CN105007312A - Method and system for controlling adaptive load-balancing of cloud computing server - Google Patents
Method and system for controlling adaptive load-balancing of cloud computing server Download PDFInfo
- Publication number
- CN105007312A CN105007312A CN201510389894.2A CN201510389894A CN105007312A CN 105007312 A CN105007312 A CN 105007312A CN 201510389894 A CN201510389894 A CN 201510389894A CN 105007312 A CN105007312 A CN 105007312A
- Authority
- CN
- China
- Prior art keywords
- server
- service request
- node
- cloud computing
- weights
- 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.)
- Pending
Links
Classifications
-
- 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
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/10—Flow control; Congestion control
- H04L47/26—Flow control; Congestion control using explicit feedback to the source, e.g. choke packets
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/50—Queue scheduling
- H04L47/62—Queue scheduling characterised by scheduling criteria
- H04L47/625—Queue scheduling characterised by scheduling criteria for service slots or service orders
- H04L47/6275—Queue scheduling characterised by scheduling criteria for service slots or service orders based on priority
-
- 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
- H04L67/1004—Server selection for load balancing
- H04L67/1008—Server selection for load balancing based on parameters of servers, e.g. available memory or workload
-
- 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
- H04L67/1004—Server selection for load balancing
- H04L67/1025—Dynamic adaptation of the criteria on which the server selection is based
-
- 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
- H04L67/50—Network services
- H04L67/60—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources
- H04L67/61—Scheduling or organising the servicing of application requests, e.g. requests for application data transmissions using the analysis and optimisation of the required network resources taking into account QoS or priority requirements
Abstract
The invention relates to the field of computers and discloses a method for controlling adaptive load-balancing of a cloud computing server. The method comprises the steps as follows: detecting response time of a service request, calculating the priority of the service request, adding the service request into a service request queue, and dispatching and distributing the service request to a corresponding server; detecting and feeding back node parameter information of the server, calculating node distribution weight of the server, and updating the weight of the server node; dynamically collecting real-time load conditions of each server, analyzing and re-calculating the performance of each server in a cluster, evaluating and monitoring whether there is a network congestion risk or not, and re-distributing the service request in the request queue to the server according to an investigating result. The invention further discloses a system for controlling adaptive load-balancing of the cloud computing server. The method and the system for controlling adaptive load-balancing of the cloud computing server of the invention could balance the load of each application server to the largest extent so as to effectively configure system resources.
Description
Technical field
The present invention relates to computer realm, particularly relate to a kind of cloud computing server adaptive load balancing control method and control system.
Background technology
In next generation network, have employed Softswitch technology, what achieve that Call-Control1 provides with business is separated.Application server is introduced, for third-party business provides exploitation and operation platform in soft switchcall server.In order to improve the Large Copacity demand meeting operation system, multiple application server is needed to form group system.Each service server in cluster all can install a configuration client software, the operating state of self is regularly reported to the server being referred to as configuration center, when configuration center server does not receive the state reporting of a certain service server within a period of time, then think that this service server breaks down, this service server is rejected to outside cluster, and when configuration center server receive be excluded due to fault the operating state that the service server outside cluster reports time, then think that this service server is resumed work, this service server is added cluster.
For the ease of the smooth access service server of client, connect each access server timing of client from the service server list configuration center server acquisition cluster, after access server receives the request bag of client, adopt load-balancing algorithm by request Packet forwarding to corresponding service server process.
Wherein, load-balancing algorithm is: by configuration center server in advance for the every platform service server in cluster arranges maximum quota, wherein, service server disposal ability is stronger, quota value is larger, and access server is according to the ratio dispense request bag of these maximum quota values.Such as, if having 3 service servers in service server list, maximum quota is respectively 100,100,200, so, in unit interval, if access server has 800 to ask bag to need distribution, the request bag that these 3 service servers receive respectively is 200,200,400.
From the visual angle of access server, the service quality of service server, not only depend on the disposal ability of service server self, also depend on the network bandwidth between the two, network delay, and the change etc. of this service server external environment condition, and the load-balancing algorithm of existing employing, only according to the maximum quota value ratio dispense request bag of service server, namely the disposal ability of service server self is only considered, and when the network leading to service server occurs congested, packet loss, or during the decline of this service server self performance, if access server is also according to the ratio dispense request bag of maximum quota value, this can cause really can not realizing load balancing between service server.
State in realization in the process of load distribution, inventor finds that in prior art, at least there are the following problems: load is distributed on each application server fifty-fifty, do not consider load capacity and the actual loading situation of each server, the server had may be caused to be in high load condition for a long time, and some servers are idle for a long time, waste system resource.
