monitor server cluster operation conditions, including data block access frequency with
And the memory capacity of service node, system is when idle state, and management node is according to data access frequency and power system capacity, scheduling
Copy deposit position；
Before files passe, service node sends write data requests to namenode, and namenode calls node preference pattern, logical
Cross dispatch monitor, obtain server cluster operation information, calculate the stored ratio of node, calculate the storage of each frame node
Ratio, and according to backup factor number, the node that prioritizing selection stored ratio is the highest forms node queue and is sent to client, by
Data to be stored are divided into multiple data block by client, are stored on different service nodes；
After All Files has been stored in server cluster, collect server cluster operation information by dispatch monitor,
Get data access frequency and the memory capacity of all nodes of all nodes, if the access frequency of data exceedes predefined threshold
Value, then by Replica placement on the minimum node of access frequency；If system spare capacity is less than threshold value, then Replica placement is being deposited
The node that storage ratio is the highest；
Server cluster operation information is measured by cloud storage platform operation information and display frame monitors, described framework is supervised
Survey the task relevant with scheduling cloud storage platform；Client selects to be divided into two ways at node: client is at server cluster
Selection strategy on node and client selection mode outside server cluster node, specific implementation is as follows:
Each frame at n rack server cluster arranges TR platform service node, and number of copies is r；If client is at server
On group service node, then
A) client sends write data requests to management node；
B) management node is according to file content and system configuration scenarios, calculates all service nodes storage of client place frame
Ratio, process is as follows:
If client is i-th frame, initialize selected node set SDN for sky；
The residual capacity of this frame jth node is CLij, the block number of storage is BLij, the storage preferred proportion RS of nodeij=
CLij/BLij, storage is preferably put into selected node set than two the highest nodes, i.e. SDN={DNia、DNib}；Wherein
DNia、DNibRepresent a and the b service node in i-th frame,
C) from remaining the node calculating r-2 storage each frame than maximum, r-2 node of maximum after sequence, is selected to put into
Selected node set SDN, altogether r node, be used for depositing data block and copy thereof；
D) the node distribution service node during SDN is gathered by management node, to client, is write by client；
When client is not on service node, then the stored ratio of all nodes, r before selecting in direct calculation server cluster
The node of individual maximum, is data memory node；FromIn individual node, according to RSij=CLij/BLijSelect stored ratio
R the highest node, puts in SDN list, is the optimum node chosen.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112559483A (en) * | 2020-12-22 | 2021-03-26 | 赛尔网络有限公司 | HDFS-based data management method and device, electronic equipment and medium |
| WO2021169953A1 (en) * | 2020-02-24 | 2021-09-02 | 深圳点链科技有限公司 | Blockchain-based data processing method and system, and computer readable storage medium |
| CN117038003A (en) * | 2023-10-10 | 2023-11-10 | 德格县藏医院(藏医药研究所) | Medicine data processing method, device, equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101187931A (en) * | 2007-12-12 | 2008-05-28 | 浙江大学 | Distribution type file system multi-file copy management method |
| CN101370030A (en) * | 2008-09-24 | 2009-02-18 | 东南大学 | Resource load stabilization method based on contents duplication |
| CN101686262A (en) * | 2009-05-14 | 2010-03-31 | 南京大学 | Multi-node collaboration based storage method for sensor network |
| CN102035884A (en) * | 2010-12-03 | 2011-04-27 | 华中科技大学 | Cloud storage system and data deployment method thereof |
| CN103139302A (en) * | 2013-02-07 | 2013-06-05 | 浙江大学 | Real-time copy scheduling method considering load balancing |
| CN103150347A (en) * | 2013-02-07 | 2013-06-12 | 浙江大学 | Dynamic replica management method based on file heat |
| CN105827744A (en) * | 2016-06-08 | 2016-08-03 | 四川新环佳科技发展有限公司 | Data processing method of cloud storage platform |
-
2016
- 2016-08-01 CN CN201610622378.4A patent/CN106302656A/en not_active Withdrawn
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101187931A (en) * | 2007-12-12 | 2008-05-28 | 浙江大学 | Distribution type file system multi-file copy management method |
| CN101370030A (en) * | 2008-09-24 | 2009-02-18 | 东南大学 | Resource load stabilization method based on contents duplication |
| CN101686262A (en) * | 2009-05-14 | 2010-03-31 | 南京大学 | Multi-node collaboration based storage method for sensor network |
| CN102035884A (en) * | 2010-12-03 | 2011-04-27 | 华中科技大学 | Cloud storage system and data deployment method thereof |
| CN103139302A (en) * | 2013-02-07 | 2013-06-05 | 浙江大学 | Real-time copy scheduling method considering load balancing |
| CN103150347A (en) * | 2013-02-07 | 2013-06-12 | 浙江大学 | Dynamic replica management method based on file heat |
| CN105827744A (en) * | 2016-06-08 | 2016-08-03 | 四川新环佳科技发展有限公司 | Data processing method of cloud storage platform |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021169953A1 (en) * | 2020-02-24 | 2021-09-02 | 深圳点链科技有限公司 | Blockchain-based data processing method and system, and computer readable storage medium |
| CN112559483A (en) * | 2020-12-22 | 2021-03-26 | 赛尔网络有限公司 | HDFS-based data management method and device, electronic equipment and medium |
| CN117038003A (en) * | 2023-10-10 | 2023-11-10 | 德格县藏医院(藏医药研究所) | Medicine data processing method, device, equipment and storage medium |
| CN117038003B (en) * | 2023-10-10 | 2023-12-12 | 德格县藏医院(藏医药研究所) | Medicine data processing method, device, equipment and storage medium |
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Application publication date: 20170104 |