CN112434938A

CN112434938A - Resource capacity management method and device

Info

Publication number: CN112434938A
Application number: CN202011321520.4A
Authority: CN
Inventors: 周政明; 张新; 陈洁; 李颖; 李颢; 吕震
Original assignee: China Construction Bank Corp
Current assignee: China Construction Bank Corp
Priority date: 2020-11-23
Filing date: 2020-11-23
Publication date: 2021-03-02

Abstract

The invention discloses a method and a device for managing resource capacity, wherein the method comprises the following steps: acquiring capacity data and capacity use data of various resources in an application system; acquiring service data of the application system; judging whether capacity adjustment is needed or not according to the capacity data and the capacity use data based on a preset capacity adjustment rule; when capacity adjustment is needed, determining a capacity adjustment strategy of the application system according to the capacity data, the capacity usage data and/or the service data based on a preset capacity adjustment calculation formula, wherein the capacity adjustment strategy comprises capacity adjustment size and capacity adjustment time. By utilizing the technical scheme provided by the invention, the connection and coordination among the resource purchasing and distributing plan, the capacity expansion plan of the application system and the actual resource increase can be realized, the unnecessary resource waste is avoided, and the cost and the production risk of resource management are reduced.

Description

Resource capacity management method and device

Technical Field

The invention relates to the field of resource capacity management, in particular to a method and a device for resource capacity.

Background

Capacity management of a data center is to enable resources of an organization to exert maximum efficiency by configuring reasonable service capacity under the dual constraints of cost and business requirements. The resources covered by the data center are mainly computing resources such as servers, Storage resources such as Storage devices of an NAS (Network Attached Storage) or an SAN (Storage Area Network), big data server resources, and the like.

As the number of service application types and the number of services carried by a data center increase, some contradictions arise in resource management: firstly, the independently increased service requirements and resource expansion requirements of each application system are not matched with a resource purchasing plan and a resource allocation plan which are preset by a resource management department, the excessive purchasing of resources can cause the waste of the resources, the insufficient purchasing of the resources can cause the shortage of the resources, the service requirements in a period of time in the future can not be met, and the service function release delay or the production event is caused; secondly, the use growth condition of the data resources of each application system is not matched with a capacity expansion plan which is preset according to business requirements, and the capacity expansion is carried out according to experience under most conditions, so that the delay of the capacity expansion time and the error of the capacity expansion are easily caused; moreover, the capacity expansion triggering rules formulated by each application system are not uniform with the calculation mode of the capacity expansion capacity, so that the resource purchasing plan of the next period is continuously changed. The procurement distribution plan of the resource management department, the expansion plan of each application system and the disjunction of the actual resource expansion situation in time and capacity are easy to cause resource overstock or shortage, and the data resource management cost is greatly increased.

Accurate prejudgment and pre-estimation expansion requirements are needed urgently, and expansion strategies of all application systems are formulated based on the same prejudgment and pre-estimation standards and rules so as to provide purchasing reference and achieve synchronous coordination management.

Disclosure of Invention

In order to solve the problems in the prior art, the invention provides a method and a device for managing resource capacity. The technical scheme is as follows:

one aspect of the present invention provides a method for managing resource capacity, where the method includes:

acquiring capacity data and capacity use data of various resources in an application system;

acquiring service data of the application system;

judging whether capacity adjustment is needed or not according to the capacity data and the capacity use data based on a preset capacity adjustment rule;

when capacity adjustment is needed, determining a capacity adjustment strategy of the application system according to the capacity data, the capacity usage data and/or the service data based on a preset capacity adjustment calculation formula, wherein the capacity adjustment strategy comprises capacity adjustment size and capacity adjustment time.

Another aspect of the present invention provides an apparatus for managing resource capacity, the apparatus comprising:

the first acquisition module is used for acquiring the capacity data and the capacity use data of various resources in the application system;

the second acquisition module is used for acquiring the service data of the application system;

the capacity adjustment judging module is used for judging whether the capacity adjustment is needed or not according to the capacity data and the capacity use data based on a preset capacity adjustment rule;

and the capacity adjustment calculation module is used for determining a capacity adjustment strategy of the application system according to the capacity data, the capacity use data and/or the service data based on a preset capacity adjustment calculation formula when capacity adjustment is needed, wherein the capacity adjustment strategy comprises a capacity adjustment size and a capacity adjustment time.

Another aspect of the present invention provides a computer device, comprising a processor and a memory, wherein at least one instruction or at least one program is stored in the memory, and the at least one instruction or the at least one program is loaded by the processor and executes the method for managing the resource capacity.

Another aspect of the present invention provides a computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the method for managing resource capacity.

The method and the device for managing the resource capacity have the following technical effects:

the method combines the service data and the service requirements on the basis of the capacity data and the capacity use data, and determines the type, the time point and the capacity expansion data volume of each application system resource in a certain period in the future based on the self-defined prejudgment and pre-estimation standards and rules so as to customize a reasonable and feasible capacity expansion plan for the application system and avoid the occurrence of production events caused by estimation errors when the capacity expansion requirements are omitted, lagged or depend on experience for capacity expansion; in addition, based on consistent pre-estimation and pre-judgment standards and rules, the difference of the calculation modes of each application system is small, and the purchasing, distribution and management of resources can be further coordinated; meanwhile, when the method is applied to a data center, the capacity expansion requirements of a plurality of application systems are collected, a decision is provided for a resource management department to purchase resources so as to further make a reasonable and feasible purchase plan and resource distribution plan, purchase is orderly acquired, and production is expanded in batches according to the capacity expansion plan; by means of the connection between the resource purchasing and distributing plan, the capacity expansion plan of the application system and the actual resource increase, unnecessary resource waste is avoided, the cost of resource management is reduced, and production risks are also reduced.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a flowchart of a method for managing resource capacity according to an embodiment of the present invention;

fig. 2 is a flowchart for determining whether capacity adjustment is required according to an embodiment of the present invention;

fig. 3 is another flowchart for determining whether capacity adjustment is needed according to an embodiment of the present invention;

fig. 4 is another flowchart for determining whether capacity adjustment is needed according to an embodiment of the present invention;

FIG. 5 is a flow chart of a method for calculating a capacity resize according to an embodiment of the present invention;

FIG. 6 is a flow chart of another method for calculating capacity resizing according to embodiments of the present invention;

FIG. 7 is a flow chart of another method for calculating capacity resizing according to embodiments of the present invention;

FIG. 8 is a flow diagram of managing resource capacity from a data center dimension provided by an embodiment of the invention;

FIG. 9 is a diagram illustrating resource capacity utilization data of an application system according to an embodiment of the present invention;

FIG. 10 is a diagram of an apparatus for managing resource capacity according to an embodiment of the present invention;

fig. 11 is a block diagram of a hardware structure of a server operating a method for managing resource capacity according to an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. Examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In order to make the objects, technical solutions and advantages disclosed in the embodiments of the present invention more clearly apparent, the embodiments of the present invention are described in further detail below with reference to the accompanying drawings and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the invention and are not intended to limit the embodiments of the invention.

In the following, the terms "first", "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present embodiment, "a plurality" means two or more unless otherwise specified. In order to facilitate understanding of the technical solutions and the technical effects thereof described in the embodiments of the present invention, the embodiments of the present invention first explain related terms:

the data center comprises: a data center is a globally collaborative network of devices that is used to communicate, accelerate, present, compute, store data information over an internet network infrastructure. A central point of data is primarily the data of the organization that runs the applications to process the business and operations, which may be developed internally by the organization or purchased from an enterprise software provider. One data center is focused on operating the architecture or providing other services. Typically, an application is run on a plurality of hosts, each running a single component, which may be a database, a file server, an application server, middleware, etc.

SAN: storage Area Network, Storage Area Network; the method adopts a mesh channel technology, and one or more network storage devices and a server host are connected through a special high-speed network to establish a regional network special for data storage. It has the advantage of being easy to integrate, can improve data availability and network myocardial infarction, can also lighten the administrative task.

NAS: network Attached Storage; the technology is a technology for integrating distributed and independent data and performing centralized management so as to facilitate access to different hosts and application servers from a technical aspect. NAS may also be defined as a special dedicated data storage server comprising storage devices (e.g., disk arrays, tape drives, or removable storage media) and embedded system software that may provide cross-platform file sharing functionality.

Virtual machine: virtual Machine, abbreviated as VM; refers to a complete computer system with complete hardware system functionality, which is simulated by software and runs in a completely isolated environment. The work that can be done in a physical computer can be implemented in a virtual machine. When creating a virtual machine in a computer, it is necessary to use a part of the hard disk and the memory capacity of the physical computer as the hard disk and the memory capacity of the virtual machine.

A physical machine: a physical machine provides a virtual machine hardware environment, also referred to as a host or host, with respect to a physical computer of the virtual machine. Through the cooperation of the physical machine and the virtual machine, a plurality of operating systems (an external operating system and a plurality of operating systems in the virtual machine) can be installed on one computer, and communication can be realized among the operating systems just like a plurality of computers.

Capacity management: capacity Management; in an effort to provide the required capacity for data processing and storage at the right time in an economical manner, there are three main sub-processes, traffic capacity management, service capacity management and resource capacity management, respectively.

Fig. 1 is a flowchart of a method for managing resource capacity according to an embodiment of the present invention, and referring to fig. 1, the method for managing resource capacity according to an embodiment of the present disclosure may include the following steps:

s101: and acquiring capacity data and capacity use data of various resources in the application system.

It can be appreciated that as application system services grow and new service requirements become more demanding, resources need to be expanded. When the operation and maintenance personnel find that the utilization rate of a CPU or a memory exceeds 80 percent or the space of a file system is close to full, the operation and maintenance personnel apply for capacity expansion capacity to a resource management department urgently, omission or delay is easily caused in time, and delayed release of production events or business functions is caused.

In the embodiment of the present specification, specifically, by deploying an acquisition script in a production server of the application system, various resources, such as a CPU, a memory, a storage space, a database table space, and an existing capacity and a use condition of a file system, are acquired at regular time every day.

S103: and acquiring the service data of the application system.

In the embodiment of the present specification, the traffic data includes, but is not limited to, indexes such as a current traffic data volume, a target traffic data volume, a system transaction volume data volume, a system target transaction volume data volume, and a system per minute processing transaction volume peak value. The accuracy of pre-judging, estimating and expanding the capacity can be enhanced by combining the real service data. Whether capacity expansion is needed or not is judged according to the transaction amount and the historical situation by virtue of the experience of workers, so that missing discovery or evaluation errors are easily caused to cause production events.

S105: and judging whether the capacity adjustment is needed or not according to the capacity data and the capacity use data based on a preset capacity adjustment rule.

It can be understood that, whether corresponding resource expansion is needed or not is artificially judged according to the working experience of operation and maintenance personnel, an emergent event or a service newly-added requirement, which is likely to cause missed discovery or estimation deviation to cause a production event, and when the expansion is needed, the expansion capacity is urgently applied to a resource management department, which is likely to cause delay in the expansion time, thereby causing delayed release of service functions. Therefore, the production risk can be effectively reduced by estimating and judging the capacity expansion requirement, and the smooth operation of business service is ensured. In the embodiment of the present specification, the capacity adjustment rule under the conditions of different types of resources, different resource architectures, and the like is specified in summary to accurately predict the capacity expansion requirement of the resources.

In an embodiment of the present specification, the types of resources at least include computing resources, storage resources, and big data server resources, as shown in fig. 2, fig. 3, and fig. 4, step S105 may include the following steps:

s501: and aiming at the computing resources of the application system, acquiring a computing resource capacity adjusting rule corresponding to the type of the computing resources, the type of the application system and/or capacity data of the computing resources, wherein the computing resource capacity adjusting rule comprises an upper limit threshold and a lower limit threshold of the computing resource capacity.

S502: and if the capacity usage data of the computing resources exceeds the upper limit threshold of the capacity usage of the computing resources, capacity expansion of the computing resources is required.

S503: if the capacity usage data of the computing resource is lower than the lower threshold of the capacity usage of the computing resource, the computing resource needs to be reduced.

In the embodiments of the present specification, the computing resource mainly refers to a computing resource required by the application system to run, and includes, but is not limited to, the number of server devices of a physical machine and a virtual machine, and configurations such as a central processing unit, a memory, and a network bandwidth of the server device.

Specifically, a rule for adjusting the capacity of the application server device according to the utilization rate of a Central Processing Unit (CPU) thereof may be as shown in table 1. One aspect of the embodiments of the present disclosure is described in detail herein. The application servers in the computing server may be divided into a physical machine and a virtual machine, and may be divided into an application server for processing an online transaction and an application server for processing a batch transaction according to a transaction type to be processed. At this time, the capacity data is embodied in that the number of the application physical machines for processing the online transaction service in the application system is 3, and the capacity adjustment rule corresponding to the application physical machine may be: if the CPU utilization rates of the current application physical machines are all higher than 30%, triggering capacity expansion requirements; and if the utilization rate of the current CPU is lower than 20%, triggering the capacity reduction requirement. The above exemplary description aims to set a corresponding capacity adjustment judgment rule based on the server type, the service processing type of the application system, and the number of the server devices, where the threshold data and the number of the devices meeting the trigger condition are only for reference, and are not specific limitations to the content of the present invention, and the same processing is performed in the rest of the embodiments of the present description, and details are not repeated.

Table 1 application server CPU usage threshold

Specifically, the server of the computing resource further comprises a database server providing data writing and reading services for the business application. In one possible capacity adjustment rule, for a database server in a normal operation mode, the upper threshold of the CPU utilization may be set to 85%; for a database server in Failover mode (a backup mode of operation), for example, using IBM (International Business Machines Corporation) Power series, the upper threshold of CPU usage may be set to 120% of reserved usage, and the lower threshold may be set to 20%. Optionally, the usage threshold in the capacity adjustment rule may be further defined according to the type of the operating system installed on the database server. If an HPUX system (Hewlett Packard UniX, which is an operating system of the Hewlett Packard 9000 series server) or LINUX system (globally called GNU/LINUX, which is a set of UniX-like operating systems free of charge and free of propagation) is running on the database server, the upper threshold of the CPU utilization can be set to 60% and the lower threshold can be set to 20%. And if the CPU index is higher than the upper limit threshold, triggering a capacity expansion request, and if the CPU index is lower than the lower limit threshold, triggering a capacity reduction request.

S504: and aiming at the storage resources of the application system, acquiring a storage resource capacity adjustment rule corresponding to a storage architecture of the storage resources and/or capacity data of the storage resources, wherein the storage resource capacity adjustment rule comprises an upper limit threshold and a lower limit threshold of the storage resource capacity.

S505: and if the capacity usage data of the storage resource exceeds the upper limit threshold of the capacity usage of the storage resource, capacity expansion of the storage resource is required.

S506: and if the capacity utilization data of the storage resources is lower than the lower limit threshold of the capacity utilization of the storage resources, the storage resources need to be reduced in capacity.

In the embodiment of the present specification, the storage resource mainly refers to a storage space required for storing data in an application system running process, and may be mainly divided into a dassan (storage Area network), a storage Area network) and a NAS (network Attached storage), which are Attached to a network for storing, where the SAN is mainly used for storing database data, and the NAS is mainly used for storing file data of the application system.

Specifically, for the NAS storage architecture, the capacity data is expressed as a storage space of a server storage device, such as a disk array, a disk drive, and the like, and is determined according to a server file system utilization rate; aiming at an SAN storage architecture, the capacity data is expressed as data table space, and judgment is carried out according to the utilization rate of the data table space, the utilization rate of the data table space is the ratio of the total data amount of a database server to the total space amount of each data table, and the total space amount of each data table does not include the table space of a database system and the filing space. The illustrative embodiment further provides a storage resource capacity adjustment rule, as shown in table 2, where T represents the unit Terabyte (Terabyte) of the computer data storage capacity. The table 2 example is intended to illustrate that storage resource capacity adjustment rules may be set corresponding to capacity data of the storage resources based on the storage architecture of the storage resources.

TABLE 2 storage resource Capacity adjustment rules

S507: and aiming at the big data server resource of the application system, obtaining a big data server resource capacity adjustment rule corresponding to the type of a big data product and/or the architecture mode of the big data server resource, wherein the big data server resource adjustment rule comprises an upper limit threshold and a lower limit threshold of the big data server resource capacity.

S508: and if the capacity usage data of the big data server resource exceeds the upper limit threshold of the capacity usage of the big data server resource, the big data server resource needs to be expanded.

S509: and if the capacity usage data of the big data server resource is lower than the lower limit threshold of the capacity usage of the big data server resource, the big data server resource needs to be reduced.

In the embodiment of the present specification, the big data server resource refers to a local hard disk, a CPU, and the like of a server, which meet the storage and calculation requirements of a big data product or a big data application, and is mainly used for distributed big data products such as Hadoop (distributed system infrastructure) or greenplus (a relational database for data warehouse application).

Specifically, the capacity data may be a storage space, and the capacity usage data may be a storage space usage rate or a memory resource usage rate. Preferably, for a Hadoop product, when the utilization rate of a storage space exceeds 80% or the utilization rate of a memory resource exceeds 50%, resource expansion is required; for greenplus products, when the utilization rate of the storage space exceeds 70%, resource expansion is required.

S107: when capacity adjustment is needed, determining a capacity adjustment strategy of the application system according to the capacity data, the capacity usage data and/or the service data based on a preset capacity adjustment calculation formula, wherein the capacity adjustment strategy comprises capacity adjustment size and capacity adjustment time.

In an embodiment of the present description, the capacity adjustment time may be determined by means of a comprehensive monitoring of a data center, and specifically, the method may further include:

storing the capacity data, the capacity usage data, and the business data of the application system in a data center;

calculating capacity growth indexes of various resources of the application system by combining historical data stored in the data center;

determining a capacity adjustment strategy of the application system according to the capacity growth index of the application system, wherein the capacity adjustment strategy comprises a capacity adjustment size and a capacity adjustment time.

In an embodiment of the present specification, the types of resources at least include a computing resource, a storage resource, and a big data server resource, as shown in fig. 5, fig. 6, and fig. 7, step S107 may include:

s701: and aiming at the computing resources of the application system, determining the equipment processing capacity of the computing resources according to the current transaction amount data in the service data and the capacity utilization data of the computing resources.

S702: and determining the quantity of the capacity expansion equipment of the computing resources according to the target transaction amount data in the service data and the equipment processing capacity.

In one possible implementation, the computing resources are expanded laterally by increasing the number of application server devices. The service data comprises transaction amount TPM (Transactions Per Minute, also called TPMC) processed by the application system Per Minute and target TPM of the application system; the capacity data is embodied as the number of devices of the existing application server; the capacity usage data is specifically expressed as a current CPU usage rate of the application server, and it is understood that the capacity expansion request is triggered only when the current CPU usage rate exceeds an upper threshold defined in this embodiment of the present specification.

Specifically, the processing capacity T of a single application server may be determined by the following formula:

further, the number N of application servers to be added can be determined by the following formula, where C represents the usage rate of the CPU, and the value thereof falls between the lower threshold and the upper threshold:

s703: and aiming at the storage resources of the application system, determining the capacity growth index of the storage resources according to the service data.

S704: and determining the capacity expansion capacity of the storage resource according to the capacity data, the capacity growth index and the capacity use data of the storage resource.

In one possible implementation, the storage resources are laterally expanded by increasing the number of storage devices or storage arrays of the storage resources. The service data can be the stored data volume in a preset time period, preferably a month period, and the month growth volume of the stored data is calculated as a capacity growth index; the capacity data is embodied as a current allocated storage resource capacity and the capacity usage data is embodied as a current data storage capacity. Specifically, the size of the capacity expansion capacity may be determined by the following formula:

capacity expansion capacity (monthly growth X + current data storage capacity) 1+ Y% — allocated capacity

Wherein the parameter X represents the data shelf life, preferably a maximum of 12 months; the parameter Y represents a redundancy rate, the size of the redundancy rate depends on the capacity of the currently allocated storage resource, and if the capacity is less than 2T, Y is 25; if the capacity is [2T, 5T), Y is 20; if the capacity is [5T, 10T), Y is 15; when the capacity is 10T or more, Y is 10. The value setting or value range setting of the parameter X, Y in the embodiments of the present specification is only a preferable embodiment, and the present invention is not particularly and exclusively limited thereto.

S705: and aiming at the large data server resource of the application system, determining the single-node space capacity according to the capacity data of the large data server resource.

S706: and determining the number of expansion nodes of the large data server resource according to the total data volume of the current service, the total data volume of the target service and the single-node space capacity in the service data.

In a feasible implementation manner, the storage space of the big data product may be increased by increasing the number of nodes or performing disk expansion on existing nodes, and this embodiment of the present specification is exemplified by increasing the number of nodes. The service data comprises the total data volume of the current service and the total data volume of the target service; the capacity data is specifically represented by the number of disks, the capacity of a single disk, a storage capacity expansion threshold value and the like.

Specifically, when a Hadoop big data product is applied, the single-node space capacity can be determined by the following formula:

wherein, the parameter 0.9 indicates that the storage space needs to be freed from the system overhead, and preferably, the storage capacity expansion threshold may be 0.8 depending on the storage device specification. In one possible implementation, a single server in a low-density cluster can support 900G of data storage, and the single node space is around 3 TB; in a high-density cluster, a single server can support 1.2T of data storage, and the single node space is about 4 TB. For a Hadoop high-availability cluster, the number of nodes that need to be expanded when the storage space utilization rate of the cluster exceeds 80% may be determined by the following formula:

for a database cluster server in Hadoop, such as a Redis (a key value pair storage system) storage cluster, the number of nodes that need to be expanded when the memory resource exceeds 50% may be determined by the following formula:

the memory capacity and the memory utilization rate of a single device are introduced, and the memory utilization rate is preferably 0.5.

Specifically, when the greenplus data product is applied, the single-node space capacity may be determined by the following formula:

wherein the storage capacity expansion threshold depends on the storage device specification, and is preferably 0.7. The greenplus can be divided into a low-density cluster and a high-density cluster, and in a feasible implementation mode, a single server in the low-density cluster can support 900G of data storage, and the single node space is 3.8 TB; in a high-density cluster, a single server can support 1.2T of data storage, and the single node space is 7.7 TB. When the storage space utilization rate of the greenplus cluster exceeds 70%, the number of nodes needing capacity expansion can be determined by the following formula:

in one embodiment of the present description, the method may further include:

s109: summarizing the capacity data and the capacity use data of a plurality of application systems, taking the capacity data and the capacity use data as the total resource data of a data center, and carrying out visual display on the total asset data according to the resource types;

and summarizing the capacity adjustment strategies of the plurality of application systems, and determining a total resource adjustment strategy of the data center, wherein the total resource adjustment strategy comprises a resource adjustment type, a resource adjustment size and resource adjustment time.

Fig. 8 is a schematic flowchart of managing resource capacity from a data center dimension, and as shown in fig. 8, the method for managing resource capacity may further include:

s1091: and acquiring capacity data and capacity use data of various resources of the data center.

Specifically, data acquisition scripts are deployed in all production servers managed by the data center, capacity data and capacity use data of various resources, including but not limited to capacities and use conditions of a server CPU, a memory, a file system and a database table space, are automatically acquired at regular time each day, and are recorded into the database to serve as data assets of the data center.

S1093: and acquiring service data processed by the data center.

Specifically, other business data such as daily transaction amount, per minute system processing transaction amount peak value and the like are collected regularly.

S1095: and calculating the capacity growth indexes of various resources of the application system according to the capacity data and the capacity use data.

Specifically, as shown in fig. 9, the monthly growth amount and/or the monthly growth rate of each type of resource of each application system are calculated in combination with the historical data stored in the data center.

S1097: and judging whether the capacity adjustment is needed or not according to the capacity data and the capacity use data based on a preset capacity adjustment rule.

Preferably, the capacity adjustment rule may be as shown in relevant parts of the embodiments of the present specification, and is not described herein again.

S1099: when capacity adjustment is needed, determining a capacity adjustment strategy of the data center according to the capacity data, the capacity usage data and/or the service data based on a preset capacity adjustment calculation formula, wherein the capacity adjustment strategy comprises capacity adjustment size and capacity adjustment time.

It can be understood that, from the dimension of the data center, in addition to calculating the capacity expansion time and the capacity expansion requirement of each application system, data needs to be summarized from the perspective of resource types, and the total capacity expansion requirement of each type of resource needs to be calculated.

In the embodiment of the present description, from the dimensionality of the data center, a resource demand plan initially specified by a management period may also be combined to visually display relevant data of the existing capacity of the resource, the resource capacity expansion and growth condition, and the resource capacity expansion and purchase demand, so as to facilitate asset purchase management by a manager of the data center, facilitate ordered capacity expansion of operation and maintenance personnel of each application system in batches, solve the problem of separation between the actual capacity expansion demand and the theoretical capacity expansion demand, and also solve the problem of separation between the purchase plan and the data center asset growth demand.

An embodiment of the present invention further provides an embodiment of a device for managing resource capacity, and as shown in fig. 10, the device may include:

a first obtaining module 1010, configured to obtain capacity data and capacity usage data of various resources of an application system;

a second obtaining module 1020, configured to obtain service data of the application system;

a capacity adjustment judging module 1030, configured to judge whether to perform capacity adjustment according to the capacity data and the capacity usage data based on a preset capacity adjustment rule;

the capacity adjustment calculating module 1040 is configured to, when capacity adjustment is required, determine, based on a preset capacity adjustment calculation formula, a capacity adjustment policy of the application system according to the capacity data, the capacity usage data, and/or the service data, where the capacity adjustment policy includes a capacity adjustment size and a capacity adjustment time.

In another embodiment of the present description, the apparatus may further include:

and the online calculation module is used for calculating the capacity increase indexes of various resources of each application system on line in the data center.

And the data visualization module is used for forming a data asset total view, a resource growth view and a resource purchasing plan view in the data center.

The embodiment of the present invention is based on the same inventive concept, and please refer to the embodiment of the method for details, which is not described herein again.

The embodiment of the present invention provides a computer device, which includes a processor and a memory, where the memory stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement a method for managing resource capacity as provided in the above method embodiment.

The memory may be used to store software programs and modules, and the processor may execute various functional applications and data processing by operating the software programs and modules stored in the memory. The memory can mainly comprise a program storage area and a data storage area, wherein the program storage area can store an operating system, application programs needed by functions and the like; the storage data area may store data created according to use of the apparatus, and the like. Further, the memory may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid state storage device. Accordingly, the memory may also include a memory controller to provide the processor access to the memory.

The method embodiments provided by the embodiments of the present invention may be executed in a mobile terminal, a computer terminal, a server, or a similar computing device, that is, the computer device may include a mobile terminal, a computer terminal, a server, or a similar computing device. Taking the example of running on a server, fig. 11 is a hardware structure block diagram of the server of the method for managing resource capacity according to the embodiment of the present invention. As shown in FIG. 11, the server 1100 may have a large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 1110Processor 1110 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 1130 for storing data, one or more storage media 1120 (e.g., one or more mass storage devices) storing applications 1123 or data 1122. The memory 1130 and the storage medium 1120 may be, among other things, transient storage or persistent storage. The program stored in the storage medium 1120 may include one or more modules, each of which may include a series of instruction operations for a server. Still further, the central processor 1110 may be configured to communicate with the storage medium 1120, and execute a series of instruction operations in the storage medium 1120 on the server 1100. The Server 1100 may also include one or more power supplies 1160, one or more wired or wireless network interfaces 1150, one or more input-output interfaces 1140, and/or one or more operating systems 1121, such as a Windows Server^TM，Mac OS X^TM，Unix^TM,Linux^TM，FreeBSD^TMAnd so on.

It will be understood by those skilled in the art that the structure shown in fig. 11 is only an illustration and is not intended to limit the structure of the electronic device. For example, server 1100 may also include more or fewer components than shown in FIG. 11, or have a different configuration than shown in FIG. 11.

Embodiments of the present invention also provide a computer-readable storage medium, where the storage medium may be disposed in a server to store at least one instruction or at least one program for implementing a method for managing resource capacity in method embodiments, where the at least one instruction or the at least one program is loaded and executed by a processor to implement the method for managing resource capacity provided in the method embodiments.

Alternatively, in this embodiment, the storage medium may be located in at least one network server of a plurality of network servers of a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The embodiment of the resource capacity management method and the device provided by the invention can be seen that the method and the device combine the service data and the service requirements on the basis of the capacity data and the capacity use data, and determine the type, the time point and the capacity expansion data volume of each application system resource in a certain period in the future based on the self-defined pre-judgment and pre-estimation standards and rules, so as to customize a reasonable and feasible capacity expansion plan for the application system and avoid the occurrence of a production event caused by prediction errors when the capacity expansion requirements are omitted, lagged or depend on experience for capacity expansion; in addition, based on consistent pre-estimation and pre-judgment standards and rules, the difference of the calculation modes of each application system is small, and the purchasing, distribution and management of resources can be further coordinated; meanwhile, when the method is applied to a data center, the capacity expansion requirements of a plurality of application systems are collected, a decision is provided for a resource management department to purchase resources so as to further make a reasonable and feasible purchase plan and resource distribution plan, purchase is orderly acquired, and production is expanded in batches according to the capacity expansion plan; by means of the connection between the resource purchasing and distributing plan, the capacity expansion plan of the application system and the actual resource increase, unnecessary resource waste is avoided, the cost of resource management is reduced, and production risks are also reduced.

It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus, device and storage medium embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for relevant points.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims

1. A method for managing resource capacity, the method comprising:

acquiring service data of the application system;

2. A method for managing resource capacity according to claim 1, said method further comprising:

3. A method for managing resource capacity according to claim 1, characterized in that said method further comprises:

summarizing the capacity data and the capacity use data of a plurality of application systems, taking the capacity data and the capacity use data as the total resource data of a data center, and carrying out visual display on the total asset data according to the resource types;

4. The method according to claim 1, wherein the types of resources at least include computing resources, storage resources, and big data server resources, and the determining whether the capacity adjustment is required according to the capacity data and the capacity usage data based on a preset capacity adjustment rule comprises:

aiming at the computing resources of the application system, acquiring a computing resource capacity adjustment rule corresponding to the type of the computing resources, the type of the application system and/or capacity data of the computing resources, wherein the computing resource capacity adjustment rule comprises an upper limit threshold and a lower limit threshold of the computing resource capacity;

if the capacity usage data of the computing resources exceeds the upper limit threshold of the capacity usage of the computing resources, capacity expansion of the computing resources is needed;

if the capacity usage data of the computing resource is lower than the lower threshold of the capacity usage of the computing resource, the computing resource needs to be reduced.

5. The method according to claim 1, wherein the types of resources at least include computing resources, storage resources, and big data server resources, and the determining whether the capacity adjustment is required according to the capacity data and the capacity usage data based on a preset capacity adjustment rule comprises:

aiming at the storage resources of the application system, acquiring a storage resource capacity adjustment rule corresponding to a storage architecture of the storage resources and/or capacity data of the storage resources, wherein the storage resource capacity adjustment rule comprises an upper limit threshold and a lower limit threshold of the storage resource capacity;

if the capacity usage data of the storage resource exceeds the upper limit threshold of the capacity usage of the storage resource, capacity expansion of the storage resource is needed;

and if the capacity utilization data of the storage resources is lower than the lower limit threshold of the capacity utilization of the storage resources, the storage resources need to be reduced in capacity.

6. The method according to claim 1, wherein the types of resources at least include computing resources, storage resources, and big data server resources, and the determining whether the capacity adjustment is required according to the capacity data and/or the capacity usage data based on a preset capacity adjustment rule includes:

aiming at big data server resources of the application system, obtaining big data server resource capacity adjustment rules corresponding to big data product types and/or architecture modes of the big data server resources, wherein the big data server resource adjustment rules comprise an upper limit threshold and a lower limit threshold for using the big data server resource capacity;

if the capacity usage data of the big data server resource exceeds the upper limit threshold of the capacity usage of the big data server resource, the big data server resource needs to be expanded;

and if the capacity usage data of the big data server resource is lower than the lower limit threshold of the capacity usage of the big data server resource, the big data server resource needs to be reduced.

7. The method according to claim 4, wherein the determining the capacity adjustment policy of the application system according to the capacity data, the capacity usage data and/or the traffic data based on the preset capacity adjustment calculation formula comprises:

aiming at the computing resource of the application system, determining the equipment processing capacity of the computing resource according to the current transaction amount data in the business data and the capacity utilization data of the computing resource;

and determining the quantity of the capacity expansion equipment of the computing resources according to the target transaction amount data in the service data and the equipment processing capacity.

8. The method according to claim 5, wherein the determining the capacity adjustment policy of the application system according to the capacity data, the capacity usage data and/or the traffic data based on the preset capacity adjustment calculation formula comprises:

determining a capacity growth index of a storage resource of the application system according to the service data;

and determining the capacity expansion capacity of the storage resource according to the capacity data, the capacity growth index and the capacity use data of the storage resource.

9. The method according to claim 6, wherein the determining the capacity adjustment policy of the application system according to the capacity data, the capacity usage data and/or the traffic data based on the preset capacity adjustment calculation formula comprises:

aiming at the big data server resource of the application system, determining the single-node space capacity according to the capacity data of the big data server resource;

and determining the number of expansion nodes of the large data server resource according to the total data volume of the current service, the total data volume of the target service and the single-node space capacity in the service data.

10. An apparatus for management of resource capacity, the apparatus comprising: