CN112948088A - Cloud workflow intelligent management and scheduling system in cloud computing platform - Google Patents
Cloud workflow intelligent management and scheduling system in cloud computing platform Download PDFInfo
- Publication number
- CN112948088A CN112948088A CN202110283971.1A CN202110283971A CN112948088A CN 112948088 A CN112948088 A CN 112948088A CN 202110283971 A CN202110283971 A CN 202110283971A CN 112948088 A CN112948088 A CN 112948088A
- Authority
- CN
- China
- Prior art keywords
- cloud
- cloud workflow
- workflow
- scheduler
- scheduling
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
- G06F9/5016—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals the resource being the memory
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/54—Interprogram communication
- G06F9/547—Remote procedure calls [RPC]; Web services
Abstract
The invention discloses an intelligent management and scheduling system for cloud workflow in a cloud computing platform, which comprises: the system comprises a system front end, a scheduling controller, a cloud workflow scheduler and a resource distributor; uploading a cloud workflow file by the front end of the system; the scheduling controller receives the cloud workflow file and then generates information for starting the cloud workflow scheduler; the resource distributor receives information for starting the cloud workflow scheduler and then obtains the residual resources through the container management platform; after receiving request information for creating a cloud workflow scheduler, a container management platform creates a plurality of cloud workflow schedulers on nodes; and each cloud workflow scheduler determines the total resource allocation requirement according to the cloud workflow file, receives the residual resources, generates a cloud workflow calculation node according to the total resource allocation requirement and the residual resources, and processes the cloud workflow file. The scheme disclosed by the invention can be arranged on a large-scale distributed cluster, can improve the receiving proportion of the cloud workflow by 20 percent, and can efficiently and reliably complete the task management and scheduling of the workflow in a multi-load environment.
Description
Technical Field
The invention relates to the field of resource scheduling and management, in particular to an intelligent management and scheduling system for cloud workflows in a cloud computing platform.
Background
Cloud computing and big data are great changes and inevitable trends in informatization development, and are combustion improvers which are new kinetic energy for making high points and economic development of international competition in the information age. At present, the management and scheduling of cloud resources and cloud workflows in a cloud control system platform have unique challenges, such as complex and uncertain workflow scheduling process, low resource utilization rate, multiple data exchange, high timeliness requirement and the like. In order to meet the requirement of user service quality, a cloud service provider has to adopt a mode of excessively providing resources in the conventional cloud computing platform, which causes great resource waste. In addition, the existing cloud computing platform runs on a super-large-scale cluster with natural barriers, the scheduling and execution completion time of the cloud computing platform is increased rapidly along with the increase of the cluster scale and load, and the number of corresponding schedulable tasks is reduced rapidly, so that the cloud workflow file receiving ratio is reduced.
Disclosure of Invention
The invention aims to provide an intelligent management and scheduling system for cloud workflow in a cloud computing platform, so as to improve the receiving ratio of cloud workflow files.
In order to achieve the above object, the present invention provides an intelligent management and scheduling system for cloud workflows in a cloud computing platform, the system comprising: the system comprises a system front end, a scheduling controller, a cloud workflow scheduler and a resource distributor;
the system front end uploads a cloud workflow file of a user and sends the cloud workflow file to the scheduling controller;
the scheduling controller receives the cloud workflow file through a gPC protocol, generates startup cloud workflow scheduler information and sends the startup cloud workflow scheduler information to the resource distributor;
the resource distributor sends request information for creating the cloud workflow scheduler to the container management platform after receiving the information for starting the cloud workflow scheduler through a gPC protocol;
after the container management platform receives the request information of the cloud workflow scheduler, a plurality of cloud workflow schedulers are created on the nodes;
the cloud workflow scheduler receives a cloud workflow file sent by the scheduling controller through a gPC protocol, determines a total resource configuration requirement according to the cloud workflow file, receives a residual resource sent by the resource distributor, judges whether the total resource configuration requirement is greater than the residual resource, generates an optimal task resource configuration scheme by using a knapsack algorithm if the total resource configuration requirement is greater than the residual resource, determines to generate n pieces of information for starting computing nodes according to the task resource configuration scheme, sends computing node creation request information to the container management platform through the resource distributor, generates cloud workflow computing nodes according to the computing node request information, and processes the cloud workflow file by using the cloud workflow computing nodes; if the total resource configuration requirement is less than or equal to the residual resources, directly generating m pieces of computing node starting information according to the total resource configuration requirement, sending computing node creating request information to the container management platform through a resource distributor, generating cloud workflow computing nodes according to the computing node request information, and processing cloud workflow files by using the cloud workflow computing nodes; wherein m and n are positive integers greater than 1, and n is less than m.
