CN112506643A - Load balancing method and device of distributed system and electronic equipment - Google Patents
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Abstract
The invention provides a load balancing method and device of a distributed system and electronic equipment, belongs to the technical field of computer internet, and solves the technical problem that data storage imbalance among nodes can be generated along with operation of online tasks and reading and writing of data. Calculating a load value of each resource node; determining a load mean value of the node cluster; and determining the resource node with the maximum difference between the load value and the load average value of the node cluster as a target resource node. According to the invention, the load balance degree of the distributed cluster is calculated by adopting the variance, and the new task is preferentially deployed on the node with the largest average difference with the cluster load so as to improve the load balance degree of the distributed cluster, so that the load balance of the cluster can be effectively ensured, the cluster storage utilization rate and the network throughput are influenced by the system load state, meanwhile, the balanced cluster can effectively avoid hot spots, and the system response speed is improved.
Description
Technical Field
The invention relates to the technical field of computer internet, in particular to a load balancing method and device of a distributed system and electronic equipment.
Background
With the rapid development of cloud computing and internet technologies, mass data are generated due to the increasing interaction between information requirements and the internet, and a conventional file system for storing data by using a single server cannot well meet the storage requirement of the mass data, so that a storage system for storing a large amount of data is needed. The distributed file system solves the limitation of single machine storage based on the design of a server client mode, and data is cooperatively stored among a plurality of servers. For the storage of mass data in a cloud environment, a distributed file system relates to a large number of data server nodes and network equipment, the nodes can be distributed in various places, the configuration of the nodes is different, imbalance of data storage among the nodes can be generated along with the operation of online tasks and the reading and writing of data, and the balance degree of the data storage has important significance on the system performance.
Load balancing is an important issue in distributed file systems. System load conditions affect cluster storage utilization and network throughput. Meanwhile, the balanced cluster can effectively avoid hot spots and improve the response speed of the system.
Disclosure of Invention
The invention aims to provide a load balancing method and device of a distributed system and electronic equipment, and the load balancing method and device and the electronic equipment are used for solving the technical problems that existing nodes are distributed in various places, the configuration of the nodes is different, and data storage imbalance among the nodes is generated along with operation of on-line tasks and reading and writing of data.
In a first aspect, the present invention provides a load balancing method for a distributed system, including:
determining a dynamic index parameter of each resource node, and calculating a load value of each resource node based on the dynamic index parameter of each resource node and a preset dynamic weighting formula;
determining a load mean value of the node cluster based on the load value of each resource node; wherein the node cluster represents a set of all resource nodes;
determining the resource node with the largest difference between the load value and the load average value of the node cluster as a target resource node; wherein the target resource node is used for deploying a current task.
Further, after determining a load average of the node cluster based on the load value of each resource node, the method further includes:
determining a load variance value of the node cluster based on the load value of each resource node, the load mean value of the node cluster and a preset variance formula; wherein the load variance value of the node cluster represents a load balance of the node cluster.
Further, calculating a load value of each resource node based on the dynamic index parameter of each resource node and a preset dynamic weighting formula, including:
determining a dynamic index parameter of the node cluster based on the dynamic index parameter of each resource node;
and calculating the load value of each resource node based on the dynamic index parameter of each resource node, the dynamic index parameter of the node cluster and a preset dynamic weighting formula.
Further, the dynamic index parameters of the resource node include at least two parameters of a first resource request amount, a first resource available time and a first resource service strength; wherein the first resource request amount represents an average number of service requests received by the resource node in a unit time, the first resource available time represents a time for the resource node to be in an available state, the first resource service strength represents a ratio of an average time taken for the resource node to complete each service request to a target time interval, and the target time interval represents a time interval for the resource node to recover from an unavailable state to an available state.
Further, the method comprises the following steps:
acquiring the parallel service capability of the resource nodes;
and calculating the first resource service strength based on the first resource request quantity, the first resource available time and the parallel service capability of the resource node.
Further, the dynamic index parameter of the node cluster includes at least two parameters of a second resource request amount, a second resource available time and a second resource service strength.
Further, the preset dynamic weighting formula is as follows:
wherein ljIs a resource nodej(j is not less than 1 and not more than num) and rjIs a resource nodejFirst amount of resource requests, hjIs a resource nodejIs available to the first resource, qjIs a resource nodejFirst resource service strength, RjSecond resource request amount for node cluster, HjTime available for a second resource, Q, of the node clusterjServing a second resource strength for a cluster of nodes, w1，w2And w3Respectively represent rj/Rj、hj/HjAnd q isj/QjThe weight of (a), the w1，w2And w3Satisfies a predetermined rule, and w1， w2And w3The value of (c) is dynamically adjusted by the preset rule.
