CN115499512B - Efficient resource allocation method and system based on super-fusion cloud virtualization - Google Patents

Efficient resource allocation method and system based on super-fusion cloud virtualization Download PDF

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CN115499512B
CN115499512B CN202211444125.4A CN202211444125A CN115499512B CN 115499512 B CN115499512 B CN 115499512B CN 202211444125 A CN202211444125 A CN 202211444125A CN 115499512 B CN115499512 B CN 115499512B
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virtual
mapping
physical
resource
cloud
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CN115499512A (en
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武应进
陈骎扬
戴燎元
贺雪飞
谭艳
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Changsha Rongshu Information Technology Co ltd
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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]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5072—Grid computing
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network

Abstract

The invention relates to the technical field of virtualized resource allocation, and discloses a virtualized resource efficient allocation method and system based on a hyper-converged cloud, wherein the method comprises the following steps: the virtual network receives and analyzes the current cloud virtual resource mapping request to form a virtual node sequence; mapping the virtual nodes in the virtual node sequence to physical nodes; solving the virtual link mapping target function by adopting an improved heuristic algorithm, and mapping the virtual link to a physical link; and dynamically determining the resource optimization adjustment time of the physical nodes according to the current quantity of the cloud virtual resource mapping requests, performing optimization adjustment on the mapping virtual resources, and performing mapping processing on the cloud virtual resource mapping requests which are currently received and are not successfully mapped. The invention realizes the mapping of the virtual network to the physical layer by utilizing the heuristic algorithm and the adaptation degree of the virtual nodes and the physical nodes, efficiently distributes the physical layer equipment resources to different virtual network users, and improves the utilization rate of the physical resources.

Description

Efficient resource allocation method and system based on super-fusion cloud virtualization
Technical Field
The invention relates to the technical field of virtualized resource allocation, in particular to a virtualized resource efficient allocation method and system based on a hyper-converged cloud.
Background
With the continuous update of network technologies (such as 5G/6G, edge computing), network function virtualization and software defined networking become the main enabling technologies of the next generation network architecture, a super-fusion cloud platform abstracts different physical resources into resource pools and performs unified management, a novel architecture is constructed, and the problem of efficient and safe resource allocation is always a huge challenge faced by the novel architecture. If the resource allocation is unreasonable, the overall performance of the super-fusion cloud platform is affected by frequent cross-network resource interaction. Aiming at the problem, the patent provides a virtualized resource mapping method based on a super-fusion cloud platform, and efficient allocation of virtual resources based on the super-fusion cloud is realized.
Disclosure of Invention
In view of this, the invention provides a method for efficiently allocating virtualized resources based on a super-fusion cloud, and aims to 1) construct a mapping relationship between a physical node and a virtual node based on the adaptation degree of the physical node and the virtual node, further map the virtual node into the physical node, and solve a virtual link mapping objective function by using a heuristic algorithm, so as to implement a link mapping scheme with the minimum switching times of communication subnets and the highest link mapping profit, implement mapping from a virtual network to a physical layer, and obtain a virtual node sequence network structure containing requested resources, wherein a user can directly call the virtual node sequence network structure to allocate physical resources for a cloud virtual resource mapping request; 2) The method comprises the steps of dynamically determining the optimized adjustment time of physical node resources according to the number of current cloud virtual resource mapping requests, adopting a larger adjustment time interval when the cloud virtual resource mapping requests are increased, or else adopting a smaller adjustment time interval to realize the self-adaptive change of the adjustment time interval, wherein the dynamic change amplitude of the adjustment time interval is related to the preset time interval and the adjustment time interval determined last time, so that the phenomenon that the excessive change of the adjustment time interval causes the existence of more or less cloud virtual resource mapping requests for mapping processing, the computing resources of a virtual network are wasted, the congestion degree of the virtual network is calculated in real time, the mapping virtualized resources in the current super-fusion cloud platform are optimized and adjusted based on the congestion degree of the virtual network, and the mapping processing efficiency of the virtual network is improved.
In order to achieve the purpose, the invention provides a virtualized resource efficient allocation method based on a hyper-converged cloud, which comprises the following steps:
s1: initializing a virtual network and physical nodes, wherein the virtual nodes in the virtual network receive and analyze a current cloud virtual resource mapping request to form a virtual node sequence based on the mapping request;
s2: mapping the virtual nodes in the virtual node sequence to physical nodes, and marking the cloud virtual resource mapping request as an unmapped cloud virtual resource mapping request if no effective physical node exists currently;
s3: constructing a virtual link mapping target function based on mapping profits, and mapping virtual links between adjacent virtual nodes to physical links by adopting an improved heuristic algorithm;
s4: dynamically determining the resource optimization adjustment time of the physical nodes according to the current quantity of the cloud virtual resource mapping requests;
s5: and optimizing and adjusting the mapping virtualization resources in the current super-fusion cloud platform at the time of optimizing and adjusting the resources of the physical nodes, mapping the cloud virtual resource mapping requests which are currently received and not successfully mapped, and allocating the physical resources for the cloud virtual resource mapping requests according to the mapping processing result.
As a further improvement of the method of the invention:
optionally, initializing the virtual network and the physical node in the step S1 includes:
constructing a super-convergence cloud platform architecture, wherein the super-convergence cloud platform architecture comprises a physical layer and a virtual network, the physical layer comprises a storage device, a computing terminal device and physical links among different devices, when a user sends a cloud virtual resource mapping request to the virtual network, the virtual network obtains a virtual node sequence network structure containing the requested resource by establishing mapping with the physical layer, and the user can directly call the virtual node sequence network structure to allocate physical resources for the cloud virtual resource mapping request;
in the embodiment of the present invention, the storage device is a device capable of storing data, and includes a computer, a server, and the like, and the computing terminal device is a computer, a server, and the like capable of executing computing operations;
initializing a virtual network, wherein the virtual network comprises a plurality of virtual nodes, and initializing physical nodes, the physical nodes are devices in a physical layer in a super-convergence cloud platform architecture, and the initialized physical nodes form a graph structure of the physical layer, wherein points in the graph structure are the physical nodes, and edges in the graph structure are communication links between the physical nodes.
Optionally, the receiving and analyzing, by the virtual node in the step S1, the current cloud virtual resource mapping request to form a virtual node sequence based on the mapping request, includes:
the method comprises the steps that a virtual network receives a current cloud virtual resource mapping request, wherein the cloud virtual resource mapping request comprises a storage resource request and a computing resource request, the storage resource request comprises data volume to be stored, and the computing resource request comprises computing complexity;
the virtual network analyzes the cloud virtual resource mapping request to obtain a plurality of storage resource requests and computing resource requests, and distributes the plurality of resource requests obtained by analysis to different virtual nodes to form a virtual node sequence of the current cloud virtual resource mapping request.