Optionally, the system further comprises: a task state tracker;
after receiving the ip and the cloud workflow file id corresponding to each cloud workflow scheduler, the task state tracker monitors the cloud workflow calculation nodes in the container management platform by using a list monitoring mechanism based on a Client-Go library to obtain task state information, stores the task state information into a cache in the container management platform, arranges the task state information according to the ip, and sends the arranged task state information to the corresponding cloud workflow scheduler; and when each cloud workflow scheduler receives the cloud workflow file, generating a corresponding cloud workflow file id.
Optionally, the creating a cloud workflow scheduler request information includes: using mirror images, the number of CPU cores and the size of a memory; the creating a computing node request message includes: the method comprises the steps of obtaining a cloud workflow file id, a namespace name, an input cloud workflow file, a used mirror image, the number of CPU cores and the size of a memory.
Optionally, the system further comprises: a cloud workflow database;
the cloud workflow database is used for receiving and storing task state information sent by each cloud workflow scheduler.
Optionally, the system front end is further configured to display the number of cloud workflow schedulers and task state information through the cloud work database.
Optionally, under the condition of multiple loads, the scheduling controller selects a performance mode to work; and when the mains supply is powered off or the oil engine supplies power, the scheduling controller selects the energy-saving mode to work.
Optionally, the cloud workflow schedulers are designed using a multi-threaded framework.
Optionally, the dispatch controller is designed using a single threaded framework.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a cloud workflow intelligent management and scheduling system in a cloud computing platform, which comprises: the system comprises a system front end, a scheduling controller, a cloud workflow scheduler and a resource distributor; uploading a cloud workflow file of a user by a system front end; the scheduling controller receives the cloud workflow file and then generates information for starting the cloud workflow scheduler; the resource distributor receives information for starting the cloud workflow scheduler and then obtains the residual resources through the container management platform; after receiving request information for creating a cloud workflow scheduler, a container management platform creates a plurality of cloud workflow schedulers on nodes; and each cloud workflow scheduler determines the total resource allocation requirements according to the cloud workflow file, receives the residual resources sent by the resource allocator, generates a cloud workflow computing node according to the total resource allocation requirements and the residual resources, and processes the cloud workflow file by using the cloud workflow computing node. The scheme disclosed by the invention can be arranged on a super-large-scale distributed cluster, can improve the receiving proportion of the cloud workflow by 20 percent, efficiently and reliably completes the task management and scheduling of the workflow in a multi-load environment, and realizes the management and scheduling technology of a cloud computing platform with observable and controllable whole flow.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a structural diagram of a cloud workflow intelligent management and scheduling system in a cloud computing platform according to the present invention.
Description of the symbols:
1. the system comprises a system front end, 2, a scheduling controller, 3, a cloud workflow scheduler, 4, a resource distributor, 5, a task state tracker, 6, a container management platform, 7 and a cloud workflow database.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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 invention aims to provide an intelligent management and scheduling system for cloud workflow in a cloud computing platform, so as to improve the receiving ratio of cloud workflow files.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
As shown in fig. 1, the present invention discloses a cloud workflow intelligent management and scheduling system in a cloud computing platform, the system comprising: the system comprises a system front end 1, a scheduling controller 2, a cloud workflow scheduler 3 and a resource allocator 4.
The system front end 1 uploads a cloud workflow file of a user and sends the cloud workflow file to the scheduling controller 2.
The scheduling controller 2 receives the cloud workflow file through the gRPC protocol, generates startup cloud workflow scheduler information, and sends the startup cloud workflow scheduler information to the resource distributor 4.
After receiving the information of the cloud workflow scheduler started through the gRPC protocol, the resource distributor 4 acquires the residual resources through the container management platform 6 based on the Client-Go library operation, and sends request information for creating the cloud workflow scheduler to the container management platform 6. The create cloud workflow scheduler request information includes, but is not limited to: mirroring, number of cpu cores and memory size are used.
After receiving the request information for creating the cloud workflow scheduler, the container management platform 6 creates a plurality of cloud workflow schedulers 3 on nodes.
The cloud workflow scheduler 3 receives a cloud workflow file sent by the scheduling controller through a gPC protocol, determines a total resource allocation requirement according to the cloud workflow file, receives a residual resource sent by the resource distributor 4, judges whether the total resource allocation requirement is greater than the residual resource, generates an optimal task resource allocation scheme by using a knapsack algorithm if the total resource allocation requirement is greater than the residual resource, determines to generate n pieces of information for starting computing nodes according to the task resource allocation scheme, sends computing node creation request information to the container management platform 6 through the resource distributor 4, generates a cloud workflow computing node according to the computing node request information, and processes the cloud workflow file by using the cloud workflow computing node; if the total resource configuration requirement is less than or equal to the residual resources, directly generating m pieces of computing node starting information according to the total resource configuration requirement, sending computing node creating request information to the container management platform 6 through a resource distributor, generating cloud workflow computing nodes according to the computing node request information, and processing cloud workflow files by using the cloud workflow computing nodes; wherein m and n are positive integers greater than 1, and n is less than m. The create compute node request information includes, but is not limited to: the method comprises the steps of obtaining a cloud workflow file id, a namespace name, an input cloud workflow file, a used mirror image, the number of CPU cores and the size of a memory. The create cloud workflow scheduler request information includes, but is not limited to: using mirror images, the number of CPU cores and the size of a memory;
the container management platform 6 disclosed by the invention uses Kubernets, which is a portable and extensible open source platform, is used for managing containerized workload and services, and can promote declarative configuration and automation.
As an optional implementation, the system of the present invention further includes: a task state tracker 5; after receiving the ip and the cloud workflow file id corresponding to each cloud workflow scheduler 3, the task state tracker 5 monitors the cloud workflow computation nodes in the container management platform 6 by using a list monitoring mechanism based on a Client-Go library to obtain task state information, stores the task state information into a cache in the container management platform 6, arranges the task state information according to the ip, and sends the arranged task state information to the corresponding cloud workflow scheduler 3; and when each cloud workflow scheduler 3 receives the cloud workflow file, generating a corresponding cloud workflow file id. The task state tracker 5 periodically accesses the cache to further obtain task state information corresponding to the cloud workflow computing node, network congestion caused by frequent access to the Kubernetes container management platform 6 is avoided, the task state information is sorted according to the IP and sent back to the corresponding cloud workflow scheduler 3.
As an optional implementation, the system of the present invention further includes: a cloud workflow database 7; the cloud workflow database 7 is configured to receive and store task state information sent by each cloud workflow scheduler 3. The task state information specifically includes success, failure, and in progress.
As an optional implementation manner, the system front end 1 of the present invention is further configured to display the number of the cloud workflow schedulers 3 and the task state information through the cloud work database, view the states of the completed cloud workflow file and the ongoing cloud workflow file, and implement operable visual interface display of the user.
As an optional implementation manner, in the invention, under a multi-load condition (i.e. a high task pressure condition), the scheduling controller selects the performance mode to work; when the mains supply is powered off or the oil engine is powered on (namely, the data center has high energy-saving requirements), the scheduling controller selects the energy-saving mode to work, and different working modes are selected under different requirements. In addition, the scheduling controller uses a single-threaded framework design and guarantees the rapidity and accuracy of cloud workflow routing to the cloud workflow scheduler 3.
As an optional implementation manner, the cloud workflow scheduler 3 of the present invention uses a multi-thread framework design to start a plurality of cloud workflow schedulers, so as to avoid a problem of module crash caused by processing a large number of cloud workflow files due to a single thread.
