Data center resource management method and device

Abstract

The invention discloses a data center resource management method and device, and the method comprises the steps: carrying out the modeling of at least one data center and at least one calculation service, and obtaining a data center model of each data center and a calculation service model corresponding to each calculation service; collecting calculation service information and data center information based on the data center model and the calculation service model; determining a target optimization function of each resource optimization target based on the data center model, the calculation service model and the at least one resource optimization target; based on the calculation service information, the data center information and at least one target constraint condition, performing iterative solution on the target optimization function to obtain solved target service deployment information; and performing resource allocation management on the data center based on the target service deployment information. Through the technical scheme of the embodiment of the invention, data center resource management under the condition of considering the multi-target influence is realized, the reasonability of resource allocation is improved, and the overall performance of the data center is improved.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a data center resource management method and device. Background Art

[0002] With the continuous development of data center technology system, data center resource management has become a key technology to improve data center resource utilization, reduce operating costs and ensure service quality.

[0003] At present, traditional data center resource management methods mainly focus on a single goal, such as minimizing energy consumption or maximizing resource utilization. However, traditional data center management methods often ignore the mutual influence and constraints between multiple goals, resulting in unreasonable resource allocation and affecting the overall performance of the data center. Summary of the invention

[0004] The present invention provides a data center resource management method and device to achieve data center resource management under the consideration of multiple objectives, improve the rationality of resource allocation, and improve the overall performance of the data center.

[0005] In a first aspect, an embodiment of the present invention provides a data center resource management method, including:

[0006] Modeling at least one data center and at least one computing service to obtain a data center model corresponding to each data center and a computing service model corresponding to each computing service;

[0007] Based on the data center model and the computing service model, collecting corresponding computing service information and data center information;

[0008] Determine a target optimization function corresponding to each resource optimization target based on the data center model, the computing service model and at least one resource optimization target;

[0009] Iteratively solving the target optimization function based on the computing service information, the data center information and at least one target constraint condition to obtain solved target service deployment information;

[0010] Based on the target service deployment information, resource allocation management is performed on the data center.

[0011] In a second aspect, an embodiment of the present invention further provides a data center resource management device, including:

[0012] A model building module, used to model at least one data center and at least one computing service, and obtain a data center model corresponding to each data center and a computing service model corresponding to each computing service;

[0013] A data collection module, used to collect corresponding computing service information and data center information based on the data center model and the computing service model;

[0014] An optimization function determination module, configured to determine a target optimization function corresponding to each resource optimization target based on the data center model, the computing service model and at least one resource optimization target;

[0015] A deployment information determination module, configured to iteratively solve the target optimization function based on the computing service information, the data center information and at least one target constraint condition to obtain the solved target service deployment information;

[0016] A resource allocation module is used to manage resource allocation for the data center based on the target service deployment information.

[0017] The technical solution of the embodiment of the present invention, by modeling at least one data center and at least one computing service, obtains the data center model corresponding to each data center and the computing service model corresponding to each computing service, thereby helping to understand the complexity of the data center and computing service, and providing a basic framework for subsequent data collection and optimization solution. Based on the data center model and the computing service model, the corresponding computing service information and data center information are collected, and the operation status of the data center is obtained in real time, providing a basis for subsequent resource allocation. Based on the data center model, the computing service model and at least one resource optimization target, the target optimization function corresponding to each resource optimization target is determined, making the optimization process more flexible and controllable. Based on the computing service information, the data center information and at least one target constraint condition, the target optimization function is iteratively solved to obtain the target service deployment information after the solution, which can meet the resource management requirements in different scenarios and improve resource utilization efficiency. Based on the target service deployment information, the data center is resource allocated and managed, and dynamic adjustment and optimal configuration of resources are realized, thereby improving the operation efficiency and flexibility of the data center. Through systematic data center modeling, status collection, multi-objective optimization solution and resource allocation, efficient and dynamic management of data center resources is achieved, which helps to improve the operating efficiency of the data center, improve the rationality of resource allocation, improve the overall performance of the data center, and accurately match computing services, thereby improving resource utilization and the efficiency and quality of computing service provision.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. 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 creative work.

[0020] Figure 1 is a flow chart of a data center resource management method provided according to Embodiment 1 of the present invention;

[0021] Figure 2 is a flow chart of a data center resource management method provided according to Embodiment 2 of the present invention;

[0022] Figure 3 It is a structural diagram of a data center resource management device provided according to Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0024] It should be noted that the terms "target", "current", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] Embodiment 1

[0026] Figure 1 A flowchart of a data center resource management method is provided for the first embodiment of the present invention. This embodiment is applicable to the case of performing resource management on a data center. Figure 1As shown, the method can be executed by a data center resource management device, which can be implemented in the form of hardware and / or software, and the data center resource management device can be configured in an electronic device. Figure 1 As shown, the method specifically comprises the following steps:

[0027] S110: Model at least one data center and at least one computing service to obtain a data center model corresponding to each data center and a computing service model corresponding to each computing service.

