CN110780992A - Cloud computing platform optimized deployment method, system, terminal and storage medium - Google Patents

Cloud computing platform optimized deployment method, system, terminal and storage medium Download PDF

Info

Publication number
CN110780992A
CN110780992A CN201910892420.8A CN201910892420A CN110780992A CN 110780992 A CN110780992 A CN 110780992A CN 201910892420 A CN201910892420 A CN 201910892420A CN 110780992 A CN110780992 A CN 110780992A
Authority
CN
China
Prior art keywords
cpus
virtual machine
tenant
residual
tasks
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201910892420.8A
Other languages
Chinese (zh)
Other versions
CN110780992B (en
Inventor
班凌航
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Suzhou Wave Intelligent Technology Co Ltd
Original Assignee
Suzhou Wave Intelligent Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Suzhou Wave Intelligent Technology Co Ltd filed Critical Suzhou Wave Intelligent Technology Co Ltd
Priority to CN201910892420.8A priority Critical patent/CN110780992B/en
Publication of CN110780992A publication Critical patent/CN110780992A/en
Application granted granted Critical
Publication of CN110780992B publication Critical patent/CN110780992B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5077—Logical partitioning of resources; Management or configuration of virtualized resources
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/45562—Creating, deleting, cloning virtual machine instances
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/45595—Network integration; Enabling network access in virtual machine instances

Abstract

The invention provides a cloud computing platform optimized deployment method, a system, a terminal and a storage medium, wherein the method comprises the following steps: calculating the number of CPUs (central processing units) required by the residual tasks of the tenants; if the number of the required CPUs exceeds the number of the occupied CPUs currently, creating a virtual machine for the tenant within the range of the number of the available CPUs of the tenant; collecting platform virtual machine communication traffic and switch load; platform communication optimization is completed by mapping the virtual machine with the largest communication volume with the switch with the lightest load. The invention provides a resource allocation strategy based on dynamic adjustment of an elastic virtual machine, and incremental deployment can be carried out on the basis of the existing cloud service architecture. By carrying out dynamic resource adjustment during application running, the condition of resource fragmentation is avoided to the maximum extent, and the utilization rate of resources and the performance of a cloud computing platform can be greatly improved.

Description

Cloud computing platform optimized deployment method, system, terminal and storage medium
Technical Field
The invention relates to the technical field of cloud computing platforms, in particular to a cloud computing platform optimized deployment method, a cloud computing platform optimized deployment system, a cloud computing platform optimized deployment terminal and a storage medium.
Background
With the rise and development of cloud computing and big data industries, all industries are involved, the storage demand of cloud data is greatly increased, and cloud computing platforms are greatly popularized. At present, for the rise and development of big data applications, the performance problem gradually becomes one of the main problems faced by the development of the current big data industry.
However, the resource allocation of the current cloud computing platform to the tenants is not dynamic, that is, the number of CPUs of the tenants is unchanged initially, which may cause that the number of CPUs is not suitable for adjustment or resource waste if the number of CPUs is adjusted by the midway tenant task. In addition, the cloud computing platform virtual machine is complex in topological structure, the topological structure is not constant, and the scientific allocation of the switches for the virtual machine nodes is an important factor for optimizing the communication performance of the cloud computing platform.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a cloud computing platform optimized deployment method, a cloud computing platform optimized deployment system, a cloud computing platform optimized deployment terminal and a storage medium, so as to solve the technical problems.
In a first aspect, the present invention provides a cloud computing platform optimized deployment method, including:
calculating the number of CPUs (central processing units) required by the residual tasks of the tenants;
if the number of the required CPUs exceeds the number of the occupied CPUs currently, creating a virtual machine for the tenant within the range of the number of the available CPUs of the tenant;
collecting platform virtual machine communication traffic and switch load;
platform communication optimization is completed by mapping the virtual machine with the largest communication volume with the switch with the lightest load.
Further, the calculating the number of CPUs required by the remaining tasks of the tenant includes:
acquiring the residual lease time of the tenant and the time required for processing the residual tasks;
acquiring the number of CPUs currently occupied by tenants;
acquiring the used time of the tenant processing task, and calculating the average CPU efficiency according to the number of the current occupied CPUs and the used time;
and if the time required by the residual tasks exceeds the residual lease time, calculating the quantity of the CPUs required by the residual tasks, which meets the condition that the time required by processing the residual tasks does not exceed the residual lease time, according to the average CPU efficiency and the residual tasks.
Further, the performing platform communication optimization by mapping the virtual machine with the largest traffic volume with the switch with the lightest load includes:
traversing virtual machine traffic;
the loop maps the virtual machine with the largest traffic volume with the least loaded switch.
In a second aspect, the present invention provides a cloud computing platform optimized deployment system, including:
the CPU calculation unit is configured for calculating the number of CPUs required by the residual tasks of the tenants;
the node increasing unit is configured for creating a virtual machine for the tenant within the range of the number of available CPUs for the tenant if the number of the required CPUs exceeds the number of the occupied CPUs currently;
the information acquisition unit is configured for acquiring the communication traffic of the platform virtual machine and the load of the switch;
and the mapping reconstruction unit is configured for establishing mapping between the virtual machine with the largest communication volume and the switch with the lightest load to complete platform communication optimization.
Further, the CPU calculation unit includes:
the time acquisition module is configured for acquiring the residual lease time of the tenant and the time required for processing the residual tasks;
the occupation acquisition module is configured for acquiring the number of CPUs currently occupied by the tenant;
the efficiency calculation module is configured for acquiring the used time of the tenant processing task and calculating the average CPU efficiency according to the number of the current occupied CPUs and the used time;
and the quantity calculating module is configured for calculating the quantity of the CPUs required by the remaining tasks, which meet the condition that the time required for processing the remaining tasks does not exceed the remaining lease time, according to the average CPU efficiency and the remaining task quantity if the time required by the remaining tasks exceeds the remaining lease time.
Further, the mapping reconstruction unit includes:
the information traversing module is configured for traversing the communication traffic of the virtual machine;
and the mapping reconstruction module is configured for circularly mapping the virtual machine with the largest communication volume with the switch with the lightest load.
In a third aspect, a terminal is provided, including:
a processor, a memory, wherein,
the memory is used for storing a computer program which,
the processor is used for calling and running the computer program from the memory so as to make the terminal execute the method of the terminal.
In a fourth aspect, a computer storage medium is provided having stored therein instructions that, when executed on a computer, cause the computer to perform the method of the above aspects.
The beneficial effect of the invention is that,
according to the cloud computing platform optimized deployment method, the system, the terminal and the storage medium, the number of CPUs can be dynamically allocated to tenants, the task completion of the tenants is guaranteed, the communication mapping relation is reconstructed after virtual machines are added to change the cloud computing platform, and the communication performance of the cloud computing platform is guaranteed. The invention provides a resource allocation strategy based on dynamic adjustment of an elastic virtual machine, and incremental deployment can be carried out on the basis of the existing cloud service architecture. By carrying out dynamic resource adjustment during application running, the condition of resource fragmentation is avoided to the maximum extent, and the utilization rate of resources and the performance of a cloud computing platform can be greatly improved.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention.
FIG. 2 is a schematic block diagram of a system of one embodiment of the present invention.
Fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all 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 invention.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention. The execution subject in fig. 1 may be a cloud computing platform optimized deployment system.
As shown in fig. 1, the method 100 includes:
step 110, calculating the number of CPUs (central processing units) required by the residual tasks of the tenants;
step 120, if the number of the required CPUs exceeds the number of the CPUs occupied currently, creating a virtual machine for the tenant within the range of the number of the CPUs available for the tenant;
step 130, collecting platform virtual machine traffic and switch load;
and step 140, mapping the virtual machine with the largest communication volume with the switch with the lightest load to complete platform communication optimization.
Optionally, as an embodiment of the present invention, the calculating the number of CPUs required by the remaining task of the tenant includes:
acquiring the residual lease time of the tenant and the time required for processing the residual tasks;
acquiring the number of CPUs currently occupied by tenants;
acquiring the used time of the tenant processing task, and calculating the average CPU efficiency according to the number of the current occupied CPUs and the used time;
and if the time required by the residual tasks exceeds the residual lease time, calculating the quantity of the CPUs required by the residual tasks, which meets the condition that the time required by processing the residual tasks does not exceed the residual lease time, according to the average CPU efficiency and the residual tasks.
Optionally, as an embodiment of the present invention, the performing platform communication optimization by mapping the virtual machine with the largest traffic volume with the switch with the lightest load includes:
traversing virtual machine traffic;
the loop maps the virtual machine with the largest traffic volume with the least loaded switch.
In order to facilitate understanding of the invention, the cloud computing platform optimized deployment method provided by the invention is further described below by using the principle of the cloud computing platform optimized deployment method of the invention and combining with the process of performing optimized deployment on the cloud computing platform in the embodiment.
Specifically, the cloud computing platform optimized deployment method includes:
and S1, calculating the number of CPUs required by the residual tasks of the tenants.
The number of virtual machine cpus is calculated according to the following three constraints: 1. the closer to the actual required configuration of the service, the better; 2. the total number of physical server CPUs and the virtualization ratio (default ratio is 1: 0.8); 3. and calculating the total CPU required by the rest tasks, if the total CPU is less than the CPU available for the tenant, increasing or reducing the number of the CPUs, otherwise, completing the rest tasks by using a traditional static virtual machine method.
The method comprises the steps of firstly, obtaining the residual lease time CTuser of a tenant on a cloud computing platform, the time CTela required by processing the residual tasks, the number of CPUs currently occupied by the tenant and the time used by the tenant for processing the tasks. These are all directly available from the cloud computing platform management platform.
The average CPU efficiency of the tenant is calculated,
Figure BDA0002209169580000061
then, the remaining task amount is divided by the product of the average CPU efficiency and the number n of CPUs to obtain the time t required by the remaining task, and the equation is as follows:
and n is a variable and does not exceed the number of CPUs (central processing units) available for the tenant, and t does not exceed the residual lease time of the tenant, so that t is close to the residual lease time as much as possible, thereby not only ensuring that the task execution is completed, but also avoiding the resource waste of the cloud platform. The number of CPUs required to complete the remaining tasks can thus be found from the above formula.
Since the time required by the current tenant exceeds the remaining lease time, the number of CPUs of the tenant needs to be increased, that is, a virtual machine needs to be added to the tenant.
And S2, if the required CPU quantity exceeds the current occupied CPU quantity, creating a virtual machine for the tenant within the range of the available CPU quantity of the tenant.
The virtual machine number is adjusted for the tenant based on the required CPU number calculated in step S1. If the number of the required CPUs exceeds the number of the currently occupied CPUs, further judging whether the number of the required CPUs exceeds the number of the available CPUs of the tenants, and if not, increasing the number of the CPUs to be the difference value between the number of the required CPUs and the number of the currently occupied CPUs; and if so, increasing the number of the CPUs to be the difference value between the number of the available CPUs and the number of the CPUs occupied currently. Virtual machines are created according to the number of CPUs that need to be increased. On the other hand, if the number of CPUs required by the tenant is far smaller than the number of CPUs currently occupied by the tenant, some virtual machines of the user can be removed.
And S3, collecting the platform virtual machine traffic and the switch load.
And after the topology structure of the virtual machine is changed, the traffic and the load of the switch of the virtual machine are acquired again.
And S4, mapping the virtual machine with the largest traffic volume with the switch with the lightest load to complete platform communication optimization.
The communication load of each switch is initialized to 0, and the switch & virtual machine mapping relationship set M is initialized to an empty set. And re-executing the distribution strategy, wherein the distribution strategy is to enable a plurality of exchangers to uniformly share the total data communication quantity of cross nodes, and reduce the time difference of data forwarding of different agents. The specific execution method comprises the following steps: and establishing a mapping relation between the virtual machine with the maximum data traffic and the communication agent with the lightest current load in each cycle until all elements in the virtual machine set are traversed.
As shown in fig. 2, the system 200 includes:
a CPU calculating unit 210 configured to calculate the number of CPUs required by the remaining tasks of the tenant;
a node adding unit 220 configured to create a virtual machine for the tenant within the range of the number of CPUs available to the tenant if the number of CPUs required exceeds the number of CPUs currently occupied;
an information collection unit 230 configured to collect platform virtual machine traffic and switch load;
and the mapping reconstruction unit 240 is configured to complete platform communication optimization by establishing mapping between the virtual machine with the largest traffic and the switch with the lightest load.
Optionally, as an embodiment of the present invention, the CPU computing unit includes:
the time acquisition module is configured for acquiring the residual lease time of the tenant and the time required for processing the residual tasks;
the occupation acquisition module is configured for acquiring the number of CPUs currently occupied by the tenant;
the efficiency calculation module is configured for acquiring the used time of the tenant processing task and calculating the average CPU efficiency according to the number of the current occupied CPUs and the used time;
and the quantity calculating module is configured for calculating the quantity of the CPUs required by the remaining tasks, which meet the condition that the time required for processing the remaining tasks does not exceed the remaining lease time, according to the average CPU efficiency and the remaining task quantity if the time required by the remaining tasks exceeds the remaining lease time.
Optionally, as an embodiment of the present invention, the mapping reconstruction unit includes:
the information traversing module is configured for traversing the communication traffic of the virtual machine;
and the mapping reconstruction module is configured for circularly mapping the virtual machine with the largest communication volume with the switch with the lightest load.
Fig. 3 is a schematic structural diagram of a terminal system 300 according to an embodiment of the present invention, where the terminal system 300 may be used to execute the cloud computing platform optimized deployment method according to the embodiment of the present invention.
The terminal system 300 may include: a processor 310, a memory 320, and a communication unit 330. The components communicate via one or more buses, and those skilled in the art will appreciate that the architecture of the servers shown in the figures is not intended to be limiting, and may be a bus architecture, a star architecture, a combination of more or less components than those shown, or a different arrangement of components.
The memory 320 may be used for storing instructions executed by the processor 310, and the memory 320 may be implemented by any type of volatile or non-volatile storage terminal or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The executable instructions in memory 320, when executed by processor 310, enable terminal 300 to perform some or all of the steps in the method embodiments described below.
The processor 310 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by operating or executing software programs and/or modules stored in the memory 320 and calling data stored in the memory. The processor may be composed of an Integrated Circuit (IC), for example, a single packaged IC, or a plurality of packaged ICs connected with the same or different functions. For example, the processor 310 may include only a Central Processing Unit (CPU). In the embodiment of the present invention, the CPU may be a single operation core, or may include multiple operation cores.
A communication unit 330, configured to establish a communication channel so that the storage terminal can communicate with other terminals. And receiving user data sent by other terminals or sending the user data to other terminals.
The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and the program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Therefore, the method and the system can dynamically allocate the number of the CPUs to the tenants, ensure that the tasks of the tenants are completed, and reconstruct the communication mapping relation after the virtual machines are added to change the cloud computing platform, so as to ensure the communication performance of the cloud computing platform. The invention provides a resource allocation strategy based on dynamic adjustment of an elastic virtual machine, and incremental deployment can be carried out on the basis of the existing cloud service architecture. By carrying out dynamic resource adjustment during application running, the condition of resource fragmentation is avoided to the maximum extent, and the utilization rate of resources and the performance of a cloud computing platform can be greatly improved. For technical effects that can be achieved by the present embodiment, reference may be made to the above description, and details are not described herein again.
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of a software product, where the computer software product is stored in a storage medium, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, and the storage medium can store program codes, and includes instructions for enabling a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, and the like) to perform all or part of the steps of the method in the embodiments of the present invention.
The same and similar parts in the various embodiments in this specification may be referred to each other. Especially, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant points can be referred to the description in the method embodiment.
In the embodiments provided by the present invention, it should be understood that the disclosed system, system and method can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (8)

1. A cloud computing platform optimized deployment method is characterized by comprising the following steps:
calculating the number of CPUs (central processing units) required by the residual tasks of the tenants;
if the number of the required CPUs exceeds the number of the occupied CPUs currently, creating a virtual machine for the tenant within the range of the number of the available CPUs of the tenant;
collecting platform virtual machine communication traffic and switch load;
platform communication optimization is completed by mapping the virtual machine with the largest communication volume with the switch with the lightest load.
2. The method of claim 1, wherein the calculating the number of CPUs required for the remaining tasks of the tenant comprises:
acquiring the residual lease time of the tenant and the time required for processing the residual tasks;
acquiring the number of CPUs currently occupied by tenants;
acquiring the used time of the tenant processing task, and calculating the average CPU efficiency according to the number of the current occupied CPUs and the used time;
and if the time required by the residual tasks exceeds the residual lease time, calculating the quantity of the CPUs required by the residual tasks, which meets the condition that the time required by processing the residual tasks does not exceed the residual lease time, according to the average CPU efficiency and the residual tasks.
3. The method of claim 1, wherein the performing platform communication optimization by mapping the most heavily trafficked virtual machine to the least heavily loaded switch comprises:
traversing virtual machine traffic;
the loop maps the virtual machine with the largest traffic volume with the least loaded switch.
4. A cloud computing platform optimized deployment system, comprising:
the CPU calculation unit is configured for calculating the number of CPUs required by the residual tasks of the tenants;
the node increasing unit is configured for creating a virtual machine for the tenant within the range of the number of available CPUs for the tenant if the number of the required CPUs exceeds the number of the occupied CPUs currently;
the information acquisition unit is configured for acquiring the communication traffic of the platform virtual machine and the load of the switch;
and the mapping reconstruction unit is configured for establishing mapping between the virtual machine with the largest communication volume and the switch with the lightest load to complete platform communication optimization.
5. The system of claim 4, wherein the CPU computing unit comprises:
the time acquisition module is configured for acquiring the residual lease time of the tenant and the time required for processing the residual tasks;
the occupation acquisition module is configured for acquiring the number of CPUs currently occupied by the tenant;
the efficiency calculation module is configured for acquiring the used time of the tenant processing task and calculating the average CPU efficiency according to the number of the current occupied CPUs and the used time;
and the quantity calculating module is configured for calculating the quantity of the CPUs required by the remaining tasks, which meet the condition that the time required for processing the remaining tasks does not exceed the remaining lease time, according to the average CPU efficiency and the remaining task quantity if the time required by the remaining tasks exceeds the remaining lease time.
6. The system according to claim 4, wherein the map reconstruction unit comprises:
the information traversing module is configured for traversing the communication traffic of the virtual machine;
and the mapping reconstruction module is configured for circularly mapping the virtual machine with the largest communication volume with the switch with the lightest load.
7. A terminal, comprising:
a processor;
a memory for storing instructions for execution by the processor;
wherein the processor is configured to perform the method of any one of claims 1-3.
8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-3.
CN201910892420.8A 2019-09-20 2019-09-20 Cloud computing platform optimized deployment method, system, terminal and storage medium Active CN110780992B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910892420.8A CN110780992B (en) 2019-09-20 2019-09-20 Cloud computing platform optimized deployment method, system, terminal and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910892420.8A CN110780992B (en) 2019-09-20 2019-09-20 Cloud computing platform optimized deployment method, system, terminal and storage medium

Publications (2)

Publication Number Publication Date
CN110780992A true CN110780992A (en) 2020-02-11
CN110780992B CN110780992B (en) 2023-01-10

Family

ID=69384165

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910892420.8A Active CN110780992B (en) 2019-09-20 2019-09-20 Cloud computing platform optimized deployment method, system, terminal and storage medium

Country Status (1)

Country Link
CN (1) CN110780992B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112671917A (en) * 2020-12-28 2021-04-16 苏州浪潮智能科技有限公司 Switch idle calculation power calling method and related equipment

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150067170A1 (en) * 2013-08-29 2015-03-05 Telefonaktiebolaget L M Ericsson (Publ) Method and system to allocate bandwidth based on task deadline in cloud computing networks
CN107045457A (en) * 2016-02-05 2017-08-15 华为技术有限公司 A kind of method and device of multi-tenant resource-sharing
CN107342895A (en) * 2017-06-26 2017-11-10 网宿科技股份有限公司 A kind of network optimized approach of multi-tenant, system, computing device and storage medium

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150067170A1 (en) * 2013-08-29 2015-03-05 Telefonaktiebolaget L M Ericsson (Publ) Method and system to allocate bandwidth based on task deadline in cloud computing networks
CN107045457A (en) * 2016-02-05 2017-08-15 华为技术有限公司 A kind of method and device of multi-tenant resource-sharing
CN107342895A (en) * 2017-06-26 2017-11-10 网宿科技股份有限公司 A kind of network optimized approach of multi-tenant, system, computing device and storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112671917A (en) * 2020-12-28 2021-04-16 苏州浪潮智能科技有限公司 Switch idle calculation power calling method and related equipment

Also Published As

Publication number Publication date
CN110780992B (en) 2023-01-10

Similar Documents

Publication Publication Date Title
CN104468803B (en) A kind of virtual data center method for mapping resource and equipment
CN104283951B (en) The method, apparatus and system of a kind of instance migration
CN111182037B (en) Mapping method and device of virtual network
CN111104219A (en) Binding method, device, equipment and storage medium of virtual core and physical core
CN111262753B (en) Method, system, terminal and storage medium for automatically configuring number of NUMA nodes
CN115460216A (en) Calculation force resource scheduling method and device, calculation force resource scheduling equipment and system
WO2020108536A1 (en) Virtual network resource allocation method and system and electronic device
CN110780992B (en) Cloud computing platform optimized deployment method, system, terminal and storage medium
CN115640113A (en) Multi-plane flexible scheduling method
CN112486664A (en) Node capacity expansion method, system, terminal and storage medium
CN112163734A (en) Cloud platform based dynamic scheduling method and device for setting computing resources
CN111124669A (en) Operation method, system, terminal and storage medium of distributed SaaS software
CN110109758A (en) A kind of cloud computing resources distribution method
WO2017133421A1 (en) Method and device for sharing resources among multiple tenants
CN114219468A (en) Micro-service charging method and device based on private container cloud and related components
CN114157583A (en) Reliability-based network resource heuristic mapping method and system
CN111949414B (en) Method, system, terminal and storage medium for optimizing performance of storage controller
CN113377652A (en) Test data generation method and device
CN111240830A (en) Public link contract resource allocation method and device, electronic equipment and storage medium
CN105824708A (en) CPU (Central Processing Unit) scheduling method and device in cloud host system
CN111796932A (en) GPU resource scheduling method
CN112416572A (en) Resource pooling method, system, terminal and storage medium for memory cloud platform
CN111061562B (en) Software collaborative packaging method, system, terminal and storage medium
US20230195527A1 (en) Workload distribution by utilizing unused central processing unit capacity in a distributed computing system
CN109144231B (en) Virtualized power management method and device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant