CN101778002A - Large-scale cluster system and building method thereof - Google Patents
Large-scale cluster system and building method thereof Download PDFInfo
- Publication number
- CN101778002A CN101778002A CN201010105065A CN201010105065A CN101778002A CN 101778002 A CN101778002 A CN 101778002A CN 201010105065 A CN201010105065 A CN 201010105065A CN 201010105065 A CN201010105065 A CN 201010105065A CN 101778002 A CN101778002 A CN 101778002A
- Authority
- CN
- China
- Prior art keywords
- cluster
- sub
- shared storage
- storage module
- service
- 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
- 238000000034 method Methods 0.000 title claims abstract description 14
- 230000006870 function Effects 0.000 claims abstract description 19
- 238000013507 mapping Methods 0.000 claims abstract description 16
- 238000012544 monitoring process Methods 0.000 claims abstract description 12
- 238000013506 data mapping Methods 0.000 claims abstract description 6
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000006386 memory function Effects 0.000 abstract 1
- 238000010276 construction Methods 0.000 description 5
- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 230000010355 oscillation Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Images
Landscapes
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a large-scale cluster system and a building method thereof, which are used for improving high availability redundancy of the cluster system. The system mainly comprises a shared memory module, a sub-cluster and a dispatching node, wherein the shared memory module is used for providing a memory function and a data high availability service function; the sub-cluster is used for carrying out data mapping with the shared memory module, providing the data high availability service function and carrying out parallel input and output with outside; and the dispatching node is used for monitoring the working condition of the sub-cluster, maintaining the mapping relation between the sub-cluster and the shared memory module, and dispatching input and output load. The invention greatly improves high availability redundancy and stability of the large-scale cluster system.
Description
Technical Field
The invention relates to a network information technology, in particular to a large-scale cluster system and a construction method thereof.
Background
In a large-scale Cluster system, such as a large-scale high-performance computing Cluster, a Network Attached Storage (NAS) Cluster storage system, and the like, a construction method for high availability of service nodes is generally a method for high availability of two-by-two (Active-Active) or high availability of all service node clusters (Cluster HA).
In the cluster systems constructed by the pairwise high-availability method, when one of two service nodes (assumed node A) fails, the other node (assumed node B) loses high-availability protection, and if the node B fails again, the whole cluster system is down, so that the cluster systems constructed by the pairwise high-availability method have the problem of insufficient high-availability redundancy. Moreover, the load balancing capability of the cluster system constructed by two high-availability methods is greatly limited, only part of services can be balanced between two nodes, and the condition that the overall performance of the cluster system is limited to one service node with the worst performance is easily caused.
The cluster scale constructed by the high-availability method of all the service node clusters is generally between 32 and 64 nodes. When the service nodes reach hundreds or even thousands of service nodes, the currently general high-availability mechanism of the cluster is no longer stable and reliable, for example, the high-availability heartbeat signal of the cluster cannot effectively maintain the online states of the nodes, and as the number of the service nodes increases, once a single node or a plurality of nodes fail, the whole cluster system enters a vibration state, which significantly affects the service quality of all the nodes, and in a severe case, the whole system is down.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a large-scale cluster system and a construction method thereof, so that the high available redundancy of the cluster system is improved.
In order to solve the above technical problem, the present invention provides a large-scale cluster system, which includes a shared storage module, a sub-cluster and a scheduling node, wherein:
the shared storage module is used for providing a storage function and a data high-availability service function;
the sub-cluster is used for carrying out data mapping with the shared storage module, providing a high-availability service function of data and carrying out parallel input and output with the outside;
the scheduling node is used for monitoring the working state of the sub-cluster, maintaining the mapping relation between the sub-cluster and the shared storage module, and scheduling the input and output loads.
Preferably, the shared storage module comprises an FC-SAN disk array or an IP-SAN disk array.
Preferably, the sub-cluster comprises a plurality of service nodes, and all the service nodes construct a cluster high availability relationship and are used for completing data input and output control, protocol conversion or application calculation.
Preferably, the scheduling node is configured to map the logical unit number mapped by at least one of the shared storage modules to two or more service nodes in the sub-cluster.
