A method, device, equipment and medium for determining resource utilization of a cloud resource pool

Abstract

The present invention discloses a method, device, equipment and medium for determining resource utilization of a cloud resource pool. Since multiple preset thresholds can be set for preset parameters of preset resources in the present invention, the basic activity index of each server corresponding to each preset threshold within a preset time range is determined, and the resource utilization of the cloud resource pool corresponding to the preset threshold of the preset resources is determined based on a first number of servers whose basic activity index is not less than the preset basic activity index threshold and a second number of servers included in the cloud resource pool. Therefore, the resource utilization corresponding to the preset parameters of the preset resources can be determined in a more fine-grained manner, thereby improving the accuracy of the determined resource utilization.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, equipment and medium for determining resource utilization of a cloud resource pool. Background Art

[0002] As cloud computing technology matures, more and more enterprises and individuals deploy applications to cloud computing platforms. Cloud computing platforms centrally place storage resources and computing resources in a public cloud resource pool, allowing customers to obtain them on a pay-as-you-go basis through the Internet. Resource utilization, as an important indicator of resource pool usage, is an important decision-making basis for the construction and operation of cloud computing platforms.

[0003] The existing methods for evaluating resource pool resource utilization mainly use three types of data: instantaneous value, average value and maximum value of specific monitoring indicators. The specific monitoring indicators include CPU utilization, memory utilization, network outflow / inflow traffic, etc.

[0004] In the prior art, when the sampling time of monitoring indicators of each server in the resource pool is not synchronized, the accuracy of evaluating the resource pool utilization by simply using instantaneous values ​​is low. In particular, when the sampling period of monitoring indicators is long, there is a time deviation between the sampling points of different servers in the same time period, and the instantaneous utilization of specific resource types of each server will not be comparable. When evaluating the utilization of the resource pool over a long period of time, using the average of specific monitoring indicators within the time interval will not reflect the resource utilization characteristics, and the evaluation result will have a large deviation, which will have an adverse impact on the accuracy. When using the peak value of a specific monitoring indicator within the time interval, the evaluation result is easily affected by abnormal extreme points and isolated points, which also affects the accuracy of the resource pool utilization evaluation.

[0005] In an existing cloud platform resource utilization evaluation method, resource usage parameters of each server included in the cloud platform are collected every first preset period, and the first resource utilization of each server is calculated based on the resource usage parameters, and the maximum value of multiple first resource utilization rates of each server included in each cluster obtained within a second preset period is used as the second resource utilization rate of each server; the second preset period includes multiple first preset periods; the average value of multiple second resource utilization rates of each server included in each cluster obtained within a third preset period is used as the third resource utilization rate of each server; the third preset period includes multiple second preset periods; the maximum value of multiple third resource utilization rates of each server included in each cluster obtained within a fourth preset period is used as the fourth resource utilization rate of each server; the fourth preset period includes multiple third preset periods; the average values ​​of the fourth resource utilization rates of each server included in each cluster are used as the comprehensive resource utilization rates of each cluster.

[0006] The technical solution of the above-mentioned prior art essentially still refers to the maximum value feature of the utilization rate resource utilization feature, but does not reflect other resource utilization features, and the evaluation result has a large deviation.

[0007] In an existing cloud data center resource utilization optimization method, the average of the resource utilization of the CPU, memory and network is used to calculate the comprehensive resource utilization of the server. According to the corresponding averages of the CPU utilization, memory utilization and network utilization of all servers, a first sum of the absolute values ​​of the differences between the CPU utilization of all servers and the corresponding averages, a second sum of the absolute values ​​of the differences between the memory utilization of all servers and the corresponding averages, and a third sum of the absolute values ​​of the differences between the network utilization of all servers and the corresponding averages are determined. The first sum is used as the total CPU utilization in the resource pool, the second sum is used as the total memory utilization in the resource pool, and the third sum is used as the total network utilization in the resource pool.

[0008] In the above-mentioned prior art, the deviation and value of the resource utilization of all servers at the same time is used as the utilization of the resource pool. However, there is a time deviation between the sampling points of different servers in the same time period, resulting in a low accuracy of the resource pool utilization finally obtained.

[0009] In summary, the accuracy of determining the resource utilization of the cloud resource pool in the prior art is low. Summary of the invention

[0010] The present invention provides a method, device, equipment and medium for determining resource utilization of a cloud resource pool, so as to solve the problems in the prior art.

[0011] The present invention provides a method for determining resource utilization of a cloud resource pool, the method comprising:

[0012] Obtaining each parameter value of a preset parameter of a preset resource collected by each server in the cloud resource pool within a preset time range and at a preset frequency;

[0013] For each server, according to each parameter value of the server and a preset threshold, determine a target parameter value that is not less than the preset threshold, and according to the number of the target parameter values, determine a basic activity index of the server;

[0014] Determine a first number of servers whose basic activity index is not less than a preset basic activity index threshold, and based on the first number and a second number of servers included in the cloud resource pool, determine the resource utilization of the preset resources of the cloud resource pool for the preset threshold.

[0015] Furthermore, the preset resource is any one of CPU resources, memory resources, network resources and disk resources.

[0016] Furthermore, if the preset resource is a CPU resource, the preset parameter is CPU utilization; if the preset resource is a memory resource, the preset parameter is memory utilization; if the preset resource is a network resource, the preset parameter is the network inflow traffic rate or the network outflow traffic rate; if the preset resource is a disk resource, the preset parameter is the disk read rate, or the disk write rate, or the disk allocation rate.

[0017] Further, determining the basic activity index of the server according to the number of the target parameter values ​​includes:

[0018] A basic activity index of the server is determined according to the number of the target parameter values ​​and the total number of parameter values ​​of the server.

[0019] Further, determining the basic activity index of the server according to the number of the target parameter values ​​includes:

[0020] According to the number of the target parameter values ​​and each sub-time range included in the preset time range, determining the number of sub-times within each sub-time range;

[0021] For each sub-time range, if the sub-time range is not less than a preset threshold, the sub-time range is determined to meet the condition;

[0022] The basic activity index of the server is determined according to the number of sub-time ranges that meet the conditions and the total number of sub-time ranges included in the preset time range.

[0023] Accordingly, the present invention provides a device for determining resource utilization of a cloud resource pool, the device comprising:

[0024] An acquisition module, used to acquire each parameter value of a preset parameter of a preset resource acquired by each server in the cloud resource pool at a preset frequency within a preset time range;

[0025] A determination module is used to determine, for each server, a target parameter value that is not less than the preset threshold value based on each parameter value of the server and the preset threshold value, and determine the basic activity index of the server based on the number of the target parameter values; determine a first number of servers whose basic activity index is not less than a preset basic activity index threshold value, and determine the resource utilization of the preset resources of the cloud resource pool for the preset threshold value based on the first number and the second number of servers included in the cloud resource pool.

[0026] Furthermore, the determination module is specifically used to determine the basic activity index of the server according to the number of the target parameter values ​​and the total number of parameter values ​​of the server.

[0027] Furthermore, the determination module is specifically used to determine the number of sub-times within each sub-time range based on the number of target parameter values ​​and each sub-time range included in the preset time range; for the number of sub-times within each sub-time range, if the number of sub-times is not less than a preset number threshold, it is determined that the sub-time range meets the condition; based on the number of sub-time ranges that meet the condition and the total number of sub-time ranges included in the preset time range, the basic activity index of the server is determined.

[0028] Accordingly, the present invention provides an electronic device, comprising a processor and a memory, wherein the memory is used to store program instructions, and the processor is used to implement the steps of any of the above-mentioned methods for determining resource utilization of a cloud resource pool when executing a computer program stored in the memory.

[0029] Accordingly, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of any of the above-mentioned methods for determining resource utilization of a cloud resource pool.

[0030] The present invention provides a method, device, equipment and medium for determining resource utilization of a cloud resource pool, in which each parameter value of a preset parameter of a preset resource collected by each server in the cloud resource pool at a preset frequency within a preset time range is obtained; for each server, a target parameter value not less than the preset threshold is determined according to each parameter value of the server and a preset threshold, and a basic activity index of the server is determined according to the number of the target parameter values; a first number of servers whose basic activity index is not less than a preset basic activity index threshold is determined, and based on the first number and a second number of servers included in the cloud resource pool, the resource utilization of the preset resource of the cloud resource pool for the preset threshold is determined. Since multiple preset thresholds can be set for the preset parameters of the preset resources in the present invention, the basic activity index of each server corresponding to each preset threshold within the preset time range is determined, and the resource utilization of the cloud resource pool corresponding to the preset threshold of the preset resource is determined according to the first number of servers whose basic activity index is not less than the preset basic activity index threshold and the second number of servers included in the cloud resource pool, so that the resource utilization corresponding to the preset parameters of the preset resource can be determined in a more fine-grained manner, and the accuracy of the determined resource utilization is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0032] Figure 1 A schematic diagram of a method for determining resource utilization of a cloud resource pool provided by an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of a process for calculating resource utilization of a cloud resource pool provided in an embodiment of the present invention;

[0034] Figure 3 A schematic diagram of a process flow of calculating a basic activity index provided by an embodiment of the present invention;

[0035] Figure 4 A schematic diagram of a basic activity index of each server provided in an embodiment of the present invention when the preset threshold of CPU utilization is 5%;

[0036] Figure 5 A schematic diagram of a basic activity index of each server provided in an embodiment of the present invention when the preset threshold of CPU utilization is 35%;

[0037] Figure 6 A schematic diagram of a basic activity index of each server provided in an embodiment of the present invention when the preset threshold of CPU utilization is 65%;

[0038] Figure 7 A schematic diagram of a basic activity index of each server provided in an embodiment of the present invention when the preset threshold of CPU utilization is 95%;

[0039] Figure 8 A server distribution diagram of different basic activity indexes when a preset threshold of CPU utilization is 5% provided in an embodiment of the present invention;

[0040] Fig. 9 A server distribution diagram of different basic activity indexes when a preset threshold of CPU utilization is 35% provided in an embodiment of the present invention;

[0041] Fig.10 A server distribution diagram of different basic activity indexes when a preset threshold of CPU utilization is 65% provided in an embodiment of the present invention;

[0042] Fig.11 A server distribution diagram of different basic activity indexes when a preset threshold of CPU utilization is 95% provided in an embodiment of the present invention;

[0043] Fig.12 A schematic diagram of a basic activity index of each server provided in an embodiment of the present invention when the preset threshold of memory utilization is 5%;

[0044] Fig.13 A schematic diagram of a basic activity index of each server provided in an embodiment of the present invention when the preset threshold of memory utilization is 25%;

[0045] Fig.14 A schematic diagram of a basic activity index of each server provided in an embodiment of the present invention when the preset threshold of memory utilization is 45%;

[0046] Fig.15 A schematic diagram of a basic activity index of each server provided in an embodiment of the present invention when the preset threshold of memory utilization is 65%;

[0047] Fig.16 A server distribution diagram of different basic activity indexes when a preset threshold of memory utilization is 5% provided in an embodiment of the present invention;

[0048] Fig.17 A server distribution diagram of different basic activity indexes when a preset threshold of memory utilization is 25% provided in an embodiment of the present invention;

[0049] Fig.18 A server distribution diagram of different basic activity indexes when a preset threshold of memory utilization is 45% provided in an embodiment of the present invention;

[0050] Fig.19 A server distribution diagram of different basic activity indexes when a preset threshold of memory utilization is 65% provided in an embodiment of the present invention;

[0051] Fig. 20 A schematic diagram of the structure of a device for determining resource utilization of a cloud resource pool provided by an embodiment of the present invention;

[0052] Fig.21 A schematic diagram of the structure of another device for determining resource utilization of a cloud resource pool provided by an embodiment of the present invention;

[0053] Fig. 22 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] In order to improve the accuracy of determining the resource utilization of a cloud resource pool, embodiments of the present invention provide a method, apparatus, device and medium for determining the resource utilization of a cloud resource pool.

[0056] Embodiment 1:

[0057] Figure 1 A schematic diagram of a method for determining resource utilization of a cloud resource pool provided in an embodiment of the present invention, the process comprising the following steps:

[0058] S101: Acquire each parameter value of a preset parameter of a preset resource collected by each server in the cloud resource pool within a preset time range and at a preset frequency.

[0059] A method for determining resource utilization of a cloud resource pool provided by an embodiment of the present invention is applied to an electronic device, wherein the electronic device may be a smart terminal such as a tablet, a computer, a smart phone, or a server, wherein the server may be a local server or a cloud server.

[0060] In order to determine the resource utilization of the cloud resource pool, in an embodiment of the present invention, for the preset parameters of the preset resources, the electronic device obtains each parameter value collected by each server in the cloud resource pool within a preset time range and at a preset frequency, wherein the preset time range is pre-set by the user, and the preset time range may be 1 hour, one day, one week, etc., and the embodiment of the present invention does not impose any restrictions on this; the preset frequency is also pre-set by the user, and the preset frequency may be once per second, once per minute, once per hour, etc.

[0061] The number of parameter values ​​collected by each server within the preset time range may be the same or different, that is, deviations are allowed between the sampling times of each server, so that the number of parameter values ​​collected within the preset time range at the preset frequency is different.

[0062] S102: For each server, determine a target parameter value that is not less than the preset threshold value according to each parameter value of the server and the preset threshold value, and determine a basic activity index of the server according to the number of the target parameter values.

[0063] The electronic device pre-stores preset thresholds for preset parameters of preset resources. There are multiple preset thresholds for the preset parameters, and the preset thresholds corresponding to each preset parameter may be the same or different.

[0064] For each server in the cloud resource pool, the electronic device determines each parameter value of the server, determines a target parameter value that is not less than the preset threshold value based on each parameter value of the service and the preset threshold value, and determines the basic activity index of the server in the preset parameters of the preset resources corresponding to the preset threshold value based on the number of target parameter values.

[0065] For example, when the preset parameter of the preset resource is CPU utilization, there are multiple preset thresholds corresponding to the CPU utilization. For each server, based on each CPU utilization of the server and any preset threshold, a target CPU utilization that is not less than the preset threshold is determined, and the basic activity index of the server is determined based on the target CPU utilization.

[0066] S103: Determine a first number of servers whose basic activity index is not less than a preset basic activity index threshold, and based on the first number and a second number of servers included in the cloud resource pool, determine the resource utilization of the preset resources of the cloud resource pool for the preset threshold.

[0067] In order to determine the resource utilization of the cloud resource pool, a preset basic activity index threshold is saved in an embodiment of the present invention. According to the basic activity index corresponding to each server, a first number of servers whose basic activity index is not less than the basic activity index threshold is determined. According to the first number and the second number of servers included in the cloud resource pool, the resource utilization of the preset resources of the cloud resource pool for the preset threshold is determined.

[0068] Specifically, based on a first number of servers whose basic activity index is not less than a basic activity index threshold and a second number of servers included in the cloud resource pool, a ratio of the first number to the second number is determined, and the ratio is determined as the resource utilization of the preset resources of the cloud resource pool for the preset threshold.

[0069] Since the method of the embodiment of the present invention obtains each parameter value of the preset parameter of the preset resource collected by each server in the cloud resource pool at a preset frequency within a preset time range; for each server, according to each parameter value of the server and the preset threshold, a target parameter value not less than the preset threshold is determined, and according to the number of the target parameter values, the basic activity index of the server is determined; the first number of servers whose basic activity index is not less than the preset basic activity index threshold is determined, and based on the first number and the second number of servers included in the cloud resource pool, the resource utilization rate of the preset resource of the cloud resource pool for the preset threshold is determined. Since multiple preset thresholds can be set for the preset parameters of the preset resources in the present invention, the basic activity index of each server corresponding to each preset threshold within the preset time range is determined, and according to the first number of servers whose basic activity index is not less than the preset basic activity index threshold, and the second number of servers included in the cloud resource pool, the resource utilization rate of the cloud resource pool corresponding to the preset threshold of the preset resource is determined, so that the resource utilization corresponding to the preset parameters of the preset resource can be determined in a more fine-grained manner, and the accuracy of the determined resource utilization rate is improved.

[0070] Embodiment 2:

[0071] In order to comprehensively and accurately determine the resource utilization of the cloud resource pool, based on the above embodiment, in an embodiment of the present invention, the preset resource is any one of CPU resources, memory resources, network resources and disk resources.

[0072] In order to more comprehensively and accurately determine the resource utilization of the cloud resource pool, in an embodiment of the present invention, the preset resource is any one of CPU resources, memory resources, network resources and disk resources. In order to determine the resource utilization of each preset resource in the cloud resource pool, in an embodiment of the present invention, if the preset resource is a CPU resource, the preset parameter is the CPU utilization; if the preset resource is a memory resource, the preset parameter is the memory utilization; if the preset resource is a network resource, the preset parameter is the network inflow traffic rate or the network outflow traffic rate; if the preset resource is a disk resource, the preset parameter is the disk read rate, or the disk write rate, or the disk allocation rate.

[0073] That is, when determining the resource utilization of the CPU resources of the cloud resource pool, the CPU utilization of each server in the cloud resource pool collected within a preset time range and at a preset frequency can be obtained, and the method in the above embodiment can be used to determine the resource utilization of the CPU resources of the cloud resource pool.

[0074] When determining the resource utilization of the memory resources of the cloud resource pool, each memory utilization collected by each server in the cloud resource pool within a preset time range and at a preset frequency can be obtained, and the method in the above embodiment can be used to determine the resource utilization of the memory resources of the cloud resource pool.

[0075] When determining the resource utilization of the network resources of the cloud resource pool, each network inflow traffic rate or each network outflow traffic rate collected by each server in the cloud resource pool within a preset time range and at a preset frequency can be obtained, and the method in the above embodiment can be used to determine the resource utilization of the cloud resource pool in memory resources.

[0076] When determining the resource utilization of the disk resources of the cloud resource pool, the read rate of each disk, or the write rate of each disk, or the allocation rate of each disk collected by each server in the cloud resource pool within a preset time range and at a preset frequency can be obtained, and the resource utilization of the cloud resource pool in memory resources can be determined using the method in the above embodiment.

[0077] Embodiment 3:

[0078] In order to determine the basic activity index of the server, based on the above embodiments, in an embodiment of the present invention, determining the basic activity index of the server according to the number of the target parameter values ​​includes:

[0079] A basic activity index of the server is determined according to the number of the target parameter values ​​and the total number of parameter values ​​of the server.

[0080] In order to determine the basic activity index of the server, in an embodiment of the present invention, the ratio of the number of target parameter values ​​to the total number is determined based on the number of target parameter values ​​and the total number of parameter values ​​of the server, and the ratio is determined as the basic activity index of the server.

