A method for allocating cloud server resources

Abstract

The application provides a cloud server resource allocation method, which can realize reasonable allocation of cloud desktop products under limited server resources. The method has the following technical effects: 1. Users can purchase and immediately allocate desktops on the cloud; 2. A large number of blind pre-creation of product configurations is avoided, reducing the pressure on the server; 3. The creation of configuration desktops with low use frequency is reduced to avoid resource occupation and storage; 4. The configuration image can be flexibly allocated according to the specific use condition, achieving optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cloud desktop, and particularly relates to a cloud server resource allocation method. BACKGROUND

[0002] The cloud desktop is deployed on a cloud server, and a user purchases a corresponding cloud service (a cloud desktop product) through a purchase portal opened by a cloud server enterprise. There are two schemes on the market: 1. The user purchases the cloud desktop product when the user needs the cloud desktop product, and the cloud server creates an initialized cloud desktop product to provide the user with use; and 2. The enterprise creates the cloud desktop product in advance, and the user is directly allocated the corresponding cloud desktop product when the user needs the cloud desktop product. The two schemes have advantages and disadvantages. The scheme 1 has a long waiting time of 3-4 minutes from the purchase of the cloud desktop product to the allocation of the cloud desktop product to the user. The scheme 2 needs to create a large number of cloud desktop products in advance, and the user can be directly allocated the corresponding cloud desktop product after the purchase. However, the biggest problem of the scheme 2 is that a large number of cloud server resources need to be prepared in advance, and the cloud desktop products created in advance do not necessarily meet the current user's configuration requirements, and a large number of cloud server resources are not fully utilized. Based on the current situation, a new allocation method is needed, which can allocate the cloud desktop product to the user quickly and does not occupy a large number of cloud server resources, so as to avoid the situation that a large number of cloud desktop products are created but are not purchased by the user for a long time.

[0003] Therefore, the prior art has defects and needs to be improved. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a cloud server resource allocation method, which can allocate the cloud desktop product to the user quickly and does not occupy a large number of cloud server resources, so as to avoid the situation that a large number of cloud desktop products are created but are not purchased by the user for a long time.

[0005] The technical scheme of the present application is as follows: a cloud server resource allocation method is provided, which comprises the following steps.

[0006] S1: The number of A-class configuration cloud desktop products in the current cloud server and the number of idle A-class configuration cloud desktop products are obtained. In the present scheme, the A-class configuration cloud desktop product is any kind of configuration cloud desktop product.

[0007] S2: The number of new users of the A-class configuration cloud desktop product in the past K hours is obtained, and the new users are profiled, including: a first age interval proportion and a first occupation proportion; wherein 48>=K>=0.1. Preferably, 12>=K>=1.

[0008] The first age interval proportion is obtained by dividing the new users according to the age stage.

[0009] The first occupation ratio: the ratio of the newly added users according to the occupation.

[0010] S3: portrait the existing non-idle A-class configured cloud desktop products in the cloud server, including: the second age interval ratio, the second occupation ratio, the historical ratio, and the historical increment ratio of four dimensions; and obtaining the dimension ratio data of the A-class configured cloud desktop products.

[0011] The second age interval ratio: the ratio of the A-class configured cloud desktop products of the historical users of the cloud server according to the age stage.

[0012] The second occupation ratio: the ratio of the A-class configured cloud desktop products of the historical users of the cloud server according to the occupation.

[0013] The historical ratio: the ratio of the A-class configured cloud desktop products of the cloud server that have been allocated to the users.

[0014] The historical increment ratio: according to the increment of the A-class configured cloud desktop products in the past N days, the increment ratio of the A-class configured cloud desktop products in N days is obtained; wherein 30≥N≥1. Preferably, 10≥N≥5, and in a more preferred embodiment, the N=7.

[0015] S4: calculating the final increment ratio of the A-class configured cloud desktop products at the current time.

[0016] The final increment ratio = (the ratio of the same age interval of the first age ratio and the second age ratio multiplied and then added) * (10±2)% + (the ratio of the same occupation of the first occupation ratio and the second occupation ratio multiplied and then added) * (40±8)% + the historical ratio * (30±6)% + the historical increment ratio * (10±2)%. Preferably, the final increment ratio = (the ratio of the same age interval of the first age ratio and the second age ratio multiplied and then added) * (10±1)% + (the ratio of the same occupation of the first occupation ratio and the second occupation ratio multiplied and then added) * (40±4)% + the historical ratio * (30±3)% + the historical increment ratio * (10±1)%. In a more preferred embodiment, the final increment ratio = (the ratio of the same age interval of the first age ratio and the second age ratio multiplied and then added) * 10% + (the ratio of the same occupation of the first occupation ratio and the second occupation ratio multiplied and then added) * 40% + the historical ratio * 30% + the historical increment ratio * 10%.

[0017] S5: obtaining the total number of resources in the cloud server, and setting the ratio M of the A-class configured cloud desktop products that can be created to the total number of resources of the cloud server; wherein 20%≥M≥10%. Preferably, M=15%.

[0018] S6: Calculate the amount of advance creation required for the A-class configured cloud desktop product at the current time; the amount of advance creation = total number of resources * M * final increment proportion.

[0019] S7: Compare the number of idle A-class configured cloud desktop products with the amount of advance creation of the A-class configured cloud desktop product.

[0020] S8: If the amount of advance creation of the A-class configured cloud desktop product is greater than the number of idle A-class configured cloud desktop products, obtain the creation amount required for the A-class configured cloud desktop product, and then create the A-class configured cloud desktop product according to the creation process of the A-class configured cloud desktop product and the creation amount required for the A-class configured cloud desktop product.

[0021] The A-class configured cloud desktop product is a green pepper cloud.

[0022] Compared with the prior art, the prior art creates cloud desktops in advance to cover the future possible purchase of cloud desktops by a large number of server resources, and the prior art wastes a large amount of resources during long-term operation of the cloud desktop product (at present, the amount of advance creation of cloud desktops accounts for 35-45% of the proportion of server resources). The present application aims to solve the problem of timely allocation and provision of corresponding cloud desktop products when a large number of users purchase cloud desktop products on demand, without occupying a large amount of server resources in advance. Even if the server resources remain above 85% utilization, the problem of blind expansion of servers is solved.

[0023] By adopting the above scheme, the present application provides a method for allocating cloud server resources, which can realize reasonable allocation of cloud desktop products under limited server resources. The present application has the following technical effects:

[0024] 1. Users can purchase and immediately allocate desktops to the cloud;

[0025] 2. A large number of blind pre-creation of various product configurations is avoided, reducing the pressure on servers;

[0026] 3. The creation of low-frequency configuration desktops is reduced to avoid resource occupation and storage;

[0027] 4. The configuration image can be flexibly allocated according to the specific use case to achieve optimization. BRIEF DESCRIPTION OF DRAWINGS

[0028] Figure 1 The flowchart of the present application;

[0029] Figure 2 The image of two cloud desktop products in one embodiment of the present application. DETAILED DESCRIPTION

[0030] The present application will be described in detail below in combination with the drawings and specific embodiments.

[0031] Referring to Figure 1 and Figure 2 The application provides a cloud server resource allocation method, comprising the following steps.

[0032] S1: Obtain the number of A-class configured cloud desktop products in the current cloud server and the number of idle A-class configured cloud desktop products. The A-class configured cloud desktop product is a green pepper cloud.

[0033] S2: Obtain the number of newly added users of the A-class configured cloud desktop product in the past K hours, and profile the newly added users, including: first age interval proportion, first occupation proportion; wherein 48≥K≥0.1. Preferably, 12≥K≥1.

[0034] First age interval proportion: the newly added users are divided by age stage to obtain the proportion.

[0035] First occupation proportion: the newly added users are divided by occupation to obtain the proportion.

[0036] S3: Profile the existing non-idle A-class configured cloud desktop product in the cloud server, including: second age interval proportion, second occupation proportion, historical proportion, and historical increment proportion four dimensions; obtain the dimension proportion data of the A-class configured cloud desktop product.

[0037] Second age interval proportion: the A-class configured cloud desktop product of the historical user of the cloud server is divided by age stage to obtain the proportion.

[0038] Second occupation proportion: the A-class configured cloud desktop product of the historical user of the cloud server is divided by occupation to obtain the proportion.

[0039] Historical proportion: the proportion of the A-class configured cloud desktop product of the existing user of the cloud server.

