CN114691368A - Resource allocation method, device, equipment and medium for edge computing terminal - Google Patents

Resource allocation method, device, equipment and medium for edge computing terminal Download PDF

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CN114691368A
CN114691368A CN202210336857.5A CN202210336857A CN114691368A CN 114691368 A CN114691368 A CN 114691368A CN 202210336857 A CN202210336857 A CN 202210336857A CN 114691368 A CN114691368 A CN 114691368A
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cpu
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张鑫
陈凤超
刘沛林
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Guangdong Power Grid Co Ltd
Dongguan Power Supply Bureau of Guangdong Power Grid Co Ltd
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Dongguan Power Supply Bureau of Guangdong Power Grid Co Ltd
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Abstract

The embodiment of the invention discloses a resource allocation method and a resource allocation device of an edge computing terminal, electronic equipment and a storage medium, wherein the resource allocation method of the edge computing terminal comprises the following steps: acquiring a daily operation data sample set; dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm; respectively calculating the CPU resource demand and the RAM resource demand of the random access memory corresponding to each group of sample data; and acquiring first sample data and second sample data, and performing resource configuration according to the first sample data and the second sample data, wherein the first sample data is sample data corresponding to the maximum CPU resource demand in the g-family sample data, and the second sample data is sample data corresponding to the maximum RAM resource demand in the g-family sample data. The invention can determine the resource allocation of the CPU and the RAM of each application, so that the terminal can provide sufficient computing power for the application in each time period, effectively cope with uncertainty in the operating environment, and realize effective control on the microgrid.

Description

Resource allocation method, device, equipment and medium for edge computing terminal
Technical Field
The present invention relates to power control technologies, and in particular, to a method, an apparatus, a device, and a medium for resource allocation of an edge computing terminal.
Background
Nowadays, people pay more and more attention to the problem of energy conservation, and more renewable energy sources are connected to a low-voltage distribution network in a distributed power supply mode, so that part of the low-voltage distribution network is promoted to be changed into a micro-grid. The original low-voltage distribution network control system cannot cope with the pressure of concurrent increase of various kinds of applications such as control and transaction. Currently, micro-service technology and container technology have some applications in the fields of cloud computing, edge computing, and power system automation. The micro-service technology can divide each application into each micro-service, avoids repeated development and deployment of the same functional element, and has the characteristics of flexible deployment, high elasticity and easy expansion. The container technology provides support for the implementation of microservices. The micro-grid is complex in operation environment, and space-time uncertainty and imbalance exist in the application amount of each part in the control system.
In order to ensure the reliable operation of the control system, each part of the existing control system mostly adopts a redundancy configuration strategy, even blind configuration, and compensation investment is carried out after the configured resource amount can not meet the requirement, so that resource loss and waste are caused. And in the microgrid control system constructed by the edge computing, the uncertainty of each application causes the uncertainty of the time distribution of the computing load of the edge computing terminal.
Disclosure of Invention
The embodiment of the invention provides a resource allocation method, a resource allocation device, equipment and a medium of an edge computing terminal, wherein equipment type selection is carried out according to resource allocation conditions of a Central Processing Unit (CPU) and a Random Access Memory (RAM), and sufficient computing power is provided for the micro-grid edge computing terminal in each time period.
In a first aspect, an embodiment of the present invention provides a resource allocation method for an edge computing terminal, including:
acquiring a daily operation data sample set;
dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm, wherein g is an integer greater than or equal to 2;
respectively calculating the CPU resource demand and the RAM resource demand of the random access memory corresponding to each group of sample data;
acquiring first sample data and second sample data, and performing resource configuration according to the first sample data and the second sample data, wherein the first sample data is sample data corresponding to the maximum CPU resource demand in the g-family sample data, and the second sample data is sample data corresponding to the maximum RAM resource demand in the g-family sample data.
In a second aspect, an embodiment of the present invention provides a resource allocation apparatus for an edge computing terminal, where the apparatus includes:
the first acquisition module is used for acquiring a daily operation data sample set;
the data classification module is used for dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm, wherein g is an integer greater than or equal to 2;
the resource demand calculation module is used for calculating the CPU resource demand and the RAM resource demand corresponding to each group of sample data respectively;
and the second acquisition module is used for performing resource configuration according to the first sample data and the second sample data, wherein the first sample data is sample data corresponding to the maximum CPU resource demand in the g family of sample data, and the second sample data is sample data corresponding to the maximum RAM resource demand in the g family of sample data.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the resource allocation method of the edge computing terminal according to any one of the embodiments of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the resource allocation method of an edge computing terminal according to any one of the embodiments of the present invention.