Preferably, the scheduling node is configured to dynamically collect utilization rates of the shared storage module through the sub-cluster, dynamically collect load conditions of each service node in the sub-cluster, and schedule computing resources and storage resources in real time.
In order to solve the above technical problem, the present invention further provides a method for constructing a large-scale cluster system, including:
setting a shared storage module;
constructing a sub-cluster, and completing data mapping between the sub-cluster and the shared storage module;
monitoring the working state of the sub-cluster, maintaining the mapping relation between the sub-cluster and a shared storage module, and scheduling input and output loads;
wherein,
the shared storage is used for providing a storage function and a data high-availability service function;
the sub-clusters are used for providing data high-availability service functions and performing parallel input and output with the outside.
Preferably, the shared storage module comprises an FC-SAN disk array or an IP-SAN disk array.
Preferably, a plurality of service nodes are provided, all the service nodes are constructed into a cluster which is highly available, and are constructed into at least one sub-cluster; the service node is used for finishing data input and output control, protocol conversion or application calculation.
Preferably, the step of maintaining the mapping relationship between the sub-clusters and the shared storage module includes:
and mapping the logic unit number mapped by at least one shared storage module to two or more service nodes in the sub-cluster.
Preferably, the utilization rate of the shared storage module is dynamically collected through the sub-cluster, the load condition of each service node in the sub-cluster is dynamically collected, and the computing resources and the storage resources are scheduled in real time.
Compared with the prior art, at least one embodiment of the invention at least improves the high availability redundancy and stability of the large-scale cluster system, and the other embodiment of the invention also at least ensures the load balance of the large-scale cluster system. In addition, the large-scale cluster system constructed by the technical scheme of the invention can be expanded to hundreds or thousands of nodes, and has good expansibility.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a cluster system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an embodiment of a method for constructing a large-scale cluster system according to the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented.
It should be noted that, if not conflicting, the embodiments of the present invention and the features of the embodiments may be combined with each other within the scope of protection of the present invention. Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions and, although a logical order is illustrated in the flow charts, in some cases, the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a schematic composition diagram of an embodiment of a cluster system of the present invention. As shown in fig. 1, the cluster system embodiment mainly includes at least one shared storage module 110, at least one sub-cluster 120, and a scheduling node 130, where:
the shared storage module 110 is a physical storage medium, is used for providing a storage function and providing a certain degree of high availability of data, and can be an FC-SAN disk array (shown as 115 in the figure) or an IP-SAN disk array;
a sub-cluster 120, which includes a plurality of service nodes (shown as 125 in the figure), where all the service nodes construct a cluster high availability relationship, implement many-to-many (N-to-N) redundancy, collectively manage Logical Unit numbers (LUNs, which refer to Logical representations of physical storage) mapped by at least one shared storage module 110, and provide a high availability service for a cluster system;
the scheduling node 130, which is connected to all the sub-clusters 120, is a management center and a monitoring center of the cluster system embodiment of the present invention, and is configured to monitor working states of all the service nodes in each sub-cluster 120, maintain a mapping relationship between each service node and the shared storage module 110, and schedule input/output (I/O) loads of the cluster system, and the like; in this embodiment, the cluster system and the outside are parallel input and output;
the service nodes in the sub-cluster 120 are configured to perform at least one service of data input/output control, protocol conversion, application computation, and the like.
The scheduling node 130 is constructed to be highly available, so as to ensure that the management, monitoring, service scheduling and the like of the whole cluster system are not out of control due to the failure of a single service node. The scheduling node 130 maintains a mapping relationship between each service node and the shared storage module 110, and maps the LUN mapped by at least one shared storage module 110 to two or more service nodes in the sub-cluster, and preferably to all service nodes in the sub-cluster, thereby ensuring that the redundancy is greater than 1, and providing a highly available service, for example, when a certain service node in a certain sub-cluster 120 fails or an overload state occurs in a certain service node according to performance monitoring, the scheduling node 130 may dynamically set a main control authority of the LUN in the cluster system according to a scheduling policy preset by a system administrator, and ensuring the performance of the cluster system, such as high availability, load balancing, and the like.
The scheduling node 130 dynamically collects the utilization rate of each shared storage module 110 and the load condition of each service node in the sub-cluster through each sub-cluster, and schedules the computing resources and the storage resources of the cluster system in real time, so that the working efficiency of the large-scale cluster can be improved.
In a cluster system consisting of a plurality of sub-clusters 120, one to a plurality of cluster services are run. All the sub-clusters 120 forming the cluster system are associated through the scheduling node 130, such as online migration of service nodes among the sub-clusters 120. Different sub-clusters 120 in the cluster system may run different cluster services, and a plurality of sub-clusters 120 may also collectively provide one cluster service.
Large-scale cluster systems typically contain hundreds or even thousands of service nodes, providing tens of GB of throughput bandwidth, and millions or even billions of computing power. The service nodes in each sub-cluster 120 are connected to the shared memory module 110 at the back end.
With reference to the system embodiment shown in fig. 1, fig. 2 shows a flow of an embodiment of a method for constructing a large-scale cluster system according to the present invention. As shown in fig. 2, the embodiment of the construction method mainly includes the following steps:
step S210, setting a shared storage module; the shared storage is used for providing a storage function and providing a data high-availability service function;
step S220, constructing a sub-cluster, and finishing data mapping between the sub-cluster and the shared storage module; the sub-cluster is used for providing a data high-availability service function and carrying out parallel input and output with the outside;
step S230, monitoring the working state of the sub-cluster, maintaining the mapping relationship between the sub-cluster and the shared storage module, and scheduling the input and output loads.
The shared storage module comprises an FC-SAN disk array or an IP-SAN disk array.
Providing a plurality of service nodes, constructing all the service nodes into a cluster with high availability, and constructing the plurality of service nodes into the sub-cluster; the service node is used for completing data input and output control, protocol conversion or application calculation.
Maintaining the mapping relationship between the sub-cluster and the shared storage module, and mapping the LUN mapped by the at least one shared storage module 110 to two or more service nodes in the sub-cluster, preferably to all service nodes in the sub-cluster, thereby ensuring that the redundancy is greater than 1 and providing highly available services.
And dynamically collecting the utilization rate of the shared storage module and the load condition of each service node in the sub-cluster through the sub-cluster, and scheduling the computing resources and the storage resources in real time.
In practical application, basic functions such as monitoring and alarming can be realized on the service node, and a system administrator can know the health state of the cluster system at any time.
The technical scheme of the invention has good stability. According to the technical scheme, a large-scale Network Attached Storage (NAS) cluster or other computer clusters are constructed through a plurality of reliable and stable small-scale die clusters, the problems that the stability of a large-scale cluster system is reduced and uncontrollable along with the increase of the scale are effectively solved under the condition that no hardware equipment is added, and the stability of the whole cluster system is guaranteed by utilizing the high redundancy and the good stability of the small-scale die clusters and the load balancing capacity provided in the sub-clusters and between the sub-clusters to a certain degree.
The technical scheme of the invention has good expandability. The number of the service nodes in the sub-cluster is generally controlled to be between 8 and 16, indexes such as high available stability, delay and the like of the cluster are ideal at the scale, and the oscillation of the whole cluster caused by the failure of a single service node is limited in the sub-cluster. The number of sub-clusters in the whole cluster system is unlimited, so that the scale of the cluster system can be easily expanded to hundreds or thousands of nodes, and the stability of the whole cluster system can be expected and controlled.
The cluster system has good load balancing capability. The master control authority of the storage resources in the sub-cluster, the cluster service and the like can be dynamically adjusted according to application requirements, and the load balance in the sub-cluster is easy to realize. The cluster system can dynamically adjust the mapping relation of the shared storage modules in the sub-clusters and the IO load and the service node affiliation relation among the sub-clusters according to the monitoring of the load and the health state of each service node, so that relatively more nodes are allocated for the high-load sub-clusters, and the overall performance of the cluster is improved. The sub-clusters are transferred on line through the service nodes, the condition that a certain sub-cluster is overloaded and other sub-clusters are lightly loaded is avoided, and the full play of the overall efficiency of the cluster is ensured.
The technical scheme of the invention can also dynamically add or remove the service nodes according to the monitoring of the load and the monitoring state of the service nodes, thereby effectively controlling the processing capacity of the large-scale cluster system.
The cluster system can run a plurality of cluster tasks or applications, and the isolation between the cluster tasks or the applications is set by the scheduling node, so that the quality of service is ensured.
According to the technical scheme, a large-scale Cluster is constructed by a plurality of reliable and stable small-scale mold clusters (Sub-clusters), the defect that redundancy is insufficient in a pairwise high-availability mode and the defect that performance load balancing is limited are overcome, and Cluster oscillation possibly caused by a high-availability mode of all data node clusters is avoided.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein. In addition, those skilled in the art will appreciate that the modules or steps of the invention described above can be implemented by a general purpose computing device, they can be centralized on a single computing device or distributed over a network of multiple computing devices, and alternatively they can be implemented by program code executable by a computing device, such that they can be stored in a storage device and executed by a computing device, or they can be separately fabricated into various integrated circuit modules, or multiple modules or steps thereof can be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Although the embodiments of the present invention have been described above, the above descriptions are only for the convenience of understanding the present invention, and are not intended to limit the present invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A large-scale cluster system is characterized by comprising a shared storage module, a sub-cluster and a scheduling node, wherein:
the shared storage module is used for providing a storage function and a data high-availability service function;
the sub-cluster is used for carrying out data mapping with the shared storage module, providing a high-availability service function of data and carrying out parallel input and output with the outside;
the scheduling node is used for monitoring the working state of the sub-cluster, maintaining the mapping relation between the sub-cluster and the shared storage module, and scheduling the input and output loads.
2. The system of claim 1, wherein:
the shared storage module comprises an FC-SAN disk array or an IP-SAN disk array.
3. The system of claim 1, wherein:
the sub-cluster comprises a plurality of service nodes, and all the service nodes construct a high cluster availability relation and are used for finishing data input and output control, protocol conversion or application calculation.
4. The system of claim 3, wherein:
the scheduling node is used for mapping the logic unit number mapped by at least one shared storage module to two or more service nodes in the sub-cluster.
5. The system of claim 3, wherein:
the dispatching node is used for dynamically collecting the utilization rate of the shared storage module through the sub-cluster, dynamically collecting the load condition of each service node in the sub-cluster, and dispatching the computing resources and the storage resources in real time.
6. A method for constructing a large-scale cluster system is characterized by comprising the following steps:
setting a shared storage module;
constructing a sub-cluster, and completing data mapping between the sub-cluster and the shared storage module;
monitoring the working state of the sub-cluster, maintaining the mapping relation between the sub-cluster and a shared storage module, and scheduling input and output loads;
wherein,
the shared storage is used for providing a storage function and a data high-availability service function;
the sub-clusters are used for providing data high-availability service functions and performing parallel input and output with the outside.
7. The method of claim 1, wherein:
the shared storage module comprises an FC-SAN disk array or an IP-SAN disk array.
8. The method of claim 1, wherein:
providing a plurality of service nodes, constructing all the service nodes into a cluster with high availability, and constructing into at least one sub-cluster;
the service node is used for finishing data input and output control, protocol conversion or application calculation.
9. The method of claim 8, wherein the step of maintaining a mapping of the sub-clusters to shared storage modules comprises:
and mapping the logic unit number mapped by at least one shared storage module to two or more service nodes in the sub-cluster.