[0040] Historical increment proportion: according to the increment of the A-class configured cloud desktop product in the past N days, the increment proportion of the A-class configured cloud desktop product in N days is obtained; wherein 30≥N≥1. Preferably, 10≥N≥5, in a more preferred embodiment, the N=7.

[0041] S4: Calculate the final increment proportion of the A-class configured cloud desktop product at the current time.

[0042] Final increment proportion = (first age proportion multiplied by second age proportion in the same age interval and then added) * (10±2)% + (first occupation proportion multiplied by second occupation proportion in the same occupation and then added) * (40±8)% + historical proportion * (30±6)% + historical increment proportion * (10±2)%. Preferably, final increment proportion = (first age proportion multiplied by second age proportion in the same age interval and then added) * (10±1)% + (first occupation proportion multiplied by second occupation proportion in the same occupation and then added) * (40±4)% + historical proportion * (30±3)% + historical increment proportion * (10±1)%. In a more preferred embodiment, final increment proportion = (first age proportion multiplied by second age proportion in the same age interval and then added) * 10% + (first occupation proportion multiplied by second occupation proportion in the same occupation and then added) * 40% + historical proportion * 30% + historical increment proportion * 10%.

[0043] S5: Obtain the total number of resources in the cloud server, and set the proportion M of the cloud desktop product of the A-class configuration that can be created in the total number of resources of the cloud server; wherein 20% ≥ M ≥ 10%. Preferably, M = 15%.

[0044] S6: Calculate the amount of advance creation required by the cloud desktop product of the A-class configuration at the current time; the amount of advance creation = total number of resources * M * final increment proportion.

[0045] S7: Compare the number of idle cloud desktop products of the A-class configuration with the amount of advance creation of the cloud desktop product of the A-class configuration.

[0046] S8: If the amount of advance creation of the cloud desktop product of the A-class configuration is greater than the number of idle cloud desktop products of the A-class configuration, obtain the creation amount required by the cloud desktop product of the A-class configuration, and then create the cloud desktop product of the A-class configuration according to the creation process of the cloud desktop product of the A-class configuration and the creation amount required by the cloud desktop product of the A-class configuration.

[0047] Compared with the prior art of covering the future possible purchase of cloud desktop by users with a large number of server resources to create cloud desktop in advance, the prior art wastes a large amount of resources when the cloud desktop product is operated for a long time (the proportion of the server resources required to create cloud desktop in advance is currently 35-45%). The present application aims to solve the problem of timely allocation and provision of corresponding cloud desktop products when a large number of users purchase cloud desktop products on demand, without occupying a large amount of server resources in advance. Even if the server resources remain above 85% utilization, the problem of blindly expanding servers is solved.

[0048] Embodiment 1

[0049] Taking the cloud desktop product A of the A-class configuration as an example

[0050] The 15% increment creation of the existing cloud server platform is 100 machines (i.e., the maximum support creation amount).

[0051] Through the cloud server platform calculation, the user increment of cloud desktop product A in the past 2 days is 100 people.

[0052] The current age distribution interval of these increments: less than 18 years old (10 people, accounting for 10%), 18-30 years old (50 people, accounting for 50%), 31-45 years old (30 people, accounting for 30%), and more than 45 years old (10 people, accounting for 10%);

[0053] The occupation distribution of these increments: 30 students, 30 designers, 10 teachers, 20 development engineers, and 10 micro-marketers.

[0054] The dimension proportion data of cloud desktop product A.

[0055] Age proportion: less than 18 years old (10%), 18-30 years old (50%), 31-45 years old (30%), and more than 45 years old (10%)

[0056] User occupation proportion: 30% students, 30% designers, 10% teachers, 20% development engineers, and 10% micro-marketers;

[0057] Historical proportion: 40%;

[0058] Historical increment proportion: 30%;

[0059] Present calculation: the increment of cloud desktop product A

[0060] The current configuration increment proportion = (10%*10%+50%*50%+30%*30%+10%*10%)*10%+ (30%*30%+30%*30%+10%*10%+20%*20%+10%*10%)*40%+40%*30%+30%*10%=28.2%

[0061] Finally, the number of cloud desktop product A that needs to be created in advance is calculated = 100*28.2%, which is approximately equal to 28.

[0062] Compare the number of idle cloud desktop product A with 28 through the platform calculation, if it is greater than 28, no creation is needed, if it is less than 28, create the number of cloud desktop product A that is different from the two.