Optionally, mapping the virtual nodes in the virtual node sequence to physical nodes in the step S2 includes:
the mapping process of the virtual node comprises the following steps:
s21: calculating the mapping capability of different current physical nodes, and for the physical nodes for storage, calculating the mapping capability power of the physical nodes for storage 1 Comprises the following steps:
Figure 8328DEST_PATH_IMAGE001
wherein:
Figure 394310DEST_PATH_IMAGE002
as in the physical nodeThe mapping capacity of the storage equipment, space is the storage capacity space of the storage equipment, and n represents that n virtual nodes are mapped to the physical node;
for a physical node used for computation, its mapping capability
Figure 642888DEST_PATH_IMAGE003
Is the remaining CPU resource;
mapping capabilities
Figure 506939DEST_PATH_IMAGE002
Less than a storage threshold
Figure 790153DEST_PATH_IMAGE004
And mapping capabilities
Figure 81457DEST_PATH_IMAGE005
Less than the calculated threshold
Figure 817332DEST_PATH_IMAGE006
If no effective physical node exists, the cloud virtual resource mapping request is marked as a cloud virtual resource mapping request which is not mapped successfully, and the mapping operation is terminated;
s22: for any virtual node in the virtual node sequence
Figure 485074DEST_PATH_IMAGE007
Wherein i represents a virtual node
Figure 888373DEST_PATH_IMAGE007
The received resource request is the ith resource request obtained by resolving,
Figure 350578DEST_PATH_IMAGE008
，
Figure 573749DEST_PATH_IMAGE009
representing virtual nodes
Figure 779603DEST_PATH_IMAGE007
The received resource request is a storage resource request,
Figure 771830DEST_PATH_IMAGE010
representing virtual nodes
Figure 404936DEST_PATH_IMAGE007
The received resource request is a computing resource request;
s23: for any virtual node with j =1
Figure 380982DEST_PATH_IMAGE011
And analyzing the received resource request information, wherein the data volume to be stored in the resource request information is
Figure 124948DEST_PATH_IMAGE012
Traversing all physical nodes for storage
Figure 237260DEST_PATH_IMAGE013
Computing physical nodes
Figure 38338DEST_PATH_IMAGE013
And virtual node
Figure 236101DEST_PATH_IMAGE011
Degree of adaptation of
Figure 783757DEST_PATH_IMAGE014
：
Figure 750576DEST_PATH_IMAGE015
Wherein:
Figure 991065DEST_PATH_IMAGE016
representing physical nodes
Figure 410545DEST_PATH_IMAGE013
The remaining storage space of (a);
Figure 761892DEST_PATH_IMAGE017
representing physical nodes
Figure 583217DEST_PATH_IMAGE013
A number of connected communication links;
Figure 260186DEST_PATH_IMAGE018
representing physical nodes
Figure 166962DEST_PATH_IMAGE013
A storage capacity space of (a);
if physical node
Figure 56421DEST_PATH_IMAGE013
If the residual storage space is less than the data amount to be stored, the physical node is enabled
Figure 997832DEST_PATH_IMAGE013
And virtual node
Figure 580123DEST_PATH_IMAGE011
The degree of adaptation of (a) is null; physical node with highest adaptation degree
Figure 974196DEST_PATH_IMAGE013
As arbitrary virtual nodes
Figure 667345DEST_PATH_IMAGE011
Mapping node of
Figure 197684DEST_PATH_IMAGE019
；
S24: for any virtual node with j =2
Figure 947946DEST_PATH_IMAGE020
For the received calculation request informationPerforming analysis, and calculating the complexity of the request information
Figure 829315DEST_PATH_IMAGE021
Traversing all physical nodes used for computation
Figure 326155DEST_PATH_IMAGE022
Computing physical nodes
Figure 976579DEST_PATH_IMAGE022
And virtual node
Figure 900673DEST_PATH_IMAGE020
Degree of adaptation of
Figure 269337DEST_PATH_IMAGE023
：
Figure 304289DEST_PATH_IMAGE024
Wherein:
Figure 74799DEST_PATH_IMAGE025
representing physical nodes
Figure 904215DEST_PATH_IMAGE026
The remaining CPU resources;
physical node with highest adaptation degree
Figure 760176DEST_PATH_IMAGE026
As arbitrary virtual nodes
Figure 598819DEST_PATH_IMAGE020
Of the mapping node
Figure 958256DEST_PATH_IMAGE027
。
Optionally, the constructing a virtual link mapping objective function based on mapping benefits in the step S3 includes:
the mapping benefit mainly comprises link mapping benefit, and for any two continuous virtual nodes
Figure 224152DEST_PATH_IMAGE028
And
Figure 301829DEST_PATH_IMAGE029
between which a virtual link is formed
Figure 944163DEST_PATH_IMAGE030
And the mapping physical nodes corresponding to the virtual nodes are respectively
Figure 423686DEST_PATH_IMAGE031
And
Figure 860484DEST_PATH_IMAGE032
mapping the physical links formed between the physical nodes to
Figure 422528DEST_PATH_IMAGE033
The virtual link mapping objective function is:
Figure 602973DEST_PATH_IMAGE034
wherein:
Figure 937003DEST_PATH_IMAGE035
representing a physical link;
Figure 544701DEST_PATH_IMAGE036
representing a physical link
Figure 862550DEST_PATH_IMAGE033
The remaining bandwidth of;
Figure 581108DEST_PATH_IMAGE037
representing a physical link
Figure 35223DEST_PATH_IMAGE033
The number of subnet switches;
m represents the total number of virtual nodes corresponding to the cloud virtual resource mapping request;
Figure 813823DEST_PATH_IMAGE038
indicating a maximum number of subnet switches.
Optionally, the mapping, in the step S3, the virtual links between adjacent virtual nodes to the physical links by using an improved heuristic algorithm includes:
solving the mapping objective function of the virtual link by adopting an improved heuristic algorithm to obtain a physical link mapped by the virtual link, wherein the mapping process of the virtual link is as follows:
s31: performing explosion operation on the m physical nodes obtained by mapping to generate explosion sparks to surrounding links, wherein the physical nodes
Figure 618968DEST_PATH_IMAGE031
The number of explosion sparks generated was:
Figure 141216DEST_PATH_IMAGE039
wherein:
Figure 449838DEST_PATH_IMAGE040
representing by physical nodes
Figure 399339DEST_PATH_IMAGE031
As a physical link of the origin is,
Figure 160622DEST_PATH_IMAGE041
a function value representing a function of substituting the physical link into the virtual link mapping objective function;
Figure 486561DEST_PATH_IMAGE042
representing by physical nodes
Figure 649689DEST_PATH_IMAGE031
The number of physical links as a starting point;
s32: updating a physical node
Figure 770092DEST_PATH_IMAGE031
Position of
Figure 284250DEST_PATH_IMAGE043
：
Figure 145371DEST_PATH_IMAGE044
Wherein:
Figure 163005DEST_PATH_IMAGE043
representing a physical link
Figure 719889DEST_PATH_IMAGE045
In physical node
Figure 455763DEST_PATH_IMAGE031
The next link intersection point that is the starting point,
Figure 389084DEST_PATH_IMAGE046
a physical link without link crossing;
Figure 261225DEST_PATH_IMAGE047
to represent
Figure 989010DEST_PATH_IMAGE046
Number of nearby explosion sparks;
Figure 212181DEST_PATH_IMAGE048
to represent
Figure 418034DEST_PATH_IMAGE046
With physical nodes
Figure 410261DEST_PATH_IMAGE049
The distance of (d);
s33: returning to the step S31 until a physical link L containing m physical nodes is obtained, and carrying out resource transportation on each physical node through the physical link;
and forming a virtual node sequence network structure containing the request resources by the physical links and the virtual links obtained by mapping, wherein a user can directly call the virtual node sequence network structure to allocate physical resources for the cloud virtual resource mapping request.
Optionally, the dynamically determining, according to the number of the current cloud virtual resource mapping requests in the step S4, a resource optimization adjustment time of the physical node includes:
the determination process of the physical node resource optimization adjustment moment comprises the following steps:
s41: calculating the current variation amplitude of the cloud virtual resource mapping request quantity
Figure 308947DEST_PATH_IMAGE050
：
Figure 753835DEST_PATH_IMAGE051
Wherein:
Figure 763379DEST_PATH_IMAGE053
the number of current cloud virtual resource mapping requests received for the virtual network;
Figure 141271DEST_PATH_IMAGE054
representing the quantity of received cloud virtual resource mapping requests after the last time of physical node resource optimization adjustment;
s42: determining the next time of the resource optimization adjustment of the physical node as t:
Figure 945279DEST_PATH_IMAGE055
Figure 143042DEST_PATH_IMAGE056
Figure 690698DEST_PATH_IMAGE057
wherein:
Figure 678025DEST_PATH_IMAGE058
representing the last time of the resource optimization adjustment of the physical node;
Figure 652934DEST_PATH_IMAGE059
representing a preset physical node resource optimization adjustment time interval;
Figure 337993DEST_PATH_IMAGE060
the method indicates that when the quantity of the cloud virtual resource mapping requests is increased, the physical node resource optimization adjusts the time interval,
Figure 689340DEST_PATH_IMAGE061
indicating the adjustment time interval of the resource optimization adjustment of the physical node when the quantity of the cloud virtual resource mapping requests increases last time;
Figure 510666DEST_PATH_IMAGE062
indicating that when the number of the cloud virtual resource mapping requests is reduced, the physical node resource optimization adjusts the time interval,
Figure 187635DEST_PATH_IMAGE063
and the adjustment time interval of the resource optimization adjustment of the physical node is represented when the quantity of the cloud virtual resource mapping requests is reduced last time. In the embodiment of the invention, when the cloud virtual resource mapping request is increased, a larger adjustment time interval is adopted, otherwise, a smaller adjustment time interval is adopted, the self-adaptive change of the adjustment time interval is realized, and the dynamic change amplitude of the adjustment time interval is related to the preset time interval and the adjustment time interval determined last time, so that the excessive change of the adjustment time interval is avoided, the mapping processing is carried out on more or less cloud virtual resource mapping requests, and the computing resources of a virtual network are wasted.
