CN108021447B - Method and system for determining optimal resource strategy based on distributed data - Google Patents
Method and system for determining optimal resource strategy based on distributed data Download PDFInfo
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- 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
Abstract
The invention relates to a method and a system for determining an optimal resource strategy based on distributed data, wherein the method comprises the following steps: collecting current resource information and establishing an intelligent training model; then calculating the increase rate of the used resources of each micro server in any current time period, and optimizing the intelligent training model; then calculating the resource quantity needed to be used by each micro server in any future time period; repeating the above process until the current used resource growth rate is calculated to be equal to the last used resource growth rate; secondly, determining a node distribution strategy scheme of the optimized micro server; and finally, generating a control instruction, and adjusting the node resources in real time. A system is also described. According to the invention, the resource information can be acquired in real time, the optimal allocation strategy can be calculated intelligently according to the resource information automation, the resources required by the micro-service can be allocated according to the optimal strategy, the resource utilization is maximized, and the resource cost of enterprises is saved.
Description
Technical Field
The invention belongs to the field of micro servers, and particularly relates to a method and a system for determining an optimal resource strategy based on distributed data.
Background
The existing micro-service system has relative independence, does not form a whole set of service network, monitors service states without calling frequency, calculates a large amount of information of service flow to carry out intellectualization, obtains a real-time optimal resource scheduling and using strategy, cannot allocate resources required by micro-service according to the optimal strategy, maximizes resource use, and saves resource cost for enterprises.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the existing micro-service system cannot obtain a real-time optimal resource scheduling and using strategy, and cannot allocate resources required by the micro-server according to the optimal strategy, so that the resource use is maximized, and the resource cost is saved for enterprises.
To solve the above technical problem, the present invention provides a method for determining an optimal resource policy based on distributed data, the method comprising:
s1, collecting current resource information of at least 2 micro servers, and establishing an intelligent training model;
s2, calculating the used resource growth rate of each micro server in any current time period according to the current resource information and the intelligent training model, and optimizing the intelligent training model according to the used resource growth rate;
s3, calculating the resource quantity needed to be used by each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model;
s4, repeating the steps S1-S3 until the current used resource increasing rate is calculated to be equal to the last used resource increasing rate, then executing the step S5;
s5, determining an optimized node distribution strategy scheme of the micro servers according to the optimized intelligent training model and the resource quantity required to be used by each micro server;
and S6, generating a control instruction according to the node distribution strategy scheme, and simultaneously adjusting the node resources of the at least 2 micro servers in real time according to the control instruction.
The invention has the beneficial effects that: by the method, the resource information can be acquired in real time, the optimal allocation strategy can be calculated intelligently according to the automation of the resource information, the resources required by the micro server can be allocated according to the optimal strategy, the resource use is maximized, and the resource cost of enterprises is saved.
Further, the current resource information includes: the method comprises the steps of ip resources, cpu and internal memory of each micro server, load pressure and network flow information, occupied resource amount and actual load amount sf.
Further, the total resource amount of the at least 2 micro servers and the load amount yf of each micro server are preset in the intelligent training model.
Further, the S3 includes: and when the ratio of the actual load sf to the load yf is greater than 0.8, calculating the resource amount required to be used by each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model.
Further, the S3 further includes: when the ratio of the actual load sf to the load yf is smaller than 0.2, calculating the resource recovery amount or the resource release amount of each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model, wherein the resource recovery amount refers to the resource released by other micro servers when the resource amount provided by the micro servers is insufficient; the resource releasing means that when the amount of the resources provided by the current micro server is too much, the resources are released to other micro servers.
The method has the following further beneficial effects: and determining the resource recovery amount or the resource release amount of each micro server according to the used resource growth rate and the optimized intelligent training model, so that the resources can be allocated according to the node distribution strategy scheme in the follow-up process.
The invention also relates to a system for determining an optimal resource policy based on distributed data, the system comprising: the system comprises a mongodb big data cluster, a big data computing center, an intelligent computing center, a strategy computing center and a micro-service cluster;
the microservice cluster is comprised of: at least 2 micro servers, wherein the at least 2 micro servers form a distributed structure;
the mongodb big data cluster is used for adopting the current resource information of the at least 2 micro servers in the micro service cluster;
the big data computing center is used for rapidly inquiring the current resource information in the mongodb big data cluster in real time and submitting the current resource information to the intelligent computing center;
the intelligent computing center is used for establishing an intelligent training model, computing the used resource growth rate of each micro server in any current time period according to the current resource information and the intelligent training model, and optimizing the intelligent training model according to the used resource growth rate; the intelligent training model is used for calculating the resource quantity required to be used by each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model; determining an optimized node distribution strategy scheme of the micro servers according to the optimized intelligent training model and the resource quantity required to be used by each micro server;
and the strategy computing center is used for generating a control instruction according to the node distribution strategy scheme and simultaneously adjusting the node resources of the at least 2 micro servers in real time according to the control instruction.
The invention has the beneficial effects that: by the system, the resource information can be acquired in real time, the optimal allocation strategy can be calculated intelligently according to the automation of the resource information, the resources required by the micro server can be allocated according to the optimal strategy, the resource utilization is maximized, and the resource cost of enterprises is saved.
Further, the current resource information includes: the method comprises the steps of ip resources, cpu and internal memory of each micro server, load pressure and network flow information, occupied resource amount and actual load amount sf.
Further, the total resource amount of the at least 2 micro servers and the load amount yf of each micro server are preset in the intelligent training model.
Further, the intelligent computing center is specifically configured to, when a ratio of the actual load amount sf to the load amount yf is greater than 0.8, compute the resource amount that each micro server needs to use in any future time period according to the used resource growth rate and the optimized intelligent training model.
Further, the intelligent computing center is specifically configured to, when a ratio of the actual load amount sf to the load amount yf is smaller than 0.2, compute a resource amount of resources recovered or released by each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model, where the recovered resources are resources released by other micro servers when the resource amount currently provided by the intelligent computing center is insufficient; the resource releasing means that when the amount of the resources provided by the current micro server is too much, the resources are released to other micro servers.
The method has the following further beneficial effects: and determining the resource recovery amount or the resource release amount of each micro server according to the used resource growth rate and the optimized intelligent training model, so that the resources can be allocated according to the node distribution strategy scheme in the follow-up process.
Drawings
FIG. 1 is a flow chart of a method of determining an optimal resource policy based on distributed data in accordance with the present invention;
FIG. 2 is a schematic diagram of a system for determining an optimal resource policy based on distributed data according to the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, provided in embodiment 1 of the present invention is a method for determining an optimal resource policy based on distributed data, where the method includes:
s1, collecting current resource information of at least 2 micro servers, and establishing an intelligent training model;
s2, calculating the used resource growth rate of each micro server in any current time period according to the current resource information and the intelligent training model, and optimizing the intelligent training model according to the used resource growth rate;
s3, calculating the resource quantity needed to be used by each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model;
s4, repeating the steps S1-S3 until the current used resource increasing rate is calculated to be equal to the last used resource increasing rate, then executing the step S5;
s5, determining an optimized node distribution strategy scheme of the micro servers according to the optimized intelligent training model and the resource quantity required to be used by each micro server;
and S6, generating a control instruction according to the node distribution strategy scheme, and simultaneously adjusting the node resources of the at least 2 micro servers in real time according to the control instruction.
It should be noted that, in this embodiment 1, first, some resource information of the multiple micro servers in the current operation is collected, and the resource information represents the current performance operation of the micro servers, for example: hardware resource information, network information, application service information, application tracking information and the like of the micro server, and meanwhile, an intelligent training model is also established; after the intelligent training model is established, the intelligent training model can calculate the use resource growth rate of each micro server in the current unfixed time period according to some resource information of the micro servers, and can optimize the intelligent training model according to the use resource growth rate (namely, parameters of the intelligent training model are adjusted according to the use resource growth rate), so that the optimized intelligent training model and the use resource growth rate are adopted to calculate the resource quantity required to be used by each micro server in the future unfixed time period; the method comprises the steps of continuously obtaining updated resource information of the micro servers, continuously adjusting and optimizing the intelligent training model until the current used resource growth rate is equal to the last used resource growth rate, indicating that the current micro servers are in a stable state, and the intelligent training model belongs to a relatively optimized state at present, so that the optimized node distribution strategy scheme of the micro servers can be determined according to the optimized intelligent training model and the resource quantity required to be used by each micro server, the resource quantity required to be used by each micro server can be adjusted timely according to the node distribution strategy scheme, the resources can be fully distributed and fully used by each micro server, the resource use is maximized, and the resource cost of enterprises is saved.
Optionally, in another embodiment 2, the current resource information includes: the method comprises the steps of ip resources, cpu and internal memory of each micro server, load pressure and network flow information, occupied resource amount and actual load amount sf.
It should be noted that embodiment 2 is a detailed description based on embodiment 1, and it is to be analyzed in embodiment 2 that all the current resource information mentioned in embodiment 1 includes: the information of ip resources, cpu and memory, load pressure and network flow information, occupied resource amount and actual load amount sf of each micro server can fully describe the operation condition of the current micro server, and is beneficial to subsequently mobilizing the micro servers.
Optionally, in another embodiment 3, the total resource amount of the at least 2 micro servers and the load amount yf of each micro server are preset in the intelligent training model.
It should be noted that, this embodiment 3 is a description and an improvement performed on the basis of the above embodiment 2, and this embodiment 3 shows that the total resource amount of all the micro servers and the load amount yf of each micro server need to be preset in the intelligent training model in advance, so that when performing subsequent calculation, the situation of the micro servers is determined by comparing the resource amount occupied by each micro server and the actual load amount sf actually measured.
Optionally, in another embodiment 4, the S3 includes: and when the ratio of the actual load sf to the load yf is greater than 0.8, calculating the resource amount required to be used by each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model.
It should be noted that the technical solution in this embodiment 4 is to improve the technology of the above embodiment 3, in this embodiment 4, a ratio between the actual load sf and the load yf in the embodiment 3 is determined, and when the ratio between the actual load sf and the load yf is greater than 0.8, it is determined that the current usage of the micro server is sufficient, and then it may be determined that the current usage of the micro server in any subsequent time period may also last for a period of time, so as to infer the amount of resources that the micro server needs to use in any future time period.
Optionally, in another embodiment 5, the S3 further includes: when the ratio of the actual load sf to the load yf is smaller than 0.2, calculating the resource recovery amount or the resource release amount of each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model, wherein the resource recovery amount refers to the resource released by other micro servers when the resource amount provided by the micro servers is insufficient; the resource releasing means that when the amount of the resources provided by the current micro server is too much, the resources are released to other micro servers.
It should be noted that, this embodiment 5 is a scheme performed in the technology of the above embodiment 3, in this embodiment 4, a ratio of an actual load sf to a load yf in embodiment 3 is determined, and when the ratio of the actual load sf to the load yf is smaller than 0.2, it is determined that the current usage rate of the micro server is not good, so that it may be determined that the current micro server needs to be improved in any subsequent time period, for example, the usage rate of the micro server is increased, resources are fully used, and all the resource amounts of resources recovered or released from each micro server in any future time period need to be calculated, where a recovered resource is a resource amount obtained when the current resource amount provided by the current micro server is insufficient, and the resource amounts released by other micro servers are obtained; releasing resources means releasing resources to other micro servers when the amount of resources provided by the micro servers is too much. In this embodiment 5, the resource amount of each micro server to be recovered or released is determined according to the resource usage growth rate and the optimized intelligent training model, which is beneficial to subsequently allocate resources according to the node distribution policy scheme (for example, a resource recovery instruction (an instruction to stop micro service application, delete application cache data, and the like, and adjust nodes of a micro service container in real time to achieve optimal use of resources).
As shown in fig. 2, embodiment 6 of the present invention further relates to a system for determining an optimal resource policy based on distributed data, where the system includes: the system comprises a mongodb big data cluster, a big data computing center, an intelligent computing center, a strategy computing center and a micro-service cluster;
the microservice cluster is comprised of: at least 2 micro servers, wherein the at least 2 micro servers form a distributed structure;
the mongodb big data cluster is used for adopting the current resource information of the at least 2 micro services in the micro service cluster;
the big data computing center is used for rapidly inquiring and computing the current resource information in the mongodb big data cluster in real time and submitting the current resource information to the intelligent computing center;
the intelligent computing center is used for establishing an intelligent training model, computing the used resource growth rate of each micro server in any current time period according to the current resource information and the intelligent training model, and optimizing the intelligent training model according to the used resource growth rate; the intelligent training model is used for calculating the resource quantity required to be used by each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model; determining an optimized node distribution strategy scheme of the micro servers according to the optimized intelligent training model and the resource quantity required to be used by each micro server;
and the strategy computing center is used for generating a control instruction according to the node distribution strategy scheme and simultaneously adjusting the node resources of the at least 2 micro servers in real time according to the control instruction.
It should be noted that, in this embodiment 6, first, some resource information of the multiple micro servers in the current operation is collected, and these resource information all represent the current performance operation of the micro servers, for example: hardware resource information, network information, application service information, application tracking information and the like of the micro server, and meanwhile, an intelligent training model is also established; after the intelligent training model is established, the intelligent training model can calculate the use resource growth rate of each micro server in the current unfixed time period according to some resource information of the micro servers, and can optimize the intelligent training model according to the use resource growth rate (namely, parameters of the intelligent training model are adjusted according to the use resource growth rate), so that the optimized intelligent training model and the use resource growth rate are adopted to calculate the resource quantity required to be used by each micro server in the future unfixed time period; continuously acquiring updated resource information of the micro servers, and continuously adjusting and optimizing the intelligent training model until the current used resource growth rate is equal to the last used resource growth rate, which indicates that the current micro server is in a stable state and the intelligent training model is in a relatively optimized state at present, so that the optimized node distribution strategy scheme of the micro servers can be determined according to the optimized intelligent training model and the resource amount required to be used by each micro server, the resource amount required to be used by each micro server can be timely adjusted according to the node distribution strategy scheme, the resources can be fully distributed and fully used by each micro server, the resource use is maximized, and the resource cost is saved for enterprises
Optionally, in another embodiment 7, the current resource information includes: the method comprises the steps of ip resources, cpu and internal memory of each micro server, load pressure and network flow information, occupied resource amount and actual load amount sf.
It should be noted that embodiment 7 is a detailed description based on embodiment 6, and what should be analyzed in embodiment 7 is that all the current resource information mentioned in embodiment 6 includes: the information of ip resources, cpu and memory, load pressure and network flow information, occupied resource amount and actual load amount sf of each micro server can fully describe the operation condition of the current micro server, and is beneficial to subsequently mobilizing the micro servers.
Optionally, in another embodiment 8, the total resource amount of the at least 2 micro servers and the load amount yf of each micro server are preset in the intelligent training model.
It should be noted that, this embodiment 8 is a description and an improvement performed on the basis of the above embodiment 7, and this embodiment 8 shows that the total resource amount of all the micro servers and the load amount yf of each micro server need to be preset in the intelligent training model in advance, so that when performing subsequent calculation, the situation of the micro servers is determined by comparing the resource amount occupied by each micro server and the actual load amount sf actually measured.
Optionally, in another embodiment 9, the intelligent computing center is further specifically configured to, when a ratio of the actual load amount sf to the load amount yf is greater than 0.8, calculate, according to the used resource growth rate and the optimized intelligent training model, an amount of resources that each micro server needs to use in any future time period.
It should be noted that the technical solution in this embodiment 9 is to improve the technology of the above embodiment 8, in this embodiment 9, a ratio between an actual load sf and a load yf in the embodiment 8 is determined, and when the ratio between the actual load sf and the load yf is greater than 0.8, it is determined that the current usage of the micro server is sufficient, and then it may be determined that the current usage of the micro server in any subsequent time period may also last for a period of time, so as to infer the amount of resources that the micro server needs to use in any future time period.
Optionally, in another embodiment 10, the intelligent computing center is further specifically configured to, when a ratio of the actual load amount sf to the load amount yf is smaller than 0.2, calculate a resource amount of resources recovered or released from each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model, where the recovered resources are resources released from other micro servers when a current resource amount provided by the intelligent computing center is insufficient; the resource releasing means that when the amount of the resources provided by the current micro server is too much, the resources are released to other micro servers.
It should be noted that, this embodiment 10 is a scheme performed in the technology of the above embodiment 8, in this embodiment 10, a ratio of an actual load sf to a load yf in embodiment 8 is determined, and when the ratio of the actual load sf to the load yf is smaller than 0.2, it is determined that the current usage rate of the micro server is not good, so that it may be determined that the current micro server needs to be improved in any subsequent time period, for example, the usage rate of the micro server is increased, resources are fully used, and all the resource amounts of resources recovered or released from each micro server in any future time period need to be calculated, where a recovered resource is a resource amount obtained when the current resource amount provided by the current micro server is insufficient, and the resource amounts released by other micro servers are obtained; releasing resources means releasing resources to other micro servers when the amount of resources provided by the micro servers is too much. In this embodiment 10, the resource amount of each micro server to be recovered or released is determined according to the resource usage growth rate and the optimized intelligent training model, which is beneficial to subsequently allocate resources according to the node distribution policy scheme (for example, a resource recovery instruction (an instruction to stop micro service application, delete application cache data, and the like, and adjust nodes of a micro service container in real time to achieve optimal use of resources).
In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A method for determining an optimal resource policy based on distributed data, the method comprising:
s1, collecting current resource information of at least 2 micro servers, and establishing an intelligent training model;
s2, calculating the used resource growth rate of each micro server in any current time period according to the current resource information and the intelligent training model, and optimizing the intelligent training model according to the used resource growth rate;
s3, calculating the resource quantity needed to be used by each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model;
s4, repeating the steps S1-S3 until the current used resource increasing rate is calculated to be equal to the last used resource increasing rate, then executing the step S5;
s5, determining an optimized node distribution strategy scheme of the micro servers according to the optimized intelligent training model and the resource quantity required to be used by each micro server;
and S6, generating a control instruction according to the node distribution strategy scheme, and simultaneously adjusting the node resources of the at least 2 micro servers in real time according to the control instruction.
2. The method of claim 1, wherein the current resource information comprises: the method comprises the steps of ip resources, cpu and internal memory of each micro server, load pressure and network flow information, occupied resource amount and actual load amount sf.
3. The method of claim 2, wherein the total resource amount of the at least 2 micro servers and the load amount yf of each micro server are preset in an intelligent training model.
4. The method according to claim 3, wherein said S3 comprises: and when the ratio of the actual load sf to the load yf is greater than 0.8, calculating the resource amount required to be used by each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model.
5. The method according to claim 3, wherein said S3 further comprises: when the ratio of the actual load sf to the load yf is smaller than 0.2, calculating the resource recovery amount or the resource release amount of each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model, wherein the resource recovery amount refers to the resource released by other micro servers when the resource amount provided by the micro servers is insufficient; the resource releasing means that when the amount of the resources provided by the current micro server is too much, the resources are released to other micro servers.
6. A system for determining an optimal resource policy based on distributed data, the system comprising: the system comprises a mongodb big data cluster, a big data computing center, an intelligent computing center, a strategy computing center and a micro-service cluster;
the microservice cluster is comprised of: at least 2 micro servers, wherein the at least 2 micro servers form a distributed structure;
the mongodb big data cluster is used for adopting the current resource information of the at least 2 micro servers in the micro service cluster;
the big data computing center is used for rapidly inquiring the current resource information in the mongodb big data cluster in real time and submitting the current resource information to the intelligent computing center;
the intelligent computing center is used for establishing an intelligent training model, computing the used resource growth rate of each micro server in any current time period according to the current resource information and the intelligent training model, and optimizing the intelligent training model according to the used resource growth rate; the intelligent training model is used for calculating the resource quantity required to be used by each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model; determining an optimized node distribution strategy scheme of the micro servers according to the optimized intelligent training model and the resource quantity required to be used by each micro server;
and the strategy computing center is used for generating a control instruction according to the node distribution strategy scheme and simultaneously adjusting the node resources of the at least 2 micro servers in real time according to the control instruction.
7. The system of claim 6, wherein the current resource information comprises: the method comprises the steps of ip resources, cpu and internal memory of each micro server, load pressure and network flow information, occupied resource amount and actual load amount sf.
8. The system of claim 7, wherein the total resource amount of the at least 2 micro servers and the load amount yf of each micro server are preset in the intelligent training model.
9. The system according to claim 8, wherein the intelligent computing center is further configured to, when a ratio of the actual load amount sf to the load amount yf is greater than 0.8, calculate an amount of resources that each micro server needs to use in any future time period according to the used resource growth rate and the optimized intelligent training model.
10. The system according to claim 8, wherein the intelligent computing center is further configured to, when a ratio of the actual load amount sf to the load amount yf is smaller than 0.2, compute a resource amount of a recovered resource or a released resource of each micro server in any future time period according to the used resource growth rate and the optimized intelligent training model, where the recovered resource is a resource that is released by another micro server when a resource amount currently provided by the intelligent computing center is insufficient; the resource releasing means that when the amount of the resources provided by the current micro server is too much, the resources are released to other micro servers.
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| CN109005223A (en) * | 2018-07-26 | 2018-12-14 | 南京邮电大学 | Internet of Things resource regulating method and system, computer readable storage medium and terminal |
| CN110971623B (en) * | 2018-09-28 | 2023-02-17 | 中兴通讯股份有限公司 | Micro-service instance elastic scaling method and device and storage medium |
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