[0063] In summary, the present application provides a cloud server resource allocation method, which can realize the reasonable allocation of cloud desktop products under limited server resources. It has the following technical effects:

[0064] 1. Users can purchase and immediately allocate desktops on the cloud;

[0065] 2. Not a large blind advance to create each product configuration, reduce server pressure;

[0066] 3. Reduce the creation of low-frequency configuration desktop to avoid resource occupation and standby;

[0067] 4. The configuration image can be flexibly allocated according to the specific use, and the optimization is achieved.

[0068] The above is only a preferred embodiment of the present application, and is not used to limit the present application, any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for allocating cloud server resources, characterized in that, Includes the following steps: S1: Get the number of cloud desktop products with Class A configuration and the number of idle cloud desktop products with Class A configuration in the current cloud server; S2: Obtain the number of new users of cloud desktop products with configuration A in the past K hours, and create a profile of the new users, including: the percentage of the first age group and the percentage of the first occupation; where 48≥K≥0.1; Percentage of new users by age group: Percentage of new users divided by age group; First occupation percentage: The percentage of new users is calculated by occupation. S3: Create a profile of the existing non-idle Class A cloud desktop products on the cloud server, including four dimensions: percentage of the second age group, percentage of the second occupation, historical percentage, and historical incremental percentage; obtain the percentage data of each dimension of the Class A cloud desktop products. The percentage of users in the second age group: The percentage of historical cloud server users with Category A cloud desktop products is calculated by age group. Second occupational percentage: The percentage of cloud desktop products with Category A configuration for historical cloud server users is determined by occupation. Historical percentage: The percentage of cloud desktop products with Class A configuration that have been allocated to users on cloud servers; Historical incremental percentage: Based on the incremental growth of cloud desktop products with Class A configuration over the past N days, the incremental percentage of cloud desktop products with Class A configuration over the past N days is calculated; where 30≥N≥1; S4: Calculate the final incremental percentage of cloud desktop products with Class A configuration at the current moment; Final incremental percentage = (the product of the percentages of the first age group and the percentages of the second age group in the same age range, then summed) * (10±2)% + (the product of the percentages of the first occupation and the percentages of the second occupation in the same occupation, then summed) * (40±8)% + historical percentage * (30±6)% + historical incremental percentage * (10±2)%; S5: Obtain the total number of resources in the cloud server, and set the percentage M of the total cloud server resources that can be created with Class A cloud desktop products; where 20% ≥ M ≥ 10%; S6: Calculate the amount of cloud desktop products with configuration A that need to be created in advance at the current moment; the amount of resources created in advance = total resources * M * final incremental percentage; S7: Compare the number of idle Class A cloud desktop products with the number of Class A cloud desktop products created in advance; S8: If the number of cloud desktop products configured in A is created in advance is greater than the number of idle cloud desktop products configured in A, the number of cloud desktop products configured in A that need to be created is obtained. Then, in combination with the creation process of cloud desktop products configured in A, cloud desktop products configured in A are created according to the number of cloud desktop products configured in A that need to be created.

2. The method for allocating cloud server resources according to claim 1, characterized in that, The cloud desktop product configured in Category A is Qingjiao Cloud.

3. The method for allocating cloud server resources according to claim 1, characterized in that, In step S4, the final incremental percentage = (the percentage of the first age and the percentage of the second age in the same age range multiplied and then added) * (10 ± 1)% + (the percentage of the first occupation and the percentage of the second occupation in the same occupation multiplied and then added) * (40 ± 4)% + historical percentage * (30 ± 3)% + historical incremental percentage * (10 ± 1)%.

4. A method for allocating cloud server resources according to claim 1 or 3, characterized in that, In step S4, the final incremental percentage = (the percentage of the first age and the percentage of the second age in the same age range multiplied and then added) * 10% + (the percentage of the first occupation and the percentage of the second occupation in the same occupation multiplied and then added) * 40% + historical percentage * 30% + historical incremental percentage * 10%.

5. A method for allocating cloud server resources according to claim 1, characterized in that, in, 10≥N≥5。 6. The method for allocating cloud server resources according to claim 1, characterized in that, in, The value of N is 7.

7. A method for allocating cloud server resources according to claim 1, characterized in that, in, M=15%。 8. A method for allocating cloud server resources according to claim 1, characterized in that, in, 12≥K≥1。

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