Summary of the invention
For overcoming the deficiencies in the prior art, the object of the invention is: provide a kind of cloud computing server adaptive load balancing control method and control system can balance the load of each application server to greatest extent, effective allocating system resource.
In order to solve the technical problem in background technology, the invention provides a kind of cloud computing server adaptive load balancing control method, comprising the following steps:
The response time of detection service request, calculate the priority of described service request, include described service request in service request queue, according to priority, each service request in described request queue is dispatched, described service request is distributed to corresponding server successively;
Detect and feed back the node parameter information of described server, according to node parameter information and the priority of service request being assigned to this server, the peer distribution weights of calculation server, take out the peer distribution weights of each server, and upgrade the weights of server node;
The real time load situation of each server of dynamic acquisition, analyze and each server performance in computing cluster again, analysis result is examined, assesses and monitor whether there is network congestion risk, according to the service request in the queue of described examination result relocation request to server.
Particularly, the real time load situation of described each server of dynamic acquisition comprises:
Monitor and collect the load information of each node in cluster, the client process on server host node is to the work of acquisition component report server self load condition.
Particularly, described detection also feeds back the node parameter information of described server, and according to node parameter information and the priority of service request being assigned to this server, the peer distribution weights of calculation server comprise:
Detect the dynamic and static state parameter of server node, calculate indicator of distribution parameter, response time, dynamic parameter, static parameter and indicator of distribution parameter are stored to data structure, utilize the peer distribution weights of cloud-based adaptive genetic algorithm calculation server, and automatically generate crossover probability and mutation probability according to the fitness value of genetic algorithm.
Particularly, according to priority, each service request in described request queue is dispatched described, before described service request is distributed to corresponding server successively, also comprises:
Using the error between response time of detecting and ideal response time as control variables, the setup control cycle, at each control cycle, minimum recurrence square law is utilized automatically to upgrade the operational factor of cluster server, according to the operational factor that described control variables and renewal obtain, control the stock number of each server.
Particularly, the peer distribution weights of described calculation server also comprise:
Setting deviation threshold and threshold value compare cycle, at each threshold value compare cycle, the difference of the distribution weights calculated and server current weight is made comparisons with described deviation threshold, if difference is greater than deviation threshold, then upgrade weights to server node, and by service request state to be allocated for this server-tag, if difference is less than deviation threshold, then do not upgrade weights to server node, described server does not accept new service request.
Present invention also offers a kind of cloud computing server adaptive load balancing control system, comprise with lower module:
Response time sensing module, for detecting the response time of service request;
Scheduler module, for calculating the priority of described service request, includes described service request in service request queue, dispatches, described service request is distributed to corresponding server successively according to priority to each service request in described request queue;
Detection module, for detecting and feeding back the node parameter information of described server;
Weight computing module, for according to node parameter information and the priority of service request being assigned to this server, the peer distribution weights of calculation server;
Right value update module, for taking out the peer distribution weights of each server, and upgrades the weights of server node;
Assessment monitoring module, for the real time load situation of each server of dynamic acquisition, analyze and each server performance in computing cluster again, analysis result is examined, assess and monitor whether there is network congestion risk, according to the service request in the queue of described examination result relocation request to server.
Preferably, described assessment monitoring module is for monitoring and collect the load information of each node in cluster, and the client process on server host node is to the work of acquisition component report server self load condition.
Particularly, described detection module is for detecting the dynamic and static state parameter of server node, described weight computing module is used for calculating indicator of distribution parameter, and response time, dynamic parameter, static parameter and indicator of distribution parameter are stored to data structure, described right value update module for utilizing the peer distribution weights of cloud-based adaptive genetic algorithm calculation server, and generates crossover probability and mutation probability automatically according to the fitness value of genetic algorithm.
Cloud computing server adaptive load balancing control system provided by the invention also comprises renewal control module, for the error between the response time that will detect and ideal response time as control variables, the setup control cycle, at each control cycle, minimum recurrence square law is utilized automatically to upgrade the operational factor of cluster server, according to the operational factor that described control variables and renewal obtain, control the stock number of each server.
Particularly, described weight computing module is also for setting deviation threshold and threshold value compare cycle, at each threshold value compare cycle, the difference of the distribution weights calculated and server current weight is made comparisons with described deviation threshold, if difference is greater than deviation threshold, then upgrades weights to server node, and by service request state to be allocated for this server-tag, if difference is less than deviation threshold, then do not upgrade weights to server node, described server does not accept new service request.
Adopt technique scheme, cloud computing server adaptive load balancing control method of the present invention and control system can balance the load of each application server to greatest extent, effective allocating system resource.