In a second aspect, the present invention further provides a load balancing apparatus for a distributed system, including:
the determining and calculating unit is used for determining the dynamic index parameter of each resource node and calculating the load value of each resource node based on the dynamic index parameter of each resource node and a preset dynamic weighting formula;
a first determining unit, configured to determine a load average of the node cluster based on the load value of each resource node; wherein the node cluster represents a set of all resource nodes;
a second determining unit, configured to determine, as a target resource node, a resource node whose load value and the load mean value of the node cluster have a largest difference; wherein the target resource node is used for deploying a current task.
In a third aspect, the present invention further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program operable on the processor, and the processor implements the steps of the load balancing method for the distributed system when executing the computer program.
In a fourth aspect, the present invention also provides a computer-readable storage medium, having stored thereon a computer program, which when executed by a processor, implements the steps of the load balancing method of the distributed system,
according to the load balancing method, the load balancing device and the electronic equipment of the distributed system, a new node load calculation index is provided, the loads of the nodes are clustered in a dynamic weighting mode, and the variance is an important calculation method for measuring the discrete data balance degree. According to the invention, the load balance degree of the distributed cluster is calculated by adopting the variance, and the new task is preferentially deployed on the node with the largest average difference with the cluster load so as to improve the load balance degree of the distributed cluster, so that the load balance of the cluster can be effectively ensured, the cluster storage utilization rate and the network throughput are influenced by the system load state, meanwhile, the balanced cluster can effectively avoid hot spots, and the system response speed is improved.
Accordingly, the load balancing method and apparatus for a distributed system, the electronic device and the computer-readable storage medium provided by the embodiments of the present invention also have the above technical effects.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a work flow of a load balancing method according to an embodiment of the present invention;
fig. 2 is a block diagram illustrating an operation principle of an electronic device according to an embodiment of the present invention.
In the figure: 800 electronic device, 801 memory, 802 processor, 803 bus, 804 communication interface.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "comprising" and "having," and any variations thereof, as referred to in embodiments of the present invention, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but may alternatively include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1-2, a load balancing method for a distributed system according to an embodiment of the present invention improves resource utilization and network throughput of the distributed file system. The method comprises the following steps:
determining a dynamic index parameter of each resource node, and calculating a load value of each resource node based on the dynamic index parameter of each resource node and a preset dynamic weighting formula;
determining a load average value of the node cluster based on the load value of each resource node; wherein a node cluster represents a collection of all resource nodes;
determining the resource node with the largest difference between the load value and the load mean value of the node cluster as a target resource node; wherein the target resource node is used for deploying the current task.
The invention provides a new node load calculation index, and clusters the load of nodes by adopting a dynamic weighting mode, wherein the variance is an important calculation method for measuring the discrete data balance degree. According to the invention, the load balance degree of the distributed cluster is calculated by adopting the variance, and the new task is preferentially deployed on the node with the largest average difference with the cluster load so as to improve the load balance degree of the distributed cluster, so that the load balance of the cluster can be effectively ensured, the cluster storage utilization rate and the network throughput are influenced by the system load state, meanwhile, the balanced cluster can effectively avoid hot spots, and the system response speed is improved.
In this embodiment of the present invention, after determining the load average of the node cluster based on the load value of each resource node, the method further includes:
determining a load variance value of the node cluster based on the load value of each resource node, the load mean value of the node cluster and a preset variance formula; and the load variance value of the node cluster represents the load balance degree of the node cluster.
In the embodiment of the present invention, calculating the load value of each resource node based on the dynamic index parameter of each resource node and a preset dynamic weighting formula includes:
determining a dynamic index parameter of the node cluster based on the dynamic index parameter of each resource node;
and calculating the load value of each resource node based on the dynamic index parameter of each resource node, the dynamic index parameter of the node cluster and a preset dynamic weighting formula.
In the embodiment of the invention, the dynamic index parameters of the resource nodes comprise at least two parameters of a first resource request quantity, a first resource available time and a first resource service strength; the first resource request quantity represents the average number of service requests received by the resource node in unit time, the first resource available time represents the time of the resource node in an available state, the first resource service strength represents the ratio of the average time taken by the resource node to complete each service request to a target time interval, and the target time interval represents the time interval for the resource node to recover from the unavailable state to the available state.
