CN111767137A - System deployment method, device, electronic equipment and storage medium - Google Patents
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Abstract
According to the method, the device, the electronic equipment and the storage medium for deploying the system, whether the difference degree of the unit time access quantities at two detection moments meets the preset difference condition or not is judged according to the unit time access quantity at the current detection moment of the system and the unit time access quantity at the previous detection moment of the system, if yes, the situation that the unit time access quantity is greatly increased or greatly reduced possibly occurs in the system is shown, at the moment, the target service mechanism number corresponding to the unit time access quantity at the current detection moment is determined according to the preset queuing theory model corresponding to the system, and the service mechanism number currently deployed by the system is adjusted to the target service mechanism number. According to the scheme, when the unit time access amount in the system is greatly increased or reduced, the number of the service mechanisms of the system is adjusted to the number suitable for the current unit time access amount, and the problems of resource waste or too slow access processing caused by unreasonable configuration of the number of the service mechanisms in the system are avoided.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a system deployment method, apparatus, electronic device, and storage medium.
Background
In the actual use process of the system, a phenomenon that the distribution of the number of threads or the number of QPS (an abbreviation of Queries per second, which means query rate per second, is the number of Queries that a server can respond per second, and is a measure of how much access a specific query server can process in a specified time) is very uneven often occurs in one day, for example, in one day, only in some time periods, the access amount is very large, the number of threads in the system is large, the QPS is high, and the access amount in other times is low. This leads to a problem: when the project is deployed, whether high access is met, multiple service mechanisms are deployed, or daily average access is followed, and fewer service mechanisms are deployed. From the perspective of resource utilization, a small number of service mechanisms should be deployed, but from the perspective of access success rate, it is necessary to satisfy the peak user access, so it is now difficult to balance the service mechanisms for multiple deployments.
Disclosure of Invention
In order to solve the technical problem of how to deploy a service organization for a system, the application provides a system deployment method, a device, an electronic device and a storage medium.
In a first aspect, an embodiment of the present application provides a system deployment method, including:
acquiring unit time access quantity of a current detection moment of a system and unit time access quantity of a previous detection moment;
judging whether the difference degree between the unit time visit amount of the current detection moment and the unit time visit amount of the previous detection moment meets a preset difference condition or not;
if so, determining the number of target service mechanisms corresponding to the unit time access amount of the current detection time according to a pre-constructed queuing theory model corresponding to the system;
and adjusting the number of service mechanisms currently deployed by the system to the target number of service mechanisms.
In a possible implementation manner, the determining whether a difference degree between the unit time access amount at the current detection time and the unit time access amount at the previous detection time satisfies a preset difference condition includes:
calculating the absolute value of the difference value between the unit time visit amount of the current detection time and the unit time visit amount of the previous detection time;
judging whether the absolute value of the difference value is larger than a preset first threshold value or not;
and if so, determining that the difference degree between the unit time visit amount of the current detection time and the unit time visit amount of the previous detection time meets a preset difference condition.
In a possible implementation manner, determining the number of target service mechanisms corresponding to the unit time visit rate at the current detection time according to a pre-constructed queuing theory model corresponding to the system includes:
acquiring the number of preset alternative service mechanisms and a preset average service rate, wherein the average service rate is the access amount processed by a single service mechanism in unit time;
inputting the unit time access amount, the number of alternative service mechanisms and the average service rate of the current detection moment into a pre-constructed queuing theory model corresponding to the system;
and determining the target service mechanism number corresponding to the unit time access amount of the current detection time as the minimum value of the number of the alternative service mechanisms which enable the parameters output by the queuing theory model to meet the preset requirements.
In a possible implementation manner, before determining the target number of service agencies corresponding to the unit time visit amount at the current detection time according to a pre-constructed queuing theory model corresponding to the system, the method further includes:
obtaining operation parameters of the system in a preset time period, wherein the operation parameters comprise: the access amount of each unit time, the average service rate, the access failure number of each unit time and the access processing time length corresponding to each unit time, wherein the average service rate is the access amount processed by a single service mechanism in unit time;
calculating the probability distribution of the access amount of the system in the preset time period according to the operation parameters;
calculating a service queue length of the system according to the operation parameters and the probability distribution of the access amount, wherein the service queue length is the highest accessible access amount waiting for processing in the system in unit time;
and determining a queuing theory model corresponding to the system according to a preset queuing rule, a preset service rule, the service queue length and the average service rate.
In a possible implementation manner, calculating a probability distribution of the access amount of the system in the preset time period according to the operation parameter includes:
calculating the average visit amount of the system in each unit time in a preset time period according to the acquired visit amount of the system in each unit time in the preset time period;
and taking the average access amount of the system in each unit time in the preset time period as a parameter of Poisson distribution, and substituting the parameter into a probability function of the preset Poisson distribution to obtain the access amount probability distribution of the system in the preset time period.
In a possible implementation manner, calculating a service queue length of the system according to the operation parameter and the probability distribution of the access amount includes:
determining a busy period of the access amount probability distribution;
determining the average access amount and the average access failure number corresponding to the busy period according to the operation parameters;
subtracting the average access failure number corresponding to the busy period from the average access amount corresponding to the busy period, and then subtracting the average service rate to obtain a difference value corresponding to the busy period;
and taking the difference value corresponding to the busy period as the service queue length of the system.
