CN117909032A - Dynamic elastic scheduling system and related method for high-performance computing and cloud computing resources - Google Patents
Dynamic elastic scheduling system and related method for high-performance computing and cloud computing resources Download PDFInfo
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Abstract
The invention relates to the technical field of computing resource management, in particular to a dynamic flexible scheduling computing resource management system, a construction method and a management method, wherein the dynamic flexible scheduling computing resource management system comprises the steps of dividing an independent high-performance computing resource pool and a cloud computing resource pool in a unified resource pool; extracting computing resources from the high-performance computing resource pool and the cloud computing resource pool according to the sharing area resource basic configuration information to form an independent resource sharing area; and setting a computing resource scheduling mode in the resource sharing area to realize the computing resource scheduling of the resource sharing area. The invention establishes the independent high-performance computing resource pool and cloud computing resource pool, and extracts the needed computing resources from each computing resource pool to establish the resource sharing area, the computing resources in the resource sharing area can be dynamically and flexibly scheduled, the computing resources can be distributed according to the needs, the high-performance computing resources and the cloud computing resource utilization rate are improved, the resources are not required to be configured according to the peak value of the computing resource usage when the resources are purchased, and the resource cost is effectively saved.
Description
Technical Field
The invention relates to the technical field of computing resource management, in particular to a dynamic elastic scheduling system and a related method for high-performance computing and cloud computing resources.
Background
The existing large-scale institutions, such as group companies with complex structures, belong to the areas, functions and field-oriented differences of the subordinate units, each subordinate unit self-builds resource pools, management of each resource pool is mutually isolated, applications are mutually independent, uniform scheduling and management of high-performance computing resources and cloud computing resources cannot be realized at the group level, and therefore the problems that the computing resources cannot be shared, the resource utilization rate is low and the like are caused.
The problem of the resource management mode that each subordinate unit is used for self-building the resource pool and each resource pool is managed and isolated from each other in the prior large-scale organization includes: 1. the cloud service levels are uneven, the IT problem is usually complicated to locate, the unified resource management capability is lacked, the hybrid cloud management capability is lacked, and the operation and maintenance management is complicated; 2. the business management is rough, the business authorization is disordered, the business online time is long, the organization and the flow model of a customer cannot be matched accurately, the data are not shared, the application is repeatedly built, and the result conversion path is not smooth; 3. the resource utilization is unreasonable, the resource utilization rate is low, the resource waste exists, and the resource can not be allocated according to the requirement; 4. the management cost is high, 70% of the data center management modes are chaotic, the management cost is huge, the development speed of cloud computing service is slowed down, resources can not be reused, resources are wasted, and the standards are non-uniform and have no uniform specifications.
Disclosure of Invention
The invention provides a dynamic elastic scheduling system and a related method for high-performance computing and cloud computing resources, which overcome the defects of the prior art, and can effectively solve the problems of low resource utilization rate and resource waste caused by the unshared resources existing in the prior high-performance computing resources and cloud computing resource management
One of the technical schemes of the invention is realized by the following measures: a construction method of a dynamic elastic scheduling system of high-performance computing and cloud computing resources comprises the following steps:
Dividing an independent high-performance computing resource pool and a cloud computing resource pool in the unified resource pool, and storing computing resources required by Fu Gesuan force into the corresponding computing resource pools;
extracting computing resources from the high-performance computing resource pool and the cloud computing resource pool according to the sharing area resource basic configuration information to form an independent resource sharing area;
Setting a computing resource scheduling mode in the resource sharing area to realize the computing resource scheduling of the resource sharing area, wherein the computing resource scheduling mode comprises manual scheduling and dynamic elastic scheduling.
The following are further optimizations and/or improvements to the above-described inventive solution:
Extracting computing resources from the high-performance computing resource pool and the cloud computing resource pool according to the sharing area resource basic configuration information to form an independent resource sharing area, including:
Setting basic configuration information of the shared area resources, wherein the basic configuration information of the shared area resources comprises the model number, cpu, memory, management IP, calculation IP and DDR of calculation resources;
using the divided computing resource pool as a management unit, and utilizing the shared area resource basic configuration information to identify the computing resources matched with the high-performance computing resource pool and the cloud computing resource pool;
And selecting matched computing resources, and establishing an independent resource sharing area after extracting from the high-performance computing resource pool and the cloud computing resource pool.
And the dynamic elastic scheduling sets a timing task and a scheduling threshold, automatically matches the idle computing resources from the resource sharing area according to the scheduling threshold and a task triggering rule, and distributes the idle computing resources to each computing resource pool after automatic deployment.