The embodiment of the invention comprises the following steps:
acquiring the parallel service capability of the resource nodes;
and calculating the first resource service strength based on the first resource request quantity, the first resource available time and the parallel service capacity of the resource node.
In the embodiment of the present invention, the dynamic index parameter of the node cluster includes at least two parameters of a second resource request amount, a second resource available time, and a second resource service strength.
In the embodiment of the present invention, the preset dynamic weighting formula is:
wherein ljIs a resource nodej(j is not less than 1 and not more than num) and rjIs a resource nodejFirst amount of resource requests, hjIs a resource nodejIs available to the first resource, qjIs a resource nodejFirst resource service strength, RjSecond resource request amount for node cluster, HjTime available for a second resource, Q, of the node clusterjServing a second resource of the node cluster with a strength, w1，w2And w3Respectively represent rj/Rj、hj/HjAnd q isj/QjWeight of, w1，w2And w3Satisfies a predetermined rule, and w1，w2And w3The value of (c) is dynamically adjusted by a preset rule.
An embodiment of the present invention further provides a load balancing apparatus for a distributed system, including:
the determining and calculating unit is used for determining the dynamic index parameter of each resource node and calculating the load value of each resource node based on the dynamic index parameter of each resource node and a preset dynamic weighting formula;
the first determining unit is used for determining the load mean value of the node cluster based on the load value of each resource node; wherein a node cluster represents a collection of all resource nodes;
the second determining unit is used for determining the resource node with the load value which has the largest difference with the load mean value of the node cluster as a target resource node; wherein the target resource node is used for deploying the current task.
The embodiment of the present invention further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program that can run on the processor, and the processor implements the steps of the load balancing method of the distributed system when executing the computer program.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the load balancing method for a distributed system are implemented.
The embodiment of the invention comprises the following specific implementation steps:
(1) computation of node load
The node resource load assessment is to extract a node resource load assessment index from the availability monitoring information of resources to assess the node resource load state and provide more accurate real-time performance information of the resources for task scheduling, the cloud environment is dynamically changed, the current node resource load assessment mainly comprises some physical performance indexes or static indexes such as storage capacity, CPU computing capacity, network bandwidth and the like, and the actual load condition of the cloud resources is difficult to reflect due to the non-symbolic and uncertainty of the indexes.
In the embodiment of the invention, the load state and the actual available capacity of the current resource node are described by adopting 3 dynamic index parameters of resource request amount r, resource available time h and resource service strength q.
Assume that a cluster Node contains num resources, i.e., Node { Node1,node2,…,nodenum}。
1) Resource request amount r: resource nodejThe average number of service requests received per unit time. Assume resource nodej(j is more than or equal to 1 and less than or equal to num) and the resource request amount is rj(j is more than or equal to 1 and less than or equal to num), the resource request quantity of the Node is as follows:
2) resource available time h: time h when the resource node is in an available state.
3) Resource service strength q: the ratio of the average time for a resource node to complete a service request to the time interval between the resource starting from unavailable and returning to available. P is the parallel service capability of the resource node, then
Assume resource nodej(j is more than or equal to 1 and less than or equal to num) parallel service capability pj(j is more than or equal to 1 and less than or equal to num), the resource Node parallel service capability is as follows:
P＝p1+p2+…+pnum (1.3)。
the embodiment of the invention adopts a dynamic weighting algorithm to evaluate the self load state of the resource node and calculate the load value l of the resource nodejAnd providing basis for the scheduling and migration of the application container.
ljIs defined as shown in formula (1.4).
The embodiment of the invention applies double threshold1And threshold2(threshold1＜threshold2) The policy of (2) divides the load of the resource node into overload, normal and idle.
By dynamically adjusting the weight wkTo change rj/Rj、hj/HjAnd q isj/QjTo ljIs adaptively and dynamically adjusted in each evaluation period by the formula (1.5)k：
wk＝w0+μ(w1-w0) (1.5)；
Wherein, w0,w1Are in the range of [0,0.5 respectively]，[0,1]And w is1＞w0(ii) a And mu is a value in the range of [0, 1%]The random number of (2).
(2) Calculating the balance of cluster load
Assume that a cluster Node includes num nodes, i.e., Node { Node1,node2,…,nodenum}. The load of each node of the cluster is L { L }1,l2,…lnumH, the mean value of the cluster load is lj：
Variance of cluster load SL 2As shown in equation (1.8):
(3) selecting a node
Calculate each node and ljAnd selecting the node with the minimum difference value as the deployment node of the new task. When a plurality of nodes with the same difference value exist, one node is randomly selected as a deployment node.