Optionally, in the step S5, performing optimization adjustment on the mapping virtualized resource in the current hyper-convergence cloud platform at the time of optimization adjustment of the physical node resource, and performing mapping processing on the cloud virtual resource mapping request that is currently received and that is not successfully mapped, includes:
performing optimization adjustment on mapping virtualization resources in the current super-convergence cloud platform at a physical node resource optimization adjustment time t, wherein the mapping virtualization resources are virtual node sequence network structures, when a cloud virtual resource mapping request is successfully processed, mapping between the virtual node sequence network structures and a physical layer is formed in a virtual network, and a user can directly call the virtual node sequence network structures so as to realize calling of resources in the physical layer;
the optimization and adjustment process comprises the following steps:
s51: calculating congestion degree of virtual network in current super-convergence cloud platform
Figure 828831DEST_PATH_IMAGE064
：
Figure 983869DEST_PATH_IMAGE065
Wherein:
Figure 925281DEST_PATH_IMAGE066
representing the current hyper-fusion cloudThe number of cloud virtual resource mapping requests that the virtual network is processing in the station,
Figure 241992DEST_PATH_IMAGE067
representing the number of virtual node sequence network structures which have completed resource mapping and wait to be deleted in the current super-fusion cloud platform when a user is in
Figure 636065DEST_PATH_IMAGE068
When the virtual node sequence network structure is not called in the time interval of (3), the virtual node sequence network structure is deleted from the virtual network and is positioned in
Figure 329214DEST_PATH_IMAGE068
The un-called virtual node sequence network structure of the time interval is the virtual node sequence network structure waiting for deletion;
Figure 125132DEST_PATH_IMAGE069
representing the average processing time length of the cloud virtual resource mapping request currently processed;
s52: when in use
Figure 878324DEST_PATH_IMAGE064
When the congestion value is larger than the preset congestion threshold value, optimizing and adjusting mapping virtualization resources in the super-fusion cloud platform, otherwise, ending the current optimizing and adjusting process;
s53: mapping the virtual node sequence network structure waiting for deletion to a storage node in a physical layer, deleting the virtual node sequence network structure waiting for deletion in the virtual network, and reducing the congestion degree of the virtual network;
s54: and mapping the cloud virtual resource mapping request which is currently received and is not successfully mapped, and allocating physical resources for the cloud virtual resource mapping request.
In order to solve the above problems, the present invention provides a system for efficiently allocating virtualized resources based on a super-converged cloud, the system comprising:
the virtual node mapping device is used for initializing a virtual network and physical nodes, the virtual nodes in the virtual network receive and analyze the current cloud virtual resource mapping request to form a virtual node sequence based on the mapping request, the virtual nodes in the virtual node sequence are mapped to the physical nodes, and if no effective physical node exists at present, the cloud virtual resource mapping request is marked as a cloud virtual resource mapping request which is not mapped successfully;
the virtual link mapping device is used for constructing a virtual link mapping target function based on mapping income and mapping virtual links between adjacent virtual nodes to physical links by adopting an improved heuristic algorithm;
and the resource optimization and adjustment module is used for dynamically determining the physical node resource optimization and adjustment time according to the current cloud virtual resource mapping request quantity, performing optimization and adjustment on the mapping virtual resources in the current super-fusion cloud platform at the physical node resource optimization and adjustment time, performing mapping processing on the currently received cloud virtual resource mapping requests which are not mapped successfully and allocating physical resources to the cloud virtual resource mapping requests according to the mapping processing result.
Compared with the prior art, the invention provides a virtualized resource efficient allocation method based on a hyper-converged cloud, which has the following advantages:
firstly, the present solution provides a mapping method from a virtual network to a physical layer, in which a virtual node in a virtual node sequence is mapped to a physical node, and the mapping process of the virtual node is as follows: calculating the mapping capability of different physical nodes at present, and for the physical nodes for storage, calculating the mapping capability of the different physical nodes
Figure 759692DEST_PATH_IMAGE070
Comprises the following steps:
Figure 990954DEST_PATH_IMAGE001
wherein:
Figure 638448DEST_PATH_IMAGE070
for storage in physical nodesThe mapping capacity of the device, space is the storage capacity space of the storage device, and n represents that n virtual nodes are mapped to the physical node; for a physical node used for computation, its mapping capability
Figure 562542DEST_PATH_IMAGE071
Is the remaining CPU resource; mapping capabilities
Figure 931206DEST_PATH_IMAGE070
Less than a storage threshold
Figure 231738DEST_PATH_IMAGE072
And mapping capabilities
Figure 736668DEST_PATH_IMAGE071
Less than the calculated threshold
Figure 831663DEST_PATH_IMAGE073
If no effective physical node exists, the cloud virtual resource mapping request is marked as a cloud virtual resource mapping request which is not mapped successfully, and the mapping operation is terminated; for any virtual node in the virtual node sequence
Figure 687624DEST_PATH_IMAGE074
Wherein i represents a virtual node
Figure 260688DEST_PATH_IMAGE074
The received resource request is the ith resource request obtained by resolving,
Figure 885704DEST_PATH_IMAGE075
，
Figure 151600DEST_PATH_IMAGE076
representing virtual nodes
Figure 494857DEST_PATH_IMAGE074
The received resource request is a storage resource request,
Figure 137191DEST_PATH_IMAGE077
representing virtual nodes
Figure 616714DEST_PATH_IMAGE074
The received resource request is a computing resource request; for any virtual node with j =1
Figure 53511DEST_PATH_IMAGE078
And analyzing the received resource request information, wherein the data volume to be stored in the resource request information is
Figure 618485DEST_PATH_IMAGE079
Traversing all physical nodes for storage
Figure 533351DEST_PATH_IMAGE080
Computing physical nodes
Figure 132960DEST_PATH_IMAGE080
And virtual node
Figure 6238DEST_PATH_IMAGE078
Degree of adaptation of
Figure 789999DEST_PATH_IMAGE081
：
Figure 508556DEST_PATH_IMAGE015
Wherein:
Figure 962671DEST_PATH_IMAGE082
representing physical nodes
Figure 6850DEST_PATH_IMAGE080
The remaining storage space of (a);
Figure 546416DEST_PATH_IMAGE083
representing physical nodes
Figure 68664DEST_PATH_IMAGE080
The number of connected communication links;
Figure 377286DEST_PATH_IMAGE084
representing physical nodes
Figure 326787DEST_PATH_IMAGE080
A storage capacity space of (a); if physical node
Figure 88070DEST_PATH_IMAGE080
If the residual storage space is less than the data amount to be stored, the physical node is enabled
Figure 414009DEST_PATH_IMAGE080
And virtual node
Figure 577137DEST_PATH_IMAGE078
Is empty; physical node with highest adaptation degree
Figure 697540DEST_PATH_IMAGE080
As arbitrary virtual nodes
Figure 211698DEST_PATH_IMAGE078
Of the mapping node
Figure 75749DEST_PATH_IMAGE085
(ii) a For any virtual node with j =2
Figure 358962DEST_PATH_IMAGE086
Analyzing the received calculation request information, wherein the calculation complexity in the calculation request information is
Figure 915846DEST_PATH_IMAGE087
Traversing all physical nodes used for computation
Figure 648791DEST_PATH_IMAGE088
Computing physicsNode point
Figure 50953DEST_PATH_IMAGE088
And virtual node
Figure 188674DEST_PATH_IMAGE089
Degree of adaptation of
Figure 916458DEST_PATH_IMAGE090
：
Figure 139629DEST_PATH_IMAGE024
Wherein:
Figure 611062DEST_PATH_IMAGE091
representing physical nodes
Figure 603289DEST_PATH_IMAGE088
The remaining CPU resources; physical node with highest adaptation degree
Figure 970816DEST_PATH_IMAGE088
As arbitrary virtual nodes
Figure 946862DEST_PATH_IMAGE089
Mapping node of
Figure 956407DEST_PATH_IMAGE092
. Constructing a virtual link mapping objective function based on mapping benefits, wherein the mapping benefits mainly comprise link mapping benefits, and for any two continuous virtual nodes
Figure 803140DEST_PATH_IMAGE093
And
Figure 872727DEST_PATH_IMAGE094
between which a virtual link is formed
Figure 804911DEST_PATH_IMAGE095
And is deficiency ofThe mapping physical nodes corresponding to the pseudo-nodes are respectively
Figure 883725DEST_PATH_IMAGE085
And
Figure 584965DEST_PATH_IMAGE096
mapping physical links formed between physical nodes to
Figure 825454DEST_PATH_IMAGE097
The virtual link mapping objective function is:
Figure 510513DEST_PATH_IMAGE034
wherein:
Figure 858930DEST_PATH_IMAGE098
representing a physical link;
Figure 680256DEST_PATH_IMAGE099
representing a physical link
Figure 826066DEST_PATH_IMAGE100
The remaining bandwidth of;
Figure 732842DEST_PATH_IMAGE101
representing a physical link
Figure 887880DEST_PATH_IMAGE100
The number of subnet switches; m represents the total number of virtual nodes corresponding to the cloud virtual resource mapping request;
Figure 563712DEST_PATH_IMAGE102
indicating the maximum number of subnet switches. And solving the virtual link mapping target function by adopting an improved heuristic algorithm to obtain a physical link mapped by the virtual link. According to the scheme, the mapping relation between the physical nodes and the virtual nodes is constructed based on the adaptation degree of the physical nodes and the virtual nodes, and then the virtual nodes are mapped to the physical nodesIn the nodes, a heuristic algorithm is utilized to solve the virtual link mapping objective function, a link mapping scheme with the minimum communication subnet switching times and the highest link mapping profit is realized, the mapping from the virtual network to the physical layer is realized, a virtual node sequence network structure containing the requested resources is obtained, and a user can directly call the virtual node sequence network structure to allocate the physical resources for the cloud virtual resource mapping request.
Meanwhile, the scheme provides a virtual network optimization method, which is used for optimizing the virtual network according to the number of the current cloud virtual resource mapping requests received by the virtual network
Figure 146003DEST_PATH_IMAGE103
Dynamically determining the optimal adjustment time of the physical node resources, wherein the determination process of the optimal adjustment time of the physical node resources comprises the following steps: calculating the current variation amplitude of the cloud virtual resource mapping request quantity
Figure 540075DEST_PATH_IMAGE050
：
Figure 233225DEST_PATH_IMAGE051
Wherein:
Figure 29143DEST_PATH_IMAGE053
the number of current cloud virtual resource mapping requests received for the virtual network;
Figure 47914DEST_PATH_IMAGE104
representing the quantity of received cloud virtual resource mapping requests after the last time of physical node resource optimization adjustment; determining the next time of the resource optimization adjustment of the physical node as t:
Figure 663703DEST_PATH_IMAGE055
Figure 160544DEST_PATH_IMAGE056
Figure 810968DEST_PATH_IMAGE057
wherein:
Figure 735062DEST_PATH_IMAGE105
representing the last time of resource optimization adjustment of the physical node;
Figure 103726DEST_PATH_IMAGE106
representing a preset physical node resource optimization adjustment time interval;
Figure 138678DEST_PATH_IMAGE107
indicating that when the quantity of the cloud virtual resource mapping requests is increased, the physical node resource optimization adjusts the time interval,
Figure 640679DEST_PATH_IMAGE108
indicating the adjustment time interval of the resource optimization adjustment of the physical node when the quantity of the cloud virtual resource mapping requests is increased last time;
Figure 735674DEST_PATH_IMAGE109
the resource optimization adjustment time interval of the physical node is adjusted when the number of the cloud virtual resource mapping requests is reduced,
Figure 591635DEST_PATH_IMAGE110
and the adjustment time interval of the resource optimization adjustment of the physical node is represented when the quantity of the cloud virtual resource mapping requests is reduced last time. Performing optimization adjustment on mapping virtualization resources in the current super-convergence cloud platform at a physical node resource optimization adjustment time t, wherein the mapping virtualization resources are virtual node sequence network structures, when a cloud virtual resource mapping request is successfully processed, mapping between the virtual node sequence network structures and a physical layer is formed in a virtual network, and a user can directly call the virtual node sequence network structures so as to realize calling of resources in the physical layer; wherein the optimization and adjustment process comprises: calculating congestion degree of virtual network in current super-convergence cloud platform
Figure 430278DEST_PATH_IMAGE111
：
Figure 55294DEST_PATH_IMAGE065
Wherein:
Figure 321190DEST_PATH_IMAGE112
represents the number of cloud virtual resource mapping requests processed by the virtual network in the current super-convergence cloud platform,
Figure 398868DEST_PATH_IMAGE113
representing the number of virtual node sequence network structures which have completed resource mapping and wait to be deleted in the current super-fusion cloud platform when a user is in
Figure 775622DEST_PATH_IMAGE114
When the virtual node sequence network structure is not called in the time interval of (3), the virtual node sequence network structure is deleted from the virtual network and is positioned in
Figure 255145DEST_PATH_IMAGE114
The non-called virtual node sequence network structure of the time interval is the virtual node sequence network structure waiting for deletion;
Figure 957522DEST_PATH_IMAGE115
representing the average processing time of the cloud virtual resource mapping request currently processed; when in use
Figure 522496DEST_PATH_IMAGE111
If the congestion value is larger than the preset congestion threshold value, optimizing and adjusting the mapping virtualization resources in the super-fusion cloud platform, otherwise, ending the current optimizing and adjusting process; mapping the virtual node sequence network structure waiting for deletion to the storage nodes in the physical layer, and deleting the virtual nodes waiting for deletion in the virtual networkThe sequence network structure reduces the congestion degree of the virtual network; and mapping the cloud virtual resource mapping request which is currently received and is not successfully mapped, and allocating physical resources for the cloud virtual resource mapping request. According to the scheme, the optimized adjustment time of the physical node resources is dynamically determined according to the number of the current cloud virtual resource mapping requests, when the cloud virtual resource mapping requests are increased, a larger adjustment time interval is adopted, otherwise, a smaller adjustment time interval is adopted, the self-adaptive change of the adjustment time interval is realized, the dynamic change amplitude of the adjustment time interval is related to the preset time interval and the adjustment time interval determined last time, the excessive change of the adjustment time interval is avoided, the mapping processing of more or less cloud virtual resource mapping requests is avoided, the computing resources of a virtual network are wasted, the congestion degree of the virtual network is calculated in real time, the mapping virtualized resources in the current super-fusion cloud platform are optimized and adjusted based on the congestion degree of the virtual network, and the mapping processing efficiency of the virtual network is improved.
Drawings
Fig. 1 is a schematic flowchart of a virtualized resource efficient allocation method based on a hyper-converged cloud according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a system for efficiently allocating resources based on a super-converged cloud virtualization according to an embodiment of the present invention.
The implementation, functional features and advantages of the present invention will be further described with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a virtualized resource efficient allocation method based on a super-fusion cloud. The execution subject of the efficient resource allocation method based on the super-converged cloud includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the efficient resource allocation method based on the super-converged cloud may be executed by software or hardware installed in the terminal device or the server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1:
s1: initializing a virtual network and physical nodes, and receiving and analyzing a current cloud virtual resource mapping request by the virtual nodes in the virtual network to form a virtual node sequence based on the mapping request.
Initializing a virtual network and a physical node in the step S1 includes:
constructing a super-convergence cloud platform architecture, wherein the super-convergence cloud platform architecture comprises a physical layer and a virtual network, the physical layer comprises a storage device, a computing terminal device and physical links among different devices, when a user sends a cloud virtual resource mapping request to the virtual network, the virtual network obtains a virtual node sequence network structure containing the requested resource by establishing mapping with the physical layer, and the user can directly call the virtual node sequence network structure to allocate physical resources for the cloud virtual resource mapping request;
initializing a virtual network, wherein the virtual network comprises a plurality of virtual nodes, and initializing physical nodes, the physical nodes are devices in a physical layer in a super-convergence cloud platform architecture, and the initialized physical nodes form a graph structure of the physical layer, wherein points in the graph structure are the physical nodes, and edges in the graph structure are communication links between the physical nodes.
In the step S1, the virtual node receives and analyzes the current cloud virtual resource mapping request to form a virtual node sequence based on the mapping request, including:
the method comprises the steps that a virtual network receives a current cloud virtual resource mapping request, wherein the cloud virtual resource mapping request comprises a storage resource request and a computing resource request, the storage resource request comprises data volume to be stored, and the computing resource request comprises computing complexity;
the virtual network analyzes the cloud virtual resource mapping request to obtain a plurality of storage resource requests and computing resource requests, and distributes the plurality of resource requests obtained by analysis to different virtual nodes to form a virtual node sequence of the current cloud virtual resource mapping request.
S2: and mapping the virtual nodes in the virtual node sequence to the physical nodes, and marking the cloud virtual resource mapping request as an unmapped cloud virtual resource mapping request if no effective physical node exists currently.
Mapping the virtual nodes in the virtual node sequence to physical nodes in the step S2 includes:
the mapping process of the virtual node comprises the following steps:
s21: calculating the mapping capability of different physical nodes at present, and for the physical nodes for storage, calculating the mapping capability of the different physical nodes
Figure 702941DEST_PATH_IMAGE116
Comprises the following steps:
Figure 302550DEST_PATH_IMAGE001
wherein:
Figure 910249DEST_PATH_IMAGE117
the mapping capacity of the storage equipment in the physical node is shown, space is the storage capacity space of the storage equipment, and n represents that n virtual nodes are mapped to the physical node;
for a physical node used for computation, its mapping capability
Figure 696939DEST_PATH_IMAGE118
Is the remaining CPU resource;
will map capabilities
Figure 681076DEST_PATH_IMAGE119
Less than a storage threshold
Figure 135191DEST_PATH_IMAGE120
And mapping capabilities
Figure 910861DEST_PATH_IMAGE121
Less than the calculated threshold
Figure 450427DEST_PATH_IMAGE122
If no effective physical node exists, the cloud virtual resource mapping request is marked as a cloud virtual resource mapping request which is not mapped successfully, and the mapping operation is terminated;
s22: for any virtual node in the virtual node sequence
Figure 972675DEST_PATH_IMAGE123
Wherein i represents a virtual node
Figure 281297DEST_PATH_IMAGE124
Received is the ith resource request obtained by parsing,
Figure 230798DEST_PATH_IMAGE125
，
Figure 257660DEST_PATH_IMAGE126
representing virtual nodes
Figure 318020DEST_PATH_IMAGE127
The received resource request is a storage resource request,
Figure 746727DEST_PATH_IMAGE128
representing virtual nodes
Figure 867130DEST_PATH_IMAGE127
The received resource request is a computing resource request;
s23: for any virtual node with j =1
Figure 381288DEST_PATH_IMAGE129
And analyzing the received resource request information, wherein the data volume to be stored in the resource request information is
Figure 245339DEST_PATH_IMAGE130
Traversing all physical nodes for storage
Figure 528552DEST_PATH_IMAGE131
Calculating physical nodes
Figure 819857DEST_PATH_IMAGE132
And virtual node
Figure 555731DEST_PATH_IMAGE129
Degree of adaptation of
Figure 489052DEST_PATH_IMAGE133
：
Figure 361193DEST_PATH_IMAGE015
Wherein:
Figure 88978DEST_PATH_IMAGE134
representing physical nodes
Figure 309219DEST_PATH_IMAGE135
The remaining storage space of (a);
Figure 780652DEST_PATH_IMAGE136
representing physical nodes
Figure 772879DEST_PATH_IMAGE135
A number of connected communication links;
Figure 405985DEST_PATH_IMAGE137
representing physical nodes
Figure 116452DEST_PATH_IMAGE138
A storage capacity space of (a);
if physical node
Figure 125997DEST_PATH_IMAGE139
If the residual storage space is less than the data amount to be stored, the physical node is enabled
Figure 972730DEST_PATH_IMAGE139
And virtual node
Figure 42317DEST_PATH_IMAGE140
Is empty; physical node with highest adaptation degree
Figure 240080DEST_PATH_IMAGE139
As arbitrary virtual nodes
Figure 53315DEST_PATH_IMAGE141
Of the mapping node
Figure 754555DEST_PATH_IMAGE142
；
S24: for any virtual node with j =2
Figure 995044DEST_PATH_IMAGE143
Analyzing the received calculation request information, wherein the calculation complexity in the calculation request information is
Figure 414524DEST_PATH_IMAGE144
Traversing all physical nodes used for computation
Figure 765871DEST_PATH_IMAGE145
Computing physical nodes
Figure 852775DEST_PATH_IMAGE145
And virtual node
Figure 264165DEST_PATH_IMAGE146
Degree of adaptation of
Figure 170941DEST_PATH_IMAGE147
：
Figure 346487DEST_PATH_IMAGE024
Wherein:
Figure 22319DEST_PATH_IMAGE148
representing physical nodes
Figure 604610DEST_PATH_IMAGE145
The remaining CPU resources;
physical node with highest adaptation degree
Figure 998682DEST_PATH_IMAGE145
As arbitrary virtual nodes
Figure 691832DEST_PATH_IMAGE149
Of the mapping node
Figure 487749DEST_PATH_IMAGE150
。
S3: and constructing a virtual link mapping objective function based on the mapping profit, and mapping the virtual links between the adjacent virtual nodes to the physical links by adopting an improved heuristic algorithm.
The step S3 of constructing a virtual link mapping objective function based on the mapping benefit includes:
the mapping benefit mainly comprises link mapping benefit, and for any two continuous virtual nodes
Figure 975362DEST_PATH_IMAGE151
And
Figure 856731DEST_PATH_IMAGE152
between which a virtual link is formed
Figure 619150DEST_PATH_IMAGE153
And the mapping physical nodes corresponding to the virtual nodes are respectively
Figure 269575DEST_PATH_IMAGE154
And
Figure 193668DEST_PATH_IMAGE155
mapping physical links formed between physical nodes to
Figure 31174DEST_PATH_IMAGE156
The virtual link mapping objective function is:
Figure 331706DEST_PATH_IMAGE034
wherein:
Figure 102215DEST_PATH_IMAGE157
representing a physical link;
Figure 197210DEST_PATH_IMAGE158
representing a physical link
Figure 787592DEST_PATH_IMAGE156
The remaining bandwidth of;
Figure 626235DEST_PATH_IMAGE159
representing a physical link
Figure 248321DEST_PATH_IMAGE156
The number of subnet switches;
m represents the total number of virtual nodes corresponding to the cloud virtual resource mapping request;
Figure 779797DEST_PATH_IMAGE160
indicating a maximum number of subnet switches.
In the step S3, an improved heuristic algorithm is adopted to map the virtual links between the adjacent virtual nodes to the physical links, including:
solving the virtual link mapping objective function by adopting an improved heuristic algorithm, and solving to obtain a physical link mapped by the virtual link, wherein the mapping process of the virtual link is as follows:
s31: performing explosion operation on the m physical nodes obtained by mapping to generate explosion sparks to surrounding links, wherein the physical nodes
Figure 857474DEST_PATH_IMAGE161
The number of explosion sparks generated is:
Figure 234229DEST_PATH_IMAGE039
wherein:
Figure 713752DEST_PATH_IMAGE162
representing by physical nodes
Figure 150550DEST_PATH_IMAGE161
As a physical link of the origin is,
Figure 715523DEST_PATH_IMAGE163
a function value representing a function of substituting the physical link into the virtual link mapping objective function;
Figure 895969DEST_PATH_IMAGE164
representing by physical nodes
Figure 229998DEST_PATH_IMAGE161
The number of physical links as a starting point;
s32: updating physical nodes
Figure 837697DEST_PATH_IMAGE161
In the position of
Figure 155546DEST_PATH_IMAGE165
：
Figure 874103DEST_PATH_IMAGE044
Wherein:
Figure 328218DEST_PATH_IMAGE165
representing a physical link
Figure 106818DEST_PATH_IMAGE166
In the physical node
Figure 646384DEST_PATH_IMAGE161
The next link intersection point that is the starting point,
Figure 168632DEST_PATH_IMAGE167
a physical link without link crossing;
Figure 742833DEST_PATH_IMAGE168
to represent
Figure 689405DEST_PATH_IMAGE169
Number of nearby explosion sparks;
Figure 716267DEST_PATH_IMAGE170
to represent
Figure 776627DEST_PATH_IMAGE167
With physical nodes
Figure 939755DEST_PATH_IMAGE171
The distance of (a);
s33: returning to the step S31 until a physical link L containing m physical nodes is obtained, and carrying out resource transportation on each physical node through the physical link;
and forming a virtual node sequence network structure containing the request resources by the physical links and the virtual links obtained by mapping, wherein a user can directly call the virtual node sequence network structure to allocate physical resources for the cloud virtual resource mapping request.
S4: and dynamically determining the resource optimization adjustment time of the physical nodes according to the current quantity of the cloud virtual resource mapping requests.
In the step S4, dynamically determining the physical node resource optimization adjustment time according to the current cloud virtual resource mapping request number, including:
the determination process of the physical node resource optimization adjustment moment comprises the following steps:
s41: calculating the current variation amplitude of the cloud virtual resource mapping request quantity
Figure 325737DEST_PATH_IMAGE172
：
Figure 574315DEST_PATH_IMAGE051
Wherein:
Figure 438366DEST_PATH_IMAGE053
the number of current cloud virtual resource mapping requests received for the virtual network;
Figure 721580DEST_PATH_IMAGE173
representing the quantity of received cloud virtual resource mapping requests after the last time of physical node resource optimization adjustment;
s42: determining the next time of the resource optimization adjustment of the physical node as t:
Figure 278463DEST_PATH_IMAGE055
Figure 14338DEST_PATH_IMAGE056
Figure 682080DEST_PATH_IMAGE057
wherein:
Figure 85379DEST_PATH_IMAGE174
representing the last time of resource optimization adjustment of the physical node;
Figure 282005DEST_PATH_IMAGE175
representing a preset physical node resource optimization adjustment time interval;
Figure 770756DEST_PATH_IMAGE176
indicating that when the quantity of the cloud virtual resource mapping requests is increased, the physical node resource optimization adjusts the time interval,
Figure 976609DEST_PATH_IMAGE177
indicating the adjustment time interval of the resource optimization adjustment of the physical node when the quantity of the cloud virtual resource mapping requests is increased last time;
Figure 968836DEST_PATH_IMAGE178
indicating that when the number of the cloud virtual resource mapping requests is reduced, the physical node resource optimization adjusts the time interval,
Figure 867522DEST_PATH_IMAGE179
and the adjustment time interval of the resource optimization adjustment of the physical node is represented when the quantity of the cloud virtual resource mapping requests is reduced last time. In the embodiment of the invention, when the cloud virtual resource mapping request is increased, a larger adjustment time interval is adopted, otherwise, a smaller adjustment time interval is adopted, the self-adaptive change of the adjustment time interval is realized, the dynamic change amplitude of the adjustment time interval is related to the preset time interval and the last determined adjustment time interval, and the excessive change of the adjustment time interval is avoided, so that the existence of more or less cloud virtual resources is avoidedThe source mapping request is subjected to mapping processing, and computing resources of the virtual network are wasted.
S5: and performing optimization adjustment on the mapping virtualization resources in the current super-fusion cloud platform at the time of optimization adjustment of the physical node resources, performing mapping processing on the currently received and unsuccessfully mapped cloud virtual resource mapping requests, and allocating the physical resources for the cloud virtual resource mapping requests according to the mapping processing result.
In the step S5, the mapping virtualized resource in the current super-fusion cloud platform is optimally adjusted at the time of optimizing and adjusting the physical node resource, and the mapping processing is performed on the cloud virtual resource mapping request that is currently received and is not successfully mapped, including:
performing optimization adjustment on mapping virtualization resources in the current super-convergence cloud platform at a physical node resource optimization adjustment time t, wherein the mapping virtualization resources are virtual node sequence network structures, when a cloud virtual resource mapping request is successfully processed, mapping between the virtual node sequence network structures and a physical layer is formed in a virtual network, and a user can directly call the virtual node sequence network structures so as to realize calling of resources in the physical layer;
wherein the optimization and adjustment process comprises the following steps:
s51: calculating congestion degree of virtual network in current super-convergence cloud platform
Figure 575059DEST_PATH_IMAGE180
：
Figure 319024DEST_PATH_IMAGE065
Wherein:
Figure 431337DEST_PATH_IMAGE181
represents the number of cloud virtual resource mapping requests processed by the virtual network in the current super-convergence cloud platform,
Figure 235345DEST_PATH_IMAGE182
the number of virtual node sequence network structures which are used for representing that resource mapping is completed and deletion is waited in the current super-fusion cloud platform is performed when a user is in
Figure 433108DEST_PATH_IMAGE183
When the virtual node sequence network structure is not called in the time interval of (3), the virtual node sequence network structure is deleted from the virtual network and is positioned in
Figure 980764DEST_PATH_IMAGE183
The un-called virtual node sequence network structure of the time interval is the virtual node sequence network structure waiting for deletion;
Figure 947583DEST_PATH_IMAGE184
representing the average processing time of the cloud virtual resource mapping request currently processed;
s52: when the temperature is higher than the set temperature
Figure 188071DEST_PATH_IMAGE180
If the congestion value is larger than the preset congestion threshold value, optimizing and adjusting the mapping virtualization resources in the super-fusion cloud platform, otherwise, ending the current optimizing and adjusting process;
s53: mapping the virtual node sequence network structure waiting for deletion to a storage node in a physical layer, deleting the virtual node sequence network structure waiting for deletion in the virtual network, and reducing the congestion degree of the virtual network;
s54: and mapping the cloud virtual resource mapping request which is currently received and is not successfully mapped, and allocating physical resources for the cloud virtual resource mapping request.
Example 2:
as shown in fig. 2, a functional block diagram of a system for efficiently allocating resources based on a super-fusion cloud provided in an embodiment of the present invention is shown, which can implement the method for efficiently allocating resources based on a super-fusion cloud in embodiment 1.
The system 100 for efficiently allocating virtualized resources based on a super-converged cloud can be installed in electronic equipment. According to the realized functions, the system for efficiently allocating resources based on the super-fusion cloud virtualization can comprise a virtual node mapping device 101, a virtual link mapping device 102 and a resource optimization adjusting module 103. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and can perform a fixed function, and are stored in a memory of the electronic device.
The virtual node mapping device 101 is configured to initialize a virtual network and a physical node, where a virtual node in the virtual network receives and analyzes a current cloud virtual resource mapping request to form a virtual node sequence based on the mapping request, maps the virtual node in the virtual node sequence to the physical node, and marks the cloud virtual resource mapping request as an unmapped cloud virtual resource mapping request if there is no valid physical node currently;
the virtual link mapping device 102 is configured to construct a virtual link mapping objective function based on mapping gains, and map virtual links between adjacent virtual nodes to physical links by using an improved heuristic algorithm;
the resource optimization and adjustment module 103 is configured to dynamically determine a physical node resource optimization and adjustment time according to the current cloud virtual resource mapping request number, perform optimization and adjustment on the mapping virtualization resources in the current hyper-converged cloud platform at the physical node resource optimization and adjustment time, perform mapping processing on currently received and unsuccessfully mapped cloud virtual resource mapping requests, and allocate physical resources to the cloud virtual resource mapping requests according to mapping processing results.
In detail, in the embodiment of the present invention, when the modules in the efficient resource allocation system 100 based on a super-fusion cloud are used, the same technical means as the efficient resource allocation method based on a super-fusion cloud described in fig. 1 are used, and the same technical effect can be produced, which is not described herein again.
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of another identical element in a process, apparatus, article, or method comprising the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (8)

1. A virtualized resource efficient allocation method based on a super-converged cloud is characterized by comprising the following steps:
s1: initializing a virtual network and physical nodes, wherein the virtual nodes in the virtual network receive and analyze a current cloud virtual resource mapping request to form a virtual node sequence based on the mapping request;
s2: mapping the virtual nodes in the virtual node sequence to physical nodes, and marking the cloud virtual resource mapping request as an unmapped cloud virtual resource mapping request if no effective physical node exists currently;
s3: constructing a virtual link mapping target function based on mapping profits, and mapping virtual links between adjacent virtual nodes to physical links by adopting an improved heuristic algorithm;
s4: dynamically determining the physical node resource optimization adjustment time according to the current cloud virtual resource mapping request quantity, wherein the method comprises the following steps:
the determination process of the physical node resource optimization adjustment moment comprises the following steps:
s41: calculating the current variation amplitude alpha of the cloud virtual resource mapping request quantity:
Figure 97389DEST_PATH_IMAGE003
wherein:
Figure 448736DEST_PATH_IMAGE005
mapping the number of requests for the current cloud virtual resource received by the virtual network;
Figure 270061DEST_PATH_IMAGE006
representing the quantity of cloud virtual resource mapping requests received after the last time of resource optimization adjustment of the physical nodes;
s42: determining the next time of the resource optimization adjustment of the physical node as t:
Figure 415872DEST_PATH_IMAGE007
Figure 588227DEST_PATH_IMAGE008
Figure 212106DEST_PATH_IMAGE009
wherein:
Figure 419097DEST_PATH_IMAGE010
representing the last time of the resource optimization adjustment of the physical node;
Figure 732879DEST_PATH_IMAGE011
representing a preset physical node resource optimization adjustment time interval;
Figure 126951DEST_PATH_IMAGE012
indicating that when the quantity of the cloud virtual resource mapping requests is increased, the physical node resource optimization adjusts the time interval,
Figure 820101DEST_PATH_IMAGE013
indicating the adjustment time interval of the resource optimization adjustment of the physical node when the quantity of the cloud virtual resource mapping requests increases last time;
Figure 616018DEST_PATH_IMAGE014
the resource optimization adjustment time interval of the physical node is adjusted when the number of the cloud virtual resource mapping requests is reduced,
Figure 103632DEST_PATH_IMAGE015
the adjustment time interval of the resource optimization adjustment of the physical node is represented when the quantity of the cloud virtual resource mapping requests is reduced last time;
s5: and optimizing and adjusting the mapping virtualization resources in the current super-fusion cloud platform at the time of optimizing and adjusting the resources of the physical nodes, mapping the cloud virtual resource mapping requests which are currently received and not successfully mapped, and allocating the physical resources for the cloud virtual resource mapping requests according to the mapping processing result.
2. The efficient resource allocation method based on the super-converged cloud virtualization according to claim 1, wherein initializing a virtual network and a physical node in the step S1 comprises:
constructing a super-convergence cloud platform architecture, wherein the super-convergence cloud platform architecture comprises a physical layer and a virtual network, the physical layer comprises a storage device, a computing terminal device and physical links among different devices, when a user sends a cloud virtual resource mapping request to the virtual network, the virtual network obtains a virtual node sequence network structure containing the requested resource by establishing mapping with the physical layer, and the user can directly call the virtual node sequence network structure to allocate physical resources for the cloud virtual resource mapping request;
initializing a virtual network, wherein the virtual network comprises a plurality of virtual nodes, and initializing physical nodes, the physical nodes are devices in a physical layer in a super-convergence cloud platform architecture, and the initialized physical nodes form a graph structure of the physical layer, wherein points in the graph structure are the physical nodes, and edges in the graph structure are communication links between the physical nodes.
3. The efficient allocation method for virtualized resources based on super-converged cloud according to claim 2, wherein the step S1, in which the virtual node receives and parses a current cloud virtual resource mapping request to form a virtual node sequence based on the mapping request, includes:
the method comprises the steps that a virtual network receives a current cloud virtual resource mapping request, wherein the cloud virtual resource mapping request comprises a storage resource request and a computing resource request, the storage resource request comprises data volume to be stored, and the computing resource request comprises computing complexity;
the virtual network analyzes the cloud virtual resource mapping request to obtain a plurality of storage resource requests and computing resource requests, and distributes the plurality of resource requests obtained by analysis to different virtual nodes to form a virtual node sequence of the current cloud virtual resource mapping request.
4. The method for efficient resource allocation based on the super-converged cloud virtualization according to claim 3, wherein the mapping the virtual nodes in the virtual node sequence to the physical nodes in the step S2 includes:
the mapping process of the virtual node comprises the following steps:
s21: calculating the mapping capability of different current physical nodes, and for the physical nodes for storage, calculating the mapping capability of the different current physical nodes
Figure 250579DEST_PATH_IMAGE016
Comprises the following steps:
Figure 481840DEST_PATH_IMAGE017
wherein:
Figure 397844DEST_PATH_IMAGE018
the mapping capacity of the storage equipment in the physical node is shown, space is the storage capacity space of the storage equipment, and n represents that n virtual nodes are mapped to the physical node;
for a physical node used for computation, its mapping capability
Figure 790779DEST_PATH_IMAGE019
Is the remaining CPU resource;
mapping capabilities
Figure 425023DEST_PATH_IMAGE020
Less than a storage threshold
Figure 459975DEST_PATH_IMAGE021
And mapping capabilities
Figure 230485DEST_PATH_IMAGE022
Less than the calculated threshold
Figure 794321DEST_PATH_IMAGE023
If no effective physical node exists, the cloud virtual resource mapping request is marked as a cloud virtual resource mapping request which is not mapped successfully, and the mapping operation is terminated;
s22: for any virtual node in the virtual node sequence
Figure 915861DEST_PATH_IMAGE024
Wherein i represents a virtual node
Figure 754504DEST_PATH_IMAGE024
Received is the ith resource request obtained by parsing,
Figure 113941DEST_PATH_IMAGE025
，
Figure 111328DEST_PATH_IMAGE026
representing virtual nodes
Figure 189006DEST_PATH_IMAGE024
The received resource request is a storage resource request,
Figure 831340DEST_PATH_IMAGE027
representing virtual nodes
Figure 576442DEST_PATH_IMAGE024
The received resource request is a computing resource request;
s23: for the
Figure 747660DEST_PATH_IMAGE028
Arbitrary virtual node of
Figure 578213DEST_PATH_IMAGE029
Analyzing the received resource request information to obtain the resource request informationThe amount of data to be stored in the source request information is
Figure 493079DEST_PATH_IMAGE030
Traversing all physical nodes for storage
Figure 827109DEST_PATH_IMAGE031
Calculating physical nodes
Figure 700387DEST_PATH_IMAGE031
And virtual node
Figure 752657DEST_PATH_IMAGE029
Degree of adaptation of
Figure 471214DEST_PATH_IMAGE032
：
Figure 925329DEST_PATH_IMAGE033
Wherein:
Figure 703929DEST_PATH_IMAGE034
representing physical nodes
Figure 243495DEST_PATH_IMAGE031
The remaining storage space of (a);
Figure 765743DEST_PATH_IMAGE035
representing physical nodes
Figure 74365DEST_PATH_IMAGE031
The number of connected communication links;
Figure 20936DEST_PATH_IMAGE036
representing physical nodes
Figure 782219DEST_PATH_IMAGE031
A storage capacity space of (a);
if the physical node
Figure 108158DEST_PATH_IMAGE031
If the residual storage space is less than the data amount to be stored, the physical node is enabled
Figure 271286DEST_PATH_IMAGE031
And virtual node
Figure 391689DEST_PATH_IMAGE029
The degree of adaptation of (a) is null; physical node with highest adaptation degree
Figure 905847DEST_PATH_IMAGE031
As arbitrary virtual nodes
Figure 769898DEST_PATH_IMAGE029
Of the mapping node
Figure 787532DEST_PATH_IMAGE037
；
S24: for the
Figure 344416DEST_PATH_IMAGE038
Arbitrary virtual node of
Figure 80290DEST_PATH_IMAGE039
Analyzing the received calculation request information, and calculating the complexity of the calculation request information into
Figure 748032DEST_PATH_IMAGE040
Traversing all physical nodes for computation
Figure 620173DEST_PATH_IMAGE041
Calculating physical nodes
Figure 347958DEST_PATH_IMAGE041
And virtual node
Figure 571129DEST_PATH_IMAGE039
Degree of adaptation of
Figure 776982DEST_PATH_IMAGE042
：
Figure 769209DEST_PATH_IMAGE043
Wherein:
Figure 664965DEST_PATH_IMAGE044
representing physical nodes
Figure 109853DEST_PATH_IMAGE041
The remaining CPU resources;
physical node with highest adaptation degree
Figure 119397DEST_PATH_IMAGE041
As arbitrary virtual nodes
Figure 231710DEST_PATH_IMAGE039
Of the mapping node
Figure 301297DEST_PATH_IMAGE045
。
5. The method for efficient allocation of virtualized resources based on a super-converged cloud according to claim 1, wherein the step S3 of constructing a mapping objective function of the virtual link based on the mapping benefits comprises:
construction is based onA virtual link mapping objective function of mapping benefits, the mapping benefits mainly including link mapping benefits, for any two consecutive virtual nodes
Figure 233481DEST_PATH_IMAGE046
And
Figure 781137DEST_PATH_IMAGE047
between which a virtual link is formed
Figure 747956DEST_PATH_IMAGE048
And the mapping physical nodes corresponding to the virtual nodes are respectively
Figure 722865DEST_PATH_IMAGE037
And
Figure 407924DEST_PATH_IMAGE049
mapping physical links formed between physical nodes to
Figure 24850DEST_PATH_IMAGE050
Said virtual link mapping an objective function
Figure 580597DEST_PATH_IMAGE051
Comprises the following steps:
Figure 991986DEST_PATH_IMAGE052
wherein:
Figure 898763DEST_PATH_IMAGE053
representing a physical link;
Figure 53800DEST_PATH_IMAGE054
representing a physical link
Figure 995212DEST_PATH_IMAGE050
The remaining bandwidth of;
Figure 311923DEST_PATH_IMAGE055
representing a physical link
Figure 437487DEST_PATH_IMAGE050
The number of subnet switches;
m represents the total number of virtual nodes corresponding to the cloud virtual resource mapping request;
Figure 396215DEST_PATH_IMAGE056
indicating a maximum number of subnet switches.
6. The method for efficient resource allocation based on the super-converged cloud virtualization according to claim 5, wherein the mapping of the virtual links between the adjacent virtual nodes to the physical links by using an improved heuristic algorithm in the step S3 comprises:
solving the virtual link mapping objective function by adopting an improved heuristic algorithm, and solving to obtain a physical link mapped by the virtual link, wherein the mapping process of the virtual link is as follows:
s31: performing explosion operation on the m physical nodes obtained by mapping to generate explosion sparks to surrounding links, wherein the physical nodes
Figure 192133DEST_PATH_IMAGE057
The number of explosion sparks generated is:
Figure 945325DEST_PATH_IMAGE058
wherein:
Figure 826694DEST_PATH_IMAGE059
representing by physical nodes
Figure 57955DEST_PATH_IMAGE060
As a physical link of the origin is,
Figure 708379DEST_PATH_IMAGE061
a function value representing a function of substituting the physical link into the virtual link mapping objective function;
Figure 898052DEST_PATH_IMAGE062
representing by physical nodes
Figure 266717DEST_PATH_IMAGE060
The number of physical links as a starting point;
s32: updating physical nodes
Figure 301669DEST_PATH_IMAGE060
In the position of
Figure 806599DEST_PATH_IMAGE063
：
Figure 901594DEST_PATH_IMAGE064
Wherein:
Figure 757555DEST_PATH_IMAGE063
representing a physical link
Figure 330619DEST_PATH_IMAGE050
In physical node
Figure 955635DEST_PATH_IMAGE060
The next link intersection point that is the starting point,
Figure 221531DEST_PATH_IMAGE065
a physical link for which no link crossing exists;
Figure 319717DEST_PATH_IMAGE066
to represent
Figure 962050DEST_PATH_IMAGE065
Number of nearby explosion sparks;
Figure 441573DEST_PATH_IMAGE067
represent
Figure 878371DEST_PATH_IMAGE065
And physical node
Figure 443344DEST_PATH_IMAGE068
The distance of (a);
s33: returning to the step S31 until a physical link L containing m physical nodes is obtained, and carrying out resource transportation on each physical node through the physical link;
and forming a virtual node sequence network structure containing the request resource by the physical link and the virtual link obtained by mapping, wherein a user can directly call the virtual node sequence network structure to allocate physical resources for the cloud virtual resource mapping request.
7. The method according to claim 1, wherein the step S5 of performing optimization adjustment on the mapped virtualized resources in the current hyper-converged cloud platform at the time of optimization adjustment of the physical node resources, and performing mapping processing on the currently received cloud virtual resource mapping request and the cloud virtual resource mapping request that is not mapped successfully includes:
performing optimization adjustment on mapping virtualization resources in the current super-convergence cloud platform at a physical node resource optimization adjustment time t, wherein the mapping virtualization resources are virtual node sequence network structures, when a cloud virtual resource mapping request is successfully processed, mapping between the virtual node sequence network structures and a physical layer is formed in a virtual network, and a user can directly call the virtual node sequence network structures so as to realize calling of resources in the physical layer;
the optimization and adjustment process comprises the following steps:
s51: calculating congestion degree of virtual network in current super-convergence cloud platform
Figure 358211DEST_PATH_IMAGE069
：
Figure 957819DEST_PATH_IMAGE070
Wherein:
Figure 565518DEST_PATH_IMAGE071
represents the number of cloud virtual resource mapping requests processed by the virtual network in the current super-convergence cloud platform,
Figure 617788DEST_PATH_IMAGE072
representing the number of virtual node sequence network structures which have completed resource mapping and wait to be deleted in the current super-fusion cloud platform when a user is in
Figure 336345DEST_PATH_IMAGE073
When the virtual node sequence network structure is not called in the time interval, the virtual node sequence network structure is deleted from the virtual network and is positioned in
Figure 790460DEST_PATH_IMAGE073
The non-called virtual node sequence network structure of the time interval is the virtual node sequence network structure waiting for deletion;
Figure 569060DEST_PATH_IMAGE074
representing the average processing time of the cloud virtual resource mapping request currently processed;
s52: when in use
Figure 108626DEST_PATH_IMAGE069
When the congestion value is larger than the preset congestion threshold value, optimizing and adjusting mapping virtualization resources in the super-fusion cloud platform, otherwise, ending the current optimizing and adjusting process;
s53: mapping the virtual node sequence network structure waiting for deletion to a storage node in a physical layer, deleting the virtual node sequence network structure waiting for deletion in the virtual network, and reducing the congestion degree of the virtual network;
s54: and mapping the cloud virtual resource mapping request which is received currently and is not mapped successfully, and allocating physical resources to the cloud virtual resource mapping request.
8. A system for efficient allocation of virtualized resources based on a super-converged cloud, the system comprising:
the virtual node mapping device is used for initializing a virtual network and physical nodes, the virtual nodes in the virtual network receive and analyze the current cloud virtual resource mapping request to form a virtual node sequence based on the mapping request, the virtual nodes in the virtual node sequence are mapped to the physical nodes, and if no effective physical node exists at present, the cloud virtual resource mapping request is marked as a cloud virtual resource mapping request which is not mapped successfully;
the virtual link mapping device is used for constructing a virtual link mapping target function based on mapping income and mapping virtual links between adjacent virtual nodes to physical links by adopting an improved heuristic algorithm;
the resource optimization and adjustment module is used for dynamically determining physical node resource optimization and adjustment time according to the number of current cloud virtual resource mapping requests, performing optimization and adjustment on mapping virtual resources in a current super-fusion cloud platform at the physical node resource optimization and adjustment time, performing mapping processing on currently received cloud virtual resource mapping requests which are not mapped successfully, and allocating physical resources to the cloud virtual resource mapping requests according to mapping processing results, so that the super-fusion cloud virtual resource-based efficient allocation method is realized according to any one of claims 1-7.
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