Cloud computing resource allocation method and system

Abstract

The invention particularly relates to a cloud computing resource allocation method and system. According to the cloud computing resource allocation method and system, historical business data is collected, demand data is collected and analyzed in real time, and the demand trend is predicted in combination with the historical business data; using conditions and states of various resources in the cloud computing environment are monitored in real time; formulating an optimized resource allocation strategy according to a demand analysis and prediction result in combination with resource state monitoring information; and executing resource allocation operation, monitoring the use condition of the cloud computing resources in real time, collecting user feedback information in real time, optimizing a resource allocation strategy in real time according to the resource monitoring data and the user feedback information, and dynamically adjusting the allocation of the cloud computing resources so as to improve the resource utilization rate. According to the cloud computing resource allocation method and system, the resource allocation rule is optimized through specific resource allocation, efficient utilization of resources is achieved, and the performance and cost effectiveness of cloud computing services are improved.

Description

Technical Field

[0001] The present invention relates to the field of cloud computing technology, and in particular to a cloud computing resource allocation method and system. Background Art

[0002] As a flexible and scalable computing model, cloud computing provides convenient computing resources for enterprises and individuals. However, the allocation and utilization of cloud computing resources still face challenges.

[0003] Traditional resource allocation methods are often based on static strategies or simple rules, which fail to fully consider actual application requirements and resource status, resulting in resource waste or limited service performance.

[0004] Based on the above problems, the present invention proposes a cloud computing resource allocation method and system, aiming to establish a standard cloud computing resource allocation and optimization method rule. Summary of the invention

[0005] In order to overcome the deficiencies of the prior art, the present invention provides a simple and efficient cloud computing resource allocation method and system.

[0006] The present invention is achieved through the following technical solutions:

[0007] A cloud computing resource allocation method comprises the following steps:

[0008] Step S1: Demand analysis and forecasting

[0009] Collect historical business data, including resource usage peak, stage average, business load, and user access volume;

[0010] Collect and analyze demand data of various applications in the cloud computing environment in real time, and combine historical business data to predict the demand trend of resources for each application in the future time period t;

[0011] Step S2: Resource status monitoring

[0012] Monitor the usage and status of various resources in the cloud computing environment in real time, and identify whether the resources are idle, busy, or overloaded;

[0013] Step S3: Optimization strategy formulation

[0014] Based on the results of demand analysis and forecasting, combined with resource status monitoring information, formulate optimized resource allocation strategies, including increasing resource supply during peak business demand and reducing resource supply during low business demand;

[0015] Step S4: Dynamic adjustment and execution

[0016] Execute resource allocation operations, monitor the usage of cloud computing resources in real time, collect user feedback information in real time, and optimize resource allocation strategies in real time based on resource monitoring data and user feedback information, and dynamically adjust the allocation of cloud computing resources to improve resource utilization.

[0017] In step S3, the resource allocation strategy includes low load (capacity reduction) judgment standard rules, capacity expansion judgment standard rules and cancellation standard rules;

[0018] The low load (capacity reduction) judgment standard rules are as follows:

[0019] 1). vCPU low load standard: peak vCPU usage ≤ 40% and vCPU average usage ≤ 20%;

[0020] 2). Memory low load standard: peak memory usage ≤ 40% and average memory usage ≤ 30%;

[0021] 3). Storage low load standard: storage utilization rate ≤ 60%;

[0022] The low load demand is determined based on the 8:00-12:00 and 15:00-18:00 periods on working days. The resource utilization value is captured every 5 minutes. The average utilization value of all values ​​is calculated every 5 working days, and the peak value is the highest value within 5 working days.

[0023] Except for servers allocated according to the minimum configuration, when any of the three indicators of vCPU, memory and disk within the period reaches the above-mentioned low load judgment standard rules, it is judged as low load demand and resource supply is reduced.

[0024] The expansion judgment standard rules are as follows:

[0025] 1) When the average usage rate of the virtual machine's vCPU during the 8:00-12:00 and 15:00-18:00 periods in the last five working days is not less than 50%, apply for vCPU expansion of IaaS resources;

[0026] 2) When the average usage rate of the virtual machine's memory during the 8:00-12:00 and 15:00-18:00 periods in the last five working days is not less than 60%, apply for memory expansion of IaaS resources;

[0027] ⒊) When the current storage usage of the virtual machine is not less than 90%, apply for storage expansion of IaaS resources

[0028] The revocation standard rules are as follows:

[0029] 1) The cloud resources have been released for more than 90 days, but security testing has not been completed and the resources have not been put into operation;

[0030] 2). The virtual machine resources are no longer used due to the user unit's reasons;

[0031] When one of the above conditions is met, apply to revoke the cloud resources of the virtual machine.