In a possible implementation manner, the operation parameter further includes a number of service mechanisms corresponding to each unit time;
determining a queuing theory model corresponding to the system according to a preset queuing rule, a preset service rule, the service queue length and the average service rate, wherein the queuing theory model comprises the following steps:
calculating the actual average waiting time of the system according to the operation parameters and the probability distribution of the access amount, wherein the actual average waiting time is the average time of queuing and waiting for access in the system;
determining an alternative queuing theory model matched with a preset queuing rule, a preset service rule, the service queue length and the average service rate;
inputting the access amount per unit time, the average service rate and the corresponding service mechanism number contained in the operation parameters into the alternative queuing theory models to obtain the simulated average waiting time corresponding to each alternative queuing theory model;
calculating the absolute value of the difference value between the simulated average waiting time length corresponding to each alternative queuing theory model and the actual average waiting time length;
and determining the alternative queuing theory model with the minimum absolute value of the difference between the corresponding simulated average waiting time length and the actual average waiting time length as the queuing theory model.
In one possible implementation, calculating an actual average waiting duration of the system according to the operation parameter and the probability distribution of the access amount includes:
determining busy periods and idle periods of the access amount probability distribution;
determining an average access processing duration corresponding to the busy period according to the operation parameters, and taking the average access processing duration as a first average access processing duration;
determining an average access processing duration corresponding to the idle period according to the operation parameters, and taking the average access processing duration as a second average access processing duration;
calculating a difference between the first average access processing duration and the second average access processing duration;
and taking the difference value of the first average access processing time length and the second average access processing time length as the actual average waiting time length of the system.
In a second aspect, an embodiment of the present application further provides a system deployment apparatus, including:
the system comprises an access amount acquisition module, a data acquisition module and a data processing module, wherein the access amount acquisition module is used for acquiring the access amount per unit time at the current detection time of the system and the access amount per unit time at the previous detection time;
the judging module is used for judging whether the difference degree between the unit time visit amount of the current detection moment and the unit time visit amount of the previous detection moment meets a preset difference condition or not;
the target service mechanism number determining module is used for determining the target service mechanism number corresponding to the unit time visit amount of the current detection moment according to a pre-constructed queuing theory model corresponding to the system if the difference degree between the unit time visit amount of the current detection moment and the unit time visit amount of the previous detection moment meets a preset difference condition;
and the adjusting module is used for adjusting the number of the service mechanisms currently deployed by the system to the target number of the service mechanisms.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor and a memory, the processor being configured to execute a data processing program stored in the memory to implement the system deployment method of the first aspect.
In a fourth aspect, the present application further provides a storage medium storing one or more programs, where the one or more programs are executable by one or more processors to implement the system deployment method of the first aspect.
Compared with the prior art, the technical scheme provided by the embodiment of the application has the following advantages:
according to the system deployment method provided by the embodiment of the application, whether the difference degree of the unit time access quantities at two detection moments meets the preset difference condition or not is judged according to the unit time access quantity at the current detection moment of the system and the unit time access quantity at the previous detection moment of the system, if yes, the situation that the unit time access quantity is greatly increased or greatly reduced possibly occurs in the system is shown, in order to prevent the number of the service mechanisms currently deployed by the system from being too small or too large, at the moment, the target service mechanism number corresponding to the unit time access quantity at the current detection moment is determined according to the preset queuing theory model corresponding to the system, and the number of the service mechanisms currently deployed by the system is adjusted to the target service mechanism number. According to the scheme, when the unit time access amount in the system is greatly increased or reduced, the number of the service mechanisms of the system is timely adjusted to the number suitable for the current unit time access amount, resource waste caused by the fact that the number of the service mechanisms is large due to low access amount is avoided, the resource utilization rate is improved, and the problem that access processing is too slow due to the fact that the number of the service mechanisms is small due to high access amount is also avoided.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a system deployment method according to an embodiment of the present application;
fig. 2 is a flowchart of a method for determining a target number of service organizations according to an embodiment of the present disclosure;
fig. 3 is a block diagram of a system deployment apparatus according to an embodiment of the present application;
fig. 4 is a schematic view of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a flowchart of a system deployment method provided in an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
s11, acquiring the unit time visit quantity of the current detection time and the unit time visit quantity of the previous detection time of the system.
The current detection time and the previous detection time are two adjacent detection times, the previous detection time is the previous detection time of the current detection time, the specific detection time and the time interval between the two adjacent detection times are set according to specific conditions, for example, the time interval between the two adjacent detection times can be 1 hour or 1 minute, that is, the unit time access amount of the system is detected every 1 hour or 1 minute.
The access amount per unit time is an access amount per unit time, and the length of the unit time is also set according to the circumstances, and may be, for example, 1 second.
If the system is a system with frequent read operations, the access amount may be the QPS of the system, which is an abbreviation for Queries PerSecond, meaning the query rate per second.
If the system is a system with frequent write operations or a system of an agent type, the access amount may be the thread number of the system.
And S12, judging whether the difference between the unit time visit amount of the current detection moment and the unit application visit amount of the previous detection moment meets a preset difference condition.
Whether the difference degree of the unit time access amount of two adjacent detection time meets the difference condition is judged to judge whether the unit time access amount is greatly increased or greatly reduced.