[0081] Exemplarily, the list R of preset parameters of preset resources is {r1,…,r i ,…r n}, where r i represents the i-th monitoring indicator, n is the number of key monitoring indicators; the server list P composed of each server in the cloud resource pool is {p1,…,p j ,…,p m}, where p j represents the jth physical server, and m is the number of servers in the resource pool.

[0082] Preset parameters for preset resources i The preset threshold composition list is in Represents the preset parameter r iThe kth preset threshold, c represents the preset parameter r i Get the number of preset thresholds for each server in the server list P within the preset time range [T s ,T e ] corresponds to the preset parameter r i Each parameter value X ij ,in Where Q represents the number of parameter values.

[0083] Calculate the value of each server in the server list P under the preset parameter r i Each preset threshold The basic activity index is calculated by the following steps:

[0084] Step 1: Select a physical server p from the server list P j .

[0085] Step 2: From the preset parameter r i List of preset thresholds for Select a preset threshold from

[0086] Step 3: Calculate server p j In monitoring indicators i The threshold point The basic activity index h ijk ,in 0≤h ijk ≤1, the function f(·) is

[0087] Step 4: Repeat steps 2 and 3 until the list is exhausted. All preset thresholds in the server p are obtained j In the preset parameter r i List of basic activity indexes for each preset threshold h ij ={h ij1 ,…,h ijk ,…,h ijc}.

[0088] Step 5: Repeat steps 1 to 4 until the list {p1,…,p j ,…,p m}, and obtain the preset parameters r of each server. i List of basic activity indexes with preset thresholds H i , where H i ={h i1 ,…,h ij ,…,h im}.

[0089] Embodiment 4:

[0090] In order to determine the basic activity index of the server, based on the above embodiments, in an embodiment of the present invention, determining the basic activity index of the server according to the number of the target parameter values ​​includes:

[0091] According to the number of the target parameter values ​​and each sub-time range included in the preset time range, determining the number of sub-times within each sub-time range;

[0092] For each sub-time range, if the sub-time range is not less than a preset threshold, the sub-time range is determined to meet the condition;

[0093] The basic activity index of the server is determined according to the number of sub-time ranges that meet the conditions and the total number of sub-time ranges included in the preset time range.

[0094] In order to determine the basic activity index of the server, in an embodiment of the present invention, when the preset time range is long or the preset frequency is high, the preset time range is divided into multiple sub-time ranges in advance, wherein the number of parameter values ​​corresponding to each sub-time range is not less than a preset number threshold, wherein the preset number threshold is a positive integer.

[0095] After determining the target parameter value among the parameter values ​​collected at a preset frequency within a preset time range, the sub-time range corresponding to the collection time of the target parameter value is determined based on the collection time of the target parameter value and the number of target parameter values ​​and each sub-time range included in the preset time range, thereby determining the sub-number of target parameter values ​​in each sub-time range.

[0096] In order to determine the basic activity index of the server, a condition corresponding to a sub-time range is also preset in an embodiment of the present invention, and the condition is that the number of sub-times corresponding to the sub-time range is not less than a preset number threshold. According to the number of sub-times in each sub-time range and the preset number threshold, the sub-time range in which the number of sub-times is not less than the preset number threshold is determined to be the sub-time range that meets the condition.

[0097] According to the number of sub-time ranges that meet the conditions and the total number of sub-time ranges included in the preset time range, a ratio of the number of sub-time ranges that meet the conditions to the total number is determined, and the ratio is determined as the basic activity index of the server.

[0098] Exemplarily, the calculation of each server in the server list P at the preset parameter r i Each preset threshold The basic activity index is calculated by the following steps:

[0099] Step 1: Determine each server p in the server list P j .

[0100] Step 2: Determine the preset parameter r i List of preset thresholds for Each preset threshold in

[0101] Step 3: Calculate server p j In monitoring indicators i The threshold point The basic activity index h ijk , specifically including the following steps:

[0102] Step 3.1: Set the preset time range [T s ,T e ] is divided into D sub-time ranges, each of which includes {[t s1 ,t e1 ],…,[t sd ,t ed ],…,[t sD ,t eD ]},t s1 =T s ,t eD =T e ,t sd and t ed They represent the start time and end time of the dth sub-time range respectively; the preset quantity threshold is N, and the number of parameter values ​​of each sub-time range must be greater than or equal to N, and the initialization parameters Y=0, d=1.

[0103] Step 3.2: Get a sub-time range [t sd ,t ed ] parameter value Where d_D represents the number of parameter values ​​in the dth subinterval, and the initialization mark w d =0;

[0104] Step 3.3: From parameter values Get a value from the If the difference is greater than or equal to 0, then w d =w d +1; otherwise, w d Remain unchanged.

[0105] Step 3.4: Calculate w d -N, if it is equal to 0, then Y=Y+1, and then execute step 3.6; otherwise, continue to execute step 3.5.

[0106] Step 3.5: From parameter values Get a new value from the collection, repeat steps 3.3 to 3.4 until the collection is traversed. All parameter values ​​in .

[0107] Step 3.6: d=d+1, if d≤D, obtain the parameter value in the new sub-time range, and repeat steps 3.2 to 3.5 until all sub-time ranges are traversed.

[0108] Step 3.7: Calculate server p j In monitoring indicators i The threshold point The basic activity index h ijk ,in Y represents the number of sub-time ranges that satisfy a sub-number not less than a preset number threshold, and D represents the total number of sub-time ranges included in the preset time range.

[0109] Step 4: Repeat steps 2 and 3 until the list is exhausted. All preset thresholds in the server p are obtained j In the preset parameter r i List of basic activity indexes for each preset threshold h ij ={h ij1 ,…,h ijk ,…,h ijc}.

[0110] Step 5: Repeat steps 1 to 4 until the list {p1,…,p j ,…,p m}, and obtain the preset parameters r of each server. i List of basic activity indexes with preset thresholds H i , where H i ={h i1 ,…,h ij ,…,h im}.

[0111] Furthermore, in order to determine the resource utilization of the cloud resource pool, the basic activity index threshold {u1,…,u g ,…,u G}, where u g represents the gth basic activity index threshold, G represents the number of basic activity index thresholds; the basic activity index list H obtained in step 5 i , calculate the resource utilization of the resource pool at each basic activity index threshold in The function f(·) is Traverse all basic activity index thresholds {u1,…,u g ,…,u G}, so as to obtain the s ,T e ] specifies the resource utilization of the resource pool at each preset threshold of the preset parameters.