10. The system of claim 8, wherein:
and dynamically collecting the utilization rate of the shared storage module through the sub-cluster, dynamically collecting the load condition of each service node in the sub-cluster, and scheduling the computing resources and the storage resources in real time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010101050654A CN101778002B (en) | 2010-02-02 | 2010-02-02 | Large-scale cluster system and building method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010101050654A CN101778002B (en) | 2010-02-02 | 2010-02-02 | Large-scale cluster system and building method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101778002A true CN101778002A (en) | 2010-07-14 |
| CN101778002B CN101778002B (en) | 2012-06-27 |
Family
ID=42514340
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2010101050654A Active CN101778002B (en) | 2010-02-02 | 2010-02-02 | Large-scale cluster system and building method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101778002B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102012891A (en) * | 2010-11-18 | 2011-04-13 | 浪潮(北京)电子信息产业有限公司 | Computer cluster management method, device and system |
| CN102402395A (en) * | 2010-09-16 | 2012-04-04 | 上海中标软件有限公司 | Quorum disk-based non-interrupted operation method for high availability system |
| CN102412988A (en) * | 2011-11-14 | 2012-04-11 | 浪潮(北京)电子信息产业有限公司 | Service information system and method for realizing continuous operation by using same |
| CN103152405A (en) * | 2013-02-19 | 2013-06-12 | 浪潮电子信息产业股份有限公司 | Application method of high availability cluster for storage pool resource |
| CN104504147A (en) * | 2015-01-04 | 2015-04-08 | 华为技术有限公司 | Resource coordination method, device and system for database cluster |
| CN104754008A (en) * | 2013-12-26 | 2015-07-01 | 伊姆西公司 | Network storage node, network storage system and device and method for network storage node |
| CN105141675A (en) * | 2015-08-10 | 2015-12-09 | 浪潮(北京)电子信息产业有限公司 | Method for accessing remote logical equipment through multiple paths, sending end and system |
| CN106293934A (en) * | 2016-07-19 | 2017-01-04 | 浪潮(北京)电子信息产业有限公司 | A kind of cluster system management optimization method and platform |
| CN106713164A (en) * | 2016-12-29 | 2017-05-24 | 北京奇艺世纪科技有限公司 | System, method and apparatus for balancing flow between switch ports |
| CN106790565A (en) * | 2016-12-27 | 2017-05-31 | 中国电子科技集团公司第五十二研究所 | A kind of network attached storage group system |
| CN112416248A (en) * | 2020-11-18 | 2021-02-26 | 海光信息技术股份有限公司 | Method and device for realizing disk array and electronic equipment |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8484213B2 (en) * | 2005-08-31 | 2013-07-09 | International Business Machines Corporation | Heterogenous high availability cluster manager |
| CN1972311A (en) * | 2006-12-08 | 2007-05-30 | 华中科技大学 | A stream media server system based on cluster balanced load |
-
2010
- 2010-02-02 CN CN2010101050654A patent/CN101778002B/en active Active
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102402395A (en) * | 2010-09-16 | 2012-04-04 | 上海中标软件有限公司 | Quorum disk-based non-interrupted operation method for high availability system |
| CN102402395B (en) * | 2010-09-16 | 2014-07-16 | 中标软件有限公司 | Quorum disk-based non-interrupted operation method for high availability system |
| CN102012891A (en) * | 2010-11-18 | 2011-04-13 | 浪潮(北京)电子信息产业有限公司 | Computer cluster management method, device and system |
| CN102012891B (en) * | 2010-11-18 | 2012-10-31 | 浪潮(北京)电子信息产业有限公司 | Computer cluster management method, device and system |
| CN102412988A (en) * | 2011-11-14 | 2012-04-11 | 浪潮(北京)电子信息产业有限公司 | Service information system and method for realizing continuous operation by using same |
| CN103152405A (en) * | 2013-02-19 | 2013-06-12 | 浪潮电子信息产业股份有限公司 | Application method of high availability cluster for storage pool resource |
| CN103152405B (en) * | 2013-02-19 | 2017-05-03 | 浪潮电子信息产业股份有限公司 | Application method of high availability cluster for storage pool resource |
| CN104754008B (en) * | 2013-12-26 | 2019-03-08 | 伊姆西公司 | Network storage node, network store system and the device and method for network storage node |
| CN104754008A (en) * | 2013-12-26 | 2015-07-01 | 伊姆西公司 | Network storage node, network storage system and device and method for network storage node |
| US10498814B2 (en) | 2013-12-26 | 2019-12-03 | EMC IP Holding Company LLC | Network storage node and network storage system |
