CN107357649B - Method and device for determining system resource deployment strategy and electronic equipment - Google Patents
Method and device for determining system resource deployment strategy and electronic equipment Download PDFInfo
- Publication number
- CN107357649B CN107357649B CN201710391766.0A CN201710391766A CN107357649B CN 107357649 B CN107357649 B CN 107357649B CN 201710391766 A CN201710391766 A CN 201710391766A CN 107357649 B CN107357649 B CN 107357649B
- Authority
- CN
- China
- Prior art keywords
- deployment
- feasible
- state information
- strategy
- service
- 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.)
- Active
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
- G06F9/50—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
Abstract
The embodiment of the invention provides a method and a device for determining a system resource deployment strategy and electronic equipment. The method comprises the following steps: receiving a first service resource request of a service to be deployed; acquiring first system resource use state information, wherein the first system resource use state information is first resource use state information of each physical machine in the system; acquiring at least one first feasible deployment strategy matched with the first system resource use state information in the system; aiming at each first feasible deployment strategy, calculating and recording the average longest continuous deployment times of each first feasible deployment strategy according to a preset simulation algorithm and simulation calculation times; and selecting a preset number of first feasible deployment strategies with the longest average and the largest continuous deployment times from the records, and taking the preset number of first feasible deployment strategies as system resource deployment strategies corresponding to the first service resource requests.
Description
Technical Field
The present invention relates to the field of resource allocation technologies, and in particular, to a method and an apparatus for determining a system resource deployment policy, and an electronic device.
Background
The resource deployment of a single machine and multiple instances refers to the deployment of multiple instances consuming resources on one physical machine, for example, in a MySQL cluster, multiple MySQL instances are deployed on each physical machine and allocated to different business personnel for use; in the MongoDB cluster, a plurality of MongoDB instances are deployed on each physical machine and are distributed to different service personnel for use.
At present, in a resource deployment scenario of a single machine with multiple instances, when an instance is to be deployed to a certain physical machine in a system, an operation and maintenance person needs to comprehensively consider the current use conditions of resources such as a CPU, a memory, a network, and a disk of the physical machine to determine a physical machine deployment instance.
However, the inventor finds that the prior art has at least the following problems in the process of implementing the invention: when a large number of physical machines exist in a cluster, an operation and maintenance worker generally selects a proper physical machine to complete system resource deployment according to resource requirements in a received service resource request and personal historical experience, and comprehensively considers the current resource use conditions of the large number of physical machines, and the selection of the proper physical machine is called a deployment strategy. In the prior art, because the deployment strategy is manually selected, the reliability of the selected deployment strategy is low. Therefore, it is desirable to provide an objective and reliable method for determining a system resource deployment policy.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for determining a system resource deployment strategy and electronic equipment, so as to solve the problem of low reliability of the deployment strategy caused by manual selection of the deployment strategy in the prior art. The specific technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a method for determining a system resource deployment policy, where the method includes:
receiving a first service resource request of a service to be deployed;
acquiring first system resource use state information, wherein the first system resource use state information is first resource use state information of each physical machine in the system;
acquiring at least one first feasible deployment strategy matched with the first system resource use state information in the system;
aiming at each first feasible deployment strategy, calculating and recording the average longest continuous deployment times of each first feasible deployment strategy according to a preset simulation algorithm and simulation calculation times;
and selecting a preset number of first feasible deployment strategies with the longest average and the largest continuous deployment times from the records, and taking the preset number of first feasible deployment strategies as system resource deployment strategies corresponding to the first service resource requests.
The method as described above, wherein the calculating and recording the average longest possible continuous deployment time of each first feasible deployment strategy according to a preset simulation algorithm and a simulation calculation time includes:
judging whether the preset simulation calculation times are reached;
if the preset simulation calculation times are not reached, randomly sampling from the pre-stored historical service resource requests to obtain a second service resource request;
calculating and recording the deployment times of the first feasible deployment strategy;
determining second resource use state information of each physical machine in the system as second system resource use state information according to the first feasible deployment strategy;
judging whether at least one second feasible deployment strategy meeting the second service resource request exists in the system according to the second system resource use state information;
if yes, returning to the step of obtaining a second service resource request by randomly sampling from the pre-stored historical service resource requests aiming at each second feasible deployment strategy;
if not, determining the recorded deployment times of the first feasible deployment strategy as the longest continuous deployment times of the first feasible deployment strategy; returning to the step of judging whether the preset simulation calculation times are reached;
and if the preset simulation calculation times are reached, calculating the average longest continuous deployment times of the first feasible deployment strategy according to the recorded longest continuous deployment times and the preset simulation calculation times.