In the embodiment of the invention, a new load calculation index is adopted, the load of the node is calculated by using a dynamic weighting mode, and the load condition of the distributed cluster is measured by using variance.
The embodiment of the invention mainly comprises the following steps:
the method comprises the following steps: and providing a new load calculation index, and calculating the load condition of the node by using a dynamic weighting method.
Step two: and calculating the load balance degree of the distributed cluster by adopting a variance method.
Step three: and deploying the task on the node with the largest mean difference with the cluster load balance.
As shown in fig. 2, an electronic device 800 according to an embodiment of the present invention includes a memory 801 and a processor 802, where the memory stores a computer program that is executable on the processor, and the processor executes the computer program to implement the steps of the method according to the embodiment.
As shown in fig. 2, the electronic device further includes: a bus 803 and a communication interface 804, the processor 802, the communication interface 804, and the memory 801 being connected by the bus 803; the processor 802 is used to execute executable modules, such as computer programs, stored in the memory 801.
The Memory 801 may include a high-speed Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 804 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 803 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 2, but this does not indicate only one bus or one type of bus.
The memory 801 is used for storing a program, the processor 802 executes the program after receiving an execution instruction, and the method performed by the apparatus defined by the process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 802, or implemented by the processor 802.
The processor 802 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 802. The Processor 802 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 801, and the processor 802 reads the information in the memory 801 and completes the steps of the method in combination with the hardware thereof.
Corresponding to the method, the embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores machine executable instructions, and when the computer executable instructions are called and executed by a processor, the computer executable instructions cause the processor to execute the steps of the method.
The apparatus provided by the embodiment of the present invention may be specific hardware on the device, or software or firmware installed on the device, etc. The device provided by the embodiment of the present invention has the same implementation principle and technical effect as the method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the method embodiments without reference to the device embodiments. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the foregoing systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
For another example, a division of elements into only one logical division may be implemented in a different manner, and multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; and the modifications, changes or substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention. Are intended to be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A load balancing method for a distributed system, comprising:
determining a dynamic index parameter of each resource node, and calculating a load value of each resource node based on the dynamic index parameter of each resource node and a preset dynamic weighting formula;
determining a load mean value of the node cluster based on the load value of each resource node; wherein the node cluster represents a set of all resource nodes;
determining the resource node with the largest difference between the load value and the load average value of the node cluster as a target resource node; wherein the target resource node is used for deploying a current task.
2. The method of claim 1, after determining a mean load value of the node cluster based on the load value of each resource node, further comprising:
determining a load variance value of the node cluster based on the load value of each resource node, the load mean value of the node cluster and a preset variance formula; wherein the load variance value of the node cluster represents a load balance of the node cluster.
3. The method according to claim 1, wherein calculating the load value of each resource node based on the dynamic index parameter of each resource node and a preset dynamic weighting formula comprises:
determining a dynamic index parameter of the node cluster based on the dynamic index parameter of each resource node;
and calculating the load value of each resource node based on the dynamic index parameter of each resource node, the dynamic index parameter of the node cluster and a preset dynamic weighting formula.
4. The method of claim 3, wherein the dynamic index parameters of the resource node comprise at least two parameters of a first resource request amount, a first resource available time and a first resource service strength; wherein the first resource request amount represents an average number of service requests received by the resource node in a unit time, the first resource available time represents a time for the resource node to be in an available state, the first resource service strength represents a ratio of an average time taken for the resource node to complete each service request to a target time interval, and the target time interval represents a time interval for the resource node to recover from an unavailable state to an available state.
5. The method of claim 4, comprising:
acquiring the parallel service capability of the resource nodes;
and calculating the first resource service strength based on the first resource request quantity, the first resource available time and the parallel service capability of the resource node.
6. The method of claim 5, wherein the dynamic index parameters of the node cluster comprise at least two of a second resource request amount, a second resource available time, and a second resource service strength.
7. The method of claim 6, wherein the predetermined dynamic weighting formula is:
wherein ljIs a resource nodej(j is not less than 1 and not more than num) and rjIs a resource nodejFirst amount of resource requests, hjIs a resource nodejIs available to the first resource, qjIs a resource nodejFirst resource service strength, RjSecond resource request amount for node cluster, HjTime available for a second resource, Q, of the node clusterjServing a second resource strength for a cluster of nodes, w1，w2And w3Respectively represent rj/Rj、hj/HjAnd q isj/QjThe weight of (a), the w1，w2And w3Satisfies a predetermined rule, and w1，w2And w3The value of (c) is dynamically adjusted by the preset rule.