[0112] Among them, the index The meaning is: preset parameter r i The basic activity index h ijk Greater than or equal to the basic activity index threshold u g Server p j (j=1,2,…,m) is the proportion of all physical servers in the resource pool; where h ijk Indicates that within the preset time range [T s ,T e ], server p j In the preset parameter r i The parameter value on is greater than or equal to the preset threshold % of.

[0113] Because in the embodiment of the present invention, by dividing sub-time ranges and setting quantity thresholds, the utilization of specific resources in the resource pool within the preset time range can be evaluated more evenly, reducing the impact of short-term fluctuations in parameter values ​​during high-frequency collection; and through the preset multiple basic activity index thresholds, the distribution of specific resource utilization of each server in the resource pool can be further determined.

[0114] Embodiment 5:

[0115] The method for determining the resource utilization rate of a cloud resource pool in an embodiment of the present invention is described below through a specific embodiment. Figure 2 A schematic diagram of a process for calculating resource utilization of a cloud resource pool provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the process includes the following steps:

[0116] S201: Obtain a preset parameter list and a server list of preset resources in a resource pool.

[0117] S202: Receive multiple preset thresholds and preset time ranges set for each preset parameter.

[0118] S203: For any preset parameter in the preset parameter list, obtain each parameter value collected by each server in the cloud resource pool within a preset time range and at a preset frequency.

[0119] S204: Calculate the basic activity index of each server at each preset threshold value of each preset parameter.

[0120] S205: Determine whether all items in the preset parameter list of the preset resource have been traversed, if so, proceed to S206, if not, proceed to S203.

[0121] S206: Obtain a basic activity index list of each server at each preset threshold value of each preset parameter.

[0122] S207: According to each preset basic activity index threshold, calculate the resource utilization rate of the resource pool at each basic activity index threshold.

[0123] Figure 3 A schematic diagram of a process flow of calculating a basic activity index provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the process includes the following steps:

[0124] S301: Determine each server in the server list.

[0125] S302: Determine each preset threshold in a preset threshold list of preset parameters.

[0126] S303: Calculate the basic activity index of the server at a preset threshold of preset parameters.

[0127] S304: Determine whether all preset thresholds in the preset threshold list of preset parameters have been traversed, if so, proceed to S305, if not, proceed to S302.

[0128] S305: Determine whether all servers in the server list have been traversed, if so, proceed to S306, if not, proceed to S301.

[0129] S306: Determine the basic activity index of each server at each preset threshold value of each preset parameter.

[0130] Embodiment 5:

[0131] The method for determining the resource utilization rate of the cloud resource pool in the embodiment of the present invention is described below by a specific embodiment, including the following steps:

[0132] Step 1: Get the server list of the cloud resource pool and the preset parameter list of the preset resources.

[0133] Optionally, the preset parameters of the preset resources include but are not limited to CPU utilization, memory utilization, network inflow traffic rate, network outflow traffic rate, disk read rate, disk write rate, and disk allocation rate.

[0134] Exemplarily, taking the data of 266 servers in the cloud resource pool as an example, the preset parameters of the preset resources are CPU utilization and memory utilization.

[0135] Step 2: Get the CPU utilization. There are multiple preset thresholds, including 5%, 35%, 65% and 95%, and the preset time range for CPU utilization is nearly 24 hours. The preset thresholds for memory utilization include 5%, 25%, 45% and 65%, and the preset time range for memory utilization is also nearly 24 hours.

[0136] Step 3: When the preset parameter of the preset resource is CPU utilization, obtain the CPU utilization of each of the 266 servers within the preset time range, the preset frequency is once per hour, and each server has 24 parameter values. Get the partial CPU utilization of 10 servers, as shown in Table 1, in %.

[0137]

[0138] Table 1

[0139] As shown in Table 1, the CPU utilization of server No. 1 is 0.0086 at the 1st hour, 0.056 at the 2nd hour, 0.0516 at the 3rd hour, 0.0833 at the 4th hour, 0.3824 at the 5th hour, and 0.0431 at the 6th hour; the CPU utilization of server No. 2 is 33.0395 at the 1st hour, 30.2886 at the 2nd hour, 39.3409 at the 3rd hour, 24.3349 at the 4th hour, 28.9465 at the 5th hour, and 24.8818 at the 6th hour. The same is true for other servers, and the embodiments of the present invention are not described in detail here.

[0140] Step 4: Calculate the basic activity index of each preset threshold value of CPU utilization of 266 servers. The calculation process of the basic activity index includes the following steps:

[0141] Step 4.1, select server number 1 from the 266 servers.

[0142] In step 4.2, a first preset threshold value 5% is selected from the preset threshold value list {5%, 35%, 65%, 95%} of CPU utilization.

[0143] Step 4.3, calculate the basic activity index of server No. 1 at the preset threshold of 5% in, represents the qth parameter value of the i-th preset parameter of the j-th server within the preset time range, j = {1, 2, ..., 266}, i = {1, 2}; represents the kth preset threshold of the i-th preset parameter, k = {1, 2, 3, 4}; the function f(·) is

[0144] Step 4.4, repeat steps 4.2 and 4.3 until the four preset thresholds in the list {5%, 35%, 65%, 95%} are traversed to obtain the basic activity index list of server No. 1 for each preset threshold on the monitoring indicator CPU utilization.

[0145] Step 4.5, repeat steps 4.1 to 4.4 until all 266 servers are traversed to obtain a list of basic activity indexes of each server at each preset threshold value on CPU utilization.

[0146] Figure 4 A schematic diagram of a basic activity index of each server when the preset threshold of CPU utilization is 5% is provided in an embodiment of the present invention, such as Figure 4 As shown, Figure 4 The figure shows the basic activity index of 266 servers when the preset threshold of CPU utilization is 5%.

[0147] Figure 5 A schematic diagram of a basic activity index of each server when the preset threshold of CPU utilization is 35% is provided in an embodiment of the present invention, such as Figure 5 As shown, Figure 5 The figure shows the basic activity index of 266 servers when the preset threshold of CPU utilization is 35%.

[0148] Figure 6 A schematic diagram of a basic activity index of each server when the preset threshold of CPU utilization is 65% is provided in an embodiment of the present invention, such as Figure 6 As shown, Figure 6 The figure shows the basic activity index of 266 servers when the preset threshold of CPU utilization is 65%.

[0149] Figure 7 A schematic diagram of a basic activity index of each server when the preset threshold of CPU utilization is 95% is provided in an embodiment of the present invention, such as Figure 7 As shown, Figure 7 The figure shows the basic activity index of 266 servers when the preset threshold of CPU utilization is 95%.

[0150] Figure 8 A server distribution diagram of different basic activity indexes when the preset threshold of CPU utilization is 5% provided in an embodiment of the present invention, such as Figure 8 As shown, Figure 8 The number of servers corresponding to each basic activity index when the preset threshold of CPU utilization is 5% is shown in FIG.

[0151] Fig. 9 A server distribution diagram of different basic activity indexes when the preset threshold of CPU utilization is 35% provided in an embodiment of the present invention, such as Fig. 9 As shown, Fig. 9 The number of servers corresponding to each basic activity index when the preset threshold of CPU utilization is 35% is shown in FIG.

[0152] Fig.10 A server distribution diagram of different basic activity indexes when the preset threshold of CPU utilization is 65% provided in an embodiment of the present invention, such as Fig.10 As shown, Fig.10 The number of servers corresponding to each basic activity index when the preset threshold of CPU utilization is 65% is shown in FIG.

[0153] Fig.11 A server distribution diagram of different basic activity indexes when the preset threshold of CPU utilization is 95% provided in an embodiment of the present invention, such as Fig.11 As shown, Fig.11 The number of servers corresponding to each basic activity index when the preset threshold of CPU utilization is 95% is shown in FIG.

[0154] Step 5, repeat steps 3 and 4, select the monitoring indicator memory utilization, and obtain the memory utilization of each of the 266 servers collected at the preset frequency within the preset time range of 24 hours. Obtain partial memory utilization of 10 servers, as shown in Table 2, with the preset frequency of once per hour, in %.

[0155]

[0156]

[0157] Table 2

[0158] As shown in Table 1, the memory utilization of server No. 1 is 4.9248 in the first hour, 4.9283 in the second hour, 4.9133 in the third hour, 4.9022 in the fourth hour, 4.9021 in the fifth hour, and 4.9143 in the sixth hour; the memory utilization of server No. 2 is 27.8825 in the first hour, 27.8905 in the second hour, 27.8762 in the third hour, 27.885 in the fourth hour, 27.8634 in the fifth hour, and 27.8825 in the sixth hour. The same is true for other servers, and the embodiments of the present invention are not described in detail here.

[0159] The basic activity index of each preset threshold {5%, 35%, 65%, 95%} of memory utilization of 266 servers is calculated, so as to obtain a list of basic activity indexes of each preset threshold of CPU utilization and memory utilization of 266 servers in the cloud resource pool.

[0160] Fig.12 A schematic diagram of a basic activity index of each server with a preset threshold of 5% memory utilization provided by an embodiment of the present invention, such as Fig.12 As shown, Fig.12 The figure shows the basic activity index of 266 servers when the preset threshold of CPU utilization is 5%.

[0161] Fig.13 A schematic diagram of a basic activity index of each server when the preset threshold of memory utilization is 25% is provided in an embodiment of the present invention, such as Fig.13 As shown, Fig.13 The figure shows the basic activity index of 266 servers when the preset threshold of memory utilization is 25%.

[0162] Fig.14 A schematic diagram of a basic activity index of each server when the preset threshold of memory utilization is 45% is provided in an embodiment of the present invention, such as Fig.14 As shown, Fig.14 Figure 2 shows the basic activity index of 266 servers at a preset threshold of 45% memory utilization.

[0163] Fig.15 A schematic diagram of a basic activity index of each server when the preset threshold of memory utilization is 65% is provided in an embodiment of the present invention, such as Fig.15 As shown, Fig.15 Figure 2 shows the basic activity index of 266 servers at a preset threshold of 65% memory utilization.

[0164] Fig.16 A server distribution diagram of different basic activity indexes when the preset threshold of memory utilization is 5% provided in an embodiment of the present invention, such as Fig.16 As shown, Fig.16 The number of servers corresponding to each basic activity index when the preset threshold of CPU utilization is 5% is shown in FIG.

[0165] Fig.17 A server distribution diagram of different basic activity indexes when the preset threshold of memory utilization is 25% provided in an embodiment of the present invention, such as Fig.17 As shown, Fig.17 The number of servers corresponding to each basic activity index when the preset threshold of memory utilization is 25% is shown in FIG.

[0166] Fig.18 A server distribution diagram of different basic activity indexes when the preset threshold of memory utilization is 45% provided in an embodiment of the present invention, such as Fig.18 As shown, Fig.18 The number of servers corresponding to each basic activity index when the preset threshold of memory utilization is 45% is shown in FIG.

[0167] Fig.19 A server distribution diagram of different basic activity indexes when the preset threshold of memory utilization is 65% provided in an embodiment of the present invention, such as Fig.19 As shown, Fig.19 The number of servers corresponding to each basic activity index when the preset threshold of memory utilization is 65% is shown in FIG.

[0168] Step 6: Set the basic activity index threshold {0.3, 0.6, 0.9}, and use the basic activity index list obtained in step 5 to calculate the activity index of the resource pool at each basic activity index threshold. The method is as follows:

[0169]

[0170]

[0171]

[0172] Where i = {1, 2}, k = {1, 2, 3, 4}, and the function f(·) is All i and k are traversed to obtain the activity index of the cloud resource pool at each preset threshold of the preset parameters within the preset time range.

[0173] The activity index of the resource pool at each preset threshold of CPU utilization is shown in Table 3:

[0174]

[0175] Table 3

[0176] As shown in Table 3, the value 0.075 in the 4th column of the 5th row in Table 3 indicates that among the 266 servers in the resource pool, 7.5% of the servers have a CPU utilization greater than 95% at a sampling point ratio of more than 90% within a specified 24-hour period. The values ​​of other positions are similar, and the comparison of the embodiment of the present invention is not repeated.

[0177] The activity index of the resource pool at each preset threshold of memory utilization is shown in Table 4:

[0178]

[0179] Table 4

[0180] As shown in Table 4, the value 0.695 of the second column of the second row in Table 4 indicates that among the 266 servers in the resource pool, 69.5% of the servers have a memory utilization rate greater than 5% at sampling points accounting for more than 30% within a specified 24-hour period. The values ​​of other positions are similar, and the comparison of the embodiment of the present invention is not repeated.