As an optional implementation manner, the resource allocator 4 of the present invention implements state acquisition of the global keep-alive cloud workflow scheduler 3, acquires the remaining resources of the container management platform 6, creates a cloud workflow scheduler and a computing node interface by the abstract container management platform 6, and introduces etcd as shared storage among the multiple resource allocators 4.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to assist in understanding the core concepts of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. An intelligent management and scheduling system for cloud workflows in a cloud computing platform, the system comprising: the system comprises a system front end, a scheduling controller, a cloud workflow scheduler and a resource distributor;
the system front end uploads a cloud workflow file of a user and sends the cloud workflow file to the scheduling controller;
the scheduling controller receives the cloud workflow file through a gPC protocol, generates startup cloud workflow scheduler information and sends the startup cloud workflow scheduler information to the resource distributor;
the resource distributor sends request information for creating the cloud workflow scheduler to the container management platform after receiving the information for starting the cloud workflow scheduler through a gPC protocol;
after the container management platform receives the request information of the cloud workflow scheduler, a plurality of cloud workflow schedulers are created on the nodes;
the cloud workflow scheduler receives a cloud workflow file sent by the scheduling controller through a gPC protocol, determines a total resource configuration requirement according to the cloud workflow file, receives a residual resource sent by the resource distributor, judges whether the total resource configuration requirement is greater than the residual resource, generates an optimal task resource configuration scheme by using a knapsack algorithm if the total resource configuration requirement is greater than the residual resource, determines to generate n pieces of information for starting computing nodes according to the task resource configuration scheme, sends computing node creation request information to the container management platform through the resource distributor, generates cloud workflow computing nodes according to the computing node request information, and processes the cloud workflow file by using the cloud workflow computing nodes; if the total resource configuration requirement is less than or equal to the residual resources, directly generating m pieces of computing node starting information according to the total resource configuration requirement, sending computing node creating request information to the container management platform through a resource distributor, generating cloud workflow computing nodes according to the computing node request information, and processing cloud workflow files by using the cloud workflow computing nodes; wherein m and n are positive integers greater than 1, and n is less than m.
2. The intelligent management and scheduling system for cloud workflows in a cloud computing platform according to claim 1, further comprising: a task state tracker;
after receiving the ip and the cloud workflow file id corresponding to each cloud workflow scheduler, the task state tracker monitors the cloud workflow calculation nodes in the container management platform by using a list monitoring mechanism based on a Client-Go library to obtain task state information, stores the task state information into a cache in the container management platform, arranges the task state information according to the ip, and sends the arranged task state information to the corresponding cloud workflow scheduler; and when each cloud workflow scheduler receives the cloud workflow file, generating a corresponding cloud workflow file id.
3. The cloud workflow intelligent management and scheduling system of claim 1 wherein the creating a cloud workflow scheduler request message comprises: using mirror images, the number of CPU cores and the size of a memory; the creating a computing node request message includes: the method comprises the steps of obtaining a cloud workflow file id, a namespace name, an input cloud workflow file, a used mirror image, the number of CPU cores and the size of a memory.
4. The intelligent management and scheduling system for cloud workflows in a cloud computing platform according to claim 2, wherein the system further comprises: a cloud workflow database;
the cloud workflow database is used for receiving and storing task state information sent by each cloud workflow scheduler.
5. The system for intelligent management and scheduling of cloud workflows in a cloud computing platform of claim 4, wherein the system front end is further configured to expose the number of cloud workflow schedulers and task status information via the cloud work database.
6. The intelligent management and scheduling system of cloud workflows in a cloud computing platform of claim 4 wherein the scheduling controller selects a performance mode to operate under a multi-load condition; and when the mains supply is powered off or the oil engine supplies power, the scheduling controller selects the energy-saving mode to work.
7. The system for intelligent management and scheduling of cloud workflows in a cloud computing platform according to claim 1, wherein each cloud workflow scheduler is designed using a multi-threaded framework.
8. The cloud workflow intelligence management and scheduling system of claim 1 wherein the scheduling controller is designed using a single threaded framework.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110283971.1A CN112948088B (en) | 2021-03-17 | 2021-03-17 | Cloud workflow intelligent management and scheduling system in cloud computing platform |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110283971.1A CN112948088B (en) | 2021-03-17 | 2021-03-17 | Cloud workflow intelligent management and scheduling system in cloud computing platform |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112948088A true CN112948088A (en) | 2021-06-11 |
| CN112948088B CN112948088B (en) | 2022-10-04 |
Family
ID=76229366
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110283971.1A Active CN112948088B (en) | 2021-03-17 | 2021-03-17 | Cloud workflow intelligent management and scheduling system in cloud computing platform |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112948088B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114115003A (en) * | 2021-11-12 | 2022-03-01 | 北京银盾泰安网络科技有限公司 | Remote start-stop control platform |
| CN116744368A (en) * | 2023-07-03 | 2023-09-12 | 北京理工大学 | Intelligent collaborative heterogeneous air-ground unmanned system based on cloud side end architecture and implementation method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015192556A1 (en) * | 2014-06-16 | 2015-12-23 | 中兴通讯股份有限公司 | Management method, management center and management system for cloud scheduling |
| CN107959588A (en) * | 2017-12-07 | 2018-04-24 | 郑州云海信息技术有限公司 | Cloud resource management method, cloud resource management platform and the management system of data center |
| CN108845878A (en) * | 2018-05-08 | 2018-11-20 | 南京理工大学 | The big data processing method and processing device calculated based on serverless backup |
| CN109639791A (en) * | 2018-12-06 | 2019-04-16 | 广东石油化工学院 | Cloud workflow schedule method and system under a kind of container environment |
-
2021
- 2021-03-17 CN CN202110283971.1A patent/CN112948088B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015192556A1 (en) * | 2014-06-16 | 2015-12-23 | 中兴通讯股份有限公司 | Management method, management center and management system for cloud scheduling |
| CN107959588A (en) * | 2017-12-07 | 2018-04-24 | 郑州云海信息技术有限公司 | Cloud resource management method, cloud resource management platform and the management system of data center |
| CN108845878A (en) * | 2018-05-08 | 2018-11-20 | 南京理工大学 | The big data processing method and processing device calculated based on serverless backup |
| CN109639791A (en) * | 2018-12-06 | 2019-04-16 | 广东石油化工学院 | Cloud workflow schedule method and system under a kind of container environment |
Non-Patent Citations (2)
| Title |
|---|
| 杨清波等: "基于容器的调控云PaaS平台的设计与实现", 《电网技术》 * |
| 王岩等: "面向云工作流的两阶段资源调度方法", 《华南理工大学学报(自然科学版)》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114115003A (en) * | 2021-11-12 | 2022-03-01 | 北京银盾泰安网络科技有限公司 | Remote start-stop control platform |
| CN114115003B (en) * | 2021-11-12 | 2023-08-22 | 浙江银盾云科技有限公司 | Remote start-stop control platform |
| CN116744368A (en) * | 2023-07-03 | 2023-09-12 | 北京理工大学 | Intelligent collaborative heterogeneous air-ground unmanned system based on cloud side end architecture and implementation method |
| CN116744368B (en) * | 2023-07-03 | 2024-01-23 | 北京理工大学 | Intelligent collaborative heterogeneous air-ground unmanned system based on cloud side end architecture and implementation method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112948088B (en) | 2022-10-04 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sotiriadis et al. | SimIC: Designing a new inter-cloud simulation platform for integrating large-scale resource management | |
| Sharma et al. | A survey of job scheduling and resource management in grid computing | |
| Fernández-Cerero et al. | SCORE: Simulator for cloud optimization of resources and energy consumption | |
| US20050188087A1 (en) | Parallel processing system | |
| Gu et al. | Greening cloud data centers in an economical way by energy trading with power grid | |
| CN102591921A (en) | Scheduling and management in a personal datacenter | |
| CN112948088B (en) | Cloud workflow intelligent management and scheduling system in cloud computing platform | |
| Li et al. | Opportunistic scheduling in clouds partially powered by green energy | |
| TW201217954A (en) | Power management in a multi-processor computer system | |
| Kaur et al. | Optimization techniques for resource provisioning and load balancing in cloud environment: a review | |
| Sun et al. | Building a fault tolerant framework with deadline guarantee in big data stream computing environments | |
| Chen et al. | Using a task dependency job-scheduling method to make energy savings in a cloud computing environment | |
| CN114138488A (en) | Cloud-native implementation method and system based on elastic high-performance computing | |
| Samadi et al. | DT-MG: many-to-one matching game for tasks scheduling towards resources optimization in cloud computing | |
| CN104468710A (en) | Mixed big data processing system and method | |
| Caux et al. | Smart datacenter electrical load model for renewable sources management | |
| Feoktistov et al. | Agent behavior model for distributed computing management in the environment with virtualized resources | |
| CN114237858A (en) | Task scheduling method and system based on multi-cluster network | |
| Goyal et al. | Energy optimised resource scheduling algorithm for private cloud computing | |
| Zhang et al. | Optimising data access latencies of virtual machine placement based on greedy algorithm in datacentre | |
| Mehenni et al. | An optimal big data processing for smart grid based on hybrid MDM/R architecture to strengthening RE integration and EE in datacenter | |
| CN113672579B (en) | File synchronization method based on webservice | |
| Biran et al. | Coordinating green clouds as data-intensive computing | |
| De Faria et al. | Network and energy-aware resource selection model for opportunistic grids | |
| Balashov et al. | Resource Management in Private Multi-Service Cloud Environments |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |