CN110597633A - Method and system for intelligent cooperative network group elastic expansion - Google Patents
Method and system for intelligent cooperative network group elastic expansion Download PDFInfo
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- 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
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
- G06F9/5022—Mechanisms to release resources
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- 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
Abstract
The invention provides a method and a system for intelligent cooperative network group elastic expansion, which are used for realizing the elastic expansion of a network group as required, periodically monitoring the resource utilization rate condition of each virtual component of the group, calling a threshold decision through the monitored resource utilization rate condition, allowing a capacity expansion mechanism to be triggered when the network capacity is overloaded, dynamically increasing the resources allocated to the network under the condition that the network service is not interrupted, and ensuring the user experience; and when the utilization rate of the network resources is too low, the resources allocated to the network are dynamically reduced, and the resource utilization rate of the computing network is improved.
Description
Technical Field
The invention relates to the technical field of internet, in particular to a method and a system for intelligent cooperative network group elastic expansion.
Background
In a computer network, a network service provided by an operator is composed of a series of network devices with special functions as required, for example, a security monitoring service may include special devices such as a firewall, intrusion detection, load balancing and the like. However, the traditional deployment mode based on dedicated hardware devices highly couples these network function middleware with the underlying hardware devices, and has the disadvantage that the deployment, operation and expansion are extremely difficult, and it is not possible to flexibly provide services for users as required and ensure user experience.
In order to solve the above drawbacks, various new network technologies have been proposed in recent years. For example, internationally, Software Defined Networking (SDN) implements on-demand forwarding of data streams based on a concept of separation of control and forwarding, so as to greatly improve network programmability and dynamic adjustment capability. Network Function Virtualization (NFV) is to implement decoupling of various Network functions and dedicated physical devices through a virtualization technology, so as to significantly improve resource adaptation efficiency and reduce service deployment cost. The intelligent cooperative network is provided in China and aims to separate the triple binding characteristics of the existing IP network, namely 'user and network separation', 'resource and position separation' and 'control and data separation', so that the management and control capability and flexibility of the network are comprehensively improved, and efficient network service is improved for the user according to needs. Specifically, the intelligent cooperative network is based on a "three-layer and two-domain" model, wherein the three layers are an intelligent service layer, a resource adaptation layer and a network component layer, and the two domains are an entity domain and an action domain. The network component layer is an infrastructure layer of the intelligent cooperative network and is responsible for sensing the performance, the state and the behavior of the components; the resource adaptation layer plays a role in starting and stopping, upwards receives the group demands issued by the intelligent service layer, and downwards schedules the virtual components; the intelligent service layer is mainly responsible for intelligent searching and dynamic matching of services. The entity domain is used for marking functional entities in the intelligent cooperative network, such as service identification, group identification, component identification and the like; the behavior domain is used for describing the behavior characteristics of the functional entity, such as service behavior description, ethnic group behavior description, component behavior description and the like.
The family group arrangement is a key concept of intelligent cooperative network resource adaptation and is also a solution way for realizing efficient deployment of network services according to needs. In the intelligent cooperative network, an intelligent service layer can analyze service requirements into group requirements, and then a group arrangement mechanism realizes the establishment and overtime disassembly of corresponding groups according to an upper layer analysis result. However, considering that the user needs are changing in the actual scene, the deviation of the pre-application resource capacity is inevitable, and the deployed service group also has the capability of dynamically increasing or decreasing the resource capacity, so as to improve the system operation efficiency while meeting the service quality.
Disclosure of Invention
The embodiment of the invention provides a method and a system for intelligent cooperative network group elastic expansion, which are used for solving the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
A method for intelligent cooperative network population elastic expansion comprises:
periodically monitoring the resource utilization rate condition of each virtual component of the group;
determining whether the clan group is flexible or not through a threshold value based on the resource utilization rate condition of each virtual component of the clan group in a monitoring period;
evaluating a population stretch specification based on the population stretch decision;
and matching the database and the template library based on the evaluation result of the population expansion specification to perform population expansion operation.
Preferably, the step of periodically monitoring the resource utilization of each virtual component of the population comprises: periodically monitoring the resource utilization rate condition of each virtual component of the monitoring group in a monitoring period; the resource utilization conditions include virtual component CPU utilization, memory utilization, and bandwidth utilization.
