CN109002342B - OpenStack-based method and system for directionally scheduling computing resources - Google Patents
OpenStack-based method and system for directionally scheduling computing resources Download PDFInfo
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Abstract
The invention provides a method for directionally scheduling computing resources based on OpenStack, which comprises the following steps: 1) dividing OpenStack computing resources into a plurality of computing areas according to relevant parameters of the virtual machine, and identifying; 2) dividing all the computing nodes into designated computing areas and inputting the computing areas into a database; 3) a user selects a calculation area through the identification, and reads all calculation node information of the calculation area from the database; 4) filtering out non-conforming computing nodes aiming at all the computing nodes read in the step 3), sequencing the rest computing nodes, and scheduling the computing nodes with the highest sequencing to generate the virtual machine. The invention also provides a system for directionally scheduling the computing resources based on the OpenStack.
Description
Technical Field
The invention relates to the technical field of cloud computing, in particular to a computing resource directional scheduling method and system based on OpenStack.
Background
In recent years, cloud computing technology has become one of the hottest IT technologies, and has attracted a high degree of attention from the industrial, academic, and government sectors. OpenStack is one of the most popular cloud computing technologies at present, and can integrate different physical resources to form a huge resource pool to provide computing, storage and network resource services for the outside. The OpenStack classifies and manages physical resources by using the available Zone, divides all control nodes into the same available Zone Internal, and divides all computing nodes into the same available Zone Nova. Currently, OpenStack performs filtering and weighted sorting processing on each computing node added to its computing resource, and then performs scheduling.
However, the disadvantages are that: the available Zone divides resources according to geography (different machine rooms or different cabinets) or physics (different equipment configurations), has larger granularity, can not carry out filtering processing according to different service purposes of the computing node in the filtering process, and can not be directionally dispatched to a designated area; when the computing resources are large in scale, a great deal of effort and time are needed for scheduling and operation maintenance, and the cost is too high.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a method and a system for directionally scheduling computing resources based on OpenStack, which can divide the computing resources of OpenStack into different computing areas, wherein the computing area range is smaller than that of a Zone, and the Nova available area can be divided into a plurality of computing areas to meet different requirements; the user can select the calculation area according to the self requirement, and the directional scheduling module can schedule the virtual machine to the designated calculation area, so that the scheduling efficiency is improved; and the operation and maintenance of the computing resources in different areas can be distinguished, the cost is saved, and the resources are utilized more efficiently.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a method for directionally scheduling computing resources based on OpenStack comprises the following steps:
1) dividing OpenStack computing resources into a plurality of computing areas according to relevant parameters of the virtual machine, and identifying;
2) dividing all computing nodes (servers) into designated computing areas and recording the designated computing areas into a database;
3) a user selects a calculation area through the identification, and reads all calculation node information of the calculation area from the database;
4) filtering out non-conforming computing nodes aiming at all the computing nodes read in the step 3), sequencing the rest computing nodes, and scheduling the computing nodes with the front sequencing to generate the virtual machine.
Further, the relevant parameters of the virtual machine include the importance of virtual machine traffic, the consumption level of CPU and memory, the amount of data generated, and the security of the virtual machine.
Further, the judgment indexes of the importance of the virtual machine service are as follows: it is important if a service interruption on a virtual machine will have an impact on other systems or services, otherwise it is not important.
Further, all the computing nodes in the step 2) are firstly recorded into a database table for directional scheduling registration, and then the computing nodes are divided into corresponding computing areas according to the relevant parameters of the virtual machine.
Further, in step 3), the user adds a mark to the virtual machine to be applied according to the relevant parameters of the virtual machine, and finds the calculation area containing the corresponding identification through the mark.
Further, in the step 4), the computing nodes whose available resources do not meet the requirements of the virtual machine are filtered according to the parameters of the CPU, the memory and the disk of the computing nodes, where the available resources refer to the capacities of the CPU, the memory and the disk available to the computing nodes.
Further, in step 4), the remaining computing nodes are subjected to weighted sorting of available resources, and sorted from large to small according to the weights.
An OpenStack-based directional scheduling system for computing resources, comprising:
the cloud computing platform based on the OpenStack comprises a plurality of computing areas containing identifications, a plurality of computing nodes, a database and cloud computing, wherein the computing areas can be divided into computing resources according to needs, the computing nodes can be divided into designated computing areas, the database stores the relationship between the computing nodes and the computing areas, and the cloud computing of each computing node is realized;
and the directional scheduling module is used for identifying the calculation region identification selected by the user and realizing the directional scheduling of the calculation nodes through filtering and sequencing.
Compared with the prior art, the invention has the following advantages:
the computing resources of OpenStack can be divided into different computing areas, the computing area range is smaller than that of a Zone, and the available area of Nova can be divided into a plurality of computing areas to meet different requirements; the OpenStack self-scheduling strategy is expanded, the computing nodes can be directionally scheduled to the specified computing area according to the relevant parameters of the virtual machine, the optimal computing node is selected to be used for generating the virtual machine, and finer-grained strategy configuration is supported; the cluster is supported to distinguish physical computing areas according to requirements, so that differentiated operation and maintenance are conveniently supported, and maintenance cost is reduced; the directional scheduling reduces the scheduling range and improves the scheduling efficiency.
Drawings
Fig. 1 is a schematic diagram of division of an OpenStack computing resource into different computing regions.
FIG. 2 is a schematic diagram of a computing node directed scheduling process.
FIG. 3 is a diagram illustrating selection of a compute region and a compute node by a virtual machine.
Detailed Description
In order to make the aforementioned and other features and advantages of the invention more comprehensible, embodiments accompanied with figures are described in detail below.
The embodiment provides a method and a system for directionally scheduling computing resources based on OpenStack, wherein the system is a cloud computing platform based on OpenStack and comprises a plurality of computing areas containing identifications, which can be divided into computing resources according to needs, a plurality of computing nodes which can be divided into specified computing areas, a database for storing the relationship between the computing nodes and the computing areas, and cloud computing for realizing the computing nodes; the system also comprises a directional scheduling module which is used for identifying the calculation area identification selected by the user and realizing the directional scheduling of the calculation nodes through filtering and sequencing. In conjunction with the system, the scheduling method is shown in fig. 2, and includes the following steps:
step 1: the OpenStack computing resource T is divided into different computing areas according to relevant parameters of a virtual machine, and is identified by T1 and T2, as shown in FIG. 1. The relevant parameters of the virtual machine comprise the importance of the virtual machine service, the consumption degree of a CPU and a memory, the generated data volume and the safety of the virtual machine, wherein the importance judgment indexes of the service are as follows: it is important if a service interruption on a virtual machine will have an impact on other systems or services, otherwise it is not important.
Step 2: and dividing all the computing nodes into designated computing areas and recording the designated computing areas into a database. For example, compute nodes C1, C2, C3 are partitioned into compute region T1, and compute nodes C4, C5, C6 are partitioned into compute region T2.
Specifically, six servers are added to the OpenStack cloud platform as six computing nodes C1, C2, C3, C4, C5 and C6. Firstly, the six computing nodes need to be recorded into a database table specially used for directional scheduling registration, if the six servers have three latest servers and three older servers, the new servers have higher and more stable performance and are suitable for storing important virtual machines (judged according to relevant parameters of the virtual machines), and the older servers have high failure probability and are suitable for storing unimportant virtual machines, and even if the servers fail, the failure has no influence. If the computing area T1 is used for storing important virtual machines and the computing area T2 is used for storing non-important virtual machines, three new servers are divided into the computing area T1 (such as C1, C2 and C3), three older servers are divided into the computing area T2 (such as C4, C5 and C6), and the three servers are recorded in the table of the database.
And step 3: when the user applies for the virtual machine, the calculation region is selected according to the requirement, for example, according to the importance degree of the virtual machine, the important virtual machine is selected as the calculation region T1, and the unimportant virtual machine is selected as the calculation region T2. Because the virtual machine is applied by the user, the user can determine in which computing area the virtual machine is suitable to be stored according to relevant parameters of the virtual machine, such as service importance, resource (CPU, memory) consumption, security, and the like. For example, a virtual machine applied by a user needs a CPU to be configured with 4 cores, and a computing node with less than 4 cores of the remaining available CPU is excluded.
To further clarify the technical content of this step, it is explained by referring to fig. 3. The computing resource T is divided into three computing areas, namely an important computing area S-T1, a non-important computing area F-T2 and a temporary computing area R-T3 according to relevant parameters (such as service importance) of the virtual machine, wherein S, F, R is identification. When a user A, B, C applies for a virtual machine (denoted by vm), which virtual machine is selected according to its own needs (such as the importance of service), if the user considers that the service is important and needs to select the important virtual machine, a mark S (such as S-vm1, S-vm4 and S-vm7) is added to the virtual machine to be applied, and the host machine is selected only in an important computing area S-T1 according to the mark during scheduling, so that the scheduling range is remarkably reduced, and the virtual machine is guaranteed to be scheduled as required. Similarly, if a non-important virtual machine or a temporary virtual machine is selected, a label F (such as F-vm2, F-vm5, F-vm8) or R (such as R-vm3, R-vm6, R-vm9) is added to the virtual machine to be applied, and hosts of the corresponding computing area F-T2 or R-T3 are scheduled to generate the required virtual machine according to the label F or R during scheduling. It should be noted that when the user applies for a virtual machine, the virtual machine to be applied is marked, and actually belongs to a directed request including a condition, for example, when the user a applies for a virtual machine storing important services, the mark is S, in fig. 3, S-vm1 under the user a belongs to the directed request of the virtual machine, and instead of the virtual machine that has been applied, the directed request is directed to (the leftmost arrow in the figure) the computing area S-T1, the computing node C1 is further found, and the virtual machine S-vm1 is generated in C1.
And 4, step 4: filtering all the computing nodes read in the steps, and filtering the computing nodes which do not meet the requirements of the available resources (namely available CPU, memory and disk capacity) of the virtual machine according to the parameters of the CPU, the memory and the disk of the computing nodes; and performing weighted sequencing on the remaining coincident computing nodes for the available resources, sequencing the computing nodes in sequence from large to small according to the weights, and scheduling the computing nodes in the front of the sequence to generate the virtual machine.
And when scheduling is carried out, the background calls a directional scheduling module, and the directional scheduling module is used for identifying the calculation region identifier selected by the user. For example, identifying a user selected computing region T1, the scheduling scope is defined as computing nodes C1, C2, C3 in T1; then, reading all the computing node information in the computing area T1 from the database, and filtering out non-compliant computing nodes according to a self filtering method, such as C3; through the weighted sorting method, the remaining computing nodes C1 and C2 are sorted, and the computing node with the top sorting is selected, so that the most suitable computing node in T1 is scheduled, as shown in fig. 2. The directional scheduling method greatly reduces the scheduling range and improves the scheduling efficiency.
In order to verify the technical effect, 80 servers are used as computing nodes and divided into a plurality of computing areas for testing, and the test data are as follows:
as can be seen from the above table, when 80 compute nodes are not divided, i.e. 1 compute region, i.e. the method is not used, the scheduling time consumption is 824 ms; when 80 computing nodes are divided into at least 2 computing areas, the method plays a role, the scheduling time is shortened to 30 milliseconds, and the effect is remarkable; it can also be seen from the table that the scheduling time is shorter when the number of the calculation regions is larger (the space is smaller), so the method can significantly improve the scheduling efficiency. In addition, due to the fact that the plurality of computing areas are divided, operation and maintenance can be distinguished according to computing nodes of different computing areas, and operation and maintenance cost can be reduced.
Claims (6)
1. A method for directionally scheduling computing resources based on OpenStack comprises the following steps:
1) dividing OpenStack computing resources into a plurality of computing areas according to relevant parameters of the virtual machine, including the importance of virtual machine service, the consumption degree of a CPU and a memory, the generated data volume and the safety of the virtual machine, and identifying;
2) dividing all the computing nodes into designated computing areas and inputting the designated computing areas into a database;
3) a user selects a calculation area through the identification, and reads all calculation node information of the calculation area from the database;
4) and (3) filtering out computing nodes with available resources which do not meet the requirements of the virtual machine according to the CPU, memory and disk parameters of the computing nodes, wherein the available resources refer to the available CPU, memory and disk capacities of the computing nodes, sequencing the rest computing nodes, and scheduling the computing nodes with the highest sequencing to generate the virtual machine.
2. The method of claim 1, wherein the determination of the importance of the virtual machine service is as follows: it is important if a service interruption on a virtual machine will have an impact on other systems or services, otherwise it is not important.
3. The method according to claim 1, wherein all the compute nodes in step 2) are first entered into a database table for directional scheduling registration, and then the compute nodes are divided into corresponding compute regions according to the relevant parameters of the virtual machine.
4. The method according to claim 1, wherein in step 3), the user marks the virtual machine to be applied according to the relevant parameters of the virtual machine, and finds the calculation region containing the corresponding identifier through the mark.
5. The method of claim 1, wherein the remaining compute nodes are weighted in step 4) in order of available resources according to a decreasing weight.
6. An OpenStack-based directed scheduling system of computing resources for implementing the method of any of claims 1-5, the system comprising:
the cloud computing platform based on the OpenStack comprises a plurality of computing areas containing identifications, a plurality of computing nodes, a database and cloud computing, wherein the computing areas can be divided into computing resources according to needs, the computing nodes can be divided into designated computing areas, the database stores the relationship between the computing nodes and the computing areas, and the cloud computing of each computing node is realized;
and the directional scheduling module is used for identifying the calculation region identification selected by the user and realizing the directional scheduling of the calculation nodes through filtering and sequencing.
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| CN110380894A (en) * | 2019-06-27 | 2019-10-25 | 苏州浪潮智能科技有限公司 | The management method and device of cloud platform medium cloud host |
| CN114064448A (en) * | 2020-08-05 | 2022-02-18 | 北京金山云网络技术有限公司 | Test environment deployment and smoking test method and device and electronic equipment |
| CN111966494A (en) * | 2020-08-18 | 2020-11-20 | 江苏安超云软件有限公司 | Resource scheduling method and device, storage medium and electronic equipment |
| CN112465359B (en) * | 2020-12-01 | 2024-03-15 | 中国联合网络通信集团有限公司 | Calculation force calling method and device |
| CN116405391A (en) * | 2023-04-10 | 2023-07-07 | 长扬科技(北京)股份有限公司 | OpenStack-based virtual machine node screening method, system and storage medium |
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