[0028] Among them, a data center may refer to a centralized computing facility (such as a computer room), which usually contains a large number of servers, storage devices, network devices, and related software and services for processing, storing, and distributing data. Computing services may refer to services that use computing resources provided by a data center to perform specific tasks or provide specific functions. A data center model may refer to an abstract representation of the physical and logical structure of a data center. A computing service model may refer to an abstract representation of computing service functions and resource requirements.

[0029] Specifically, the elements and their relationships in at least one data center are abstracted, an abstract representation of the physical and logical structure of each data center is constructed, and a data center model corresponding to each data center is obtained; at least one computing service is modeled, the computing service functions and resource requirements corresponding to each computing service are abstracted, and a computing service model corresponding to each computing service is obtained, so that the complexity of the data center can be understood through clear model construction, which can provide a basis for subsequent data collection and optimization solutions.

[0030] Exemplarily, S110 may include: modeling at least one data center to obtain a first type of multivariate mathematical expression corresponding to each data center; wherein the dependent variable in the first type of multivariate mathematical expression is the operating performance parameter of the data center, and the independent variable is the operating resource parameter of the data center; modeling at least one computing service to obtain a second type of multivariate mathematical expression corresponding to each computing service; wherein the dependent variable in the second type of multivariate mathematical expression is the operating performance parameter of the computing service, and the independent variable is the operating resource parameter for providing the computing service. Of course, modeling at least one data center and modeling at least one computing service to obtain the mathematical model corresponding to each data center and each service may also be in other forms, for example, containing only multiple independent variables or dependent variables, or containing both independent variables and dependent variables, but the positions of the independent variables and the dependent variables are different from those in the first type of multivariate mathematical expression and the second type of multivariate mathematical expression, or containing both independent variables and dependent variables, but the contents of the independent variables and the dependent variables are different from those in the first type of multivariate mathematical expression and the second type of multivariate mathematical expression. It is understandable that those skilled in the art can establish models from different perspectives to achieve the description of at least one data center and at least one computing service, and this patent is not limited here.

[0031] Among them, the first type of multivariate mathematical expression may refer to a data center model, which is used to describe the relationship between the operating performance of a data center and its operating resources. The second type of multivariate mathematical expression may refer to a computing service model, which is used to describe the relationship between the operating performance of a computing service and its required operating resources. The operating performance parameters may include: any one or more of service delay, service throughput, computing power matching, network bandwidth, computer room energy consumption, and computer room resource utilization. The operating resource parameters may include: any one or more of the number, model, and performance of the central processing unit and / or graphics processing unit, and may also include the number of computing service requests, etc.

[0032] Specifically, the operating performance parameters of each data center are defined as dependent variables (such as service throughput, computing power matching, network bandwidth, computer room energy consumption and computer room resource utilization), and the operating resource parameters are defined as independent variables (such as the number, model, performance, etc. of central processing units and / or graphics processors), and the first type of multivariate mathematical expression corresponding to each data center is constructed based on the above independent variables and dependent variables to describe the complex relationship between the operating performance parameters of each data center and its operating resource parameters; the operating performance parameters of each computing service are defined as dependent variables (such as service delay, service throughput, computing power matching, network bandwidth, computer room energy consumption and computer room resource utilization), and the operating resource parameters required for providing computing services are defined as independent variables (such as the number, model, performance, etc. of central processing units and / or graphics processors), and the second type of multivariate mathematical expression corresponding to each computing service is constructed based on the above independent variables and dependent variables to describe the complex relationship between the operating performance parameters of each computing service and its operating resource parameters. Through accurate model construction, the resources of the data center can be more reasonably allocated and managed, computing services can be accurately matched, resource waste and over-configuration can be avoided, thereby improving resource utilization and the efficiency and quality of computing service provision.

[0033] S120: Based on the data center model and the computing service model, collect corresponding computing service information and data center information.

[0034] Computing service information may refer to various data and indicators related to the current operating status of computing services determined according to the computing service model. Data center information may refer to various data and indicators related to the current operating status of the data center as a whole and its various components determined according to the data center model. These data and indicators are used to evaluate the performance, resource usage, health status, resource demand, etc. of the data center and computing services.

[0035] Specifically, based on the data center model and the computing service model, real-time collection of computing service information such as the number of requests for each computing service, service delay, service throughput, service quality evaluation and other parameters; data center information such as network bandwidth, transmission delay within and / or between computer rooms, computer room power consumption, computing power model and quantity, computer room resource utilization, computing power matching degree and other parameters can ensure the accuracy and real-time nature of data collection, and provide a basis for subsequent reallocation of resources and optimization decisions.

[0036] Exemplarily, S120 may include: determining target information corresponding to each data center and each computing service based on the data center model and the computing service model; and obtaining computing service information corresponding to the computing service and current data center information corresponding to the data center based on the target information corresponding to each data center and each computing service.

[0037] The target information may refer to key data and indicators that need to be collected according to the computing service model and the data center model.

[0038] Specifically, according to the first type of multivariate mathematical expressions in the data center model, the key performance parameters and resource usage that need to be monitored and collected for each data center, i.e., the target information, are determined; according to the second type of multivariate mathematical expressions corresponding to the computing service model, the performance indicators and resource consumption that need to be tracked for each computing service, i.e., the target information, are determined. Using monitoring tools and system logs, the computing service information corresponding to the computing service and the data center information corresponding to the data center are collected in real time according to the target information. By real-time monitoring and analysis of the resource usage of the data center and computing services, the demand for computing services and the idle or over-allocated resources of the data center can be accurately reflected, thereby facilitating the subsequent optimization and adjustment of resources, improving resource utilization, and the efficiency and quality of computing service provision.