Accompanying drawing explanation
In order to be illustrated more clearly in technical scheme of the present invention, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the present invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
Fig. 1 is the schematic block diagram of group system in prior art;
Fig. 2 is the schematic flow sheet of the cloud computing server adaptive load balancing control method that the embodiment of the present invention provides;
Fig. 3 is the structured flowchart of the cloud computing server adaptive load balancing control system that the embodiment of the present invention provides.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, the every other embodiment that those of ordinary skill in the art obtain under the prerequisite of not making creative work, all belongs to the scope of protection of the invention.
The schematic flow sheet of the cloud computing server adaptive load balancing control method that embodiment 1: Fig. 2 provides for the embodiment of the present invention, can clearly be seen that from figure, the cloud computing server adaptive load balancing control method that the present embodiment provides comprises the following steps:
The response time of detection service request, calculate the priority of described service request, include described service request in service request queue, according to priority, each service request in described request queue is dispatched, described service request is distributed to corresponding server successively;
Detect and feed back the node parameter information of described server, according to node parameter information and the priority of service request being assigned to this server, the peer distribution weights of calculation server, take out the peer distribution weights of each server, and upgrade the weights of server node;
The real time load situation of each server of dynamic acquisition, analyze and each server performance in computing cluster again, analysis result is examined, assesses and monitor whether there is network congestion risk, according to the service request in the queue of described examination result relocation request to server.
Particularly, the real time load situation of described each server of dynamic acquisition comprises:
Monitor and collect the load information of each node in cluster, the client process on server host node is to the work of acquisition component report server self load condition.
Particularly, described detection also feeds back the node parameter information of described server, and according to node parameter information and the priority of service request being assigned to this server, the peer distribution weights of calculation server comprise:
Detect the dynamic and static state parameter of server node, calculate indicator of distribution parameter, response time, dynamic parameter, static parameter and indicator of distribution parameter are stored to data structure, utilize the peer distribution weights of cloud-based adaptive genetic algorithm calculation server, and automatically generate crossover probability and mutation probability according to the fitness value of genetic algorithm.
Particularly, according to priority, each service request in described request queue is dispatched described, before described service request is distributed to corresponding server successively, also comprises:
Using the error between response time of detecting and ideal response time as control variables, the setup control cycle, at each control cycle, minimum recurrence square law is utilized automatically to upgrade the operational factor of cluster server, according to the operational factor that described control variables and renewal obtain, control the stock number of each server.
Particularly, the peer distribution weights of described calculation server also comprise:
Setting deviation threshold and threshold value compare cycle, at each threshold value compare cycle, the difference of the distribution weights calculated and server current weight is made comparisons with described deviation threshold, if difference is greater than deviation threshold, then upgrade weights to server node, and by service request state to be allocated for this server-tag, if difference is less than deviation threshold, then do not upgrade weights to server node, described server does not accept new service request.
The structured flowchart of the cloud computing server adaptive load balancing control system that embodiment 2: Fig. 3 provides for the embodiment of the present invention, can clearly be seen that from figure, the cloud computing server adaptive load balancing control system that the present embodiment provides comprises with lower module:
Response time sensing module, for detecting the response time of service request;
Scheduler module, for calculating the priority of described service request, includes described service request in service request queue, dispatches, described service request is distributed to corresponding server successively according to priority to each service request in described request queue;
Detection module, for detecting and feeding back the node parameter information of described server;
Weight computing module, for according to node parameter information and the priority of service request being assigned to this server, the peer distribution weights of calculation server;
Right value update module, for taking out the peer distribution weights of each server, and upgrades the weights of server node;
Assessment monitoring module, for the real time load situation of each server of dynamic acquisition, analyze and each server performance in computing cluster again, analysis result is examined, assess and monitor whether there is network congestion risk, according to the service request in the queue of described examination result relocation request to server.
Preferably, described assessment monitoring module is for monitoring and collect the load information of each node in cluster, and the client process on server host node is to the work of acquisition component report server self load condition.
Particularly, described detection module is for detecting the dynamic and static state parameter of server node, described weight computing module is used for calculating indicator of distribution parameter, and response time, dynamic parameter, static parameter and indicator of distribution parameter are stored to data structure, described right value update module for utilizing the peer distribution weights of cloud-based adaptive genetic algorithm calculation server, and generates crossover probability and mutation probability automatically according to the fitness value of genetic algorithm.
Cloud computing server adaptive load balancing control system provided by the invention also comprises renewal control module, for the error between the response time that will detect and ideal response time as control variables, the setup control cycle, at each control cycle, minimum recurrence square law is utilized automatically to upgrade the operational factor of cluster server, according to the operational factor that described control variables and renewal obtain, control the stock number of each server.