[0181] The larger the preset threshold of the preset parameter of the preset resource, the higher the basic active point index threshold, and the larger the corresponding activity index, the higher the resource utilization rate of the resource pool for the preset resource.

[0182] Furthermore, if the CPU utilization is collected at a preset frequency of once per minute, and the preset time range is 24 hours, the preset time range is divided into 24 sub-time ranges with hours as the basic unit, and then the basic activity index in step 4.3 is calculated using the sparse processing method. The specific method is as follows:

[0183] Step 4.3.1: Divide the preset time range of 24 hours into 24 sub-time ranges, preset the number threshold N=10, and initialize the parameters Y=0, d=1.

[0184] Step 4.3.2: Get the parameter value of a sub-time range and initialize the marker w d =0.

[0185] Step 4.3.3: Get a value from the parameter value described in step 4.3.2, calculate the difference between it and the preset threshold, if it is greater than or equal to 0, then w d =w d +1; otherwise, w d Remain unchanged.

[0186] Step 4.3.4: Calculate w d -10, if it is equal to 0, it means that there are 10 parameter values ​​greater than or equal to the preset threshold in this sub-time range, add 1 to the Y value, and then execute step 4.3.6; otherwise, continue to step 4.3.5.

[0187] Step 4.3.5: Get a new value from the parameter value, and repeat steps 4.3.3 to 4.3.4 until all parameter values ​​in the sub-time range are traversed.

[0188] Step 4.3.6: d=d+1, if d≤24, obtain the parameter value of the new sub-time range, and repeat steps 4.3.2 to 4.3.5 until all sub-time ranges are traversed.

[0189] Step 4.3.6: Calculate server p j In the preset parameter r i The preset threshold The basic activity index h ijk ,in

[0190] Since the embodiments of the present invention can perform multi-dimensional and fine-grained active situation awareness on the resource pool, the utilization of specific resources in the resource pool within a preset time range can be determined more accurately, thereby providing support for decision-making in resource pool operation, capacity expansion, resource optimization, and resource scheduling.

[0191] Embodiment 6:

[0192] Based on the above embodiments, Fig. 20 A schematic diagram of a structure of a device for determining resource utilization of a cloud resource pool provided in an embodiment of the present invention, the device comprising:

[0193] An acquisition module 2001 is used to acquire each parameter value of a preset parameter of a preset resource acquired by each server in the cloud resource pool within a preset time range and at a preset frequency;

[0194] Determination module 2002 is used to determine, for each server, a target parameter value that is not less than the preset threshold value based on each parameter value of the server and the preset threshold value, and determine the basic activity index of the server based on the number of the target parameter values; determine a first number of servers whose basic activity index is not less than the preset basic activity index threshold value, and determine the resource utilization of the preset resources of the cloud resource pool for the preset threshold value based on the first number and the second number of servers included in the cloud resource pool.

[0195] Furthermore, the determination module is specifically used to determine the basic activity index of the server according to the number of the target parameter values ​​and the total number of parameter values ​​of the server.

[0196] Furthermore, the determination module is specifically used to determine the number of sub-times within each sub-time range based on the number of target parameter values ​​and each sub-time range included in the preset time range; for the number of sub-times within each sub-time range, if the number of sub-times is not less than a preset number threshold, it is determined that the sub-time range meets the condition; based on the number of sub-time ranges that meet the condition and the total number of sub-time ranges included in the preset time range, the basic activity index of the server is determined.

[0197] The following describes a resource utilization determination device for a cloud resource pool according to an embodiment of the present invention through a specific embodiment. Fig.21 A schematic diagram of the structure of another device for determining resource utilization of a cloud resource pool provided by an embodiment of the present invention, the device comprising:

[0198] The data acquisition module 2101 is used to acquire a list of physical servers in the cloud resource pool, a list of preset parameters of preset resources, and each parameter value of the preset parameters within a preset time range, which is equivalent to the acquisition module 2001 in the above embodiment.

[0199] The parameter setting module 2102 is used to set the configuration parameter values ​​of each preset parameter, such as the preset threshold, preset time range, number of sub-time ranges, basic activity index threshold and quantity threshold.

[0200] The index calculation module 2103 is used to calculate the basic activity index of each server in the resource pool at each preset threshold value on each preset parameter and the resource utilization rate of the resource pool at each basic activity index threshold value, which is equivalent to the determination module 2002 in the above embodiment.

[0201] The data storage module 2104 is used to store the acquired parameter values, configuration parameter values ​​and calculation results.

[0202] The data display module 2105 is used to display parameter values, configuration parameter values, resource utilization of the resource pool at each basic activity index threshold, and the basic activity index of each physical server in the resource pool at each preset threshold on each preset parameter; the display forms include but are not limited to curve charts, radar charts, heat maps, grouped heat maps, multi-group clustered heat maps, frequency distribution histograms, cumulative frequency curve charts, box plots, tables, etc.

[0203] Embodiment 7:

[0204] Fig. 22 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. On the basis of the above embodiments, the present invention further provides an electronic device, including a processor 2201, a communication interface 2202, a memory 2203 and a communication bus 2204, wherein the processor 2201, the communication interface 2202, and the memory 2203 communicate with each other through the communication bus 2204;

[0205] The memory 2203 stores a computer program. When the program is executed by the processor 2201, the processor 2201 performs the following steps:

[0206] Obtaining each parameter value of a preset parameter of a preset resource collected by each server in the cloud resource pool within a preset time range and at a preset frequency;

[0207] For each server, according to each parameter value of the server and a preset threshold, determine a target parameter value that is not less than the preset threshold, and according to the number of the target parameter values, determine a basic activity index of the server;

[0208] Determine a first number of servers whose basic activity index is not less than a preset basic activity index threshold, and based on the first number and a second number of servers included in the cloud resource pool, determine the resource utilization of the preset resources of the cloud resource pool for the preset threshold.

[0209] Furthermore, the processor 2201 is also used for the preset resource to be any one of CPU resources, memory resources, network resources and disk resources.

[0210] Furthermore, the processor 2201 is also used to, if the preset resource is a CPU resource, the preset parameter is the CPU utilization; if the preset resource is a memory resource, the preset parameter is the memory utilization; if the preset resource is a network resource, the preset parameter is the network inflow traffic rate or the network outflow traffic rate; if the preset resource is a disk resource, the preset parameter is the disk read rate, or the disk write rate, or the disk allocation rate.