[0039] S130. Determine a target optimization function corresponding to each resource optimization target based on a data center model, a computing service model, and at least one resource optimization target.

[0040] The resource optimization goal may refer to the purpose to be achieved by effectively managing and allocating resources in data centers and computing services. For example, the resource optimization goal may refer to the highest resource utilization, lowest power consumption, lowest bandwidth, lowest latency, and highest service throughput.

[0041] Specifically, according to the resource optimization goals (such as minimizing energy consumption, maximizing resource utilization, etc.), combined with the data center model and computing service model, a target optimization function corresponding to each resource optimization goal is constructed, so that the optimization direction can be clarified, and resource allocation can be made more reasonable and efficient, so as to flexibly respond to different business needs and management goals.

[0042] Exemplarily, S130 may include: obtaining at least one resource optimization target corresponding to at least one data center and at least one computing service; taking each resource optimization target as a dependent variable, and taking a deployment scheme of at least one computing service in at least one data center as an independent variable, and constructing a target optimization function corresponding to each resource optimization target based on a data center model and a computing service model.

[0043] The target optimization function may refer to a function used to describe and quantify how the deployment scheme of computing services in a data center affects resource optimization targets.

[0044] Specifically, at least one resource optimization target corresponding to at least one data center and at least one computing service is obtained, and the key factors affecting each resource optimization target, that is, the deployment scheme of each computing service in each data center, are analyzed. Each resource optimization target is taken as a dependent variable (that is, the target value to be optimized), and the deployment scheme of the computing service in the data center is taken as an independent variable (that is, an adjustable parameter). Based on the data center model and the computing service model, combined with mathematical and statistical methods, a target optimization function that can reflect the relationship between the resource optimization target and the deployment scheme is constructed. This function is used to quantitatively describe the changes in resource optimization targets under different deployment schemes. By optimizing the deployment scheme of computing services in the data center, resources can be allocated and utilized more reasonably, resource idleness and waste can be reduced, and resource utilization and the efficiency and quality of computing service provision can be improved.

[0045] S140. Based on the computing service information, the data center information, and at least one target constraint condition, iteratively solve the target optimization function to obtain solved target service deployment information.

[0046] Among them, the target constraint condition may refer to a series of restrictions that must be followed in the process of achieving the resource optimization target. These conditions limit the solution space of the resource optimization target and improve the feasibility and effectiveness of resource allocation and optimization results in actual operations. The target service deployment information may refer to specific information about how to deploy computing services in the data center.

[0047] In one embodiment, before iteratively solving the target optimization function, it also includes obtaining various parameter values ​​and / or parameter value functions of different computing services under deployment schemes in different data centers through stress testing, simulated deployment, actual deployment, etc. Then, according to the various parameter values ​​and / or parameter value functions, the function values ​​of various target optimization functions of different computing services under deployment schemes in different data centers are determined.

[0048] Specifically, according to the computing service information, data center information and target constraints, an appropriate solution and / or optimization algorithm (such as genetic algorithm, ant colony algorithm, linear programming, etc.) is used to iteratively solve the target optimization function. In the solution process, the resource allocation plan is continuously adjusted until all constraints are met and the optimal or near-optimal solution is achieved, and the target service deployment information after the solution is obtained, so as to find the optimal or near-optimal resource allocation plan, improve resource utilization, reduce energy consumption and costs, and improve the efficiency and quality of computing service provision.

[0049] S150: Perform resource allocation management on the data center based on the target service deployment information.

[0050] Specifically, according to the target service deployment information after solution, specific resource scheduling and configuration operations are implemented to adjust the resource allocation plan of the data center to provide computing services, realize dynamic resource allocation management of the data center, improve the overall performance and service quality of the data center, and at the same time improve the efficiency and quality of computing services.

[0051] Exemplarily, S150 may include: allocating, based on the target service deployment information, at least one resource in the corresponding at least one data center to each computing service in the at least one computing service to meet computing service requirements in a next time period.

[0052] Among them, computing service requirements can refer to the specific requirements and expectations for the computing resources, storage resources, network resources and other related resources and service capabilities corresponding to the data center.

[0053] Specifically, according to the target service deployment information, the specific computing service requirements of each computing service in the next time period are clarified, and according to the computing service requirements and the resource conditions of the data center, the corresponding data center resources are allocated to each computing service to meet the computing service requirements in the next time period. Through refined resource allocation management, it can ensure that computing services obtain sufficient resources when needed, ensure the efficiency and quality of computing service provision, and avoid over-allocation and waste of resources.

[0054] It should be noted that after completing the resource allocation management of the data center, the next round of model abstraction (data center model and computing service model) can be revised based on the management results. The revision operation can be performed regularly, such as solving once every half an hour, or in real time. Through regular or real-time model revision, the current data center resource usage and business demand changes can be more accurately reflected. This helps to more finely adjust the resource allocation strategy, reduce resource waste, and improve the overall resource utilization efficiency and the efficiency and quality of computing service provision.