The method as above, wherein the randomly sampling from the pre-stored historical service resource requests to obtain the second service resource request, comprises:
carrying out probability distribution statistics on the historical service resource requests to obtain probability distribution statistical results;
and randomly sampling according to the probability distribution statistical result to obtain the second service resource request.
In a second aspect, an embodiment of the present invention provides an apparatus for determining a system resource deployment policy, where the apparatus includes:
the system comprises a receiving module, a service configuration module and a service configuration module, wherein the receiving module is used for receiving a first service resource request of a service to be deployed;
a first obtaining module, configured to obtain first system resource usage state information, where the first system resource usage state information is first resource usage state information of each physical machine in the system;
a second obtaining module, configured to obtain at least one first feasible deployment policy that is matched with the first system resource usage state information in the system;
the calculation recording module is used for calculating and recording the average longest continuous deployment times of each first feasible deployment strategy according to a preset simulation algorithm and simulation calculation times aiming at each first feasible deployment strategy;
and the determining module is used for selecting a preset number of first feasible deployment strategies with the longest average and the largest continuous deployment times from the records, and taking the preset number of first feasible deployment strategies as the system resource deployment strategies corresponding to the first service resource request.
The apparatus as described above, wherein the calculation recording module is specifically configured to:
judging whether the preset simulation calculation times are reached;
if the preset simulation calculation times are not reached, randomly sampling from the pre-stored historical service resource requests to obtain a second service resource request;
calculating and recording the deployment times of the first feasible deployment strategy;
determining second resource use state information of each physical machine in the system as second system resource use state information according to the first feasible deployment strategy;
judging whether at least one second feasible deployment strategy meeting the second service resource request exists in the system according to the second system resource use state information;
if yes, returning to the step of obtaining a second service resource request by randomly sampling from the pre-stored historical service resource requests aiming at each second feasible deployment strategy;
if not, determining the recorded deployment times of the first feasible deployment strategy as the longest continuous deployment times of the first feasible deployment strategy; returning to the step of judging whether the preset simulation calculation times are reached;
and if the preset simulation calculation times are reached, calculating the average longest continuous deployment times of the first feasible deployment strategy according to the recorded longest continuous deployment times and the preset simulation calculation times.
The apparatus as described above, wherein the calculation recording module is specifically configured to:
carrying out probability distribution statistics on the historical service resource requests to obtain probability distribution statistical results;
and randomly sampling according to the probability distribution statistical result to obtain the second service resource request.
In a third aspect, an embodiment of the present invention provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, where the processor and the communication interface complete communication between the memory and the processor through the communication bus;
a memory for storing a computer program;
a processor, configured to implement any one of the method steps of the first aspect of the embodiments of the present invention when executing the program stored in the memory.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the computer is caused to execute any one of the above-mentioned method for determining a system resource deployment policy.
In a fifth aspect, an embodiment of the present invention further provides a computer program product containing instructions, which when run on a computer, causes the computer to execute any of the above-mentioned method for determining a system resource deployment policy.
The method, the device and the electronic equipment for determining the system resource deployment strategy provided by the embodiment of the invention receive a first service resource request of a service to be deployed; acquiring first system resource use state information, wherein the first system resource use state information is first resource use state information of each physical machine in the system; acquiring at least one first feasible deployment strategy matched with the first system resource use state information in the system; aiming at each first feasible deployment strategy, calculating and recording the average longest continuous deployment times of each first feasible deployment strategy according to a preset simulation algorithm and simulation calculation times; and selecting a preset number of first feasible deployment strategies with the longest average and the largest continuous deployment times from the records, and taking the preset number of first feasible deployment strategies as system resource deployment strategies corresponding to the first service resource requests. It can be seen that after receiving the first service resource request, the system resource deployment strategy with higher reliability is finally determined through a large amount of simulation calculation. Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
Fig. 1 is a schematic flowchart of a method for determining a system resource deployment policy according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating the step 104 in the embodiment shown in FIG. 1;
fig. 3 is a schematic structural diagram of a device for determining a system resource deployment policy according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an electronic device according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described below with reference to the drawings in the embodiments of the present invention.
Firstly, it should be noted that, after receiving a service resource request of a service to be deployed, it may first determine whether the service to be deployed is an important service according to a tag in the service resource request, if the service to be deployed is an important service, select a new physical machine to deploy the service resource request, and if the service to be deployed is a common service, determine a deployment policy of the service resource request by using the method provided in the embodiment of the present invention.
When deploying a common service resource request, it is generally desirable to be able to deploy as many service resource requests as possible to an existing physical machine in the system, and since migration of a deployed service is generally burdened and may affect stability of service operation, migration of the deployed service needs to be avoided as much as possible. Therefore, the method for measuring the quality of the system resource deployment strategy can ensure that the current system can deploy new service resource requests as much as possible on the premise of not expanding the capacity under the condition of not migrating the existing deployed service resources.
The following describes in detail a method for determining a system resource deployment policy provided in an embodiment of the present invention.
Fig. 1 is a flowchart illustrating a method for determining a system resource deployment policy according to an embodiment of the present invention. As shown in fig. 1, the method provided in this embodiment may be executed by a device for determining a system resource deployment policy, and specifically, the method provided in this embodiment may include:
The first service resource request may include information such as CPU occupancy and memory occupancy required by the service to be deployed. For example, for a computing operation, the first service resource request may include information about required memory occupancy rate, and for an inquiry operation, the first service resource request may include information about CPU occupancy rate, memory occupancy rate, and the like.
102, acquiring first system resource use state information, wherein the first system resource use state information is first resource use state information of each physical machine in the system;
the first resource usage state information may specifically include information of CPU usage rate, memory usage rate, spare disk capacity, network traffic size, and the like of each physical machine.
specifically, after receiving a first service resource request of a service to be deployed and determining first system resource usage state information (i.e., CPU usage, memory usage, disk spare capacity, and network traffic size of each physical machine), for a single physical machine, when all parameter values in the state information do not exceed corresponding preset upper limit values, the state information of the physical machine satisfies a resource restriction condition, at this time, it is determined that at least one deployable physical machine capable of satisfying the first service resource request exists in the system, and the at least one deployable physical machine is determined as the at least one first feasible deployment policy.
It can be understood that, if it is determined that at least one first feasible deployment policy that satisfies the first service resource request does not exist in the system, the first service resource request may be satisfied only by expanding system capacity or migrating a deployed service. The expansion or service migration can refer to the prior art, and is not described herein again.
104, aiming at each first feasible deployment strategy, calculating and recording the average longest continuous deployment times of each first feasible deployment strategy according to a preset simulation algorithm and simulation calculation times;
in this step, specifically, referring to fig. 2, for each of the first feasible deployment policies, the following steps may be performed:
and 1041, judging whether the preset simulation calculation times are reached.
And 1042, if the preset simulation calculation times are not reached, randomly sampling from the pre-stored historical service resource requests to obtain a second service resource request.
Specifically, the determining device of the system resource deployment policy may perform probability distribution statistics on the historical service resource request to obtain a probability distribution statistical result; and randomly sampling according to the probability distribution statistical result to obtain the second service resource request.
In practical application, for application scenarios in which various resource requests are independent of each other, probability estimation can be performed on the distribution of historical resource requests of a single resource, during random sampling, the historical resource requests of the resources are sampled according to respective distributions, and then the samples of the resources are combined together to obtain the second service resource request; for application scenes that the requirements of various resources are related to each other, probability estimation can be carried out on joint probability distribution of historical resource requests of the resources, the joint probability distribution is divided into two conditions of continuous distribution and discrete distribution, and for the condition of continuous distribution, multivariate Gaussian kernel density estimation is adopted to carry out maximum likelihood estimation on the resources to obtain joint probability distribution density; and for the discrete distribution condition, performing maximum likelihood estimation on each resource by adopting discrete probability distribution decomposition to obtain a joint probability density, and sampling the joint probability density of the historical resource requests to obtain the second service resource request during random sampling.
And 1043, calculating and recording the deployment times of the first feasible deployment strategy.
Specifically, the longest possible continuous deployment time of the first feasible deployment policy may be calculated by a formula maxlen _ x (i) ═ 1+ max { maxlen _ xj (i +1) }; wherein, maxlen _ x (i) represents the deployment times of the first feasible deployment strategy x in the ith simulation calculation, maxlen _ xj (i +1) represents the longest continuous deployment times of the jth feasible deployment strategy in the (i +1) th simulation calculation, and i is a positive integer.
The determining device of the system resource deployment strategy outputs the candidate system resource deployment strategy for selection by operation and maintenance personnel; and the determining device of the system resource deployment strategy receives the selection instruction, and determines the system resource deployment strategy corresponding to the selection instruction as the system resource deployment strategy corresponding to the first service resource request.
In the process of applying the method for determining a system resource deployment policy provided in this embodiment, for a first service resource request r, it is assumed that first resource usage state information of each physical machine in the system is S0 ═ { S1(0), S2(0),. ·, sM (0) }, where S1(0) to sM (0) are resource usage state information of each of M physical machines in the system; in this resource usage state, the first service resource request r can be selectively deployed to any one physical machine, so there are M possible deployment strategies.
Assuming that the first service resource request r is deployed to the mth physical machine, the new resource usage state of the mth physical machine is expressed by sm (0) + r since various resources are linearly additive. Judging whether the new resource use state of the physical machine meets the resource limitation condition or not through a judgment function f (sm (0) + r), namely, if the new resource use state of the mth physical machine does not exceed the maximum resource utilization rate of the mth physical machine, determining that the deployment of the first service resource request r to the mth physical machine is a first feasible deployment strategy; and if the resource use state exceeds the maximum resource utilization rate of the mth physical machine, determining that the deployment of the first service resource request r to the mth physical machine is not a currently feasible deployment strategy.
For infeasible deployment strategies, zero is recorded for the maximum number of consecutive successful deployments.
For the first feasible deployment strategy, recording the current deployment times as one, continuously sampling from the pre-stored historical service resource requests for simulating a second service resource request which is possible in the future, and calculating the longest continuous deployment times of each first feasible deployment strategy. Therefore, through a large amount of simulation calculation, the average longest continuous deployment times of each first feasible deployment strategy is obtained and is taken as the consideration of the advantages and disadvantages of the deployment strategies.
Specifically, for the first feasible deployment strategy, the present embodiment needs to estimate a new system resource usage state generated by the first feasible deployment strategy, and can continuously and successfully deploy the times of service resource requests without capacity expansion or migration of the system in the future.
Assuming that the M possible deployment policies are all the first feasible deployment policies, the resource usage states of the M systems can be generated at present. To simulate the next traffic resource request, we randomly sample the next possible second traffic resource request from the historical resource requests.
And for the M system resource use states, all the feasible deployment strategies under each system resource use state are solved respectively. Assuming that all deployment policies for M system resource usage states are feasible, there will be M × M system resource usage states. And after all the second feasible deployment strategies of the M system resource use states are obtained, simulating the next service resource request, and continuously searching all the feasible deployment strategies of all the system resource use states until no feasible deployment strategy exists. In this way, the longest continuous deployment times of the first M first feasible deployment strategies can be calculated.
It can be seen that the calculation of the longest possible continuous deployment times for solving the first feasible deployment strategy is obtained by a breadth-first search algorithm, each sampling of a new service resource request and the solving of all the current feasible deployment strategies are an iterative process, and the longest possible continuous deployment times for solving a certain deployment strategy can be decomposed into sub-problems for solving. The longest number of consecutive deployments maxlen _ x (i) ═ 1+ max { maxlen _ xj (i +1) } for a certain deployment policy x in the ith simulation calculation, where maxlen _ xj (i +1) represents the longest number of consecutive deployments of the jth feasible deployment policy in i +1, and i is a positive integer.
After the respective longest possible continuous deployment times of the initial first feasible deployment policies are obtained, the average longest possible continuous deployment times of the first feasible deployment policies can be calculated according to the respective longest possible continuous deployment times of the first feasible deployment policies and the preset simulation calculation times.
And 105, selecting a preset number of first feasible deployment strategies with the longest average and the largest continuous deployment times from the records, and taking the preset number of first feasible deployment strategies as system resource deployment strategies corresponding to the first service resource request.
In the method for determining a system resource deployment policy provided in this embodiment, after receiving a first service resource request, according to a first system resource usage state and the first service resource request, the number of times that different feasible deployment policies successively deploy service resource requests without capacity expansion or migration in the future of the system is calculated through a large number of simulations, a preset number of first feasible deployment policies with the largest average longest possible successive deployment times is finally determined, and the preset number of first feasible deployment policies are used as system resource deployment policies corresponding to the first service resource request. Therefore, compared with the prior art that the deployment strategy is manually selected, the method has higher reliability.
Fig. 3 is a schematic structural diagram of a device for determining a system resource deployment policy according to an embodiment of the present invention. As shown in fig. 3, the determining device 30 of the system resource deployment policy provided in this embodiment may specifically include:
a receiving module 301, configured to receive a first service resource request of a service to be deployed;
a first obtaining module 302, configured to obtain first system resource usage state information, where the first system resource usage state information is first resource usage state information of each physical machine in the system;
a second obtaining module 303, configured to obtain at least one first feasible deployment policy that is matched with the first system resource usage state information in the system;
a calculation recording module 304, configured to calculate and record, according to a preset simulation algorithm and simulation calculation times, an average longest continuous deployability time of each first feasible deployment policy;
the determining module 305 is configured to select a preset number of first feasible deployment policies with the longest average and the largest number of continuous deployments from the records, and use the preset number of first feasible deployment policies as the system resource deployment policy corresponding to the first service resource request.
The apparatus for determining a system resource deployment policy provided in this embodiment may be used to implement the technical solution of the foregoing method embodiment, and the implementation principle and the technical effect are similar, which are not described herein again.
An embodiment of the present invention further provides an electronic device, as shown in fig. 4, including a processor 401, a communication interface 402, a memory 403, and a communication bus 404, where the processor 401, the communication interface 402, and the memory 403 complete mutual communication through the communication bus 404.
A memory 403 for storing a computer program;
the processor 401 is configured to execute the program stored in the memory 403, and implement the following steps:
receiving a first service resource request of a service to be deployed;
acquiring first system resource use state information, wherein the first system resource use state information is first resource use state information of each physical machine in the system;
acquiring at least one first feasible deployment strategy matched with the first system resource use state information in the system;
aiming at each first feasible deployment strategy, calculating and recording the average longest continuous deployment times of each first feasible deployment strategy according to a preset simulation algorithm and simulation calculation times;
and selecting a preset number of first feasible deployment strategies with the longest average and the largest continuous deployment times from the records, and taking the preset number of first feasible deployment strategies as system resource deployment strategies corresponding to the first service resource requests.
The communication bus mentioned in the electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface is used for communication between the electronic equipment and other equipment.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, discrete hardware component.
In another embodiment of the present invention, a computer-readable storage medium is further provided, where instructions are stored in the computer-readable storage medium, and when the instructions are executed on a computer, the computer is caused to execute the method for determining a system resource deployment policy in any one of the above embodiments.
In yet another embodiment of the present invention, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform the method for determining a system resource deployment policy as described in any of the above embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The term "comprising", without further limitation, means that the element so defined is not excluded from the group consisting of additional identical elements in the process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (7)
1. A method for determining a system resource deployment strategy is characterized by comprising the following steps:
receiving a first service resource request of a service to be deployed;
acquiring first system resource use state information, wherein the first system resource use state information is first resource use state information of each physical machine in the system;
acquiring at least one first feasible deployment strategy matched with the first system resource use state information in the system;
for each first feasible deployment strategy, solving the longest continuous deployment times of the first feasible deployment strategy through a breadth-first search algorithm; calculating and recording the average longest continuous deployment times of each first feasible deployment strategy according to the respective longest continuous deployment times of each first feasible deployment strategy and a preset simulation calculation time;
and selecting a preset number of first feasible deployment strategies with the longest average and the largest continuous deployment times from the records, and taking the preset number of first feasible deployment strategies as system resource deployment strategies corresponding to the first service resource requests.