8. A load balancing apparatus for a distributed system, comprising:
the determining and calculating unit is used for determining the dynamic index parameter of each resource node and calculating the load value of each resource node based on the dynamic index parameter of each resource node and a preset dynamic weighting formula;
a first determining unit, configured to determine a load average of the node cluster based on the load value of each resource node; wherein the node cluster represents a set of all resource nodes;
a second determining unit, configured to determine, as a target resource node, a resource node whose load value and the load mean value of the node cluster have a largest difference; wherein the target resource node is used for deploying a current task.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program operable on the processor, and wherein the processor implements the steps of the method of any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113055479A (en) * | 2021-03-17 | 2021-06-29 | 中国工商银行股份有限公司 | Self-adaptive processing method, device and system for distributed service cluster load |
| CN113342517A (en) * | 2021-05-17 | 2021-09-03 | 北京百度网讯科技有限公司 | Resource request forwarding method and device, electronic equipment and readable storage medium |
| CN113377495A (en) * | 2021-05-17 | 2021-09-10 | 杭州中港科技有限公司 | Method for optimizing docker cluster deployment based on heuristic ant colony algorithm |
| CN113672372A (en) * | 2021-08-30 | 2021-11-19 | 福州大学 | Multi-edge cooperative load balancing task scheduling method based on reinforcement learning |
| CN114466019A (en) * | 2022-04-11 | 2022-05-10 | 阿里巴巴（中国）有限公司 | Distributed computing system, load balancing method, device and storage medium |
| CN114546610A (en) * | 2022-01-17 | 2022-05-27 | 山西省信息通信网络技术保障中心 | Mass data distributed desensitization device |
| CN115396515A (en) * | 2022-08-19 | 2022-11-25 | 中国联合网络通信集团有限公司 | Resource scheduling method, device and storage medium |
| CN116089847A (en) * | 2023-04-06 | 2023-05-09 | 国网湖北省电力有限公司营销服务中心(计量中心) | Distributed adjustable resource clustering method based on covariance agent |
-
2020
- 2020-10-12 CN CN202011081934.4A patent/CN112506643A/en not_active Withdrawn
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113055479A (en) * | 2021-03-17 | 2021-06-29 | 中国工商银行股份有限公司 | Self-adaptive processing method, device and system for distributed service cluster load |
| CN113342517A (en) * | 2021-05-17 | 2021-09-03 | 北京百度网讯科技有限公司 | Resource request forwarding method and device, electronic equipment and readable storage medium |
| CN113377495A (en) * | 2021-05-17 | 2021-09-10 | 杭州中港科技有限公司 | Method for optimizing docker cluster deployment based on heuristic ant colony algorithm |
| CN113377495B (en) * | 2021-05-17 | 2024-02-27 | 杭州中港科技有限公司 | Dock cluster deployment optimization method based on heuristic ant colony algorithm |
| CN113672372A (en) * | 2021-08-30 | 2021-11-19 | 福州大学 | Multi-edge cooperative load balancing task scheduling method based on reinforcement learning |
| CN113672372B (en) * | 2021-08-30 | 2023-08-08 | 福州大学 | Multi-edge collaborative load balancing task scheduling method based on reinforcement learning |
| CN114546610A (en) * | 2022-01-17 | 2022-05-27 | 山西省信息通信网络技术保障中心 | Mass data distributed desensitization device |
| CN114546610B (en) * | 2022-01-17 | 2022-11-18 | 山西省信息通信网络技术保障中心 | Mass data distributed desensitization device |
| CN114466019A (en) * | 2022-04-11 | 2022-05-10 | 阿里巴巴（中国）有限公司 | Distributed computing system, load balancing method, device and storage medium |
| CN114466019B (en) * | 2022-04-11 | 2022-09-16 | 阿里巴巴（中国）有限公司 | Distributed computing system, load balancing method, device and storage medium |
| CN115396515A (en) * | 2022-08-19 | 2022-11-25 | 中国联合网络通信集团有限公司 | Resource scheduling method, device and storage medium |
| CN116089847A (en) * | 2023-04-06 | 2023-05-09 | 国网湖北省电力有限公司营销服务中心(计量中心) | Distributed adjustable resource clustering method based on covariance agent |
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