[0032] The peak vCPU usage is the highest value among all records of the day;

[0033] The vCPU average usage is the average of all recorded values ​​for the day;

[0034] The peak memory usage is the highest value recorded on that day.

[0035] The average memory usage is the average of all recorded values ​​for the day;

[0036] The storage usage ratio is the used storage capacity / allocated storage capacity.

[0037] In step S4, business operation data and user feedback information are collected in real time as factors for optimizing resource allocation strategies, and the process jumps to step S1 to continuously improve resource allocation efficiency and business service quality through continuous iteration and optimization.

[0038] A system based on the cloud computing resource allocation method comprises:

[0039] The demand analysis and prediction module is responsible for collecting historical business data, including resource usage peak, stage average, business load and user access volume; at the same time, it collects and analyzes the demand data of each application in the cloud computing environment in real time, and combines historical business data to predict the demand trend of each application for resources in the future time period t;

[0040] The resource status monitoring module is responsible for real-time monitoring of the usage and status of various resources in the cloud computing environment and identifying the idle, busy or overloaded status of resources;

[0041] The optimization strategy formulation module is responsible for formulating optimized resource allocation strategies based on the results of demand analysis and prediction, combined with resource status monitoring information, and taking into account resource availability, application priority, and service level agreement (SLA) requirements. This includes increasing resource supply during peak business demand and reducing resource supply during low business demand.

[0042] The dynamic adjustment and execution module is responsible for executing resource allocation operations, monitoring the usage of cloud computing resources in real time, collecting user feedback information in real time, and optimizing resource allocation strategies in real time based on resource monitoring data and user feedback information, and dynamically adjusting the allocation of cloud computing resources to improve resource utilization.

[0043] A cloud computing resource allocation device, characterized in that it includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the above-mentioned method steps when executing the computer program.

[0044] A readable storage medium, characterized in that: a computer program is stored on the readable storage medium, and the computer program implements the above method steps when executed by a processor.

[0045] The beneficial effects of the present invention are as follows: the cloud computing resource allocation method and system optimize resource allocation rules through specific resource allocation, thereby achieving efficient resource utilization and improving the performance and cost-effectiveness of cloud computing services. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Attached Figure 1 Schematic diagram of the cloud computing resource allocation method of the present invention. DETAILED DESCRIPTION

[0048] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0049] The cloud computing resource allocation method comprises the following steps:

[0050] Step S1: Demand analysis and forecasting

[0051] Collect historical business data, including resource usage peak, stage average, business load, and user access volume;

[0052] Collect and analyze demand data of various applications in the cloud computing environment in real time, and combine historical business data to predict the demand trend of resources for each application in the future time period t;

[0053] Step S2: Resource status monitoring

[0054] Monitor the usage and status of various resources in the cloud computing environment in real time, and identify whether the resources are idle, busy, or overloaded;

[0055] Step S3: Optimization strategy formulation

[0056] Based on the results of demand analysis and forecasting, combined with resource status monitoring information, comprehensive consideration of resource availability, application priority and service level agreement (SLA) requirements, formulate an optimized resource allocation strategy, including increasing resource supply during peak business demand and reducing resource supply during low business demand.

[0057] Step S4: Dynamic adjustment and execution

[0058] Execute resource allocation operations, monitor the usage of cloud computing resources in real time, collect user feedback information in real time, and optimize resource allocation strategies in real time based on resource monitoring data and user feedback information, and dynamically adjust the allocation of cloud computing resources to improve resource utilization.

[0059] The dynamic adjustment mechanism is an adaptive resource adjustment mechanism that adjusts resource allocation in real time according to business demand forecast results, and takes into account resource constraints and cost-effectiveness to ensure the economy and feasibility of resource allocation.

[0060] Real-time monitoring of cloud computing resource usage is achieved through cloud computing management platforms or third-party monitoring tools, and efficient resource monitoring algorithms are designed to ensure the accuracy and real-time nature of monitoring data.

[0061] The continuous improvement strategy includes establishing a feedback mechanism, collecting business operation data and user feedback, using data analysis methods to identify problems and bottlenecks in resource allocation, and continuously improving and optimizing the resource allocation strategy based on the analysis results.