Particularly, described weight computing module is also for setting deviation threshold and threshold value compare cycle, at each threshold value compare cycle, the difference of the distribution weights calculated and server current weight is made comparisons with described deviation threshold, if difference is greater than deviation threshold, then upgrades weights to server node, and by service request state to be allocated for this server-tag, if difference is less than deviation threshold, then do not upgrade weights to server node, described server does not accept new service request.
Cloud computing server adaptive load balancing control method of the present invention and control system can balance the load of each application server to greatest extent, effective allocating system resource.
Above disclosedly be only several preferred embodiment of the present invention, certainly can not limit the interest field of the present invention with this, therefore according to the equivalent variations that the claims in the present invention are done, still belong to the scope that the present invention is contained.
Claims (10)
1. a cloud computing server adaptive load balancing control method, is characterized in that, comprises the following steps:
The response time of detection service request, calculate the priority of described service request, include described service request in service request queue, according to priority, each service request in described request queue is dispatched, described service request is distributed to corresponding server successively;
Detect and feed back the node parameter information of described server, according to node parameter information and the priority of service request being assigned to this server, the peer distribution weights of calculation server, take out the peer distribution weights of each server, and upgrade the weights of server node;
The real time load situation of each server of dynamic acquisition, analyze and each server performance in computing cluster again, analysis result is examined, assesses and monitor whether there is network congestion risk, according to the service request in the queue of described examination result relocation request to server.
2. cloud computing server adaptive load balancing control method as claimed in claim 1, it is characterized in that, the real time load situation of described each server of dynamic acquisition comprises:
Monitor and collect the load information of each node in cluster, the client process on server host node is to the work of acquisition component report server self load condition.
3. cloud computing server adaptive load balancing control method as claimed in claim 1, it is characterized in that, described detection also feeds back the node parameter information of described server, according to node parameter information and the priority of service request being assigned to this server, the peer distribution weights of calculation server comprise:
Detect the dynamic and static state parameter of server node, calculate indicator of distribution parameter, response time, dynamic parameter, static parameter and indicator of distribution parameter are stored to data structure, utilize the peer distribution weights of cloud-based adaptive genetic algorithm calculation server, and automatically generate crossover probability and mutation probability according to the fitness value of genetic algorithm.
4. cloud computing server adaptive load balancing control method as claimed in claim 1, it is characterized in that, according to priority, each service request in described request queue is dispatched described, before described service request is distributed to corresponding server successively, also comprises:
Using the error between response time of detecting and ideal response time as control variables, the setup control cycle, at each control cycle, minimum recurrence square law is utilized automatically to upgrade the operational factor of cluster server, according to the operational factor that described control variables and renewal obtain, control the stock number of each server.
5. as the cloud computing server adaptive load balancing control method in claim 1-4 as described in any one, it is characterized in that, the peer distribution weights of described calculation server also comprise:
Setting deviation threshold and threshold value compare cycle, at each threshold value compare cycle, the difference of the distribution weights calculated and server current weight is made comparisons with described deviation threshold, if difference is greater than deviation threshold, then upgrade weights to server node, and by service request state to be allocated for this server-tag, if difference is less than deviation threshold, then do not upgrade weights to server node, described server does not accept new service request.
6. a cloud computing server adaptive load balancing control system, is characterized in that, comprises
Response time sensing module, for detecting the response time of service request;
Scheduler module, for calculating the priority of described service request, includes described service request in service request queue, dispatches, described service request is distributed to corresponding server successively according to priority to each service request in described request queue;
Detection module, for detecting and feeding back the node parameter information of described server;
Weight computing module, for according to node parameter information and the priority of service request being assigned to this server, the peer distribution weights of calculation server;
Right value update module, for taking out the peer distribution weights of each server, and upgrades the weights of server node;
Assessment monitoring module, for the real time load situation of each server of dynamic acquisition, analyze and each server performance in computing cluster again, analysis result is examined, assess and monitor whether there is network congestion risk, according to the service request in the queue of described examination result relocation request to server.
7. cloud computing server adaptive load balancing control system as claimed in claim 6, it is characterized in that, described assessment monitoring module is for monitoring and collect the load information of each node in cluster, and the client process on server host node is to the work of acquisition component report server self load condition.
8. cloud computing server adaptive load balancing control system as claimed in claim 6, it is characterized in that, described detection module is for detecting the dynamic and static state parameter of server node, described weight computing module is used for calculating indicator of distribution parameter, and response time, dynamic parameter, static parameter and indicator of distribution parameter are stored to data structure, described right value update module for utilizing the peer distribution weights of cloud-based adaptive genetic algorithm calculation server, and generates crossover probability and mutation probability automatically according to the fitness value of genetic algorithm.