[0211] Further, the processor 2201 is specifically configured to determine the basic activity index of the server according to the number of the target parameter values, including:

[0212] A basic activity index of the server is determined according to the number of the target parameter values ​​and the total number of parameter values ​​of the server.

[0213] Further, the processor 2201 is specifically configured to determine the basic activity index of the server according to the number of the target parameter values, including:

[0214] According to the number of the target parameter values ​​and each sub-time range included in the preset time range, determining the number of sub-times within each sub-time range;

[0215] For each sub-time range, if the sub-time range is not less than a preset threshold, the sub-time range is determined to meet the condition;

[0216] The basic activity index of the server is determined according to the number of sub-time ranges that meet the conditions and the total number of sub-time ranges included in the preset time range.

[0217] The communication bus mentioned in the above electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0218] The communication interface 2202 is used for communication between the above electronic device and other devices.

[0219] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0220] The above-mentioned processor can be a general-purpose processor, including a central processing unit, a network processor (Network Processor, NP), etc.; it can also be a digital signal processing processor (Digital Signal Processing, DSP), an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc.

[0221] Embodiment 9:

[0222] Based on the above embodiments, the present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program is executed by a processor to perform the following steps:

[0223] Obtaining each parameter value of a preset parameter of a preset resource collected by each server in the cloud resource pool within a preset time range and at a preset frequency;

[0224] For each server, according to each parameter value of the server and a preset threshold, determine a target parameter value that is not less than the preset threshold, and according to the number of the target parameter values, determine a basic activity index of the server;

[0225] Determine a first number of servers whose basic activity index is not less than a preset basic activity index threshold, and based on the first number and a second number of servers included in the cloud resource pool, determine the resource utilization of the preset resources of the cloud resource pool for the preset threshold.

[0226] Furthermore, the preset resource is any one of CPU resources, memory resources, network resources and disk resources.

[0227] Furthermore, if the preset resource is a CPU resource, the preset parameter is CPU utilization; if the preset resource is a memory resource, the preset parameter is memory utilization; if the preset resource is a network resource, the preset parameter is the network inflow traffic rate or the network outflow traffic rate; if the preset resource is a disk resource, the preset parameter is the disk read rate, or the disk write rate, or the disk allocation rate.

[0228] Further, determining the basic activity index of the server according to the number of the target parameter values ​​includes:

[0229] A basic activity index of the server is determined according to the number of the target parameter values ​​and the total number of parameter values ​​of the server.

[0230] Further, determining the basic activity index of the server according to the number of the target parameter values ​​includes:

[0231] According to the number of the target parameter values ​​and each sub-time range included in the preset time range, determining the number of sub-times within each sub-time range;

[0232] For each sub-time range, if the sub-time range is not less than a preset threshold, the sub-time range is determined to meet the condition;

[0233] The basic activity index of the server is determined according to the number of sub-time ranges that meet the conditions and the total number of sub-time ranges included in the preset time range.

[0234] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0235] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0236] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0237] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0238] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for determining resource utilization of a cloud resource pool, It is characterized in that The method comprises: Obtaining each parameter value of a preset parameter of a preset resource collected by each server in the cloud resource pool within a preset time range and at a preset frequency; For each server, according to each parameter value of the server and a preset threshold, determine a target parameter value that is not less than the preset threshold, and according to the number of the target parameter values, determine a basic activity index of the server; Determine a first number of servers whose basic activity index is not less than a preset basic activity index threshold, and determine a resource utilization rate of the preset resource of the cloud resource pool for the preset threshold based on the first number and a second number of servers included in the cloud resource pool; Determining the basic activity index of the server according to the number of the target parameter values ​​includes: According to the number of the target parameter values ​​and each sub-time range included in the preset time range, determining the number of sub-times within each sub-time range; For each sub-time range, if the sub-time range is not less than a preset threshold, the sub-time range is determined to meet the condition; The basic activity index of the server is determined according to the number of sub-time ranges that meet the conditions and the total number of sub-time ranges included in the preset time range.

2. The method according to claim 1, It is characterized in that The preset resource is any one of CPU resources, memory resources, network resources and disk resources.

3. The method according to claim 2, It is characterized in that If the preset resource is a CPU resource, the preset parameter is the CPU utilization; if the preset resource is a memory resource, the preset parameter is the memory utilization; if the preset resource is a network resource, the preset parameter is the network inflow flow rate or the network outflow flow rate; If the preset resource is a disk resource, the preset parameter is a disk read rate, a disk write rate, or a disk allocation rate.

4. The method according to claim 1, It is characterized in that Determining the basic activity index of the server according to the number of the target parameter values ​​includes: A basic activity index of the server is determined according to the number of the target parameter values ​​and the total number of parameter values ​​of the server.

5. A device for determining resource utilization of a cloud resource pool, It is characterized in that The device comprises: An acquisition module, used to acquire each parameter value of a preset parameter of a preset resource acquired by each server in the cloud resource pool at a preset frequency within a preset time range; A determination module is used to determine, for each server, a target parameter value that is not less than a preset threshold value based on each parameter value of the server and a preset threshold value, and determine a basic activity index of the server based on the number of the target parameter values; determine a first number of servers whose basic activity index is not less than a preset basic activity index threshold value, and determine the resource utilization of the preset resources of the cloud resource pool for the preset threshold value based on the first number and a second number of servers included in the cloud resource pool; the determination module is specifically used to determine the number of sub-times within each sub-time range based on the number of the target parameter values ​​and each sub-time range included in the preset time range; for the number of sub-times within each sub-time range, if the number of sub-times is not less than the preset number threshold value, determine that the sub-time range meets the condition; determine the basic activity index of the server based on the number of sub-time ranges that meet the condition and the total number of sub-time ranges included in the preset time range.

6. The device according to claim 5, It is characterized in that The determination module is specifically used to determine the basic activity index of the server according to the number of the target parameter values ​​and the total number of parameter values ​​of the server.

7. An electronic device, It is characterized in that include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; A computer program is stored in the memory, and when the program is executed by the processor, the processor executes the method for determining resource utilization of a cloud resource pool according to any one of claims 1 to 4.

8. A computer-readable storage medium, It is characterized in that It stores a computer program executable by a processor, and when the program runs on the processor, the processor executes the resource utilization determination method of the cloud resource pool according to any one of claims 1 to 4.

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