| CN104504147A (en) * | 2015-01-04 | 2015-04-08 | 华为技术有限公司 | Resource coordination method, device and system for database cluster |
| WO2016107610A1 (en) * | 2015-01-04 | 2016-07-07 | 华为技术有限公司 | Resource coordination method, device, and system for database cluster |
| US10642822B2 (en) | 2015-01-04 | 2020-05-05 | Huawei Technologies Co., Ltd. | Resource coordination method, apparatus, and system for database cluster |
| CN104504147B (en) * | 2015-01-04 | 2018-04-10 | 华为技术有限公司 | A kind of resource coordination method of data-base cluster, apparatus and system |
| CN105141675A (en) * | 2015-08-10 | 2015-12-09 | 浪潮(北京)电子信息产业有限公司 | Method for accessing remote logical equipment through multiple paths, sending end and system |
| CN106293934A (en) * | 2016-07-19 | 2017-01-04 | 浪潮(北京)电子信息产业有限公司 | A kind of cluster system management optimization method and platform |
| CN106293934B (en) * | 2016-07-19 | 2019-02-01 | 浪潮(北京)电子信息产业有限公司 | A kind of cluster system management optimization method and platform |
| CN106790565B (en) * | 2016-12-27 | 2019-11-29 | 中国电子科技集团公司第五十二研究所 | A kind of network attached storage group system |
| CN106790565A (en) * | 2016-12-27 | 2017-05-31 | 中国电子科技集团公司第五十二研究所 | A kind of network attached storage group system |
| CN106713164A (en) * | 2016-12-29 | 2017-05-24 | 北京奇艺世纪科技有限公司 | System, method and apparatus for balancing flow between switch ports |
| CN112416248A (en) * | 2020-11-18 | 2021-02-26 | 海光信息技术股份有限公司 | Method and device for realizing disk array and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101778002B (en) | 2012-06-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101778002B (en) | Large-scale cluster system and building method thereof | |
| Verma et al. | A survey on network methodologies for real-time analytics of massive IoT data and open research issues | |
| CN102857577B (en) | A kind of system and method for cluster-based storage automatic load balancing | |
| US8949847B2 (en) | Apparatus and method for managing resources in cluster computing environment | |
| Guo et al. | The building of cloud computing environment for e-health | |
| CN106020934A (en) | Optimized deployment method based on virtual cluster online migration | |
| CN101937368A (en) | Cloud computation-oriented data center management system | |
| CN103124299A (en) | Distributed block-level storage system in heterogeneous environment | |
| CN105094982A (en) | Multi-satellite remote sensing data processing system | |
| US20200042608A1 (en) | Distributed file system load balancing based on available node capacity | |
| CN101662495A (en) | Backup method, master server, backup servers and backup system | |
| JP2011516998A (en) | Workload scheduling method, system, and computer program | |
| CN103441918A (en) | Self-organizing cluster server system and self-organizing method thereof | |
| CN105975378A (en) | Distributed layering autonomous monitoring and management system facing supercomputer | |
| CN111858033A (en) | Load balancing method based on cluster and multiple processes | |
| CN104021029A (en) | Spatial information cloud computing system and implementing method thereof | |
| Zhou et al. | Goldilocks: Adaptive resource provisioning in containerized data centers | |
| CN104123183A (en) | Cluster assignment dispatching method and device | |
| Fazio et al. | Managing volunteer resources in the cloud | |
| US20150160864A1 (en) | Systems and methods for high availability in multi-node storage networks | |
| CN112039985A (en) | Heterogeneous cloud management method and system | |
| CN104125292A (en) | Data processing device, cloud server and use method thereof | |
| CN105847428A (en) | Mobile cloud platform | |
| CN110647399A (en) | High-performance computing system and method based on artificial intelligence network | |
| CN114936071A (en) | Civil aircraft airborne distributed simulation system based on edge calculation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20201118 Address after: 215100 No. 1 Guanpu Road, Guoxiang Street, Wuzhong Economic Development Zone, Suzhou City, Jiangsu Province Patentee after: SUZHOU LANGCHAO INTELLIGENT TECHNOLOGY Co.,Ltd. Address before: 100085 Beijing, Haidian District on the road to information on the ground floor, building 2-1, No. 1, C Patentee before: Inspur (Beijing) Electronic Information Industry Co.,Ltd. |
|
| TR01 | Transfer of patent right |