9. cloud computing server adaptive load balancing control system as claimed in claim 6, it is characterized in that, also comprise renewal control module, for the error between the response time that will detect and ideal response time as control variables, in the setup control cycle, at each control cycle, minimum recurrence square law is utilized automatically to upgrade the operational factor of cluster server, according to the operational factor that described control variables and renewal obtain, control the stock number of each server.
10. as the cloud computing server adaptive load balancing control method in claim 6-9 as described in any one, it is characterized in that, described weight computing module is also for setting deviation threshold and threshold value compare cycle, at each threshold value compare cycle, the difference of the distribution weights calculated and server current weight is made comparisons with described deviation threshold, if difference is greater than deviation threshold, then upgrade weights to server node, and by service request state to be allocated for this server-tag, if difference is less than deviation threshold, then do not upgrade weights to server node, described server does not accept new service request.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510389894.2A CN105007312A (en) | 2015-07-03 | 2015-07-03 | Method and system for controlling adaptive load-balancing of cloud computing server |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510389894.2A CN105007312A (en) | 2015-07-03 | 2015-07-03 | Method and system for controlling adaptive load-balancing of cloud computing server |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105007312A true CN105007312A (en) | 2015-10-28 |
Family
ID=54379830
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510389894.2A Pending CN105007312A (en) | 2015-07-03 | 2015-07-03 | Method and system for controlling adaptive load-balancing of cloud computing server |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105007312A (en) |
Cited By (32)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105282259A (en) * | 2015-11-13 | 2016-01-27 | 深圳联友科技有限公司 | Load balancing allocation method, agent and system used for background cluster service |
| CN105681217A (en) * | 2016-04-27 | 2016-06-15 | 深圳市中润四方信息技术有限公司 | Dynamic load balancing method and system for container cluster |
| CN106453122A (en) * | 2016-09-23 | 2017-02-22 | 北京奇虎科技有限公司 | Method and device for selecting streaming data transmission node |
| CN106534281A (en) * | 2016-10-25 | 2017-03-22 | 广东欧珀移动通信有限公司 | Data request responding method, apparatus and system |
| CN106789699A (en) * | 2016-12-23 | 2017-05-31 | 航天星图科技（北京）有限公司 | A kind of distributed online stream process service system |
| CN107124472A (en) * | 2017-06-26 | 2017-09-01 | 杭州迪普科技股份有限公司 | Load-balancing method and device, computer-readable recording medium |
| CN107196865A (en) * | 2017-06-08 | 2017-09-22 | 中国民航大学 | A kind of adaptive threshold overload moving method of Load-aware |
| CN108234565A (en) * | 2016-12-21 | 2018-06-29 | 天脉聚源（北京）科技有限公司 | A kind of method and system of server cluster processing task |
| CN105282259B (en) * | 2015-11-13 | 2018-08-31 | 深圳联友科技有限公司 | For the load balanced sharing method of backstage cluster service, agency and system |
| US20180278646A1 (en) * | 2015-11-27 | 2018-09-27 | Alibaba Group Holding Limited | Early-Warning Decision Method, Node and Sub-System |
| US10237339B2 (en) | 2016-08-19 | 2019-03-19 | Microsoft Technology Licensing, Llc | Statistical resource balancing of constrained microservices in cloud PAAS environments |
| CN109561054A (en) * | 2017-09-26 | 2019-04-02 | 华为技术有限公司 | A kind of data transmission method, controller and access device |
| CN109857574A (en) * | 2019-01-10 | 2019-06-07 | 暨南大学 | Under a kind of low energy consumption cluster environment can overloaded load perception Service Promotion method |
| CN110099083A (en) * | 2018-01-30 | 2019-08-06 | 贵州白山云科技股份有限公司 | A kind of load equilibration scheduling method and device for server cluster |
| CN110138747A (en) * | 2019-04-23 | 2019-08-16 | 微梦创科网络科技（中国）有限公司 | A kind of method and system for verifying account logging state |
| CN110348681A (en) * | 2019-06-04 | 2019-10-18 | 国网浙江省电力有限公司衢州供电公司 | A kind of electric power CPS dynamic load distribution method |
| CN110781006A (en) * | 2019-10-28 | 2020-02-11 | 重庆紫光华山智安科技有限公司 | Load balancing method, device, node and computer readable storage medium |
| CN111049919A (en) * | 2019-12-19 | 2020-04-21 | 上海米哈游天命科技有限公司 | User request processing method, device, equipment and storage medium |
| CN111083213A (en) * | 2019-12-09 | 2020-04-28 | 苏宁云计算有限公司 | Communication method and system |
| CN111567076A (en) * | 2018-01-12 | 2020-08-21 | 三星电子株式会社 | User terminal device, electronic device, system including the same, and control method |
| CN107172504B (en) * | 2017-05-08 | 2020-12-22 | 苏州中科集成电路设计中心有限公司 | Distributed processing method and device for streaming audio and video data |
| CN112231075A (en) * | 2020-09-07 | 2021-01-15 | 武汉市九格合众科技有限责任公司 | Server cluster load balancing control method and system based on cloud service |
| CN112350952A (en) * | 2020-10-28 | 2021-02-09 | 武汉绿色网络信息服务有限责任公司 | Controller distribution method and network service system |
| CN112565829A (en) * | 2020-11-26 | 2021-03-26 | 湖南快乐阳光互动娱乐传媒有限公司 | Network scheduling method, device and system and readable storage medium |
| CN112929408A (en) * | 2021-01-19 | 2021-06-08 | 郑州阿帕斯数云信息科技有限公司 | Dynamic load balancing method and device |
| CN113271335A (en) * | 2020-08-20 | 2021-08-17 | 丁禹 | System for managing and controlling operation of cloud computing terminal and cloud server |
| CN114615275A (en) * | 2022-03-04 | 2022-06-10 | 国家工业信息安全发展研究中心 | Distributed load balancing control method and device for cloud storage |
| CN114866614A (en) * | 2022-05-05 | 2022-08-05 | 浙江工业大学 | Service self-adaptive elastic adjustment method based on network environment and server load |
| CN115086334A (en) * | 2022-06-15 | 2022-09-20 | 北京奇艺世纪科技有限公司 | Server marking method and related device |
| CN115174583A (en) * | 2022-06-28 | 2022-10-11 | 福州大学 | Server load balancing method based on programmable data plane |
| CN115827757A (en) * | 2022-11-30 | 2023-03-21 | 西部科学城智能网联汽车创新中心(重庆)有限公司 | Data operation method and device for multiple HBase clusters |
| CN116405500A (en) * | 2023-06-05 | 2023-07-07 | 济南大陆机电股份有限公司 | System resource management method based on data analysis and cloud computing data analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101753444A (en) * | 2009-12-31 | 2010-06-23 | 卓望数码技术（深圳）有限公司 | Method and device for load balancing |
| CN101986272A (en) * | 2010-11-05 | 2011-03-16 | 北京大学 | Task scheduling method under cloud computing environment |
| CN102394931A (en) * | 2011-11-04 | 2012-03-28 | 北京邮电大学 | Cloud-based user visit request scheduling method |
| US20130086411A1 (en) * | 2011-09-30 | 2013-04-04 | Alcatel-Lucent Usa, Inc. | Hardware consumption architecture |
| CN104168318A (en) * | 2014-08-18 | 2014-11-26 | 中国联合网络通信集团有限公司 | Resource service system and resource distribution method thereof |
-
2015
- 2015-07-03 CN CN201510389894.2A patent/CN105007312A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101753444A (en) * | 2009-12-31 | 2010-06-23 | 卓望数码技术（深圳）有限公司 | Method and device for load balancing |
| CN101986272A (en) * | 2010-11-05 | 2011-03-16 | 北京大学 | Task scheduling method under cloud computing environment |
| US20130086411A1 (en) * | 2011-09-30 | 2013-04-04 | Alcatel-Lucent Usa, Inc. | Hardware consumption architecture |
| CN102394931A (en) * | 2011-11-04 | 2012-03-28 | 北京邮电大学 | Cloud-based user visit request scheduling method |
| CN104168318A (en) * | 2014-08-18 | 2014-11-26 | 中国联合网络通信集团有限公司 | Resource service system and resource distribution method thereof |
Cited By (51)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105282259B (en) * | 2015-11-13 | 2018-08-31 | 深圳联友科技有限公司 | For the load balanced sharing method of backstage cluster service, agency and system |
| CN105282259A (en) * | 2015-11-13 | 2016-01-27 | 深圳联友科技有限公司 | Load balancing allocation method, agent and system used for background cluster service |
| US11102240B2 (en) * | 2015-11-27 | 2021-08-24 | Alibaba Group Holding Limited | Early-warning decision method, node and sub-system |
| US20180278646A1 (en) * | 2015-11-27 | 2018-09-27 | Alibaba Group Holding Limited | Early-Warning Decision Method, Node and Sub-System |
| CN105681217A (en) * | 2016-04-27 | 2016-06-15 | 深圳市中润四方信息技术有限公司 | Dynamic load balancing method and system for container cluster |
| CN105681217B (en) * | 2016-04-27 | 2019-02-01 | 深圳市中润四方信息技术有限公司 | Dynamic load balancing method and system for container cluster |