[0062] Resource allocation strategies include low load (capacity reduction) judgment standard rules, capacity expansion judgment standard rules, and cancellation standard rules;

[0063] The low load (capacity reduction) judgment standard rules are as follows:

[0064] 1). vCPU low load standard: peak vCPU usage ≤ 40% and vCPU average usage ≤ 20%;

[0065] 2). Memory low load standard: peak memory usage ≤ 40% and average memory usage ≤ 30%;

[0066] 3). Storage low load standard: storage utilization rate ≤ 60%;

[0067] The low load demand is determined based on the 8:00-12:00 and 15:00-18:00 periods on working days. The resource utilization value is captured every 5 minutes. The average utilization value of all values ​​is calculated every 5 working days, and the peak value is the highest value within 5 working days.

[0068] Except for servers allocated according to the minimum configuration, when any of the three indicators of vCPU, memory and disk within the period reaches the above-mentioned low load judgment standard rules, it is judged as low load demand and resource supply is reduced.

[0069] The expansion judgment standard rules are as follows:

[0070] 1) When the average usage rate of the virtual machine's vCPU during the 8:00-12:00 and 15:00-18:00 periods in the last five working days is not less than 50%, apply for vCPU expansion of IaaS resources;

[0071] 2) When the average usage rate of the virtual machine's memory during the 8:00-12:00 and 15:00-18:00 periods in the last five working days is not less than 60%, apply for memory expansion of IaaS resources;

[0072] ⒊) When the current storage usage of the virtual machine is not less than 90%, apply for storage expansion of IaaS resources

[0073] The revocation standard rules are as follows:

[0074] 1) The cloud resources have been released for more than 90 days, but security testing has not been completed and the resources have not been put into operation;

[0075] 2). The virtual machine resources are no longer used due to the user unit's reasons;

[0076] When one of the above conditions is met, apply to revoke the cloud resources of the virtual machine.

[0077] The peak vCPU usage is the highest value among all records of the day;

[0078] The vCPU average usage is the average of all recorded values ​​for the day;

[0079] The peak memory usage is the highest value recorded on that day.

[0080] The average memory usage is the average of all recorded values ​​for the day;

[0081] The storage usage ratio is the used storage capacity / allocated storage capacity.

[0082] In step S4, business operation data and user feedback information are collected in real time as factors for optimizing the resource allocation strategy, and the process jumps to step S1. Through continuous iteration and optimization, the allocation of cloud computing resources is dynamically adjusted to continuously improve resource allocation efficiency and business service quality.

[0083] A system based on the cloud computing resource allocation method includes:

[0084] The demand analysis and prediction module is responsible for collecting historical business data, including resource usage peak, stage average, business load and user access volume; at the same time, it collects and analyzes the demand data of each application in the cloud computing environment in real time, and combines historical business data to predict the demand trend of each application for resources in the future time period t;

[0085] The resource status monitoring module is responsible for real-time monitoring of the usage and status of various resources in the cloud computing environment and identifying the idle, busy or overloaded status of resources;

[0086] The optimization strategy formulation module is responsible for formulating optimized resource allocation strategies based on the results of demand analysis and prediction, combined with resource status monitoring information, and taking into account resource availability, application priority, and service level agreement (SLA) requirements. This includes increasing resource supply during peak business demand and reducing resource supply during low business demand.

[0087] The dynamic adjustment and execution module is responsible for executing resource allocation operations, monitoring the usage of cloud computing resources in real time, collecting user feedback information in real time, and optimizing resource allocation strategies in real time based on resource monitoring data and user feedback information, and dynamically adjusting the allocation of cloud computing resources to improve resource utilization.

[0088] The cloud computing resource allocation device includes a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the above method steps when executing the computer program.

[0089] The readable storage medium stores a computer program, and when the computer program is executed by a processor, the above method steps are implemented.

[0090] Compared with the prior art, the cloud computing resource allocation method and system has the following characteristics:

[0091] (1) When formulating resource allocation strategies, we comprehensively consider multiple factors such as application peak, average, long-term application demand, resource status, SLA, etc., and comprehensively evaluate resource allocation to maximize resource utilization.

[0092] (2) Through real-time monitoring data analysis, optimized resource allocation strategies are formulated to improve resource utilization.

[0093] (3) Dynamically adjust resource allocation strategies based on actual application requirements and changes in resource status to achieve efficient resource utilization.

[0094] The embodiment described above is only one specific implementation of the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A cloud computing resource allocation method, characterized in that: The following steps are involved: Step S1: Demand analysis and forecasting Collect historical business data, including resource usage peak, stage average, business load, and user access volume; Collect and analyze demand data of various applications in the cloud computing environment in real time, and combine historical business data to predict the demand trend of resources for each application in the future time period t; Step S2: Resource status monitoring Monitor the usage and status of various resources in the cloud computing environment in real time, and identify whether the resources are idle, busy, or overloaded; Step S3: Optimization strategy formulation Based on the results of demand analysis and forecasting, combined with resource status monitoring information, formulate optimized resource allocation strategies, including increasing resource supply during peak business demand and reducing resource supply during low business demand; Step S4: Dynamic adjustment and execution Execute resource allocation operations, monitor the usage of cloud computing resources in real time, collect user feedback information in real time, and optimize resource allocation strategies in real time based on resource monitoring data and user feedback information, and dynamically adjust the allocation of cloud computing resources to improve resource utilization.

2. The cloud computing resource allocation method according to claim 1, characterized in that: The resource allocation strategy is provided with low load judgment standard rules, which are as follows: 1). vCPU low load standard: peak vCPU usage ≤ 40% and vCPU average usage ≤ 20%; 2). Memory low load standard: peak memory usage ≤ 40% and average memory usage ≤ 30%; 3). Storage low load standard: storage utilization rate ≤ 60%; The low load demand is determined based on the 8:00-12:00 and 15:00-18:00 periods on working days. The resource utilization value is captured every 5 minutes. The average utilization value of all values ​​is calculated every 5 working days, and the peak value is the highest value within 5 working days. Except for servers allocated according to the minimum configuration, when any of the three indicators of vCPU, memory and disk within the period reaches the above-mentioned low load judgment standard rules, it is judged as low load demand and resource supply is reduced.

3. The cloud computing resource allocation method according to claim 2, characterized in that: The resource allocation strategy is provided with expansion judgment standard rules, which are as follows: 1.) When the average usage rate of the virtual machine's vCPU during the 8:00-12:00 and 15:00-18:00 periods in the last five working days is not less than 50%, apply for vCPU expansion of IaaS resources; 2.) If the average usage rate of the virtual machine's memory during the 8:00-12:00 and 15:00-18:00 periods in the last five working days is not less than 60%, apply for memory expansion of IaaS resources; 3.) When the current storage usage of the virtual machine is not less than 90%, apply for storage expansion of IaaS resources.

4. The cloud computing resource allocation method according to claim 1, characterized in that: The revocation standard rules are provided in the resource allocation strategy, which are as follows: 1). Cloud resources have been allocated in excess of When one of the above conditions is met, apply to revoke the cloud resources of the virtual machine.

5. The cloud computing resource allocation method according to claim 3, characterized in that: The peak vCPU usage is the highest value among all records of the day; The vCPU average usage is the average of all recorded values ​​for the day; The peak memory usage is the highest value recorded on that day. The average memory usage is the average of all recorded values ​​for the day; The storage usage ratio is the used storage capacity / allocated storage capacity.

6. The cloud computing resource allocation method according to claim 1, characterized in that: In step S4, business operation data and user feedback information are collected in real time as factors for optimizing resource allocation strategies, and the process jumps to step S1 to continuously improve resource allocation efficiency and business service quality through continuous iteration and optimization.

7. A system based on the cloud computing resource allocation method according to any one of claims 1 to 6, characterized in that: include: The demand analysis and prediction module is responsible for collecting historical business data, including resource usage peak, stage average, business load, and user access volume; At the same time, the demand data of each application in the cloud computing environment is collected and analyzed in real time, and the demand trend of each application for resources in the future time period t is predicted by combining historical business data; The resource status monitoring module is responsible for real-time monitoring of the usage and status of various resources in the cloud computing environment and identifying the idle, busy or overloaded status of resources; The optimization strategy formulation module is responsible for formulating optimized resource allocation strategies based on the results of demand analysis and prediction, combined with resource status monitoring information, and taking into account resource availability, application priority, and service level agreement (SLA) requirements. This includes increasing resource supply during peak business demand and reducing resource supply during low business demand. The dynamic adjustment and execution module is responsible for executing resource allocation operations, monitoring the usage of cloud computing resources in real time, collecting user feedback information in real time, and optimizing resource allocation strategies in real time based on resource monitoring data and user feedback information, and dynamically adjusting the allocation of cloud computing resources to improve resource utilization.

8. A cloud computing resource allocation device, characterized in that: The method comprises a memory and a processor; the memory is used to store a computer program, and the processor is used to implement the method according to any one of claims 1 to 6 when executing the computer program.

9. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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