[0055] The technical solution of the embodiment of the present invention, by modeling at least one data center and at least one computing service, obtains the data center model corresponding to each data center and the computing service model corresponding to each computing service, thereby helping to understand the complexity of the data center and computing services, and providing a basic framework for subsequent data collection and optimization solutions. Based on the data center model and the computing service model, the corresponding computing service information and data center information are collected, and the operating status of the data center is obtained in real time, providing a basis for subsequent resource allocation. Based on the data center model, the computing service model and at least one resource optimization target, the target optimization function corresponding to each resource optimization target is determined, making the optimization process more flexible and controllable. Based on the computing service information, the data center information and at least one target constraint condition, the target optimization function is iteratively solved to obtain the target service deployment information after the solution, which can meet the resource management requirements in different scenarios, improve the resource utilization efficiency and the efficiency and quality of computing service provision. Based on the target service deployment information, the data center is resource allocated and managed, and the dynamic adjustment and optimal configuration of resources are realized, which improves the operating efficiency and flexibility of the data center. Through systematic data center modeling, status collection, multi-objective optimization solution and resource allocation, efficient and dynamic management of data center resources is achieved, which helps to improve the operating efficiency of the data center, improve the rationality of resource allocation, improve the overall performance of the data center, and at the same time improve the efficiency and quality of computing service provision.

[0056] It should be noted that a data center often includes many computer rooms (data centers), which may be located in different regions to meet the access needs of users from all over the world. Some of these computer rooms are deployed in various places to improve the access experience of local users, some are for business backup in computer rooms, and some are for cluster business expansion. No matter what business is deployed, when doing cross-computer room deployment, you must fully consider the possible problems to avoid mistakes when deploying across computer rooms.

[0057] 1. Take the bandwidth issue as an example. If two or more computer rooms in different regions need to be connected, they often have to go through a wide area network. It is necessary to rent bandwidth from the operator and establish a VPN tunnel between the two computer rooms to achieve the purpose of connecting. The size of this bandwidth should be based on the actual needs of the computer room and also consider how much bandwidth the operator can provide. In principle, if local computer room forwarding can be performed, try not to cross computer rooms to reduce the traffic burden across computer rooms, which can reduce the cost of renting operator network bandwidth. When deploying services across computer rooms, the first thing to consider is whether the bandwidth of the computer room entrance and exit can be met. If it cannot be met, it will be vetoed and such cross-computer room services cannot be built and deployed.

[0058] 2. Taking security as an example, various software and hardware security protection measures can be deployed inside the data center to ensure data security. However, when data is exchanged between computer rooms, these data are not controlled by the computer room. When transmitted over the wide area network, the security of the computer room in the data center is difficult to play a role. At this time, some of the data may have an outer IP header added and encapsulated, but the inner message may still be intercepted and restored by someone. Some may be encrypted on the security equipment of the sending and receiving computer rooms, but these encryption algorithms are not undecipherable. As long as someone has mastered the deciphering method, the data may still be stolen during the transmission process. The network protocols of various wide area networks themselves have security vulnerabilities. In the event of an attack, if they are broken and exploited by someone, the data will be leaked, and the loss may be fatal. Therefore, try to transmit computing data or intermediate process data between computer rooms. For some data involving personal information or commercial secrets, try not to transmit them repeatedly in cross-computer room business.

[0059] 3. Taking the latency issue as an example, the latency of data transmission is also an important indicator. The latency within a data center is usually very low, at the microsecond level. When data is transmitted between data centers, it is affected by the latency of the cross-data center network, and the latency will be relatively high, usually at the millisecond level. The data transmission speed and latency of an Internet data center depend on multiple factors, including network bandwidth, network topology, and device performance within the data center. Generally speaking, the data transmission speed within a data center can be very fast, usually at a speed of gigabit or even 10 gigabit, and the internal network communication latency is generally around 300us (0.3ms). When data is transmitted between data centers, it will be limited by the bandwidth of the cross-data center network, and will generally be slower than the transmission speed within the data center. The latency of cross-computer room communication may be as high as 50,000us (50ms). When deploying services, the latency issue across computer rooms must be fully considered.

[0060] Embodiment 2

[0061] Figure 2 This is a flow chart of a data center resource management method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment optimizes the step of "iteratively solving the target optimization function based on computing service information, data center information and at least one target constraint condition to obtain the solved target resource deployment information". The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here.

[0062] See also Figure 2 Another data center resource management method provided in this embodiment specifically includes the following steps:

[0063] S210: Model at least one data center and at least one computing service to obtain a data center model corresponding to each data center and a computing service model corresponding to each computing service.

[0064] S220: Based on the data center model and the computing service model, collect corresponding computing service information and data center information.

[0065] S230. Determine a target optimization function corresponding to each resource optimization target based on a data center model, a computing service model, and at least one resource optimization target.

[0066] S240: Obtain at least one target constraint condition.

[0067] Specifically, the target constraint condition can refer to a series of restrictions that must be followed in the process of achieving the resource optimization target. These conditions limit the solution space of the resource optimization target and improve the feasibility and effectiveness of the optimization result in actual operation.

[0068] It should be noted that the target constraint condition can be in the same dimension as the target optimization function, for example, the power consumption of each data center does not exceed the preset value, and a sub-constraint target is the minimum power consumption of the data center (computer room) (the power consumption is different when the computer room load is light or heavy); the network bandwidth across computer rooms is limited to less than 100Gb / s, and a sub-constraint target is the minimum network bandwidth across computer rooms. Etc., where power consumption does not exceed the constraint value and bandwidth does not exceed 100G are strong constraints, and sub-constraint targets can be weak constraints.

[0069] Exemplarily, S240 may further include: acquiring a constraint level corresponding to at least one target constraint condition.

[0070] Specifically, each objective constraint has a corresponding constraint level, which is used to indicate the importance and priority of the condition in the solution and optimization process.

[0071] S250: Based on the computing service information, the data center information and at least one target constraint condition, a preliminary solution is performed on the target optimization function to obtain at least one original resource deployment information after the preliminary solution.

[0072] The original resource deployment information may refer to one or more resource allocation schemes obtained after preliminarily solving the target optimization function, and these schemes are used to explain how to allocate computing resources and other related resources to different computing services.

[0073] Specifically, based on computing service information, data center information and target constraints, a target optimization function is preliminarily solved through linear programming and other methods to obtain at least one original resource deployment information, providing a basis for subsequent optimization iterations.

[0074] Exemplarily, according to the current various computing service requirements and the data center information corresponding to each data center, it is determined which data center each service is deployed in, and it can even be accurate to certain computing resources in a certain data center. The value of each independent variable and the value of each objective optimization function obtain at least one original resource deployment information. For example, at a certain moment, there are four computing requirements of 1234, and there are four data centers ABCD. If the original resource deployment information is deployed according to the 1A, 2B, 3C, and 4D schemes, then the values ​​of variables such as power consumption, delay, and bandwidth requirements are what, and the values ​​of each objective function can be determined at this time; if the original resource deployment information is deployed according to the 1B, 2A, 3D, and 4C schemes, then the values ​​of variables such as power consumption, delay, and bandwidth requirements are what, and the values ​​of each objective function can also be determined at this time. The solution of each objective function is a potential original solution of a multi-computer room service deployment scheme based on multi-objective constraint programming, that is, potential original resource deployment information, and then the original solution, that is, original resource deployment information, is determined by judging whether at least one objective constraint condition is met.

[0075] Exemplarily, S250 may include: based on computing service information, data center information, at least one target constraint condition and the constraint level corresponding to the target constraint condition, preliminarily solving the target optimization function to obtain at least one original resource deployment information after the preliminary solution.

[0076] Specifically, based on computing service information, data center information and at least one target constraint, each target optimization function is initially solved by greedy, linear programming, enumeration, exhaustive, etc., to obtain the original resource deployment information corresponding to each target optimization function after the initial solution. By considering the target constraint and its constraint level, it can ensure that the resource deployment plan is more in line with actual needs and priorities, and improve the rationality of resource deployment.

[0077] Exemplarily, based on computing service information, data center information, at least one target constraint and the constraint level corresponding to the target constraint, a target optimization function is preliminarily solved to obtain at least one original resource deployment information after the preliminary solution, and also includes: if there is no original resource deployment information after the preliminary solution of the target optimization function, or the amount of original resource deployment information is lower than a preset value, then at least one constraint level corresponding to the target constraint is downgraded; based on computing service information, data center information, at least one target constraint and the adjusted target constraint corresponding to the target constraint, the target optimization function is re-preliminarily solved to obtain at least one original resource deployment information after the preliminary solution.

[0078] Specifically, if there is no original resource deployment information, or the amount of original resource deployment information is lower than the preset value (such as not meeting the minimum solution set size requirement), it is judged that the current constraint condition is too strict. Select the target constraint condition corresponding to the target optimization function for which there is no original resource deployment information, and downgrade the corresponding constraint level. This usually means relaxing the restriction range of these constraints. Based on the computing service information, data center information, at least one target constraint condition and the adjusted target constraint condition corresponding to the target constraint condition, the target optimization function is re-preliminarily solved to obtain at least one original resource deployment information after the preliminary solution. Check the new solution result to confirm whether the original resource deployment information that meets the requirements is obtained. If the requirements are still not met after the re-solution, the constraint level can be further adjusted and the above process can be repeated. Through iterative adjustment and solution, the optimal solution set that meets all conditions is gradually approached. By downgrading and adjusting the constraint level, the difficulty of solving can be reduced and the success rate of finding a feasible solution can be increased.

[0079] Exemplarily, if the original resource deployment information does not exist, the target constraint condition is adjusted and downgraded, and an attempt is made to re-solve it. At this time, the target constraint condition can be downgraded through preset adjustment rules. This usually occurs when the target optimization function cannot obtain any solution. For example, during the first solution, a possible strong constraint is that computing service demand 1 is deployed entirely in data center A. At this time, according to the collected data, demand 1 is greater than the service capacity of computer room A. At this time, under this strong constraint condition, any deployment plan cannot meet the target constraint condition, that is, the target optimization function has no solution. At this time, the strong constraint must be reduced to a weak constraint through constraint adjustment, that is, demand 1 is deployed first in data center A, and demands that exceed the service capacity can be provided across data centers. At this time, there may be an optimal solution.

[0080] S260: Optimize and iterate at least one original resource deployment information corresponding to the target optimization function based on a multi-objective optimization algorithm to obtain target resource deployment information after optimization and iteration.

[0081] Among them, a multi-objective optimization algorithm can refer to an algorithm for simultaneously processing multiple optimization objectives. The goal of this algorithm is to find the best trade-off solution between multiple conflicting or interrelated objectives so that all objectives can be met to a certain extent, rather than just optimizing one of the objectives.

[0082] Specifically, since resource deployment problems often involve multiple optimization objectives (such as cost minimization, performance maximization, resource utilization maximization, etc.), it is necessary to use a multi-objective optimization algorithm to find the optimal solution. The multi-objective optimization algorithm used may include any one or more heuristic algorithms such as genetic algorithm, particle swarm optimization algorithm, ant colony algorithm, greedy algorithm, etc. In one embodiment, when multiple multi-objective optimization algorithms are used, weights can be set for the optimal solutions obtained by each multi-objective optimization algorithm, and then the computing service deployment schemes of multiple optimal solutions are multiplied by the corresponding weights to obtain the final solution. The original resource deployment information obtained by the preliminary solution is optimized multiple times by the multi-objective optimization algorithm, and the resource allocation scheme is continuously adjusted to approach the optimal solution. During the iteration process, the algorithm will gradually improve the effect of resource deployment according to the feedback of the target optimization function and the restrictions of the target constraint conditions, and obtain the target resource deployment information after optimization iteration. Through optimization iteration, it can ensure that resources are more reasonably allocated and utilized, reduce resource waste, and improve the overall resource utilization efficiency and the efficiency and quality of computing service provision.

[0083] S270: Based on the target service deployment information, perform resource allocation management on the data center.

[0084] The technical solution of the embodiment of the present invention can ensure that the optimization process is always carried out in the direction of meeting actual needs by obtaining at least one target constraint. Based on computing service information, data center information and at least one target constraint, the target optimization function is preliminarily solved, and at least one original resource deployment information after the preliminary solution is obtained, which can verify the rationality of the optimization function and the effectiveness of the solution algorithm, reduce the risk in the subsequent optimization iteration process, and provide a basis for subsequent multi-objective optimization iterations. Based on the multi-objective optimization algorithm, at least one original resource deployment information corresponding to the target optimization function is optimized and iterated, and the target resource deployment information after optimization iteration is obtained, which can find a balance point between multiple optimization goals, thereby allocating resources more reasonably and improving resource utilization. By clarifying multiple target constraints, preliminary solutions and iterations of multi-objective optimization algorithms, the problem of data center resource deployment can be effectively solved, and efficient and reasonable allocation of data center resources is achieved, the overall performance and resource utilization of the data center are improved, and the efficiency and quality of computing service provision are improved.

[0085] For example, the greedy algorithm (also known as the greedy algorithm) means that when solving a problem, it always makes the best choice at the moment. In other words, it does not consider the overall optimal solution, and the algorithm obtains a local optimal solution in a certain sense. The greedy algorithm does not get the overall optimal solution for all problems. The key is the choice of the greedy strategy.

[0086] The basic idea of ​​the greedy algorithm is to proceed step by step from an initial solution to the problem. According to a certain optimization measure, each step must ensure that a local optimal solution can be obtained. Only one data is considered at each step, and its selection should meet the conditions of local optimization. If the next data is no longer a feasible solution when connected with the partial optimal solution, the data will not be added to the partial solution until all data are enumerated or the algorithm stops if no more data can be added.

[0087] The greedy algorithm generally proceeds as follows:

[0088] ① Establish a mathematical model to describe the problem.

[0089] ② Divide the problem to be solved into several sub-problems.

[0090] ③Solve each sub-problem and obtain the local optimal solution of the sub-problem.

[0091] ④Synthesize the local optimal solutions of the sub-problems into a solution to the original problem.

[0092] Greedy algorithm is a simpler and faster design technology for some optimal solution problems. The characteristic of greedy algorithm is that it proceeds step by step, often based on the current situation and making the best choice according to a certain optimization measure, without considering various possible overall situations, saving a lot of time that must be spent to exhaust all possibilities to find the optimal solution. Greedy algorithm uses a top-down, iterative method to make successive greedy choices. Each time a greedy choice is made, the problem is simplified into a smaller sub-problem. Through each step of greedy choice, an optimal solution to the problem can be obtained. Although it is necessary to ensure that the local optimal solution can be obtained at each step, the resulting global solution is sometimes not necessarily the best, so greedy algorithm should not backtrack.

[0093] Linear programming (LP) is an important branch of operations research that has been studied early, developed rapidly, is widely used, and has relatively mature methods. It is a mathematical method that assists people in scientific management. It is a mathematical theory and method for studying the extreme value problem of linear objective functions under linear constraints.

[0094] Linear programming is an important branch of operations research, which is widely used in military operations, economic analysis, business management, engineering technology, etc. It provides a scientific basis for making optimal decisions by rationally utilizing limited human, material, financial and other resources.

[0095] Introduction to Linear Programming

[0096] (1) List the constraints and objective function

[0097] (2) Draw the feasible region represented by the constraints

[0098] (3) Find the optimal solution and optimal value of the objective function within the feasible domain.

[0099] Embodiment 3

[0100] Figure 3 This is a schematic diagram of the structure of a data center resource management device provided by Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a model building module 310, a data collection module 320, an optimization function determination module 330, a deployment information determination module 340 and a resource allocation module 350.

[0101] The model building module 310 is used to model at least one data center and at least one computing service to obtain a data center model corresponding to each data center and a computing service model corresponding to each computing service;

[0102] A data collection module 320 is used to collect corresponding computing service information and data center information based on the data center model and the computing service model;

[0103] An optimization function determination module 330, configured to determine a target optimization function corresponding to each resource optimization target based on the data center model, the computing service model and at least one resource optimization target;

[0104] A deployment information determination module 340 is used to iteratively solve the target optimization function based on the computing service information, the data center information and at least one target constraint condition to obtain the solved target service deployment information;

[0105] The resource allocation module 350 is used to manage resource allocation for the data center based on the target service deployment information.

[0106] The technical solution of this embodiment, by modeling at least one data center and at least one computing service, obtains the data center model corresponding to each data center and the computing service model corresponding to each computing service, which helps to understand the complexity of data centers and computing services, and provides a basic framework for subsequent data collection and optimization solutions. Based on the data center model and the computing service model, the corresponding computing service information and data center information are collected, and the operating status of the data center is obtained in real time, providing a basis for subsequent resource allocation. Based on the data center model, the computing service model and at least one resource optimization target, the target optimization function corresponding to each resource optimization target is determined, making the optimization process more flexible and controllable. Based on the computing service information, the data center information and at least one target constraint condition, the target optimization function is iteratively solved to obtain the target service deployment information after the solution, which can meet the resource management requirements in different scenarios and improve resource utilization efficiency. Based on the target service deployment information, the data center is resource allocated and managed, and dynamic adjustment and optimal configuration of resources are realized, which improves the operating efficiency and flexibility of the data center. Through systematic data center modeling, status collection, multi-objective optimization solution and resource allocation, efficient and dynamic management of data center resources is achieved, which helps to improve the operating efficiency of the data center, improve the rationality of resource allocation, and improve the overall performance of the data center.

[0107] Optionally, the model building module 310 is specifically used to: model at least one data center to obtain a first type of multivariate mathematical expression corresponding to each data center; wherein the dependent variable in the first type of multivariate mathematical expression is the operating performance parameter of the data center, and the independent variable is the operating resource parameter of the data center; model at least one computing service to obtain a second type of multivariate mathematical expression corresponding to each computing service; wherein the dependent variable in the second type of multivariate mathematical expression is the operating performance parameter of the computing service, and the independent variable is the operating resource parameter of providing the computing service.

[0108] Optionally, the operating performance parameters include: any one or more of service delay, service throughput, computing power matching, network bandwidth, computer room energy consumption and computer room resource utilization; the operating resource parameters include: any one or more of the number, model and performance of the central processing unit and / or graphics processing unit.

[0109] Optionally, the data acquisition module 320 is specifically used to: determine the target information corresponding to each data center and each computing service based on the data center model and the computing service model; based on the target information corresponding to each data center and each computing service, obtain the computing service information corresponding to the computing service and the current data center information corresponding to the data center.

[0110] Optionally, the optimization function determination module 330 is specifically used to: obtain at least one resource optimization target corresponding to the at least one data center and the at least one computing service; take each resource optimization target as a dependent variable, and take the deployment scheme of the at least one computing service in the at least one data center as an independent variable, and construct a target optimization function corresponding to each resource optimization target based on the data center model and the computing service model.

[0111] Optionally, the deployment information determination module 340 includes:

[0112] A constraint condition acquisition unit, used to acquire at least one target constraint condition;

[0113] An original information acquisition unit, configured to preliminarily solve the target optimization function based on the computing service information, the data center information and at least one target constraint condition, and obtain at least one original resource deployment information after the preliminarily solution;

[0114] The target information acquisition unit is used to optimize and iterate at least one original resource deployment information corresponding to the target optimization function based on a multi-objective optimization algorithm to obtain the target resource deployment information after optimization and iteration.

[0115] Optionally, the constraint condition acquisition unit is specifically used to: acquire a constraint level corresponding to at least one of the target constraint conditions.

[0116] Optionally, the original information acquisition unit is specifically used to: based on the computing service information, the data center information, at least one target constraint condition and the constraint level corresponding to the target constraint condition, preliminarily solve the target optimization function to obtain at least one original resource deployment information after the preliminary solution.

[0117] Optionally, the original information acquisition unit is specifically used to: if there is no original resource deployment information after the preliminary solution of the target optimization function, or the amount of the original resource deployment information is lower than a preset value, then downgrade at least one constraint level corresponding to the target constraint condition; based on the computing service information, the data center information, at least one target constraint condition and the adjusted target constraint condition corresponding to the target constraint condition, re-preliminarily solve the target optimization function to obtain at least one original resource deployment information after the preliminary solution.

[0118] Optionally, the resource allocation module 350 is specifically used to: allocate at least one resource in at least one corresponding data center to each computing service in at least one computing service based on the target service deployment information to meet the computing service demand in the next time period.

[0119] The data center resource management device provided in the embodiment of the present invention can execute the data center resource management method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0120] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0121] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A data center resource management method, characterized in that: include: Modeling at least one data center and at least one computing service to obtain a data center model corresponding to each data center and a computing service model corresponding to each computing service; Based on the data center model and the computing service model, collecting corresponding computing service information and data center information; Determine a target optimization function corresponding to each resource optimization target based on the data center model, the computing service model and at least one resource optimization target; Iteratively solving the target optimization function based on the computing service information, the data center information and at least one target constraint condition to obtain solved target service deployment information; Based on the target service deployment information, resource allocation management is performed on the data center.

2. The method according to claim 1, characterized in that The step of modeling at least one data center and at least one computing service to obtain a data center model corresponding to each data center and a computing service model corresponding to each computing service includes: Modeling at least one data center to obtain a first-type multivariate mathematical expression corresponding to each data center; wherein the dependent variable in the first-type multivariate mathematical expression is an operating performance parameter of the data center, and the independent variable is an operating resource parameter of the data center; Model at least one computing service to obtain a second type of multivariate mathematical expression corresponding to each computing service; wherein the dependent variable in the second type of multivariate mathematical expression is an operating performance parameter of the computing service, and the independent variable is an operating resource parameter providing the computing service.

3. The method according to claim 2, characterized in that The operating performance parameters include: any one or more of service delay, service throughput, computing power matching, network bandwidth, computer room energy consumption and computer room resource utilization; the operating resource parameters include: any one or more of the number, model and performance of the central processing unit and / or graphics processing unit.

4. The method according to claim 1, characterized in that: The collecting corresponding computing service information and data center information based on the data center model and the computing service model includes: Based on the data center model and the computing service model, determining target information corresponding to each data center and each computing service; Based on the target information corresponding to each data center and each computing service, computing service information corresponding to the computing service and current data center information corresponding to the data center are acquired.

5. The method according to claim 1, characterized in that: The determining, based on the data center model, the computing service model and at least one resource optimization target, a target optimization function corresponding to each resource optimization target includes: Obtaining at least one resource optimization target corresponding to the at least one data center and the at least one computing service; Taking each resource optimization target as a dependent variable and the deployment scheme of the at least one computing service in the at least one data center as an independent variable, a target optimization function corresponding to each resource optimization target is constructed based on the data center model and the computing service model.

6. The method according to claim 1, characterized in that The iteratively solving the target optimization function based on the computing service information, the data center information and at least one target constraint condition to obtain the solved target resource deployment information includes: Obtain at least one target constraint; Based on the computing service information, the data center information and at least one target constraint condition, preliminarily solving the target optimization function to obtain at least one piece of original resource deployment information after the preliminarily solution; At least one original resource deployment information corresponding to the target optimization function is optimized and iterated based on a multi-objective optimization algorithm to obtain target resource deployment information after optimization and iteration.

7. The method according to claim 6, characterized in that The obtaining of at least one target constraint condition further includes: Obtaining a constraint level corresponding to at least one of the target constraint conditions; The preliminarily solving the target optimization function based on the computing service information, the data center information and the at least one target constraint condition to obtain at least one original resource deployment information after the preliminarily solution includes: Based on the computing service information, the data center information, at least one target constraint condition and the constraint level corresponding to the target constraint condition, the target optimization function is preliminarily solved to obtain at least one original resource deployment information after the preliminary solution.

8. The method according to claim 7, characterized in that The performing a preliminary solution to the target optimization function based on the computing service information, the data center information, the at least one target constraint condition and the constraint level corresponding to the target constraint condition to obtain at least one original resource deployment information after the preliminary solution further includes: If there is no original resource deployment information after the target optimization function is initially solved, or the amount of the original resource deployment information is lower than a preset value, downgrading at least one constraint level corresponding to the target constraint condition; Based on the computing service information, the data center information, at least one target constraint and the adjusted target constraint corresponding to the target constraint, the target optimization function is re-preliminarily solved to obtain at least one original resource deployment information after preliminary solution.

9. The method according to claim 1, characterized in that: The performing resource allocation management on the data center based on the target service deployment information includes: Based on the target service deployment information, at least one resource in at least one corresponding data center is allocated to each computing service in the at least one computing service to meet the computing service demand in the next time period.

10. A data center resource management device, characterized in that: include: A model building module, used to model at least one data center and at least one computing service, and obtain a data center model corresponding to each data center and a computing service model corresponding to each computing service; A data collection module, used to collect corresponding computing service information and data center information based on the data center model and the computing service model; An optimization function determination module, configured to determine a target optimization function corresponding to each resource optimization target based on the data center model, the computing service model and at least one resource optimization target; A deployment information determination module, configured to iteratively solve the target optimization function based on the computing service information, the data center information and at least one target constraint condition to obtain the solved target service deployment information; A resource allocation module is used to manage resource allocation for the data center based on the target service deployment information.

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