| US10237339B2 (en) | 2016-08-19 | 2019-03-19 | Microsoft Technology Licensing, Llc | Statistical resource balancing of constrained microservices in cloud PAAS environments |
| CN106453122A (en) * | 2016-09-23 | 2017-02-22 | 北京奇虎科技有限公司 | Method and device for selecting streaming data transmission node |
| CN106453122B (en) * | 2016-09-23 | 2019-06-04 | 北京奇虎科技有限公司 | A kind of choosing method and device of Stream Data Transmission node |
| WO2018054369A1 (en) * | 2016-09-23 | 2018-03-29 | 北京奇虎科技有限公司 | Method and apparatus for selecting streaming data transmission node |
| CN106534281B (en) * | 2016-10-25 | 2019-09-24 | Oppo广东移动通信有限公司 | A kind of response method of request of data, apparatus and system |
| WO2018076812A1 (en) * | 2016-10-25 | 2018-05-03 | 广东欧珀移动通信有限公司 | Data request response method and device, storage medium, server and system |
| CN106534281A (en) * | 2016-10-25 | 2017-03-22 | 广东欧珀移动通信有限公司 | Data request responding method, apparatus and system |
| CN108234565A (en) * | 2016-12-21 | 2018-06-29 | 天脉聚源（北京）科技有限公司 | A kind of method and system of server cluster processing task |
| CN106789699B (en) * | 2016-12-23 | 2019-03-19 | 中科星图股份有限公司 | A kind of distributed online stream process service system |
| CN106789699A (en) * | 2016-12-23 | 2017-05-31 | 航天星图科技（北京）有限公司 | A kind of distributed online stream process service system |
| CN107172504B (en) * | 2017-05-08 | 2020-12-22 | 苏州中科集成电路设计中心有限公司 | Distributed processing method and device for streaming audio and video data |
| CN107196865A (en) * | 2017-06-08 | 2017-09-22 | 中国民航大学 | A kind of adaptive threshold overload moving method of Load-aware |
| CN107196865B (en) * | 2017-06-08 | 2020-07-24 | 中国民航大学 | Load-aware adaptive threshold overload migration method |
| CN107124472A (en) * | 2017-06-26 | 2017-09-01 | 杭州迪普科技股份有限公司 | Load-balancing method and device, computer-readable recording medium |
| CN109561054A (en) * | 2017-09-26 | 2019-04-02 | 华为技术有限公司 | A kind of data transmission method, controller and access device |
| CN111567076B (en) * | 2018-01-12 | 2024-05-10 | 三星电子株式会社 | User terminal device, electronic device, system including the same, and control method |
| CN111567076A (en) * | 2018-01-12 | 2020-08-21 | 三星电子株式会社 | User terminal device, electronic device, system including the same, and control method |
| CN110099083A (en) * | 2018-01-30 | 2019-08-06 | 贵州白山云科技股份有限公司 | A kind of load equilibration scheduling method and device for server cluster |
| CN109857574A (en) * | 2019-01-10 | 2019-06-07 | 暨南大学 | Under a kind of low energy consumption cluster environment can overloaded load perception Service Promotion method |
| CN110138747B (en) * | 2019-04-23 | 2021-03-23 | 微梦创科网络科技（中国）有限公司 | Method and system for verifying login state of account |
| CN110138747A (en) * | 2019-04-23 | 2019-08-16 | 微梦创科网络科技（中国）有限公司 | A kind of method and system for verifying account logging state |
| CN110348681A (en) * | 2019-06-04 | 2019-10-18 | 国网浙江省电力有限公司衢州供电公司 | A kind of electric power CPS dynamic load distribution method |
| CN110348681B (en) * | 2019-06-04 | 2022-02-18 | 国网浙江省电力有限公司衢州供电公司 | Power CPS dynamic load distribution method |
| CN110781006B (en) * | 2019-10-28 | 2022-06-03 | 重庆紫光华山智安科技有限公司 | Load balancing method, device, node and computer readable storage medium |
| CN110781006A (en) * | 2019-10-28 | 2020-02-11 | 重庆紫光华山智安科技有限公司 | Load balancing method, device, node and computer readable storage medium |
| CN111083213A (en) * | 2019-12-09 | 2020-04-28 | 苏宁云计算有限公司 | Communication method and system |
| CN111083213B (en) * | 2019-12-09 | 2022-09-02 | 苏宁云计算有限公司 | Communication method and system |
| CN111049919A (en) * | 2019-12-19 | 2020-04-21 | 上海米哈游天命科技有限公司 | User request processing method, device, equipment and storage medium |
| CN113271335A (en) * | 2020-08-20 | 2021-08-17 | 丁禹 | System for managing and controlling operation of cloud computing terminal and cloud server |
| CN112231075A (en) * | 2020-09-07 | 2021-01-15 | 武汉市九格合众科技有限责任公司 | Server cluster load balancing control method and system based on cloud service |
| CN112231075B (en) * | 2020-09-07 | 2023-09-01 | 武汉市九格合众科技有限责任公司 | Cloud service-based server cluster load balancing control method and system |
| CN112350952A (en) * | 2020-10-28 | 2021-02-09 | 武汉绿色网络信息服务有限责任公司 | Controller distribution method and network service system |