2. The method according to claim 1, wherein for each of the first feasible deployment strategies, the longest possible number of consecutive deployments of the first feasible deployment strategy is solved through a breadth-first search algorithm; calculating and recording the average longest continuous deployment times of each first feasible deployment strategy according to the respective longest continuous deployment times of each first feasible deployment strategy and a preset simulation calculation time, wherein the calculation comprises the following steps:
judging whether the preset simulation calculation times are reached;
if the preset simulation calculation times are not reached, randomly sampling from the pre-stored historical service resource requests to obtain a second service resource request;
calculating and recording the deployment times of the first feasible deployment strategy;
determining second resource use state information of each physical machine in the system as second system resource use state information according to the first feasible deployment strategy;
judging whether at least one second feasible deployment strategy meeting the second service resource request exists in the system according to the second system resource use state information;
if yes, returning to the step of obtaining a second service resource request by randomly sampling from the pre-stored historical service resource requests aiming at each second feasible deployment strategy;
if not, determining the recorded deployment times of the first feasible deployment strategy as the longest continuous deployment times of the first feasible deployment strategy; returning to the step of judging whether the preset simulation calculation times are reached;
and if the preset simulation calculation times are reached, calculating the average longest continuous deployment times of the first feasible deployment strategy according to the recorded longest continuous deployment times and the preset simulation calculation times.
3. The method of claim 2, wherein randomly sampling the pre-stored historical traffic resource requests to obtain the second traffic resource request comprises:
carrying out probability distribution statistics on the historical service resource requests to obtain probability distribution statistical results; and randomly sampling according to the probability distribution statistical result to obtain the second service resource request.
4. An apparatus for determining a system resource deployment policy, comprising:
the system comprises a receiving module, a service configuration module and a service configuration module, wherein the receiving module is used for receiving a first service resource request of a service to be deployed;
a first obtaining module, configured to obtain first system resource usage state information, where the first system resource usage state information is first resource usage state information of each physical machine in the system;
a second obtaining module, configured to obtain at least one first feasible deployment policy that is matched with the first system resource usage state information in the system;
the calculation recording module is used for solving the longest continuous deployment times of each first feasible deployment strategy through a breadth-first search algorithm; calculating and recording the average longest continuous deployment times of each first feasible deployment strategy according to the respective longest continuous deployment times of each first feasible deployment strategy and a preset simulation calculation time;
and the determining module is used for selecting a preset number of first feasible deployment strategies with the longest average and the largest continuous deployment times from the records, and taking the preset number of first feasible deployment strategies as the system resource deployment strategies corresponding to the first service resource request.
5. The apparatus of claim 4, wherein the computation logging module is specifically configured to:
judging whether the preset simulation calculation times are reached;
if the preset simulation calculation times are not reached, randomly sampling from the pre-stored historical service resource requests to obtain a second service resource request;
calculating and recording the deployment times of the first feasible deployment strategy;
determining second resource use state information of each physical machine in the system as second system resource use state information according to the first feasible deployment strategy;
judging whether at least one second feasible deployment strategy meeting the second service resource request exists in the system according to the second system resource use state information;
if yes, returning to the step of obtaining a second service resource request by randomly sampling from the pre-stored historical service resource requests aiming at each second feasible deployment strategy;
if not, determining the recorded deployment times of the first feasible deployment strategy as the longest continuous deployment times of the first feasible deployment strategy; returning to the step of judging whether the preset simulation calculation times are reached;
and if the preset simulation calculation times are reached, calculating the average longest continuous deployment times of the first feasible deployment strategy according to the recorded longest continuous deployment times and the preset simulation calculation times.
6. The apparatus of claim 5, wherein the computation logging module is specifically configured to:
carrying out probability distribution statistics on the historical service resource requests to obtain probability distribution statistical results;
and randomly sampling according to the probability distribution statistical result to obtain the second service resource request.
7. An electronic device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1 to 3 when executing a program stored in the memory.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710391766.0A CN107357649B (en) | 2017-05-27 | 2017-05-27 | Method and device for determining system resource deployment strategy and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710391766.0A CN107357649B (en) | 2017-05-27 | 2017-05-27 | Method and device for determining system resource deployment strategy and electronic equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107357649A CN107357649A (en) | 2017-11-17 |
| CN107357649B true CN107357649B (en) | 2020-07-03 |
Family
ID=60271027
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710391766.0A Active CN107357649B (en) | 2017-05-27 | 2017-05-27 | Method and device for determining system resource deployment strategy and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107357649B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2583992B (en) * | 2018-12-28 | 2021-06-09 | Fujitsu Client Computing Ltd | Information processing device, inference processing device, and information processing system |
| CN116225192B (en) * | 2023-05-08 | 2023-08-01 | 之江实验室 | Control method and device of heat dissipation system, storage medium and electronic equipment |
| CN116956756B (en) * | 2023-09-21 | 2024-02-09 | 浪潮电子信息产业股份有限公司 | Model deployment method, task processing method, device, equipment and storage medium |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102760073A (en) * | 2011-04-29 | 2012-10-31 | 中兴通讯股份有限公司 | Method, system and device for scheduling task |
| CN103297499A (en) * | 2013-04-19 | 2013-09-11 | 无锡成电科大科技发展有限公司 | Scheduling method and system based on cloud platform |
| CN103365713A (en) * | 2012-04-01 | 2013-10-23 | 华为技术有限公司 | Resource dispatch and management method and device |
| CN105553882A (en) * | 2015-12-23 | 2016-05-04 | 清华大学 | Method for scheduling SDN data plane resources |
| CN105630601A (en) * | 2014-11-03 | 2016-06-01 | 阿里巴巴集团控股有限公司 | Resource allocation method and system based on real-time computing |
| CN105656973A (en) * | 2014-11-25 | 2016-06-08 | 中国科学院声学研究所 | Distributed method and system for scheduling tasks in node group |
| CN106534284A (en) * | 2016-10-26 | 2017-03-22 | 航天恒星科技有限公司 | Resource load balancing method and device in distributed system |
-
2017
- 2017-05-27 CN CN201710391766.0A patent/CN107357649B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102760073A (en) * | 2011-04-29 | 2012-10-31 | 中兴通讯股份有限公司 | Method, system and device for scheduling task |
| CN103365713A (en) * | 2012-04-01 | 2013-10-23 | 华为技术有限公司 | Resource dispatch and management method and device |
| CN103297499A (en) * | 2013-04-19 | 2013-09-11 | 无锡成电科大科技发展有限公司 | Scheduling method and system based on cloud platform |
| CN105630601A (en) * | 2014-11-03 | 2016-06-01 | 阿里巴巴集团控股有限公司 | Resource allocation method and system based on real-time computing |
| CN105656973A (en) * | 2014-11-25 | 2016-06-08 | 中国科学院声学研究所 | Distributed method and system for scheduling tasks in node group |
| CN105553882A (en) * | 2015-12-23 | 2016-05-04 | 清华大学 | Method for scheduling SDN data plane resources |
| CN106534284A (en) * | 2016-10-26 | 2017-03-22 | 航天恒星科技有限公司 | Resource load balancing method and device in distributed system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107357649A (en) | 2017-11-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107341029B (en) | Method and device for acquiring system resource deployment strategy and electronic equipment | |
| US11036552B2 (en) | Cognitive scheduler | |
| US20220004931A1 (en) | Root cause discovery engine | |
| CN108595157B (en) | Block chain data processing method, device, equipment and storage medium | |
| US10346249B2 (en) | Resolving and preventing computer system failures caused by changes to the installed software | |
| US11295242B2 (en) | Automated data and label creation for supervised machine learning regression testing | |
| US11223642B2 (en) | Assessing technical risk in information technology service management using visual pattern recognition | |
| US20210209481A1 (en) | Methods and systems for dynamic service performance prediction using transfer learning | |
| CN107357649B (en) | Method and device for determining system resource deployment strategy and electronic equipment | |
| US10795937B2 (en) | Expressive temporal predictions over semantically driven time windows | |
| US10838767B2 (en) | Distributed computing utilizing a recovery site | |
| US10108520B2 (en) | Systems and methods for service demand based performance prediction with varying workloads | |
| CN109413694B (en) | Small cell caching method and device based on content popularity prediction | |
| CN111784401A (en) | Order taking rate prediction method, device, equipment and readable storage medium | |
| US10313457B2 (en) | Collaborative filtering in directed graph | |
| CN110896344A (en) | Redundant service deployment method and device | |
| WO2017131696A1 (en) | Database server to predict sales | |
| CN112463058A (en) | Fragmented data sorting method and device and storage node | |
| Tsai et al. | Simulation optimization in security screening systems subject to budget and waiting time constraints | |
| CN114281474A (en) | Resource adjusting method and device | |
| CN113656046A (en) | Application deployment method and device | |
| CN113076451B (en) | Abnormal behavior identification and risk model library establishment method and device and electronic equipment | |
| CN111142898B (en) | Data anti-leakage terminal upgrading method and system based on group intelligent mode | |
| CN117370473B (en) | Data processing method, device, equipment and storage medium based on integrity attack | |
| CN112949887B (en) | Method, device and equipment for planning dispatch path and computer readable storage medium |
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 |