The second technical scheme of the invention is realized by the following measures: a high performance computing and cloud computing resource dynamic resilient scheduling system, comprising:
The computing resource pool area comprises an independent high-performance computing resource pool and a cloud computing resource pool, and each computing resource pool stores computing resources meeting the same computing power requirement;
The resource sharing area comprises a shared resource area and a scheduling control module; the shared resource area stores computing resources extracted from a high-performance computing resource pool and a cloud computing resource pool according to the shared area resource basic configuration information; the scheduling control module completes the scheduling of the computing resources of the resource sharing area by a set scheduling mode of the computing resources, wherein the scheduling mode of the computing resources comprises manual scheduling and dynamic elastic scheduling.
The following are further optimizations and/or improvements to the above-described inventive solution:
The system also comprises a management unit, wherein the management unit comprises a right management module and a maintenance module;
the authority management module operates the user authority, wherein the operation comprises adding, deleting and modifying the user, setting and distributing the user authority;
The maintenance module combines the user authority pair to create a computing resource pool, records computing resources for each computing resource pool, and performs daily maintenance on the computing resources in the computing resource pool and the resource sharing area.
The third technical scheme of the invention is realized by the following measures: the scheduling method of the dynamic elastic scheduling system for the high-performance computing and cloud computing resources comprises the following steps:
determining a computing resource scheduling mode;
If the computing resource scheduling mode is determined to be manual scheduling, acquiring resource acquisition data information, confirming required computing resources, manually scheduling idle computing resources from a resource sharing area according to the required computing resources, and distributing the idle computing resources to each computing resource pool;
If the computing resource scheduling mode is dynamic elastic scheduling, setting a resource pool scheduling threshold, creating a timing task, establishing a judging rule, automatically matching idle computing resources from a resource sharing area according to the time of the timing task and combining the resource pool scheduling threshold and the judging rule, and automatically deploying and distributing the idle computing resources to each computing resource pool.
The following are further optimizations and/or improvements to the above-described inventive solution:
The manual scheduling includes:
acquiring resource acquisition data information;
Judging whether the computing resources in each computing resource pool need to be added according to the resource acquisition data information,
In response, a resource sharing region is searched, whether the needed computing resource exists is judged,
In response, the desired computing resources are scheduled from the resource sharing region to the respective computing resource pools,
In response to no, the required computing resources are manually increased for each computing resource pool.
The dynamic elastic scheduling includes:
setting a scheduling threshold of a certain computing resource pool, creating a corresponding timing task, and establishing a judgment rule;
automatically monitoring a threshold of the computational resource pool by a timed task;
determining whether the computing resources in the computing resource pool are lower than a preset scheduling threshold,
In response, a decision rule trigger is generated, the resource sharing region is searched, whether the needed computing resources exist is determined,
In response, the desired computing resource is scheduled from the resource sharing region to the corresponding computing resource pool,
And if not, triggering an alarm notice to prompt that the computing resources of the computing resource pool are insufficient, and prompting that the manual scheduling is started.
When the manual scheduling or dynamic flexible scheduling is completed, the resource scheduling information is recorded, and the resource scheduling is completed
The method also comprises the steps of creating a computing resource pool, inputting computing resources for each computing resource pool, and carrying out daily maintenance on the computing resources in the computing resource pool and the resource sharing area.
The beneficial effects of the invention include:
1. Unified management of computing resources: the invention uniformly manages the high-performance computing resources and cloud computing resources, refines the computing resources according to the service type and the corresponding computing power requirement, and forms a plurality of independent high-performance computing resource pools and cloud computing resource pools.
The resource utilization rate is improved: according to the method, the needed computing resources are extracted from each independent high-performance computing resource pool and cloud computing resource pool to establish the resource sharing area, the computing resources in the resource sharing area can be dynamically and flexibly scheduled, the computing resources can be distributed according to the needs, and the utilization rate of the high-performance computing resources and the cloud computing resources is effectively improved.
The resource cost is saved: the establishment of the resource sharing area and the setting of the dynamic flexible scheduling can dynamically configure the computing resources according to the service demands, so that the resources do not need to be configured according to the peak value of the computing resource usage when the resources are purchased, and the resource cost is effectively saved.
Drawings
FIG. 1 is a schematic diagram of a system construction method according to the present invention.
Fig. 2 is a schematic diagram of the system structure of the present invention.
FIG. 3 is a schematic diagram of a system management method according to the present invention.
Fig. 4 is a schematic diagram of a manual scheduling method in the present invention.
FIG. 5 is a schematic diagram of a dynamic flexible scheduling method in the present invention.
Detailed Description
The present invention is not limited by the following examples, and specific embodiments can be determined according to the technical scheme and practical situations of the present invention.
The invention is further described below with reference to examples and figures:
Example 1: as shown in fig. 1, a method for constructing a dynamic elastic scheduling system of high-performance computing and cloud computing resources includes:
step S101, dividing an independent high-performance computing resource pool and a cloud computing resource pool in the unified resource pool, and storing computing resources required by Fu Gesuan force into the corresponding computing resource pools;
In this embodiment, the independent high-performance computing resource pool and cloud computing resource pool are divided in the unified resource pool, and computing resources required by Fu Gesuan force are stored in the corresponding computing resource pool, so that each computing resource pool comprises a plurality of computing nodes.
Step S102, extracting computing resources from each computing resource pool according to the sharing area resource basic configuration information to form an independent resource sharing area; the method specifically comprises the following steps:
(1) Setting basic configuration information of the shared area resources, wherein the basic configuration information of the shared area resources comprises the model number, cpu, memory, management IP, calculation IP and DDR of calculation resources; setting the basic configuration information of the shared area resource according to specific requirements;
(2) Using the divided computing resource pool as a management unit, and utilizing the shared area resource basic configuration information to identify the computing resources matched with the high-performance computing resource pool and the cloud computing resource pool;
(3) And selecting matched computing resources, and establishing an independent resource sharing area after extracting from the high-performance computing resource pool and the cloud computing resource pool. The computing resources extracted here may be independently shared stored by type.
The above-mentioned requirement to be satisfied in order to ensure the normal execution of the schedule when establishing the resource sharing region includes:
1. the unified management network is used for managing the computing resources through the high-performance computing resource pool and the cloud computing resource pool, and the unified management network can meet the dynamic scheduling requirement.
The server configuration meets the demands of each resource pool, if the demands of computing power and dividing the demands of a high-performance computing resource pool and a cloud computing resource pool are simultaneously met, when computing resources need to be scheduled, the needed operating system and software information can be directly deployed without any change to hardware.
The high-performance computing resource pool and the cloud computing resource pool are provided with independent shared storage areas, and when the computing resources of the resource pool are scheduled to other resource pools, the use of the shared storage is not affected, so that the data independence is realized.
When the matched computing resources are selected, the functions of ping impi, ping management network, ping service network and the like, and the information of CPU utilization rate, memory utilization rate, disk use condition, network card sending and receiving rate and the like of the computing resources can be provided in advance, so that the computing resources are more accurately selected for creating the resource sharing area.
Step S103, a computing resource scheduling mode is set in the resource sharing area, and computing resource scheduling of the resource sharing area is achieved. The computing resource scheduling mode in the step comprises manual scheduling and dynamic elastic scheduling, wherein the manual scheduling does not create a timing task, the dynamic elastic scheduling sets a timing task and a scheduling threshold, and proper idle computing resources are automatically matched from a resource sharing area according to the scheduling threshold and a task triggering rule and distributed to each computing resource pool after automatic deployment.
Example 2: as shown in fig. 2, a dynamic resilient scheduling system for high performance computing and cloud computing resources includes:
The computing resource pool area comprises an independent high-performance computing resource pool and a cloud computing resource pool, and each computing resource pool stores computing resources meeting the same computing power requirement;
The resource sharing area comprises a shared resource area and a scheduling control module; the shared resource area stores computing resources extracted from a high-performance computing resource pool and a cloud computing resource pool according to the shared area resource basic configuration information; the scheduling control module completes the scheduling of the computing resources of the resource sharing area by a set computing resource scheduling mode, wherein the computing resource scheduling mode comprises manual scheduling and dynamic elastic scheduling;
the management unit comprises a right management module and a maintenance module;
(1) The authority management module operates the user authority, wherein the operation comprises adding, deleting and modifying the user, setting and distributing the user authority; the user rights herein may be, but are not limited to, normal users and administrators.
(2) The maintenance module is combined with creating a computing resource pool, inputting computing resources for each computing resource pool, and carrying out daily maintenance on the computing resources in the computing resource pool and the resource sharing area, wherein the daily maintenance comprises checking, creating, modifying, deleting, maintaining and the like on the resources.
Example 3: as shown in fig. 3, a scheduling method of a dynamic elastic scheduling system for high-performance computing and cloud computing resources is characterized by comprising the following steps:
Step S301, determining a computing resource scheduling mode;
Step S302, if the computing resource scheduling mode is determined to be manual scheduling, acquiring resource acquisition data information, confirming required computing resources, manually scheduling idle computing resources from a resource sharing area according to the required computing resources, and distributing the idle computing resources to each computing resource pool;
Step S303, if the computing resource scheduling mode is dynamic elastic scheduling, setting a resource pool scheduling threshold, creating a timing task, establishing a judging rule, automatically matching idle computing resources from a resource sharing area according to the time of the timing task and combining the resource pool scheduling threshold and the judging rule, and automatically deploying and distributing the idle computing resources to each computing resource pool.
If the computing resource scheduling mode is determined to be manual scheduling, the corresponding scheduling flow includes:
(1) Acquiring resource acquisition data information;
(2) Judging whether the computing resources in each computing resource pool need to be added according to the resource acquisition data information,
(3) In response, a resource sharing region is searched, whether the needed computing resource exists is judged,
(4) In response, the desired computing resources are scheduled from the resource sharing region to the respective computing resource pools,
(5) In response to no, the required computing resources are manually increased for each computing resource pool.
If the computing resource scheduling mode is determined to be dynamic flexible scheduling, the corresponding scheduling flow includes:
(1) Setting a scheduling threshold of a certain computing resource pool, creating a corresponding timing task, and establishing a judgment rule;
(2) Automatically monitoring a threshold of the computational resource pool by a timed task;
(3) Determining whether the computing resources in the computing resource pool are lower than a preset scheduling threshold,
(4) In response, a decision rule trigger is generated, the resource sharing region is searched, whether the needed computing resources exist is determined,
(5) In response, the desired computing resource is scheduled from the resource sharing region to the corresponding computing resource pool,
(6) And if not, triggering an alarm notice to prompt that the computing resources of the computing resource pool are insufficient, and prompting that the manual scheduling is started.
The resource scheduling information is required to be recorded when the manual scheduling or dynamic elastic scheduling is completed, so that the statistical analysis of the scheduling information is convenient to carry out later, and a data basis is provided for reasonably planning resources in a resource pool. The recorded resource scheduling information comprises the information of an original resource pool, a scheduled resource pool, scheduling time, scheduling reasons and the like.
The management method of the embodiment further comprises the steps of creating a computing resource pool, inputting computing resources for each computing resource pool, and carrying out daily maintenance on the computing resources in the computing resource pool and the resource sharing area, wherein the daily maintenance comprises the steps of checking, creating, modifying, deleting, maintaining and the like on the resources.
In summary, the invention changes the problem that the existing method can not realize the uniform scheduling and management of the high-performance computing resources and cloud computing resources, so that the high-performance computing resources and cloud computing resources can not be shared, and the resource utilization rate is lower, and the method is divided into an independent high-performance computing resource pool and a cloud computing resource pool in a uniform resource pool, thereby realizing the uniform management and classified management of the computing resources. And the needed computing resources are extracted from the high-performance computing resource pool and the cloud computing resource pool to establish a resource sharing area, and the high-performance computing resource pool and the cloud computing resource pool in the resource sharing area can be dynamically and flexibly scheduled, so that the computing resources can be distributed according to the needs, and the utilization rate of the resources is effectively improved. Meanwhile, the establishment of the resource sharing area and the setting of dynamic flexible scheduling can dynamically configure the computing resources according to the service demands, so that resources do not need to be configured according to the peak value of the use of the computing resources when the resources are purchased, and the resource cost is effectively saved.
The technical characteristics form the embodiment of the invention, have stronger adaptability and implementation effect, and can increase or decrease unnecessary technical characteristics according to actual needs so as to meet the requirements of different situations.
Claims (10)
1. The method for constructing the dynamic elastic scheduling system of the high-performance computing and cloud computing resources is characterized by comprising the following steps of:
Dividing an independent high-performance computing resource pool and a cloud computing resource pool in the unified resource pool, and storing computing resources required by Fu Gesuan force into the corresponding computing resource pools;
extracting computing resources from the high-performance computing resource pool and the cloud computing resource pool according to the sharing area resource basic configuration information to form an independent resource sharing area;
Setting a computing resource scheduling mode in the resource sharing area to realize the computing resource scheduling of the resource sharing area, wherein the computing resource scheduling mode comprises manual scheduling and dynamic elastic scheduling.
2. The method for constructing a dynamic flexible scheduling system for high-performance computing and cloud computing resources according to claim 1, wherein the extracting computing resources from the high-performance computing resource pool and the cloud computing resource pool according to the basic configuration information of the shared area resources to form an independent resource shared area comprises:
Setting basic configuration information of the shared area resources, wherein the basic configuration information of the shared area resources comprises the model number, cpu, memory, management IP, calculation IP and DDR of calculation resources;
using the divided computing resource pool as a management unit, and utilizing the shared area resource basic configuration information to identify the computing resources matched with the high-performance computing resource pool and the cloud computing resource pool;
And selecting matched computing resources, and establishing an independent resource sharing area after extracting from the high-performance computing resource pool and the cloud computing resource pool.
3. The method for constructing the dynamic elastic scheduling system of the high-performance computing and cloud computing resources according to claim 1 or 2, wherein computing resources in the resource sharing area are classified and independently shared and stored, or/and the manual scheduling is not provided with a timing task, idle computing resources are manually matched from the resource sharing area and distributed to each computing resource pool, the dynamic elastic scheduling is provided with a timing task and a scheduling threshold, idle computing resources are automatically matched from the resource sharing area according to the scheduling threshold and a task triggering rule, and the idle computing resources are automatically deployed and distributed to each computing resource pool.
4. A high performance computing and cloud computing resource dynamic resilient scheduling system constructed using the method of any one of claims 1 to 3, comprising:
The computing resource pool area comprises an independent high-performance computing resource pool and a cloud computing resource pool, and each computing resource pool stores computing resources meeting the same computing power requirement;
The resource sharing area comprises a shared resource area and a scheduling control module; the shared resource area stores computing resources extracted from a high-performance computing resource pool and a cloud computing resource pool according to the shared area resource basic configuration information; the scheduling control module completes the scheduling of the computing resources of the resource sharing area by a set scheduling mode of the computing resources, wherein the scheduling mode of the computing resources comprises manual scheduling and dynamic elastic scheduling.
5. The dynamic elastic scheduling system of high performance computing and cloud computing resources of claim 4, further comprising a management unit comprising a rights management module and a maintenance module;
the authority management module operates the user authority, wherein the operation comprises adding, deleting and modifying the user, setting and distributing the user authority;
The maintenance module combines the user rights to create a computing resource pool, records computing resources for each computing resource pool, and performs daily maintenance on the computing resources in the computing resource pool and the resource sharing area.
6. A scheduling method of the dynamic elastic scheduling system for high-performance computing and cloud computing resources according to claim 4 or 5, comprising:
determining a computing resource scheduling mode;
If the computing resource scheduling mode is determined to be manual scheduling, acquiring resource acquisition data information, confirming required computing resources, manually scheduling idle computing resources from a resource sharing area according to the required computing resources, and distributing the idle computing resources to each computing resource pool;
If the computing resource scheduling mode is dynamic elastic scheduling, setting a resource pool scheduling threshold, creating a timing task, establishing a judging rule, automatically matching idle computing resources from a resource sharing area according to the time of the timing task and combining the resource pool scheduling threshold and the judging rule, and automatically deploying and distributing the idle computing resources to each computing resource pool.
7. The scheduling method of the dynamic elastic scheduling system for high-performance computing and cloud computing resources of claim 6, wherein the manual scheduling comprises:
acquiring resource acquisition data information;
judging whether the computing resources in the existing computing resource pool need to be added according to the resource acquisition data information,
In response, a resource sharing region is searched, whether the needed computing resource exists is judged,
In response, the desired computing resources are scheduled from the resource sharing region to the respective computing resource pools,
In response to no, the required computing resources are manually increased for each computing resource pool.
8. The scheduling method of the dynamic flexible scheduling system for high-performance computing and cloud computing resources according to claim 6 or 7, wherein the dynamic flexible scheduling comprises:
setting a scheduling threshold of a certain computing resource pool, creating a corresponding timing task, and establishing a judgment rule;
automatically monitoring a threshold of the computational resource pool by a timed task;
determining whether the computing resources in the computing resource pool are lower than a preset scheduling threshold,
In response, a decision rule trigger is generated, the resource sharing region is searched, whether the needed computing resources exist is determined,
In response, the desired computing resource is scheduled from the resource sharing region to the corresponding computing resource pool,
And if not, triggering an alarm notice to prompt that the computing resources of the computing resource pool are insufficient, and prompting that the manual scheduling is started.
9. The scheduling method of the dynamic flexible scheduling system for high-performance computing and cloud computing resources according to claim 7 or 8, wherein when the manual scheduling or the dynamic flexible scheduling is completed, the resource scheduling information is recorded, and the resource scheduling is completed.
10. The scheduling method of a dynamic flexible scheduling system for high-performance computing and cloud computing resources according to any one of claims 6 to 9, further comprising creating computing resource pools, entering computing resources for each computing resource pool, and performing daily maintenance on computing resources in the computing resource pools and resource sharing areas.
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