| CN112565829A (en) * | 2020-11-26 | 2021-03-26 | 湖南快乐阳光互动娱乐传媒有限公司 | Network scheduling method, device and system and readable storage medium |
| CN112565829B (en) * | 2020-11-26 | 2023-03-17 | 湖南快乐阳光互动娱乐传媒有限公司 | Network scheduling method, device and system and readable storage medium |
| CN112929408A (en) * | 2021-01-19 | 2021-06-08 | 郑州阿帕斯数云信息科技有限公司 | Dynamic load balancing method and device |
| CN114615275A (en) * | 2022-03-04 | 2022-06-10 | 国家工业信息安全发展研究中心 | Distributed load balancing control method and device for cloud storage |
| CN114615275B (en) * | 2022-03-04 | 2024-05-10 | 国家工业信息安全发展研究中心 | Cloud storage-oriented distributed load balancing control method and device |
| CN114866614A (en) * | 2022-05-05 | 2022-08-05 | 浙江工业大学 | Service self-adaptive elastic adjustment method based on network environment and server load |
| CN115086334A (en) * | 2022-06-15 | 2022-09-20 | 北京奇艺世纪科技有限公司 | Server marking method and related device |
| CN115174583B (en) * | 2022-06-28 | 2024-03-29 | 福州大学 | Server load balancing method based on programmable data plane |
| CN115174583A (en) * | 2022-06-28 | 2022-10-11 | 福州大学 | Server load balancing method based on programmable data plane |
| CN115827757B (en) * | 2022-11-30 | 2024-03-12 | 西部科学城智能网联汽车创新中心(重庆)有限公司 | Data operation method and device for multi-HBase cluster |
| CN115827757A (en) * | 2022-11-30 | 2023-03-21 | 西部科学城智能网联汽车创新中心(重庆)有限公司 | Data operation method and device for multiple HBase clusters |
| CN116405500B (en) * | 2023-06-05 | 2023-08-08 | 济南大陆机电股份有限公司 | System resource management method based on data analysis and cloud computing data analysis |
| CN116405500A (en) * | 2023-06-05 | 2023-07-07 | 济南大陆机电股份有限公司 | System resource management method based on data analysis and cloud computing data analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105007312A (en) | Method and system for controlling adaptive load-balancing of cloud computing server | |
| Andreolini et al. | Dynamic load management of virtual machines in cloud architectures | |
| US20210006505A1 (en) | A bursty traffic allocation method, device and proxy server | |
| US10581756B2 (en) | Nonintrusive dynamically-scalable network load generation | |
| US20230093389A1 (en) | Service request allocation method and apparatus, computer device, and storage medium | |
| US10362100B2 (en) | Determining load state of remote systems using delay and packet loss rate | |
| CN105912399B (en) | Task processing method, device and system | |
| CN104836819A (en) | Dynamic load balancing method and system, and monitoring and dispatching device | |
| CN103401947A (en) | Method and device for allocating tasks to multiple servers | |
| CN105872061B (en) | A kind of server set group managing means, apparatus and system | |
| CN104536804A (en) | Virtual resource dispatching system for related task requests and dispatching and distributing method for related task requests | |
| US20240007522A1 (en) | Dynamically updating load balancing criteria | |
| CN107835262A (en) | A kind of streaming media server dynamical load distribution method | |
| CN112261120A (en) | Cloud-side cooperative task unloading method and device for power distribution internet of things | |
| CN104301241B (en) | A kind of SOA dynamic load distributing methods and system | |
| CN108737543B (en) | Distributed Internet of things middleware and working method | |
| CN105872082B (en) | Fine granularity resource response system based on container cluster load-balancing algorithm | |
| CN113885794B (en) | Data access method and device based on multi-cloud storage, computer equipment and medium | |
| CN104320433A (en) | Data processing method and distributed data processing system | |
| Xue et al. | Tale of tails: Anomaly avoidance in data centers | |
| Lee et al. | Development of an optimal load balancing algorithm based on ANFIS modeling for the clustering web-server | |
| US7903571B1 (en) | System and method for improving multi-node processing | |
| CN112732451A (en) | Load balancing system in cloud environment | |
| CN111459651B (en) | Load balancing method, device, storage medium and scheduling system | |
| Hossfeld et al. | Comparing the Scalability of Communication Networks and Systems |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20151028 |
|
| WD01 | Invention patent application deemed withdrawn after publication |