And S13, if so, determining the number of target service mechanisms corresponding to the unit time access amount of the current detection time according to a pre-constructed queuing theory model corresponding to the system.
When the access amount per unit time is greatly increased or greatly reduced, the number of service mechanisms currently deployed by the system may be too small or too large, so in order to provide the utilization rate of resources, the target number of service mechanisms suitable for the access amount per unit time at the current detection time is determined through a preset queuing theory model corresponding to the system.
Wherein the queuing theory model can be one of M/M/1, M/D/1, M/Ek/1, M/M/c/∞/M, M/M/c/N/∞, M/Ek/c/N, etc. types according to the characteristics of the system.
The service mechanism may be a virtual machine or a server, and the number of the service mechanisms is the number of the service mechanisms.
And S14, adjusting the number of service mechanisms currently deployed by the system to the target number of service mechanisms.
If the difference between the unit time access amount at the current detection time and the unit time access amount at the previous detection time meets the difference condition, the current number of service mechanisms may not well meet the requirement of the current detection time, so that in order to enable the system to meet the requirement of the current detection time, the number of currently deployed service mechanisms is adjusted to the target number of service mechanisms, the deployed service mechanisms are service mechanisms in a use state, the system may have multiple service mechanisms, but each service mechanism is not necessarily a deployed service mechanism, and only the service mechanism in the use state is a deployed service mechanism.
According to the system deployment method provided by the embodiment of the application, whether the difference degree of the unit time access quantities at two detection moments meets the preset difference condition or not is judged according to the unit time access quantity at the current detection moment of the system and the unit time access quantity at the previous detection moment of the system, if yes, the situation that the unit time access quantity is greatly increased or greatly reduced possibly occurs in the system is shown, in order to prevent the number of the service mechanisms currently deployed by the system from being too small or too large, at the moment, the target service mechanism number corresponding to the unit time access quantity at the current detection moment is determined according to the preset queuing theory model corresponding to the system, and the number of the service mechanisms currently deployed by the system is adjusted to the target service mechanism number. According to the scheme, when the unit time access amount in the system is greatly increased or reduced, the number of the service mechanisms of the system is timely adjusted to the number suitable for the current unit time access amount, so that the problem that the access amount is low, the number of the service mechanisms is large, resource waste is caused, the resource utilization rate is improved, and the problem that the access processing is too slow due to the fact that the number of the service mechanisms is small is solved.
On the basis of the above embodiment, in S12, whether the difference between the unit time visit amount at the current detection time and the unit time visit amount at the previous detection time satisfies the preset difference condition may be determined by:
calculating an absolute value of a difference value between the unit time visit amount at the current detection time and the unit time visit amount at the previous detection time, judging whether the absolute value of the difference value is larger than a preset first threshold value, if so, determining that the difference degree between the unit time visit amount at the current detection time and the unit time visit amount at the previous detection time meets a preset difference condition, and if not, determining that the difference degree between the unit time visit amount at the current detection time and the unit time visit amount at the previous detection time does not meet the preset difference condition.
The first threshold value is a value set according to specific conditions.
If the absolute value of the difference between the unit time access amount at the current detection time and the unit time access amount at the previous detection time is greater than the preset first threshold, it indicates that the unit time access amount at the current detection time is greatly increased or greatly reduced compared with the unit time access amount at the previous detection time.
In this embodiment, the difference degree of the access amount per unit time at two adjacent detection times is determined by calculating the absolute value of the difference, so as to determine whether the access amount is suddenly increased or greatly reduced, and the calculation is simple.
On the basis of the above embodiment, in S12, it is determined whether the difference between the unit time visit amount at the current detection time and the unit time visit amount at the previous detection time satisfies the preset difference condition, and the following manner may be adopted:
and calculating the ratio of the unit time access amount of the current detection moment to the unit time access amount of the previous detection moment, if the ratio is greater than a preset second threshold or smaller than a preset third threshold, determining that the difference between the unit time access amount of the current detection moment and the unit time access amount of the previous detection moment meets a preset difference condition, otherwise, determining that the difference between the unit time access amount of the current detection moment and the unit time access amount of the previous detection moment does not meet the preset difference condition.
The second threshold and the third threshold are values set according to actual conditions, for example, the second threshold is 1.2, and the third threshold is 0.8.
Wherein the ratio of the current detection time unit time visit amount to the previous detection time unit time visit amount is obtained by dividing the current detection time unit time visit amount by the previous detection time unit time visit amount
In this embodiment, the ratio may indicate whether the unit time access amount at the current detection time is increased or decreased compared with the unit time access amount at the previous detection time, and the increase or decrease amplitude, when the ratio is greater than 1, the increase is indicated, and when the ratio is less than 1, the decrease is indicated, and the difference between the unit time access amount at the current detection time and the unit time access amount at the previous detection time is indicated by the ratio, which is more intuitive.
Based on the foregoing embodiment, as shown in fig. 2, the step S13 of determining the number of target service mechanisms corresponding to the unit-time visit rate at the current detection time according to a pre-constructed queuing theory model corresponding to the system may include:
s131, acquiring the number of preset alternative service mechanisms and a preset average service rate, wherein the average service rate is the access amount processed by a single service mechanism in unit time.
The number of the alternative service mechanisms is the number of the service mechanisms that can be provided by the system, wherein one system may have a plurality of the number of the alternative service mechanisms, for example, if there are 6 total available service mechanisms in one system, the number of the service mechanisms that can be provided by the system is 1, 2, 3, 4, 5, 6, and these 6 numbers can all be used as the number of the alternative service mechanisms of the system.
The average service rate is determined according to the processing capability of the service mechanism in the system, for example, if the service mechanism is a server, the corresponding average service rate can be determined according to the number of CPU cores of the server.
And S132, inputting the unit time access amount, the number of alternative service mechanisms and the average service rate of the current detection moment into a pre-constructed queuing theory model corresponding to the system.
And if a plurality of alternative service mechanisms exist, respectively inputting the alternative service mechanism numbers into the queuing theory model.
The queuing theory model takes the number of service mechanisms, the average service rate and the unit time access amount as input, and takes parameters such as the average queue length, the average stay time, the average waiting time, the average busy hour, the system state and the like as output.
Wherein, average captain: refers to the mathematical expectation of the amount of access within the system (including the amount of access being processed and the amount of access queued for processing), denoted Ls.
Average captain: a mathematical expectation, denoted Lq, of the amount of accesses queued for processing within the system.
Average length of stay: refers to the mathematical expectation of the time spent accessing the system (including the time of queuing and the time of processing), denoted as Ws.
Average waiting time length: refers to a mathematical expectation of access to the queuing latency in the queuing system, denoted as Wq.
Average busy period: the mathematical expectation, denoted Tb, indicates the length of the service organization's continuous busy time (the time from the visit to the idle service organization to the service organization's re-idle).
The state of the system: refers to the amount of access in the system.
And S133, determining the number of the candidate service mechanisms which enable the parameters output by the queuing theory model to meet the preset requirements and have the smallest value as the number of the target service mechanisms corresponding to the unit time access amount of the current detection time.
When the number of the input alternative service mechanisms is different, the values of the parameters output by the queuing theory model are also different.
The threshold of the parameter output by any one or more queuing theory models may be preset, for example, the threshold of the average queue length, the threshold of the average lingering time, the threshold of the average waiting time and/or the threshold of the average busy hour are set, and then the queuing theory models are compared with the preset corresponding parameter threshold according to the parameter values output by the unit time access amount, the number of alternative service mechanisms and the average service rate at the current detection time, and the corresponding comparison result is determined to meet the preset condition (the preset condition is also set according to the requirement, the preset conditions corresponding to different parameters are different, for example, the threshold of the average lingering time is preset, and the preset condition may be that the average lingering time is smaller than the threshold of the average lingering time), and when the comparison result corresponding to the number of alternative service mechanisms meets the preset condition, it is indicated that the number of alternative service mechanisms can meet the unit time access amount at the current detection time And the quantity demand takes the minimum value of the alternative service mechanism numbers which can meet the unit time access quantity demand at the current detection time as the target service mechanism number.
For example, the number of the alternative service mechanisms is respectively 1, 2, 3, 4, 5 and 6, the 6 numbers are respectively input into the queuing theory model corresponding to the system to obtain the average stay time corresponding to each alternative service mechanism, the average stay time corresponding to each alternative service mechanism is respectively compared with the preset threshold value of the average stay time, the number of the alternative service mechanisms of which the corresponding average stay time is smaller than the threshold value of the average stay time is determined to be 4, 5 and 6, and finally the number of the alternative service mechanisms of which the corresponding average stay time is smaller than the threshold value of the average stay time is determined to be 4, 5 and 6, and the number of the alternative service mechanisms is finally determined to be 4.
In this embodiment, the number of alternative service mechanisms that can meet the unit-time access amount at the current detection time is determined by comparing the output parameter of the queuing theory with a preset parameter threshold, which is simple and fast, and the minimum value of the number of alternative service mechanisms that meet the unit-time access amount at the current detection time is selected as the target service mechanism number, thereby improving the utilization rate of a single service mechanism.
On the basis of the above embodiment, before determining the target number of services corresponding to the unit time visit amount at the current detection time according to the pre-constructed queuing theory model corresponding to the system in step S13, the method further needs to construct the queuing theory model corresponding to the system, and the method may include the following steps:
step 1: obtaining operation parameters of the system in a preset time period, wherein the operation parameters comprise: the access amount of each unit time, the average service rate, the access failure number of each unit time and the access processing time length corresponding to each unit time, wherein the average service rate is the access amount processed by a single service mechanism in unit time.
The duration and the specific time of the preset time period are set according to specific conditions, for example, the duration of the preset time period may be one day.
If the system is a system with frequent read operations, the access amount may be the QPS of the system, which is an abbreviation for Queries PerSecond, meaning the query rate per second.
If the system is a system with frequent write operations or a system of an agent type, the access amount may be the thread number of the system.
Wherein the access processing duration is the average duration consumed by processing each access.
The access failure number of each unit time and the access processing time length corresponding to each unit time can be obtained from the log of the system.
The number of access failures per unit time can also be calculated according to the following formula:
the unit time access quantity failure number is the unit time access quantity-unit time access success number, wherein the unit time access success number can be directly acquired.
And 2, calculating the probability distribution of the access amount of the system in the preset time period according to the operation parameters.
Wherein the probability distribution of the access amount can be calculated by adopting the following method:
calculating the average access amount of the system in each unit time in a preset time period according to the obtained access amount of the system in each unit time in the preset time period, taking the average access amount of the system in each unit time in the preset time period as a parameter of Poisson distribution, and substituting the parameter into a preset probability function of Poisson distribution to obtain the access amount probability distribution of the system in the preset time period.
Wherein the probability function of the preset poisson distribution is as follows:
wherein k represents the actual access amount of each unit time in the preset time period, and λ represents the average access amount of the system in each unit time in the preset time period.
And 3, calculating the service queue length of the system according to the operation parameters and the probability distribution of the access amount, wherein the service queue length is the highest access amount which can be accommodated in the system and waits for processing in a queue in unit time.
Wherein, the service captain can adopt the following mode to calculate:
determining the busy period of the access amount probability distribution, determining the average access amount and the average access failure number corresponding to the busy period according to the operation parameters, subtracting the average access failure number corresponding to the busy period from the average access amount corresponding to the busy period, and then subtracting the average service rate to obtain the difference value corresponding to the busy period, wherein the difference value corresponding to the busy period is used as the service queue length of the system.
Since one busy period may include a plurality of unit times, the average access amount corresponding to the busy period is an average of the access amounts corresponding to each unit time in the busy period, and the average access failure number corresponding to the busy period is an average of the access failure numbers corresponding to each unit time in the busy period.
If only one busy period exists in the probability distribution of the access amount, taking the difference value corresponding to the busy period as the service queue length of the system; and if the access quantity probability distribution has a plurality of busy periods, taking the average value of the difference values corresponding to the busy periods as the service queue length of the system.
And 4, determining a queuing theory model corresponding to the system according to a preset queuing rule, a preset service rule, the service queue length and the average service rate.
The queuing rules mainly include:
queuing mode: the waiting system is a/instant system, wherein when the waiting system is that the service mechanism is occupied, the access queue is processed, when the service mechanism is occupied, the access process fails, and the order rule of the waiting system queuing mode comprises the following steps: first come first serve, anytime serve, first serve with priority, etc.
Queuing system capacity: with/without limitations;
the number of queuing queues is single column/multiple columns;
whether to quit in the middle, enable/disable;
whether inter-column transfer is allowed// inhibited.
The service rules mainly include:
number of service organizations: single// multiple;
service organization arrangement form: parallel// series// hybrid;
service organization service mode: one by one// batch by batch;
service time distribution: random// deterministic;
whether the service time distribution is stable: smooth// non-smooth.
And presetting a corresponding queuing rule and a corresponding service rule according to the characteristics of the system.
And selecting a queuing theory model matched with a preset queuing rule, a preset service rule, the service queue length and the average service rate as the queuing theory model corresponding to the system according to the existing queuing theory model selecting mode, wherein the specific selecting process is not repeated here, and the service queue length is used for determining the capacity of the queuing system in the queuing theory model.
In this embodiment, the queuing theory model corresponding to the system can be constructed in the above manner, and it is ensured that the constructed queuing theory model can be used for determining the number of target service mechanisms.
Since a plurality of queuing theory models may be determined according to the queuing rule, the service queue length and the average service rate, on the basis of the above embodiment, in order to make the finally obtained queuing theory model more suitable for the system, the operation parameter further includes the number of service mechanisms corresponding to each unit time, and in step 4, the following manner may be adopted for determining the queuing theory model corresponding to the system according to the preset queuing rule, the service queue length and the average service rate:
calculating the actual average waiting time of the system according to the operation parameters and the probability distribution of the access amount, determining an alternative queuing theory model matched with the queuing rules, the service queue length and the average service rate, inputting the access amount and the average service rate of unit time contained in the operation parameters and the corresponding service mechanism number into the queuing theory model and inputting the alternative queuing theory model to obtain a simulated average waiting time, calculating the absolute value of the difference between the simulated average waiting time and the actual average waiting time, and determining the alternative queuing theory model with the minimum absolute value of the difference between the simulated average waiting time and the actual average waiting time as the queuing theory model.
Where the average wait period is the average period of time an access is queued in the system.
The corresponding number of the service mechanisms is the number of the service mechanisms corresponding to the access amount per unit time in the input candidate queuing theory model, for example, if the time corresponding to the input access amount per unit time is 3 minutes 16 seconds at 14 points on 12 days 2 month and 12 2019, the time corresponding to the input number of the service mechanisms is also 3 minutes 16 seconds at 14 points on 12 days 2 month and 12 2019.
In this embodiment, the queuing theory model determined in the above manner and corresponding to the system has high accuracy.
On the basis of the above embodiment, the actual average waiting time of the system may be calculated according to the operation parameters and the probability distribution of the access amount in the following manner:
determining a busy period and an idle period of the access amount probability distribution, determining an average access processing duration corresponding to the busy period according to the operation parameters, using the average access processing duration as a first average access processing duration, determining an average access processing duration corresponding to the idle period according to the operation parameters, using the average access processing duration as a second average access processing duration, calculating a difference value between the first average access processing duration and the second average access processing duration, and using the difference value between the first average access processing duration and the second average access processing duration as an actual average waiting duration of the system.
Namely, the actual average waiting time of the system is the first average access processing time length to the second average access processing time length.
The average access processing duration corresponding to the busy period is the average of the access processing durations corresponding to all unit time in the busy period, and the average access processing duration corresponding to the idle period is the average of the access processing durations corresponding to all unit time in the idle period.
And if the access amount probability distribution comprises a plurality of busy periods and/or a plurality of idle periods, taking the average value of the average access processing time lengths corresponding to all busy periods as a first average access processing time length, and taking the average value of the average access processing time lengths corresponding to all idle periods as a second average access processing time length.
In a possible implementation manner, the queuing theory model corresponding to the system may be determined in the following manner in step 4:
determining alternative queuing theory models matched with the queuing rules, the service queue lengths and the average service rates, simulating the alternative queuing theory models by adopting simulation software according to the operation parameters to obtain simulated access response success numbers corresponding to each unit time in a preset time period corresponding to each alternative queuing theory model, subtracting the access failure numbers of each unit time from the access amount of each unit time contained in the operation parameters to obtain actual access response success numbers corresponding to each unit time in the preset time period, respectively calculating error values corresponding to each alternative queuing theory model according to the simulated access response success numbers and the actual access response success numbers corresponding to each alternative queuing theory model, and selecting the alternative queuing theory model with the smallest error value as the queuing theory model corresponding to the system.
For each alternative queuing theory model, the corresponding error value can be calculated in the following way:
wherein SjRepresenting the error value, C, corresponding to the alternative queuing theory model jjiRepresenting the simulation access response success number R of the ith unit time of the alternative queuing theory model j in a preset time periodiRepresents the actual access response success number of the ith unit time in the preset time period, and m represents the total number of unit times included in the preset time period.
The simulation software can be MATLAB and other simulation software.
In this embodiment, the queuing theory model is screened in a simulation and calculation manner, so that the finally obtained queuing theory model is more accurate.
The system deployment method provided by the embodiment of the application starts a queuing theory model corresponding to a system when the system access amount is increased or decreased sharply, analyzes whether the current service mechanism number is sufficient or excessive in real time, then calls a related interface to realize dynamic expansion or reduction of the service mechanism number in the system according to the target service mechanism number obtained by the queuing theory model, improves the utilization rate of resources through dynamic expansion or reduction of the service mechanism number, can acquire and release the resources of service mechanisms (virtual machines or servers) as required, further improves the service satisfaction of users, can dynamically increase the number of the service mechanisms deployed in the system when the access amount is increased, provides efficient services for the users, and reduces the probability of access failure.
On the basis of the above embodiment, when the service success rate of the system decreases, the number of service mechanisms deployed in the system can be adjusted according to the queuing theory model corresponding to the system, so as to improve the service success rate, wherein the service success rate can be determined by analyzing the log returned by the interface.
Fig. 3 is a block diagram of a system deployment apparatus provided in an embodiment of the present application, and as shown in fig. 3, the apparatus may include:
an access amount obtaining module 301, configured to obtain an access amount per unit time at a current detection time of the system and an access amount per unit time at a previous detection time;
a determining module 302, configured to determine whether a difference between the unit time access amount at the current detection time and the unit time access amount at the previous detection time meets a preset difference condition;
a target service mechanism number determining module 303, configured to determine, according to a pre-constructed queuing theory model corresponding to the system, a target service mechanism number corresponding to the unit time access amount at the current detection time if a difference between the unit time access amount at the current detection time and the unit time access amount at the previous detection time satisfies a preset difference condition;
an adjusting module 304, configured to adjust the number of service mechanisms currently deployed by the system to the target number of service mechanisms.
On the basis of the foregoing embodiment, the determining module 302 is specifically configured to:
calculating the absolute value of the difference value between the unit time visit amount of the current detection time and the unit time visit amount of the previous detection time;
judging whether the absolute value of the difference value is larger than a preset first threshold value or not;
and if so, determining that the difference degree between the unit time visit amount of the current detection time and the unit time visit amount of the previous detection time meets a preset difference condition.
On the basis of the foregoing embodiment, the target service organization number determining module 303 is specifically configured to:
acquiring the number of preset alternative service mechanisms and a preset average service rate, wherein the average service rate is the access amount processed by a single service mechanism in unit time;
inputting the unit time access amount, the number of alternative service mechanisms and the average service rate of the current detection moment into a pre-constructed queuing theory model corresponding to the system;
and determining the target service mechanism number corresponding to the unit time access amount of the current detection time as the minimum value of the number of the alternative service mechanisms which enable the parameters output by the queuing theory model to meet the preset requirements.
On the basis of the above embodiment, the apparatus further includes: and the model construction module is used for constructing a queuing theory model corresponding to the system before determining the number of the target service mechanisms corresponding to the unit time visit amount of the current detection time according to the pre-constructed queuing theory model corresponding to the system.
The model building module may include:
the obtaining submodule is used for obtaining the operating parameters of the system in a preset time period, and the operating parameters comprise: the access amount of each unit time, the average service rate, the access failure number of each unit time and the access processing time length corresponding to each unit time, wherein the average service rate is the access amount processed by a single service mechanism in unit time;
the probability distribution determining submodule is used for calculating the probability distribution of the access amount of the system in the preset time period according to the operation parameters;
a service queue length determining submodule, configured to calculate a service queue length of the system according to the operation parameter and the probability distribution of the access amount, where the service queue length is a highest accessible access amount to be queued in the system in a unit time;
and the model determining submodule is used for determining a queuing theory model corresponding to the system according to a preset queuing rule, a preset service rule, the service queue length and the average service rate.
On the basis of the above embodiment, the probability distribution determination submodule is specifically configured to:
calculating the average access amount of the system in each unit time according to the acquired access amount of the system in each unit time;
and taking the average access amount of the system in each unit time as a parameter of Poisson distribution, and bringing the average access amount into a preset probability function of Poisson distribution to obtain the access amount probability distribution of the system in the preset time period.
On the basis of the above embodiment, the service captain determination sub-module is specifically configured to:
determining a busy period of the access amount probability distribution;
determining the average access amount and the average access failure number corresponding to the busy period according to the operation parameters;
and subtracting the average access failure number corresponding to the busy period from the average access amount corresponding to the busy period, and then subtracting the average service rate to obtain a difference value corresponding to the busy period, wherein the difference value corresponding to the busy period is used as the service queue length of the system.
On the basis of the above embodiment, the operation parameters further include the number of service mechanisms corresponding to each unit time;
the model determination submodule is specifically configured to:
calculating the actual average waiting time of the system according to the operation parameters and the probability distribution of the access amount, wherein the actual average waiting time is the average time of queuing and waiting for access in the system;
determining an alternative queuing theory model matched with a preset queuing rule, a preset service rule, the service queue length and the average service rate;
inputting the access amount per unit time, the average service rate and the corresponding service mechanism number contained in the operation parameters into the alternative queuing theory models to obtain the simulated average waiting time corresponding to each alternative queuing theory model;
calculating the absolute value of the difference value between the simulated average waiting time length corresponding to each alternative queuing theory model and the actual average waiting time length;
and determining the alternative queuing theory model with the minimum absolute value of the difference between the corresponding simulated average waiting time length and the actual average waiting time length as the queuing theory model.
On the basis of the above embodiment, the calculating, by the model determining submodule, the actual average waiting time of the system according to the operation parameter and the probability distribution of the access amount specifically includes:
determining busy periods and idle periods of the access amount probability distribution;
determining an average access processing duration corresponding to the busy period according to the operation parameters, and taking the average access processing duration as a first average access processing duration;
determining an average access processing duration corresponding to the idle period according to the operation parameters, and taking the average access processing duration as a second average access processing duration;
and calculating the difference value of the first average access processing time length and the second average access processing time length to be used as the actual average waiting time length of the system.
The system deployment device provided by the embodiment of the application starts a queuing theory model corresponding to a system when the access amount of the system is increased or decreased sharply, analyzes whether the number of current service mechanisms is sufficient or excessive in real time, then obtains a proper target number of service mechanisms according to the queuing theory model corresponding to the system, calls related interfaces to realize dynamic expansion or reduction of the number of the service mechanisms in the system, improves the utilization rate of resources through dynamic expansion or reduction of the number of the service mechanisms, can acquire and release the resources of the service mechanisms as required, further improves the service satisfaction of users, can dynamically increase the number of the service mechanisms deployed in the system when the QPS is high, provides efficient service for users, and reduces the probability of access failure.
In another embodiment of the present application, an electronic device is further provided, 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 communication with each other through the communication bus 404;
a memory 403 for storing a computer program;
the processor 401, when executing the program stored in the memory 403, implements the following steps:
acquiring unit time access quantity of a current detection moment of a system and unit time access quantity of a previous detection moment;
judging whether the difference degree between the unit time visit amount of the current detection moment and the unit time visit amount of the previous detection moment meets a preset difference condition or not;
if so, determining the number of target service mechanisms corresponding to the unit time access amount of the current detection time according to a pre-constructed queuing theory model corresponding to the system;
and adjusting the number of service mechanisms currently deployed by the system to the target number of service mechanisms.
The communication bus 404 mentioned in the above electronic device may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus 404 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
The communication interface 402 is used for communication between the above-described electronic apparatus and other apparatuses.
The Memory 403 may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), 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 401 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
In another embodiment of the present application, there is provided a computer-readable storage medium, wherein a system-deploying method program is stored on the computer-readable storage medium, and when executed by a processor, the method program implements the steps of any of the above-described system-deploying methods.
When the embodiments of the present application are specifically implemented, reference may be made to the above embodiments, and corresponding technical effects are achieved.
It is noted that, in this document, 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. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (11)
1. A method of system deployment, comprising:
acquiring unit time access quantity of a current detection moment of a system and unit time access quantity of a previous detection moment;
judging whether the difference degree between the unit time visit amount of the current detection moment and the unit time visit amount of the previous detection moment meets a preset difference condition or not;
if so, determining the number of target service mechanisms corresponding to the unit time access amount of the current detection time according to a pre-constructed queuing theory model corresponding to the system;
and adjusting the number of service mechanisms currently deployed by the system to the target number of service mechanisms.
2. The method of claim 1, wherein determining whether the difference between the unit time visit amount at the current detection time and the unit time visit amount at the previous detection time satisfies a preset difference condition comprises:
calculating the absolute value of the difference value between the unit time visit amount of the current detection time and the unit time visit amount of the previous detection time;
judging whether the absolute value of the difference value is larger than a preset first threshold value or not;
and if so, determining that the difference degree between the unit time visit amount of the current detection time and the unit time visit amount of the previous detection time meets a preset difference condition.
3. The method of claim 1, wherein determining the number of target service mechanisms corresponding to the unit time visit rate at the current detection time according to a pre-constructed queuing theory model corresponding to the system comprises:
acquiring the number of preset alternative service mechanisms and a preset average service rate, wherein the average service rate is the access amount processed by a single service mechanism in unit time;
inputting the unit time access amount, the number of alternative service mechanisms and the average service rate of the current detection moment into a pre-constructed queuing theory model corresponding to the system;
and determining the target service mechanism number corresponding to the unit time access amount of the current detection time as the minimum value of the number of the alternative service mechanisms which enable the parameters output by the queuing theory model to meet the preset requirements.
4. The method according to claim 1, wherein before determining the target number of services corresponding to the amount of access per unit time at the current detection time according to a pre-constructed queuing theory model corresponding to the system, the method further comprises:
obtaining operation parameters of the system in a preset time period, wherein the operation parameters comprise: the access amount of each unit time, the average service rate, the access failure number of each unit time and the access processing time length corresponding to each unit time, wherein the average service rate is the access amount processed by a single service mechanism in unit time;
calculating the probability distribution of the access amount of the system in the preset time period according to the operation parameters;
calculating a service queue length of the system according to the operation parameters and the probability distribution of the access amount, wherein the service queue length is the highest accessible access amount waiting for processing in the system in unit time;
and determining a queuing theory model corresponding to the system according to a preset queuing rule, a preset service rule, the service queue length and the average service rate.
5. The method of claim 4, wherein calculating the probability distribution of the access volume of the system over the preset time period according to the operation parameters comprises:
calculating the average visit amount of the system in each unit time in a preset time period according to the acquired visit amount of the system in each unit time in the preset time period;
and taking the average access amount of the system in each unit time in the preset time period as a parameter of Poisson distribution, and substituting the parameter into a probability function of the preset Poisson distribution to obtain the access amount probability distribution of the system in the preset time period.
6. The method of claim 4, wherein calculating a service captain for the system based on the operational parameters and the probability distribution of the access volumes comprises:
determining a busy period of the access amount probability distribution;
determining the average access amount and the average access failure number corresponding to the busy period according to the operation parameters;
subtracting the average access failure number corresponding to the busy period from the average access amount corresponding to the busy period, and then subtracting the average service rate to obtain a difference value corresponding to the busy period;
and taking the difference value corresponding to the busy period as the service queue length of the system.
7. The method of claim 4, wherein the operational parameters further include a number of service units per unit time;
determining a queuing theory model corresponding to the system according to a preset queuing rule, a preset service rule, the service queue length and the average service rate, wherein the queuing theory model comprises the following steps:
calculating the actual average waiting time of the system according to the operation parameters and the probability distribution of the access amount, wherein the average waiting time is the average time of queuing and waiting for access in the system;
determining an alternative queuing theory model matched with a preset queuing rule, a preset service rule, the service queue length and the average service rate;
inputting the access amount per unit time, the average service rate and the corresponding service mechanism number contained in the operation parameters into the alternative queuing theory models to obtain the simulated average waiting time corresponding to each alternative queuing theory model;
calculating the absolute value of the difference value between the simulated average waiting time length corresponding to each alternative queuing theory model and the actual average waiting time length;
and determining the alternative queuing theory model with the minimum absolute value of the difference between the corresponding simulated average waiting time length and the actual average waiting time length as the queuing theory model.
8. The method of claim 7, wherein calculating an actual average wait duration for the system based on the operational parameters and the probability distribution of visitors comprises:
determining busy periods and idle periods of the access amount probability distribution;
determining an average access processing duration corresponding to the busy period according to the operation parameters, and taking the average access processing duration as a first average access processing duration;
determining an average access processing duration corresponding to the idle period according to the operation parameters, and taking the average access processing duration as a second average access processing duration;
calculating a difference between the first average access processing duration and the second average access processing duration;
and taking the difference value of the first average access processing time length and the second average access processing time length as the actual average waiting time length of the system.
9. A system deployment apparatus, comprising:
the system comprises an access amount acquisition module, a data acquisition module and a data processing module, wherein the access amount acquisition module is used for acquiring the access amount per unit time at the current detection time of the system and the access amount per unit time at the previous detection time;
the judging module is used for judging whether the difference degree between the unit time visit amount of the current detection moment and the unit time visit amount of the previous detection moment meets a preset difference condition or not;
the target service mechanism number determining module is used for determining the target service mechanism number corresponding to the unit time visit amount of the current detection moment according to a pre-constructed queuing theory model corresponding to the system if the difference degree between the unit time visit amount of the current detection moment and the unit time visit amount of the previous detection moment meets a preset difference condition;
and the adjusting module is used for adjusting the number of the service mechanisms currently deployed by the system to the target number of the service mechanisms.
10. An electronic device, comprising: a processor and a memory, said processor for executing a data processing program stored in said memory to implement the system deployment method of any of claims 1-8.
11. A storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the method of system deployment of any one of claims 1-8.
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