Preferably, the determining whether the population is scaled by a threshold value based on the resource utilization of each virtual component of the population in a monitoring period includes:
if a virtual component needing to be extended or retracted exists, adding the virtual component into a corresponding preset extended virtual component list or retracted virtual component list, and executing a group expansion decision-based step of evaluating a group expansion specification;
and if the virtual components needing to be extended or retracted do not exist, judging that the cluster does not stretch, and waiting for the next monitoring period to execute the step of periodically monitoring the resource utilization rate condition of each virtual component of the cluster.
Preferably, based on the population scaling decision, evaluating the population scaling specification comprises:
evaluating the population stretch specification comprises evaluating a population overhang operation and a population collapse operation;
the group overhanging operation comprises the step of expanding virtual components with the same specification as each virtual component in a preset overhanging virtual component list; the virtual component specification comprises the CPU number, the memory space, the hard disk space and the service function borne by the virtual component of the virtual component;
the population retraction operation includes deleting each virtual component in a preset retraction virtual component list.
Preferably, the matching of the evaluation result based on the population scaling specification with the database and the template library, the population scaling operation including:
the database comprises a component information database and a flow table rule information database;
the component information database comprises an ethnic group and a port pair corresponding to a virtual component contained in the ethnic group, and the flow table rule information database comprises the ethnic group, a flow table rule, a port pair group and a port pair;
the template library comprises a mirror image resource library, a virtual component template library, template indexes and template descriptions corresponding to the mirror image resource library and the virtual component template library respectively, wherein the template indexes and the template descriptions are key value pairs in one-to-one correspondence.
Preferably, the matching of the evaluation result based on the population scaling specification with the database and the template library, the population scaling operation includes the following sub-steps:
based on the port pair corresponding to the virtual component, deleting the port pair and updating the flow table rule information database, deleting the virtual component corresponding to the port pair and updating the component information database;
and constructing a new virtual component based on the template index corresponding to the virtual component template library, constructing a new port pair corresponding to the new virtual component, updating the component information database, adding the new port pair into the port pair group of the flow mark rule information database, and updating the flow mark rule information database.
In a second aspect, the present invention provides a system for intelligent cooperative network population elastic expansion and contraction for executing the above method, comprising:
the virtual resource sensing module is used for periodically monitoring the resource utilization rate condition of each virtual component of the family group;
the elastic expansion module is used for deciding whether the clan group expands or contracts through a threshold value based on the resource utilization rate condition of each virtual component of the clan group in a monitoring period; and for evaluating a population stretch specification based on the population stretch decision;
and the matching module is used for matching the database and the template library based on the evaluation result to perform the population expansion operation.
Preferably, the virtual resource awareness module includes: the system comprises a visual configuration interface, a command line configuration interface, a CPU perception module and a memory perception module;
the visual configuration interface and the command line configuration interface are used for receiving an instruction of an external user terminal, wherein the instruction comprises a step of judging whether to start to execute the resource utilization rate condition of each virtual component of the periodic monitoring group, and a threshold value used for configuring the monitoring period and the resource utilization rate of the virtual components;
the CPU perception module is used for perceiving the CPU utilization rate of each virtual component of the group;
the memory sensing module is used for sensing the memory utilization rate of each virtual component of the group.
Preferably, the elastic expansion module includes: the system comprises a virtual resource utilization rate receiving module, a trigger expansion module and an elasticity evaluation module;
the virtual resource utilization receiving module is used for receiving the resource utilization of each virtual assembly in the family group and the resource utilization threshold of each virtual assembly;
the triggering expansion module is used for deciding whether the clan group expands or contracts based on the resource utilization rate of each virtual component of the clan group in a monitoring period and the resource utilization rate threshold of each virtual component;
when the triggering expansion module finds that a virtual component with the virtual component CPU utilization rate or the virtual component memory utilization rate higher than a preset virtual component resource utilization rate reference threshold exists, the triggering expansion module adds the virtual component into a corresponding preset overhanging virtual component list;
when the triggering expansion module finds that a virtual component with a virtual component CPU utilization rate or a virtual component memory utilization rate lower than a preset virtual component resource utilization rate reference threshold exists and other virtual components with the same service functions as those borne by the virtual component exist in the group, the triggering expansion module adds the virtual component into a corresponding preset contracted virtual component list;
when the triggering expansion module does not find that the virtual component CPU utilization rate or the virtual component memory utilization rate is higher or lower than the virtual component resource utilization rate threshold value, the triggering expansion module judges that the family group does not expand and wait for the next monitoring period;
the elasticity evaluation module is used for evaluating the population expansion specification based on the population expansion decision; evaluating the population stretch specification comprises evaluating a population overhang operation and a population collapse operation;
the group overhanging operation comprises the step of expanding virtual components with the same specification as each virtual component in a preset overhanging virtual component list; the virtual component specification comprises the CPU number, the memory space, the hard disk space and the service function borne by the virtual component of the virtual component;
the population retraction operation includes deleting each virtual component in a preset retraction virtual component list.
Preferably, the matching module includes: the system comprises a database matching module, a template library matching module and an instruction issuing module;
the database matching module is used for matching a database based on a preset overhanging virtual component list and/or an inwardly shrinking virtual component list; the database matching includes: matching virtual component template descriptions and mirror image descriptions corresponding to the overhanging virtual components; matching a port pair corresponding to the retracted virtual component;
the template library matching module is used for matching a template library for the group overhanging operation, and obtaining a template index and a mirror image index which respectively correspond to the template description and the mirror image description according to the template description and the mirror image description obtained by the database matching module;
the instruction issuing module is used for calling an external downstream system and issuing an extended and/or retracted instruction, and the instruction comprises: issuing a virtual component creating instruction based on the template index and the mirror image index; and issuing an instruction for deleting the virtual component and the port pair corresponding to the virtual component.
It can be seen from the technical solutions provided by the embodiments of the present invention that, the method and system for intelligent cooperative network population elastic expansion and contraction provided by the present invention are used to realize the elastic expansion and contraction of the network population as required, and can automatically add new virtual components or allocate more resources to the population during the peak period of the user demand to maintain the network performance, and automatically release the virtual components or reduce the allocated resources during the idle period. Therefore, the method meets the user requirements under different conditions, reduces the cost of the adaptive network, saves the bottom hardware resources and reduces the economic expenditure. .
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 technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a flowchart illustrating a method for intelligent cooperative network population elastic expansion according to the present invention;
FIG. 2 is a schematic flowchart illustrating a second and a third steps of a method for intelligent cooperative network population elastic expansion and contraction according to the present invention;
FIG. 3 is a schematic flowchart illustrating a fourth step of the method for intelligent cooperative network population elastic expansion according to the present invention;
FIG. 4 is a block diagram of a system for intelligent cooperative network population elastic expansion and contraction according to the present invention;
FIG. 5 is a schematic flow chart of a system for intelligent cooperative network population elastic expansion and contraction according to the present invention;
FIG. 6 is a block diagram illustrating an exemplary intelligent collaborative network population architecture using the method and system provided by the present invention;
FIG. 7 is a flow chart of elastic expansion and contraction using the intelligent cooperative network population of FIG. 6.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, 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. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
Referring to fig. 1, the method for elastically stretching a smart cooperative network population provided by the present invention is mainly used for elastically stretching a resource adaptation layer population in a smart cooperative network, and includes:
periodically monitoring the resource utilization rate condition of each virtual component of the group;
determining whether the clan group is flexible or not through a threshold value based on the resource utilization rate condition of each virtual component of the clan group in a monitoring period;
evaluating a population stretch specification based on the population stretch decision;
and matching the database and the template library based on the evaluation result to perform the population expansion operation.
The method provided by the invention allows triggering the capacity expansion mechanism when the network capacity is overloaded, and dynamically increases the resources allocated to the network under the condition of uninterrupted network service, thereby ensuring the user experience; and when the utilization rate of the network resources is too low, the resources allocated to the network are dynamically reduced, and the resource utilization rate of the computing network is improved.
It should be understood that the group in the intelligent collaborative network is a group of user devices classified according to behavior, function, etc., which includes, but is not limited to, user computers, servers, sensors, etc.; for example, the division of the group is based on a specific behavior, different types of behaviors can have different group division modes, and after the network group is formed, each group has certain behavior similarity, so that the specific behavior characteristics of the network group resource module are formed; the population can be divided into a core population and an access population according to topological properties; further, the method can be further subdivided into a plurality of regional sub-groups according to specific positions, such as a high-performance group, a medium-performance group and a low-performance group; according to the function, the method can be divided into: server population, transmission device population, sensor population, network storage population, intelligent management population, etc.
Further, in some preferred embodiments, as shown in fig. 2, the step of periodically monitoring the resource utilization of each virtual component of the population includes: the periodic monitoring is to be understood as monitoring the resource utilization condition of each virtual component of the group in one monitoring period, wherein one monitoring period can be set according to the actual condition; the source utilization conditions include virtual component CPU utilization, memory utilization, and bandwidth utilization.
Further, in some preferred embodiments, as shown in fig. 2, the step of deciding whether the population is scaled by a threshold based on the resource utilization of each virtual component of the population in a monitoring period includes:
if a virtual component needing to be extended or retracted exists, adding the virtual component into a corresponding preset extended virtual component list or retracted virtual component list, and executing the group-based expansion decision to evaluate the expansion specification of the group;
if the virtual component needing to be extended or retracted does not exist, judging that the cluster does not stretch, and waiting for the next monitoring period to execute the resource utilization rate condition of each virtual component of the periodic monitoring cluster;
the above-mentioned manner of making a decision by the threshold is to obtain a threshold of resource utilization rate of each virtual component of the group in a monitoring period, and add the virtual component into a preset overhanging virtual component list when finding that there is a virtual component whose resource utilization rate is higher than a preset reference threshold of virtual component resource utilization rate; when a virtual component with a virtual component resource utilization rate lower than a preset virtual component resource utilization rate reference threshold value is found to exist, and other virtual components with the same service functions as those carried by the virtual component exist in the family group, the virtual component is added into a preset contracted virtual component list.
Further, in some preferred embodiments, as shown in fig. 2, evaluating the population stretch specification based on the population stretch decision comprises:
evaluating the population stretch specification comprises evaluating a population overhang operation and a population collapse operation;
the group overhang operation comprises the step of expanding virtual components with the same specification as each virtual component in a preset overhang virtual component list; the virtual component specification comprises the CPU number, the memory space, the hard disk space and the service function borne by the virtual component of the virtual component;
the population retraction operation includes deleting each virtual component in a preset retraction virtual component list.
Further, in some preferred embodiments, as shown in fig. 3, the matching of the evaluation result based on the population scaling specification with the database and the template library, the performing the population scaling operation includes:
the database comprises a component information database and a flow table rule information database;
the component information database comprises an ethnic group and a port pair corresponding to a virtual component contained in the ethnic group, and the flow table rule information database comprises the ethnic group, a flow table rule, a port pair group and a port pair;
the template library comprises a mirror image resource library, a virtual component template library, template indexes and template descriptions which are respectively corresponding to the mirror image resource library and the virtual component template library, wherein the template indexes and the template descriptions are key value pairs which are in one-to-one correspondence;
further, in other preferred embodiments, performing the population scaling operation comprises the sub-steps of:
for the intragroup contraction operation, based on the port pair corresponding to the virtual component, deleting the port pair and updating the flow table rule information database, deleting the virtual component corresponding to the port pair and updating the component information database;
aiming at the out-extending operation of the group, constructing a new virtual component based on the template index corresponding to the virtual component template library, constructing a new port pair corresponding to the new virtual component, updating a component information database, adding the new port pair into the port pair group of the flow mark rule information database, and updating the flow mark rule information database;
it should be understood that the above sub-steps are selectively performed according to preset conditions.
It will be understood by those skilled in the art that the above described types of telescoping operation applications are exemplary only, and that other types of telescoping operation applications, now known or later developed, such as may be suitable for use with embodiments of the present invention, are also within the scope of the present invention and are hereby incorporated by reference.
In a second aspect, the present invention provides a system for intelligent cooperative network population elastic expansion for executing the above method, as shown in fig. 4 and 5, comprising:
a virtual resource sensing module 401, configured to periodically monitor a resource utilization condition of each virtual component of the family group;
an elastic expansion module 402, configured to determine whether the population expands or contracts according to a threshold based on a resource utilization condition of each virtual component of the population in a monitoring period; and for evaluating a population stretch specification based on the population stretch decision;
and a matching module 403, configured to match the database with the template library based on the evaluation result, and perform population expansion and contraction operations.
Further, in some preferred embodiments, as shown in fig. 5, the virtual resource awareness module 401 includes: the system comprises a visual configuration interface 4011, a command line configuration interface 4012, a CPU perception module 4013 of a virtual component and a memory perception module 4014 of the virtual component;
the visual configuration interface 4011 and the command line configuration interface 4012 are configured to receive an instruction from an external user terminal, where the instruction includes a step of whether to start to execute resource utilization of each virtual component of the periodic monitoring population, and a threshold value used to configure a monitoring period and resource utilization of the virtual component;
the CPU perception module 4013 of the virtual component is configured to perceive the CPU utilization of each virtual component of the family group;
the memory sensing module 4014 of the virtual component is configured to sense a memory utilization of each virtual component of the group.
Further, in some preferred embodiments, the elastic expansion module 402 comprises: the system comprises a virtual resource utilization rate receiving module 4021, a trigger scaling module 4022 and an elasticity evaluation module 4023;
the virtual resource utilization receiving module 4021 is configured to receive a resource utilization of each virtual component in the cluster and a resource utilization threshold of each virtual component;
the triggering scaling module 4022 is configured to determine whether the population is scaled based on a resource utilization threshold of each virtual component of the resource utilization of each virtual component of the population in one monitoring period;
when the telescopic trigger module 4022 finds that a virtual component with a virtual component CPU utilization rate or a virtual component memory utilization rate higher than a preset virtual component resource utilization rate reference threshold exists, the telescopic trigger module 4022 adds the virtual component to a corresponding preset overhanging virtual component list;
when the scaling module 4022 is triggered to find that a virtual component with a virtual component CPU utilization rate or a virtual component memory utilization rate lower than a preset virtual component resource utilization rate reference threshold exists and another virtual component with the same service function as the virtual component exists in the family, the scaling module 4022 is triggered to add the virtual component into a corresponding preset scaled-in virtual component list;
when the triggering scaling module 4022 does not find that the utilization rate of the CPU of the virtual component or the utilization rate of the memory of the virtual component is higher or lower than the threshold of the resource utilization rate of the virtual component, the triggering scaling module 4022 determines that the population is not scaled, and waits for the next monitoring period;
the elasticity evaluation module 4023 is configured to evaluate a population expansion specification based on the population expansion decision; evaluating the population stretch specification comprises evaluating a population overhang operation and a population collapse operation;
the group overhanging operation comprises the step of expanding virtual components with the same specification as each virtual component in a preset overhanging virtual component list; the virtual component specification comprises the CPU number, the memory space, the hard disk space and the service function borne by the virtual component of the virtual component;
the population retraction operation includes deleting each virtual component in a preset retraction virtual component list.
Further, in some preferred embodiments, the matching module 403 includes: a database matching module 4031, a template library matching module 4032 and an instruction issuing module 4033;
the database matching module 4031 is used for performing database matching based on a preset overhanging virtual component list and/or a shrinking virtual component list; the database matching includes: matching virtual component template descriptions and mirror image descriptions corresponding to the overhanging virtual components; matching a port pair corresponding to the retracted virtual component;
the template library matching module 4032 is used for performing template library matching on the group overhanging operation, and obtaining a template index and a mirror image index respectively corresponding to the template description and the mirror image description according to the template description and the mirror image description obtained by the database matching module;
the instruction issuing module 4033 is configured to invoke an external downstream system to issue an instruction for extension and/or retraction, where the instruction includes: issuing a virtual component creating instruction based on the template index and the mirror image index; and issuing an instruction for deleting the virtual component and the port pair corresponding to the virtual component.
In a third aspect, the present invention provides an embodiment of an intelligent cooperative network population elastic scaling process for exemplarily demonstrating the present invention, which is shown in fig. 6 and 7, and the specific process is as follows:
there is a population 1 as shown in fig. 6, wherein A, B, C represent different service functions, respectively. The user inputs the requirements: and starting the resource utilization rate condition of each virtual component in the periodic monitoring group, wherein each monitoring period is 1 minute, and the selected CPU and memory utilization rate threshold values are 20-80%.
And monitoring the resource utilization rate condition of each virtual component in the group by the virtual resource sensing module according to the requirement input by the user. In a monitoring period, the CPU utilization rate of the virtual component a is 83%, the memory utilization rate is 72%, the CPU utilization rate of the virtual component B1 is 11%, the memory utilization rate is 5%, the CPU utilization rate of the virtual component B2 is 55%, the memory utilization rate is 30%, the CPU utilization rate of the virtual component C is 30%, and the memory utilization rate is 17%.
And adding the virtual component A into the outward extending virtual component list and adding the virtual component B1 into the inward contracting virtual component list according to the judgment result of triggering the telescopic module.
And for the virtual component A, matching the database to obtain the virtual component template description and the mirror image description of the component A, matching the template library to obtain the template index and the mirror image index of the component A, and finally issuing a command for creating the virtual component A1 according to the template index and the mirror image index. For virtual component B1, the database is matched, the port pair of B1 is obtained, and a command to delete virtual component B1 and its corresponding port pair is issued.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
It should be understood by those skilled in the art that the above-mentioned example flow of intelligent cooperative network group elastic expansion and contraction is only for better describing the technical solution of the embodiment of the present invention, and is not to be construed as limiting the embodiment of the present invention. Any method for determining the resource allocation based on the utilization of the virtual component resources is included in the scope of the embodiments of the present invention.
In summary, the method and system for intelligent cooperative network population elastic expansion and contraction provided by the present invention are used to realize the elastic expansion and contraction of the network population as required, periodically monitor the resource utilization condition of each virtual component of the population, invoke a threshold decision through the monitored resource utilization condition, allow triggering an expansion mechanism when the network capacity is overloaded, and dynamically increase the resources allocated to the network under the condition that the network service is not interrupted, thereby ensuring the user experience; meanwhile, when the utilization rate of network resources is too low, the resources allocated to the network are dynamically reduced, and the resource utilization rate of the computing network is improved; therefore, the method meets the user requirements under different conditions, reduces the cost of the adaptive network, saves the bottom hardware resources and reduces the economic expenditure.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for intelligent cooperative network population elastic expansion, comprising:
periodically monitoring the resource utilization rate condition of each virtual component of the group;
determining whether the clan group is flexible or not through a threshold value based on the resource utilization rate condition of each virtual component of the clan group in a monitoring period;
evaluating a population stretch specification based on the population stretch decision;
and matching the database and the template library based on the evaluation result of the population expansion specification to perform population expansion operation.
2. The method of claim 1, wherein the step of periodically monitoring the resource utilization of each virtual component of the population comprises: the periodic monitoring is the resource utilization rate condition of each virtual component of the monitoring group in one monitoring period; the resource utilization condition comprises virtual component CPU utilization, memory utilization and bandwidth utilization.
3. The method of claim 1, wherein the determining whether the population is scaled by a threshold based on resource utilization of each virtual component of the population during a monitoring period comprises:
if a virtual component needing to be extended or retracted exists, adding the virtual component into a corresponding preset extended virtual component list or retracted virtual component list, and executing the group-based expansion decision to evaluate the expansion specification of the group;
and if the virtual components needing to be extended or retracted do not exist, judging that the cluster does not stretch, and waiting for the next monitoring period to execute the resource utilization rate condition of each virtual component of the periodic monitoring cluster.
4. The method of claim 1, wherein evaluating a population scaling specification based on the population scaling decision comprises:
the estimated population stretch specification comprises an estimation of population overhang and retract operations;
the group overhang operation comprises the step of expanding virtual components with the same specification as each virtual component in a preset overhang virtual component list; the virtual component specification comprises the CPU number, the memory space, the hard disk space and the service function borne by the virtual component of the virtual component;
the population retraction operation includes deleting each virtual component in a preset retraction virtual component list.
5. The method of claim 1, wherein the population scaling operation performed based on the evaluation result of the population scaling specification matching the database and the template library comprises:
the database comprises a component information database and a flow table rule information database;
the component information database comprises a family group and a port pair corresponding to a virtual component contained in the family group, and the flow table rule information database comprises the family group, a flow table rule, a port pair group and a port pair;
the template library comprises a mirror image resource library, a virtual component template library, template indexes and template descriptions corresponding to the mirror image resource library and the virtual component template library respectively, wherein the template indexes and the template descriptions are key value pairs in one-to-one correspondence.
6. The method of claim 5, wherein the population scaling operation based on the evaluation result of the population scaling specification matching the database and the template library comprises the sub-steps of:
based on the port pair corresponding to the virtual component, deleting the port pair and updating a flow table rule information database, deleting the virtual component corresponding to the port pair and updating a component information database;
and constructing a new virtual component based on the template index corresponding to the virtual component template library, constructing a new port pair corresponding to the new virtual component, updating a component information database, adding the new port pair into the port pair group of the flow mark rule information database, and updating the flow mark rule information database.
7. A system for intelligent cooperative network population elastic expansion, comprising:
the virtual resource sensing module is used for periodically monitoring the resource utilization rate condition of each virtual component of the family group;
the elastic expansion module is used for deciding whether the clan group expands or contracts through a threshold value based on the resource utilization rate condition of each virtual component of the clan group in a monitoring period; and for evaluating a population stretch specification based on the population stretch decision;
and the matching module is used for matching the database and the template library based on the evaluation result to perform the population expansion operation.
8. The system of claim 7, wherein the virtual resource aware module comprises: the system comprises a visual configuration interface, a command line configuration interface, a CPU perception module and a memory perception module;
the visual configuration interface and the command line configuration interface are used for receiving an instruction of an external user terminal, wherein the instruction comprises a step of judging whether to start to execute the resource utilization rate condition of each virtual component of the periodic monitoring swarm, and a threshold value used for configuring the monitoring period and the resource utilization rate of the virtual component;
the CPU perception module is used for perceiving the CPU utilization rate of each virtual component of the family group;
the memory sensing module is used for sensing the memory utilization rate of each virtual component of the group.
9. The system of claim 7, wherein the elastic expansion module comprises: the system comprises a virtual resource utilization rate receiving module, a trigger expansion module and an elasticity evaluation module;
the virtual resource utilization receiving module is used for receiving the resource utilization of each virtual component in the family group and the resource utilization threshold of each virtual component;
the triggering expansion module is used for deciding whether the clan group expands or contracts based on the resource utilization rate of each virtual component of the clan group in a monitoring period and the resource utilization rate threshold of each virtual component;
when the trigger expansion module finds that a virtual component with a virtual component CPU utilization rate or a virtual component memory utilization rate higher than a preset virtual component resource utilization rate reference threshold exists, the trigger expansion module adds the virtual component into a corresponding preset overhanging virtual component list;
when the triggering expansion module finds that a virtual component with a virtual component CPU utilization rate or a virtual component memory utilization rate lower than a preset virtual component resource utilization rate reference threshold exists and other virtual components with the same service functions as those borne by the virtual component exist in the family, the triggering expansion module adds the virtual component into a corresponding preset contracted virtual component list;
when the triggering expansion module does not find that the utilization rate of a CPU of the virtual component exists or the utilization rate of a memory of the virtual component is higher or lower than the threshold value of the resource utilization rate of the virtual component, the triggering expansion module judges that the family group does not expand and waits for the next monitoring period;
the elasticity evaluation module is used for evaluating the population expansion specification based on the population expansion decision; the estimated population stretch specification comprises an estimation of population overhang and retract operations;
the group overhang operation comprises the step of expanding virtual components with the same specification as each virtual component in a preset overhang virtual component list; the virtual component specification comprises the CPU number, the memory space, the hard disk space and the service function borne by the virtual component of the virtual component;
the population retraction operation includes deleting each virtual component in a preset retraction virtual component list.
10. The system of claim 7, wherein the matching module comprises: the system comprises a database matching module, a template library matching module and an instruction issuing module;
the database matching module is used for matching a database based on a preset overhanging virtual component list and/or an inwardly shrinking virtual component list; the database matching includes: matching virtual component template descriptions and mirror image descriptions corresponding to the overhanging virtual components; matching a port pair corresponding to the retracted virtual component;
the template library matching module is used for matching a template library for the group overhanging operation, and obtaining a template index and a mirror image index which respectively correspond to the template description and the mirror image description according to the template description and the mirror image description obtained by the database matching module;
the instruction issuing module is used for calling an external downstream system and issuing an instruction which extends and/or retracts, and the instruction comprises: issuing a virtual component creating instruction based on the template index and the mirror image index; and issuing an instruction for deleting the virtual component and the port pair corresponding to the virtual component.
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