In the embodiment of the invention, a daily operation data sample set is obtained; dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm, wherein g is an integer greater than or equal to 2; respectively calculating the CPU resource demand and the RAM resource demand of the random access memory corresponding to each group of sample data; and acquiring first sample data and second sample data, and performing resource configuration according to the first sample data and the second sample data, wherein the first sample data is sample data corresponding to the maximum CPU resource demand in the g-family sample data, and the second sample data is sample data corresponding to the maximum RAM resource demand in the g-family sample data. In other words, in the embodiment of the invention, the resource allocation of the CPU and the RAM of each application can be determined by using the resource demand of each application to the CPU and the RAM, so that the terminal can provide sufficient computing power for the application in each time period, effectively cope with uncertainty in the running environment of the edge computing terminal, and realize effective control on the microgrid.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flowchart of a resource allocation method of an edge computing terminal according to an embodiment of the present invention;
FIG. 2 is a flowchart of resource allocation to CPU and RAM according to an embodiment of the present invention;
fig. 3 is another schematic flowchart of a resource allocation method of an edge computing terminal according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a resource allocation terminal of an edge computing terminal according to an embodiment of the present invention;
FIG. 5 is a block diagram of a resource allocation apparatus of an edge computing terminal according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Fig. 1 is a schematic flow chart of a resource allocation method for an edge computing terminal according to an embodiment of the present invention, where the method according to the embodiment of the present invention is applicable to device type selection according to resource allocation conditions of a CPU and a RAM, and provides sufficient computing power for the edge computing terminal of a microgrid at each time interval. The method can be executed by the resource configuration device of the edge computing terminal provided by the embodiment of the invention, and the device can be realized in a software and/or hardware manner. The following embodiments will be described by taking as an example that the apparatus is integrated in an electronic device, and referring to fig. 1, the method may specifically include the following steps:
step 101, acquiring a daily operation data sample set.
The sample data is data generated when each application deployed on the microgrid edge computing terminal is triggered on a running day. The micro-grid is a small power generation and distribution system composed of a distributed power supply, an energy storage device, an energy conversion device, a load, a monitoring and protecting device and the like. The micro-grid can realize flexible and efficient application of the distributed power supply and solve the problem of grid connection of the distributed power supply with large quantity and various forms. The edge calculation is an open platform integrating network, calculation, storage and application core capabilities on one side close to an object or a data source, nearest-end service is provided nearby, and the basic requirements of the industry on real-time application, application intelligence, safety, privacy protection and the like are met. The daily operation data sample set is a set of data samples generated by triggering each application deployed on the micro-grid edge computing terminal on an operation dayAnd (6) mixing. In the embodiment of the invention, one operation day is divided into 96 time periods. Assume that n applications are deployed on a microgrid edge computing terminal: (A)1,A2,...An)TWith AiDenotes the ith application and denotes the period by k. By fiRepresents application AiFrequency of triggering over 96 periods of the day of operation, i.e. fi＝(fi,1,fi,2,...,fi,k,...,fi,96). Further, with f ═ f (f)1,f2,...,fn)TRepresenting daily operating data. And collecting daily operation data F of each application to obtain a daily operation data sample set F of each application. Wherein, the data sample set F is composed of F, and has the same structure with F.
And 102, dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm, wherein g is an integer greater than or equal to 2.
The triggering frequency of each application on the operation day is influenced by season change, small probability events and the like, so that data generated by triggering each application presents certain fluctuation. For example, a load forecasting microservice is deployed on a microgrid terminal. In general, the electricity consumption of residents is more in summer than in winter, and the data generated by the load forecasting micro-service in different seasons may be different. Therefore, the daily operation sample data of each application needs to be clustered, and the daily operation sample data of each application is divided into g families. The cluster analysis is an analysis process for grouping a set of physical or abstract objects and dividing data into a plurality of classes composed of similar objects. In the embodiment of the scheme, the daily operation sample data of each application can be preprocessed by utilizing cluster analysis, so that the influence of seasonal change, small probability events and the like on the daily operation sample data of each application is avoided. In an embodiment, the value of g may be set according to actual needs or computing capability of the resource configuration device, for example, the value of g is 10, 50, or 100. In particular, the daily run data sample set may be divided into g-family sample data using a clustering algorithm such as k-means. Wherein, the k-means algorithm can judge the similar relations of different samples by calculating the distance between the different samplesThe neighbors are placed in the same category. By the aid of a clustering analysis algorithm, daily operation data of each application can be preprocessed once, and influences of small-probability burst time and the like on the daily operation data of each application are reduced. Specifically, each application day operation data sample set F is divided into g families. Wherein group i is
Figure BDA0003574665530000061
Wherein, Fj (i)Representing the jth day of operation data in family i.
And 103, respectively calculating the CPU resource demand and the RAM resource demand corresponding to each group of sample data.
Specifically, after g family sample data is acquired, a daily operation data sample of a certain family is acquired. Assuming that a daily run data sample of the i-th family is taken, further, an average of the daily run data samples of the family is calculated. The average of the daily run data samples of the family is used to estimate the overall expected value for the family. The expectation value is the probability of each possible result in the test multiplied by the sum of the results, and can reflect the average value of the random variables. Further, the expected triggering frequency of each application in each time interval on a typical day corresponding to the operation data sample of the ith family day is obtained from the total expected value. Wherein, by determining typical days in the power system, the line loss of each node in the system, the peak-valley period of each day and the distribution of loads in the system can be calculated. The resources occupied by each application in the edge computing terminal under certain conditions of the microgrid structure are known. Assuming that n applications are deployed on the edge computing terminal, resources occupied by each application in the edge computing terminal can be represented by a 2 × n matrix. With the 2 × n matrix × the expected trigger frequency for each period in the above description, an expected computation load matrix for each application can be obtained. And calculating the CPU resource demand corresponding to the first behavior sample data of each family in the matrix, and the RAM resource demand corresponding to the second behavior sample data of each family.
And 104, acquiring first sample data and second sample data, and performing resource configuration according to the first sample data and the second sample data, wherein the first sample data is sample data corresponding to the maximum CPU resource demand in the g-family sample data, and the second sample data is sample data corresponding to the maximum RAM resource demand in the g-family sample data.
Specifically, after the load matrix is calculated for each application, the time period during which the maximum value occurs in the first and second rows in the matrix is obtained. Let k be the period during which the first row maximum in the matrix occurs1The period of time during which the second row maximum occurs in the matrix is k2. Then explain each application k1Time slot has the greatest demand for CPU resources, at k2The time period has the greatest demand on RAM resources. Further, for each application k1Time period and k2Analyzing the resource demand conditions of the CPU and the RAM in time intervals to obtain k1Time period and k2And (3) calculating resource demand curves of the CPU and the RAM when the applications are concurrent in the time interval. Further, get applications k1Time period and k2All subsets of the set of time period concurrent applications. Suppose at k1All subsets of the set of time period concurrent applications are nθ1At k, in2All subsets of the set of time period concurrent applications are nθ2. Further, for nθ1And nθ2Analyzing the resource demand conditions of the CPU and the RAM to obtain nθ1At k1Resource demand curve component of CPU of a time period, and nθ2At k is2Resource demand curve component of RAM for a time period. In obtaining nθ1And nθ2Are respectively at k1Time period and k2And adding constraint conditions to the two curve components for optimization after the resource demand curve components of the CPU and the RAM are subjected to time interval. Wherein, the constraint condition can be that each application does not exceed n for the maximum resource demand of CPU and RAM in actual operationθ1And nθ2The probability of resource demand on CPU and RAM is 99.99%. Suppose that for nθ1And nθ2Is optimized to obtain
Figure BDA0003574665530000071
And
Figure BDA0003574665530000072
then
Figure BDA0003574665530000073
And
Figure BDA0003574665530000074
respectively allocating R for the resources of CPUCPUAnd resource allocation R of RAMRAM. Further, sample data for each family is traversed. Fig. 2 is a flowchart of resource allocation to a CPU and a RAM according to an embodiment of the present invention. As shown in fig. 2, the sample data of the m-th family is denoted by m. When m is greater than or equal to g, it indicates that the sample data of each family has been traversed. Specifically, the resource configuration of each family of sample data to the CPU and the resource configuration of the RAM are obtained. Further, take the corresponding R of each groupCPUAnd RRAMMaximum value of (2). Wherein each group corresponds to RCPUThe maximum sample data in (1) is the first sample data, and R corresponding to each groupRAMThe sample data of the maximum value in (b) is the second sample data. Further, resource configuration is carried out according to the first sample data and the second sample data.
According to the technical scheme of the embodiment, a daily operation data sample set is obtained; dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm, wherein g is an integer greater than or equal to 2; respectively calculating the CPU resource demand and the RAM resource demand of the random access memory corresponding to each group of sample data; and acquiring first sample data and second sample data, and performing resource configuration according to the first sample data and the second sample data, wherein the first sample data is sample data corresponding to the maximum CPU resource demand in the g-family sample data, and the second sample data is sample data corresponding to the maximum RAM resource demand in the g-family sample data. Through the technical scheme of the embodiment, the resource configuration of the CPU and the RAM of each application can be determined by utilizing the resource demand of each application to the CPU and the RAM. The terminal can provide sufficient computing power for application in each time period, effectively cope with uncertainty in the operating environment, and effectively control the micro-grid.
Fig. 3 is another schematic flow chart of a resource allocation method of an edge computing terminal according to an embodiment of the present invention, which is detailed based on the foregoing embodiment. A specific method can be shown in fig. 3, and the method can include the following steps:
step 201, acquiring a daily operation data sample set.
Step 202, dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm.
Step 203, obtaining the application trigger frequency matrix expectation P of the sample data of the ith group(i)I-family sample data expected computation load matrix L(i)Application set nθ1In a period k1CPU demand curve component of
Figure BDA0003574665530000091
And set of applications nθ2In a period k2Component of the RAM demand curve
Figure BDA0003574665530000092
Wherein i is more than or equal to 1 and less than or equal to g.
The expectation value is the probability of each possible result in the test multiplied by the sum of the results, and can reflect the average value of the random variables. The calculation load matrix may represent the CPU resource demand and the RAM resource demand corresponding to each group of sample data.
In this embodiment, optionally, the application of the sample data of the ith group triggers the frequency matrix expectation
Figure BDA0003574665530000093
Fj (i)For the jth day of operation data in family i,/iThe number of samples included in the sample data of the i-th family.
Specifically, the data sample F is run on the day of the family i(i)Then, to F(i)Get the mean value
Figure BDA0003574665530000094
Further, the overall expectation of daily running data samples is denoted by E, namely:
Figure BDA0003574665530000095
overall expectation of running data samples at the time of day of acquisition E (F)(i)) Then, by E (F)(i)) Calculating application trigger frequency matrix expectation P of i-family sample data(i). Wherein, P(i)Is a matrix of n × 96 dimensions, i.e.:
P(i)＝E(F(i))
by aiRepresenting the number of samples contained in the i-th group of sample data, P can be obtained(i)Comprises the following steps:
Figure BDA0003574665530000096
under the condition determined by the micro-grid structure, the resources occupied by each application in the edge computing terminal are known. The resource occupation state is represented by a 2 × n-dimensional matrix C. Further, a desired computation load matrix L of the i-th family of sample data is defined based on the matrix C(i)Namely:
L(i)＝CP(i)
wherein it is desired to calculate the load matrix L(i)The CPU resource demand and the RAM resource demand corresponding to the sample data of each family can be represented. And calculating the CPU resource demand corresponding to the first behavior sample data of each family in the matrix, and the RAM resource demand corresponding to the second behavior sample data of each family.
In this embodiment, optionally, the set n is appliedθ1In a period k1CPU demand curve component of (a):
Figure BDA0003574665530000101
set of applications nθ2In a period k2RAM demand curve component of (a):
Figure BDA0003574665530000102
where, i ═ 1, 2., n denotes that n applications are deployed on the edge computing terminal, and j ═ 1, 2., miRepresenting that the ith application contains miA micro service, ci,j,CPU、ci,j,RAMRespectively representing CPU and RAM resources occupied by jth micro service of ith application, epsilon (t) is step function, ti,j,startStarting execution time of jth microservice for ith application, di,j、βi,jRespectively, the data size and the computational complexity coefficient of the jth microservice for the ith application.
Specifically, the expected computation load matrix L is obtained(i)Then, the matrix L is divided into(i)Let the period of occurrence of the maximum in the first row be k1And the period of time when the maximum value appears in the second row is denoted as k2. Wherein, is composed of L(i)The computational resource demand curve of each application to the CPU and RAM can be obtained. In particular, is obtained at k1And k2And in the time period, calculating resource demand curves of the CPU and the RAM are generated when the applications are concurrent. Wherein each application is to k1The CPU resource demand curve component for a time period is:
Figure BDA0003574665530000103
each application is to k2The RAM resource demand curve components for a time period are:
Figure BDA0003574665530000111
where, i ═ 1, 2., n denotes that n applications are deployed on the edge computing terminal, and j ═ 1, 2., miRepresenting that the ith application contains miA micro service, ci,j,CPU、ci,j,RAMRespectively representing CPU and RAM resources occupied by jth micro service of ith application, epsilon (t) is step function, ti,j,startStarting execution time of jth microservice for ith application, di,j、βi,jRespectively, the data size and the computational complexity coefficient of the jth microservice for the ith application. The step function is a special continuous time function, and signal processing, integral conversion and the like can be performed by using the step function. Further, set at the period k1All subsets of the set of concurrent applications are nθ1In a period k2All subsets of the set of concurrent applications are nθ2. Then the set n of applicationsθ1In a period k1The CPU demand curve components of (1) are:
Figure BDA0003574665530000112
set of applications nθ2In a period k2The RAM demand curve components of (a):
Figure BDA0003574665530000113
direct utilization of
Figure BDA0003574665530000114
And
Figure BDA0003574665530000115
the resource allocation is carried out on the CPU and the RAM, so that the resource allocation amount is excessively increased due to some extremely small probability events, and resource waste is caused. In the embodiment of the invention, the method is used for calculating
Figure BDA0003574665530000116
And
Figure BDA0003574665530000117
and then the CPU and the RAM are subjected to resource allocation, so that resource waste is avoided.
Step 204, according to P(i)、L(i)、
Figure BDA0003574665530000118
And
Figure BDA0003574665530000119
computing a first set of reference applications
Figure BDA00035746655300001110
And a second set of reference applications
Figure BDA00035746655300001111
Wherein the first reference application set
Figure BDA00035746655300001112
And CPU resource demand RCPU,iCorresponding, second set of reference applications
Figure BDA00035746655300001113
And RAM resource demand RRAM,iAnd (7) corresponding.
Specifically, the set n of applications is obtainedθ1In a period k1CPU demand curve component and application set nθ2In a period k2After the RAM demand curve components are obtained, constraint conditions are added to the two demand curve components for optimization to obtain a first reference application set
Figure BDA0003574665530000121
And a second set of reference applications
Figure BDA0003574665530000122
In this embodiment, optionally, the first reference application set
Figure BDA0003574665530000123
Satisfies the following conditions:
Figure BDA0003574665530000124
second set of reference applications
Figure BDA0003574665530000125
Satisfies the following conditions:
Figure BDA0003574665530000126
where min max represents the maximum-minimum criterion decision method, which is one of the decision criteria for an indeterminate decision. It may be shown that the decision maker should select the worst possible outcome in each scenario and then select the best one that provides the worst possible outcome. The minimum possible result can be maximized by min max. Wherein the constraint condition in s.t. is that the maximum resource demand of CPU and RAM is not more than n when each application is actually operatedθ1And nθ2The probability of resource demand on CPU and RAM is 99.99%.
Computing a first set of reference applications by the method described above
Figure BDA0003574665530000127
And a second set of reference applications
Figure BDA0003574665530000128
And to
Figure BDA0003574665530000129
And
Figure BDA00035746655300001210
and the constraint and optimization are carried out, so that the accuracy of resource allocation is improved, and the edge computing terminal can provide sufficient computing power for each application in each time period.
Step 205, apply the set according to the first reference
Figure BDA00035746655300001211
Calculating the CPU resource demand R corresponding to the i-family sample dataCPUAnd according to the second reference application set
Figure BDA00035746655300001212
Calculating the RAM resource demand R corresponding to the i-family sample dataRAM。
In particular toGround, first reference application set
Figure BDA00035746655300001213
And CPU resource demand RCPU,iCorresponding, second set of reference applications
Figure BDA00035746655300001214
And RAM resource demand RRAM,iAnd (7) corresponding. By using
Figure BDA00035746655300001215
And
Figure BDA00035746655300001216
can respectively calculate RCPU,iAnd RRAM,i。
In this embodiment, optionally, the CPU resource demand corresponding to the i-family sample data is:
Figure BDA0003574665530000131
the RAM resource demand corresponding to the sample data of the i family is as follows:
Figure BDA0003574665530000132
wherein alpha is1、α2And respectively configuring a margin coefficient for the CPU and a margin coefficient for the RAM.
In the power system, the margin coefficient can be used for measuring the stability of a negative feedback system and can be used for predicting the overshoot of the step response of the closed-loop system. In the development process of the micro-grid, new applications may appear to need to be deployed on the edge computing terminal. Therefore, before the equipment is selected, the equipment needs to be selected
Figure BDA0003574665530000133
And
Figure BDA0003574665530000134
respectively multiplying the corresponding resource demand quantities of the CPU and the RAM by the corresponding configuration margin coefficients to obtain the CPU resource demand quantity R corresponding to the sample data of the ith groupCPUAnd RAM resource demand RRAM. Wherein R isCPUAnd RRAMAnd calculating resource allocation quantities of the edge calculation terminals defined by the micro-grid respectively. Further, R from each family of daily operating data is comparedCPUAnd RRAMAnd taking the maximum value as a calculation resource configuration result. And selecting the equipment by taking the resource configuration result as a reference.
By calculating RCPUAnd RRAMAnd the maximum value is taken as reference for equipment type selection, so that resource loss and waste caused by blind configuration can be avoided, and the efficiency of resource configuration is improved.
In the embodiment of the invention, a daily operation data sample set is obtained; dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm; application trigger frequency matrix expectation P for acquiring ith group of sample data(i)Expected calculation load matrix L of sample data of group i(i)Application set nθ1In a period k1CPU demand curve component of
Figure BDA0003574665530000135
And set of applications nθ2In a period k2Component of the RAM demand curve
Figure BDA0003574665530000136
According to P(i)、L(i)、
Figure BDA0003574665530000137
And
Figure BDA0003574665530000138
computing a first set of reference applications
Figure BDA0003574665530000139
And a second set of reference applications
Figure BDA00035746655300001310
Wherein the first reference application set
Figure BDA00035746655300001311
And CPU resource demand RCPU,iCorresponding, second set of reference applications
Figure BDA00035746655300001312
And RAM resource demand RRAM,iCorresponding; wherein i is more than or equal to 1 and less than or equal to g; applying a set according to a first reference
Figure BDA0003574665530000141
Calculating the CPU resource demand R corresponding to the i-family sample dataCPUAnd according to the second reference application set
Figure BDA0003574665530000142
Calculating the RAM resource demand R corresponding to the i-family sample dataRAM. By the technical scheme of the embodiment, resource waste caused by excessive increase of resource allocation amount due to some extremely-small-probability events is avoided, the accuracy of resource allocation is improved, and the edge computing terminal can provide sufficient computing power for each application in each time period. The uncertainty in the operation environment of the computing terminal can be effectively dealt with, and the effective control on the micro-grid is realized.
Fig. 4 is a schematic structural diagram of a resource allocation terminal of an edge computing terminal according to an embodiment of the present invention, as shown in fig. 4. The resource configuration terminal of the edge computing terminal comprises:
the container is used for providing a running environment for the functional microservice, wherein the functional microservice supports the realization of each application;
the data interaction interface is used for realizing data interaction among the applications;
and the network bridge is used for providing channels for data interaction of different containers.
The functional micro services include a message analysis micro service, a photovoltaic control micro service, a wind power control micro service, an energy storage control micro service, a breaker control micro service, a load flow calculation micro service, a topology analysis micro service, a harmonic analysis micro service, a load prediction micro service, a state estimation micro service and the like as shown in fig. 4.
Fig. 5 is a structural diagram of a resource allocation apparatus of an edge computing terminal according to an embodiment of the present invention, where the apparatus is adapted to execute a resource allocation method of the edge computing terminal according to the embodiment of the present invention. As shown in fig. 5, the apparatus may specifically include:
a first obtaining module 501, configured to obtain a daily operation data sample set;
a data classification module 502, configured to classify the daily operation data sample set into g groups of sample data according to a clustering algorithm, where g is an integer greater than or equal to 2;
a resource demand calculation module 503, configured to calculate the CPU resource demand and the RAM resource demand of the random access memory corresponding to each group of sample data respectively;
a second obtaining module 504, configured to perform resource configuration according to first sample data and second sample data, where the first sample data is sample data corresponding to a maximum CPU resource requirement in the g family of sample data, and the second sample data is sample data corresponding to a maximum RAM resource requirement in the g family of sample data.
Optionally, the second obtaining module 503 is specifically configured to:
for the sample data of the ith group, i is more than or equal to 1 and less than or equal to g;
calculating the CPU resource demand R corresponding to the i-family sample dataCPUAnd RAM resource demand RRAMThe method comprises the following steps:
an application set calculation unit: calculating a first reference application set corresponding to the i-family sample data
Figure BDA0003574665530000151
And a second set of reference applications
Figure BDA0003574665530000152
Wherein the first reference application set
Figure BDA0003574665530000153
And CPU resource demand RCPU,iCorrespondingly, the second reference application set
Figure BDA0003574665530000154
And RAM resource demand RRAM,iCorresponding;
applying the set according to the first reference
Figure BDA0003574665530000155
Calculating the CPU resource demand R corresponding to the i-family sample dataCPUAnd applying the set according to the second reference
Figure BDA0003574665530000156
Calculating the RAM resource demand R corresponding to the i-family sample dataRAM。
Optionally, the application set calculating unit is specifically configured to:
application trigger frequency matrix expectation P for acquiring ith group of sample data(i)I-family sample data expected computation load matrix L(i)Application set nθ1In a period k1CPU demand curve component of
Figure BDA0003574665530000157
And set of applications nθ2In a period k2Component of the RAM demand curve
Figure BDA0003574665530000158
According to P(i)、L(i)、
Figure BDA0003574665530000159
And
Figure BDA00035746655300001510
computing the first set of reference applications
Figure BDA00035746655300001511
And the second reference application set
Figure BDA0003574665530000161
Optionally, the application set calculating unit is further configured to:
application trigger frequency matrix expectation of i-family sample data
Figure BDA0003574665530000162
Fj (i)For the jth day of operation data in family i,/iThe number of samples contained in the sample data of the ith family;
expected computation load matrix L of i-th group sample data(i)＝CP(i)C is an application resource occupation matrix;
set of applications nθ1In a period k1CPU demand curve component of
Figure BDA0003574665530000163
Set of applications nθ2In a period k2Component of the RAM demand curve
Figure BDA0003574665530000164
Figure BDA0003574665530000165
Where, i ═ 1, 2., n denotes that n applications are deployed on the edge computing terminal, and j ═ 1, 2., miRepresenting that the ith application contains miA micro service, ci,j,CPU、ci,j,RAMRespectively representing CPU and RAM resources occupied by jth micro service of ith application, epsilon (t) is step function, ti,j,startStarting execution time of jth microservice for ith application, di,j、βi,jRespectively, the data size and the computational complexity coefficient of the jth microservice for the ith application.
Optionally, the application set calculating unit is further configured to:
the first set of reference applications
Figure BDA0003574665530000166
Satisfies the following conditions:
Figure BDA0003574665530000167
the second set of reference applications
Figure BDA0003574665530000168
Satisfies the following conditions:
Figure BDA0003574665530000169
optionally, the second obtaining module 503 is further configured to:
the CPU resource demand corresponding to the i family sample data
Figure BDA0003574665530000171
The RAM resource demand corresponding to the i family sample data
Figure BDA0003574665530000172
Wherein alpha is1、α2And respectively configuring a margin coefficient for the CPU and a margin coefficient for the RAM.
The resource allocation device of the edge computing terminal provided by the embodiment of the invention can execute the resource allocation method of the edge computing terminal provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. Reference may be made to the description of any method embodiment of the invention not specifically described in this embodiment.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and referring to fig. 6, a schematic structural diagram of a computer system 12 suitable for implementing the electronic device according to the embodiment of the present invention is shown. The electronic device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
Bus 18 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
Electronic device 12 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by electronic device 12 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 6, and commonly referred to as a "hard drive"). Although not shown in FIG. 6, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 40 having a set (at least one) of program modules 42 may be stored, for example, in memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 42 generally carry out the functions and/or methodologies of the described embodiments of the invention.
Electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), with one or more devices that enable a user to interact with electronic device 12, and/or with any devices (e.g., network card, modem, etc.) that enable electronic device 12 to communicate with one or more other computing devices. Such communication may be through an input/output (I/O) interface 22. In the electronic device 12 of the present embodiment, the display 24 is not provided as a separate body but is embedded in the mirror surface, and when the display surface of the display 24 is not displayed, the display surface of the display 24 and the mirror surface are visually integrated. Also, the electronic device 12 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via the bus 18. It should be understood that although not shown in the figures, other hardware and/or software modules may be used in conjunction with electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 16 executes various functional applications and resource allocation by running the program stored in the system memory 28, for example, implementing the resource allocation method of the edge computing terminal provided by the embodiment of the present invention: acquiring a daily operation data sample set; dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm, wherein g is an integer greater than or equal to 2; respectively calculating the CPU resource demand and the RAM resource demand of the random access memory corresponding to each group of sample data; and acquiring first sample data and second sample data, and performing resource configuration according to the first sample data and the second sample data, wherein the first sample data is sample data corresponding to the maximum CPU resource demand in the g-family sample data, and the second sample data is sample data corresponding to the maximum RAM resource demand in the g-family sample data.
Embodiments of the present invention provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the resource allocation method of an edge computing terminal according to all embodiments of the present invention: acquiring a daily operation data sample set; dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm, wherein g is an integer greater than or equal to 2; respectively calculating the CPU resource demand and the RAM resource demand of the random access memory corresponding to each group of sample data; and acquiring first sample data and second sample data, and performing resource configuration according to the first sample data and the second sample data, wherein the first sample data is sample data corresponding to the maximum CPU resource demand in the g-family sample data, and the second sample data is sample data corresponding to the maximum RAM resource demand in the g-family sample data. Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments illustrated herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims (9)

1. A resource allocation method of an edge computing terminal is characterized by comprising the following steps:
acquiring a daily operation data sample set;
dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm, wherein g is an integer greater than or equal to 2;
respectively calculating the CPU resource demand and the RAM resource demand of the random access memory corresponding to each group of sample data;
acquiring first sample data and second sample data, and performing resource configuration according to the first sample data and the second sample data, wherein the first sample data is sample data corresponding to the maximum CPU resource demand in the g-family sample data, and the second sample data is sample data corresponding to the maximum RAM resource demand in the g-family sample data.
2. The method of claim 1, wherein i is 1 ≦ g for the ith group of sample data; calculating the CPU resource demand R corresponding to the i-family sample dataCPUAnd RAM resource demand RRAMThe method comprises the following steps:
calculating a first reference application set corresponding to the i-family sample data
Figure FDA0003574665520000011
And a second set of reference applications
Figure FDA0003574665520000012
Wherein the first reference application set
Figure FDA0003574665520000013
And CPU resource demand RCPU,iCorrespondingly, the second reference application set
Figure FDA0003574665520000014
And RAM resource demand RRAM,iCorresponding;
applying the set according to the first reference
Figure FDA0003574665520000015
Calculating the CPU resource demand R corresponding to the i-family sample dataCPUAnd according to said second reference application set
Figure FDA0003574665520000016
Calculating the RAM resource demand R corresponding to the i-family sample dataRAM。
3. The method of claim 2, wherein the computing the first reference application set corresponding to the i-th family of sample data
Figure FDA0003574665520000017
And a second set of reference applications
Figure FDA0003574665520000018
The method comprises the following steps:
application trigger frequency matrix expectation P for acquiring ith group of sample data(i)I-family sample data expected computation load matrix L(i)Application set nθ1In a period k1CPU demand curve component of
Figure FDA0003574665520000021
And set of applications nθ2In a period k2Component of the RAM demand curve
Figure FDA0003574665520000022
According to P(i)、L(i)、
Figure FDA0003574665520000023
And
Figure FDA0003574665520000024
computing the first set of reference applications
Figure FDA0003574665520000025
And the second reference application set
Figure FDA0003574665520000026
4. The resource allocation method of the edge computing terminal according to claim 3, wherein the application trigger frequency matrix expectation P for obtaining the ith group of sample data(i)I-family sample data expected computation load matrix L(i)Application set nθ1In a period k1CPU demand curve component of
Figure FDA0003574665520000027
And set of applications nθ2In a period k2Component of the RAM demand curve
Figure FDA0003574665520000028
The method comprises the following steps:
application trigger frequency matrix expectation for obtaining i-th group sample data
Figure FDA0003574665520000029
Figure FDA00035746655200000210
For the jth day of operation data in family i,/iObtaining expected calculation load matrix L of the ith group of sample data for the number of samples contained in the ith group of sample data(i)＝CP(i)And C is an application resource occupation matrix, and an application set n is obtainedθ1In a period k1CPU demand curve component of
Figure FDA00035746655200000211
Figure FDA00035746655200000212
And obtaining a set of applications nθ2In a period k2Component of the RAM demand curve
Figure FDA00035746655200000213
Where, i ═ 1, 2., n denotes that n applications are deployed on the edge computing terminal, and j ═ 1, 2., miRepresenting that the ith application contains miA micro service, ci,j,CPU、ci,j,RAMRespectively representing CPU and RAM resources occupied by jth micro service of ith application, epsilon (t) is step function, ti,j,startStarting execution time of jth microservice for ith application, di,j、βi,jRespectively, the data size and the computational complexity coefficient of the jth microservice for the ith application.
5. The method of claim 4, wherein the P is a number of terms(i)、L(i)、
Figure FDA0003574665520000031
And
Figure FDA0003574665520000032
computing the first set of reference applications
Figure FDA0003574665520000033
And the second reference application set
Figure FDA0003574665520000034
The method comprises the following steps:
according to P(i)、L(i)、
Figure FDA0003574665520000035
And
Figure FDA0003574665520000036
computing the first set of reference applications
Figure FDA0003574665520000037
The first set of reference applications
Figure FDA0003574665520000038
Satisfies the following conditions:
Figure FDA0003574665520000039
according to P(i)、L(i)、
Figure FDA00035746655200000310
And
Figure FDA00035746655200000311
computing the second set of reference applications
Figure FDA00035746655200000312
The second set of reference applications
Figure FDA00035746655200000313
Satisfies the following conditions:
Figure FDA00035746655200000314
6. the method of claim 5, wherein the set of applications is applied according to the first reference
Figure FDA00035746655200000315
Computing group i sample dataCorresponding CPU resource demand RCPUAnd according to said second reference application set
Figure FDA00035746655200000316
Calculating the RAM resource demand R corresponding to the i-family sample dataRAMThe method comprises the following steps:
applying the set according to the first reference
Figure FDA00035746655200000317
Calculating the CPU resource demand corresponding to the i-th group of sample data
Figure FDA00035746655200000318
And applying the set according to the second reference
Figure FDA00035746655200000319
Calculating the RAM resource demand corresponding to the sample data of the ith family
Figure FDA00035746655200000320
Wherein alpha is1、α2And respectively configuring a margin coefficient for the CPU and a margin coefficient for the RAM.
7. A resource allocation apparatus of an edge computing terminal, comprising:
the first acquisition module is used for acquiring a daily operation data sample set;
the data classification module is used for dividing the daily operation data sample set into g groups of sample data according to a clustering algorithm, wherein g is an integer greater than or equal to 2;
the resource demand calculation module is used for calculating the CPU resource demand and the RAM resource demand corresponding to each group of sample data respectively;
and the second acquisition module is used for performing resource configuration according to the first sample data and the second sample data, wherein the first sample data is sample data corresponding to the maximum CPU resource demand in the g family of sample data, and the second sample data is sample data corresponding to the maximum RAM resource demand in the g family of sample data.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the resource allocation method of the edge computing terminal according to any one of claims 1 to 6 when executing the program.
9. A computer-readable storage medium, on which a computer program is stored, the program, when executed by a processor, implementing a resource allocation method of an edge computing terminal according to any one of claims 1 to 6.
CN202210336857.5A 2022-03-31 2022-03-31 Resource allocation method, device, equipment and medium for edge computing terminal Pending CN114691368A (en)

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