CN114741154A - Virtual machine network resource allocation system - Google Patents

Virtual machine network resource allocation system Download PDF

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CN114741154A
CN114741154A CN202210142928.8A CN202210142928A CN114741154A CN 114741154 A CN114741154 A CN 114741154A CN 202210142928 A CN202210142928 A CN 202210142928A CN 114741154 A CN114741154 A CN 114741154A
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target object
network resource
target
request
acquiring
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徐宏
梁青霜
陈亮亮
于瀛江
甄兴
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Hangzhou Zhonggang Technology Co ltd
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Hangzhou Zhonggang Technology Co ltd
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    • 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
    • H04L67/104—Peer-to-peer [P2P] networks
    • H04L67/1074—Peer-to-peer [P2P] networks for supporting data block transmission mechanisms
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G06F9/5077—Logical partitioning of resources; Management or configuration of virtualized resources
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/45579—I/O management, e.g. providing access to device drivers or storage
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45533—Hypervisors; Virtual machine monitors
    • G06F9/45558—Hypervisor-specific management and integration aspects
    • G06F2009/45595—Network integration; Enabling network access in virtual machine instances
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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Abstract

The invention discloses a virtual machine network resource allocation system, which comprises a single acquisition unit, a single analysis unit and a target object management unit, wherein the single acquisition unit is used for acquiring habit data of a target object, calculating the demand calculation power range of the target object according to the habit data, and the single analysis unit is used for carrying out demand analysis according to the habit data of the target object to acquire the priority of the target object; the resource time control unit monitors the real-time computing power of the virtual machine and is in communication connection with the processor; the abnormity identification unit performs abnormity analysis on the non-target object according to habit data of the target object, and divides the non-target object into an abnormal target object and a normal target object; the processor allocates resources to the normal target object according to the priority, the demand computing power range and the real-time computing power; distributing network resource priority to each target object to ensure the throughput of the target object and the service quality of the access application; and abnormal targets are screened out through analysis of historical data, the calculation amount is reduced, and the operation efficiency and the network resource utilization rate are improved.

Description

Virtual machine network resource allocation system
Technical Field
The invention belongs to the field of virtual machines, relates to a network resource allocation technology, and particularly relates to a virtual machine network resource allocation system.
Background
The demands of the virtual machines on the network resources are dynamically changed, but the currently created resource allocation mechanism can cause unreasonable network resources of the virtual machines, so that the network resources of part of the virtual machines are surplus.
For example, chinese patent CN103001953B discloses a method and an apparatus for allocating network resources of a virtual machine, the method includes: a first physical host receives a virtual machine resource request message sent by a user, wherein the virtual machine resource request message is used for requesting to allocate network resources of at least one virtual machine; if the virtual machine resource request message requests to allocate network resources of one virtual machine, the first physical host allocates private VLAN resources for users in a private VLAN resource pool of the first physical host, and the private VLAN resources are used for isolating the virtual machines in a two-layer network; if the virtual machine resource request message requests to allocate network resources of at least two virtual machines, the first physical host allocates community VLAN resources for the user in a community VLAN resource pool of the first physical host, and the community VLAN resources are used for communicating the virtual machines in a two-layer network.
Further, for example, chinese patent CN109614229B relates to a virtual network resource allocation system based on software definition, chinese patent CN109412865B relates to a virtual network resource allocation method, and the like, all disclose a network resource allocation technique.
However, none of the above prior arts can allocate network resources based on the characteristics of the target object itself.
Disclosure of Invention
The invention aims to provide a virtual machine network resource allocation system.
The purpose of the invention can be realized by the following technical scheme:
a virtual machine network resource allocation system, comprising: a single item acquisition unit: the system comprises a habit data acquisition unit, a requirement calculation unit, a single item analysis unit and a power calculation unit, wherein the habit data acquisition unit is used for acquiring habit data of a target object, calculating a requirement calculation range of the target object according to the habit data, and uploading the habit data and the requirement calculation range to the single item analysis unit; single item analysis unit: the method comprises the steps of carrying out demand analysis according to habit data of a target object, obtaining the priority of the target object, and transmitting the priority to a processor; the resource time control unit: the real-time computing power of the virtual machine is monitored, and the real-time computing power is in communication connection with the processor; an abnormality determination unit: according to habit data of a target object, carrying out abnormity analysis on a non-target object, and dividing the non-target object into an abnormal target object and a normal target object; the processor allocates resources to the normal target object according to the priority, the demand computing power range and the real-time computing power; a quota notification unit: which generates an anomaly early warning upon receiving the anomalous target object.
Further, the single item acquiring unit is further configured to acquire history data of the request object, and divide the request object into a target object and a non-target object according to the history data, and execute the following algorithm when dividing the object:
s1: calling historical data corresponding to the request object from a storage library;
s2: if the corresponding historical data exists, the target object is determined, otherwise, S3 is performed;
s3: preprocessing a request object:
acquiring an IP address of a request object;
performing association analysis on the request object according to a target analysis rule to acquire the association degree Gg of the request object and historical data stored in a storage library;
if the association Gg is less than or equal to G1, the request object is a non-target object, and G1 is a preset value;
otherwise, the request object is the target object.
Further, the obtaining mode of the association degree Gg is as follows:
g001: acquiring a predicted transmission path of a request object, and extracting an IP address of a transmission end point;
g002: calling an IP address corresponding to the IP address of the transmission end point from a storage library, and marking the IP address as a historical IP address;
g003: acquiring the corresponding network resource occupation amount of the historical IP address during each scheduling in the last half year, calculating the average value of the occupation amount of the network resources used for one time, and marking the average value as an occupation average value Jz;
g004: a single acquisition unit calls a predicted network resource occupation value Yz of a request object;
g005: calculating a difference value Cz, wherein the Cz is | Jz-Yz |;
g006: if Cz is greater than or equal to C, the degree of correlation
Figure BDA0003507169190000031
Wherein 1 is<α<2；
Otherwise, degree of association
Figure BDA0003507169190000032
Wherein, Zz is the average of single-use network resource occupation amounts corresponding to all IP addresses in the repository, 1<α<β<2。
Further, the method for predicting the expected network resource occupation value Yz by the single acquisition unit comprises the following steps:
acquiring data transmission quantity corresponding to the request object, and marking the data transmission quantity as pre-transmission quantity Qy;
retrieving a reference target from a repository: marking a history target object corresponding to the transmission quantity Cy which meets the condition that the absolute value of Cy-Qy is less than or equal to Q1 as a reference target; cy is the transmission amount corresponding to the target object stored in the repository, wherein Q1 is a preset value;
retrieving the network resource occupation value Qzi corresponding to each reference target i from the repository, and calculating the reference network resource occupation value Qcc,
Figure BDA0003507169190000033
i＝1、2、3、…、n；
projected network resource occupancy value
Figure BDA0003507169190000034
If no transmission amount Cy satisfying | Cy-Qy | ≦ Q1 exists, then Qcc ≦ 0;
qcs is the average value of the network resource occupation corresponding to all the target objects stored in the repository.
Further, the habit data comprises historical transmission efficiency and a network resource occupation value corresponding to each target object j; when the single acquisition unit calculates the required computing power range of the target object according to the habit data, the following algorithm is executed:
estimating transmission time Ty according to historical transmission efficiency;
acquiring Qzj a network resource occupation value corresponding to each target object j stored in the repository;
the required computational power range is as follows:
Figure BDA0003507169190000041
to is that
Figure BDA0003507169190000042
Wherein, Qzmax is a maximum network resource occupation value corresponding to all target objects stored in the repository.
Further, the method comprises the following steps: the method for estimating the transmission time comprises the following steps:
acquiring data transmission efficiency corresponding to each target object j stored in a storage library, and respectively marking the data transmission efficiency as Xj, wherein j is 1, 2, 3, … and m;
acquiring a predicted network resource occupation value Yz corresponding to a request target;
estimating transmission time
Figure BDA0003507169190000043
Further, the habit data further includes a network resource occupation duration corresponding to the target object and a number of transmission nodes corresponding to the target object, and the process of acquiring the priority of the target object by the single analysis unit is as follows:
calling habit data of a historical target object which is the same as the target object from a storage library to serve as pseudo-parameter habit data;
acquiring the maximum network resource occupation duration Tmax in the corresponding historical target object according to the habit-drawing data, and acquiring the average value Tzz of the network resource occupation durations corresponding to all the historical target objects according to the habit data;
acquiring request time of target objects, numbering each target object according to the numbering rule of 0, 1, 0 and 1 … in sequence according to the request time, taking the numbering as a request target time sequence weight, and marking the request target time sequence weight as Sx;
the number D of transmission nodes corresponding to the historical target object which is the same as the target object is called from a storage library;
the priority of the target object is:
Figure BDA0003507169190000051
wherein the content of the first and second substances,
Figure BDA0003507169190000052
presentation pair
Figure BDA0003507169190000053
And rounding to obtain 0.432, 0.352 and 0.216 as preset weights.
Further, the resource time control unit calculates the real-time residual computing power of the virtual machine in real time according to the real-time computing power of the virtual machine obtained through monitoring, the maximum required computing power corresponding to the required computing power range of the target object is obtained, and the processor sequentially allocates network resources to the target object according to the priority.
Further, the process of performing anomaly analysis by the anomaly determination unit according to the habit data of the non-target object combined with the target object is as follows:
acquiring a network resource occupation value mean value corresponding to each target object stored in a storage library;
acquiring a predicted network resource occupation value and predicted transmission time corresponding to a non-target object;
if the predicted network resource occupation value-network resource occupation value mean value is greater than F1 and the predicted transmission time is greater than F2, the non-target object is an abnormal target object;
otherwise, the non-target object is a normal target object.
Further, the quota notification unit generates an abnormal early warning after receiving the abnormal target object and displays the abnormal target object through the display unit, and an administrator deletes the abnormal target object or modifies the abnormal target object into a normal target object through the management unit.
The invention has the beneficial effects that:
the habit data of the target object is obtained through the single-item obtaining unit, the demand calculation power range of the target object is calculated according to the habit data, and the single-item analyzing unit carries out demand analysis according to the habit data of the target object to obtain the priority of the target object; the resource time control unit monitors the real-time computing power of the virtual machine and is in communication connection with the processor; the abnormity identification unit performs abnormity analysis on the non-target object according to the habit data of the target object, and divides the non-target object into an abnormal target object and a normal target object; the processor allocates resources to the normal target object according to the priority and the demand computing power range and by combining real-time computing power; distributing network resource priority to each target object to ensure the throughput of the target object and the service quality of the access application; and abnormal targets are screened out through analysis of historical data, the operation amount is reduced, and the operation efficiency and the network resource utilization rate are improved.
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To facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a system diagram of a virtual machine network resource allocation system according to the present invention;
FIG. 2 is a flow chart of virtual machine network resource allocation according to the present invention.
Detailed Description
As shown in fig. 1-2, a virtual machine network resource allocation system includes: a single item acquisition unit: the system comprises a habit data acquisition unit, a requirement calculation unit, a single item analysis unit and a power calculation unit, wherein the habit data acquisition unit is used for acquiring habit data of a target object, calculating a requirement calculation range of the target object according to the habit data, and uploading the habit data and the requirement calculation range to the single item analysis unit; single item analysis unit: the method comprises the steps of performing demand analysis according to habit data of a target object, obtaining the priority of the target object, and transmitting the priority to a processor; the resource time control unit: the real-time computing power of the virtual machine is monitored, and the virtual machine is in communication connection with the processor;
as an embodiment provided by the present invention, preferably, the habit data includes historical transmission efficiency and a network resource occupation value corresponding to each target object j; when the single acquisition unit calculates the required computing power range of the target object according to the habit data, the following algorithm is executed:
estimating transmission time Ty according to historical transmission efficiency;
acquiring Qzj a network resource occupation value corresponding to each target object j stored in the repository;
the required calculated force range is as follows:
Figure BDA0003507169190000061
to
Figure BDA0003507169190000062
Wherein, Qzmax is a maximum network resource occupation value corresponding to all target objects stored in the repository.
An abnormality determination unit: according to habit data of a target object, carrying out abnormity analysis on a non-target object, and dividing the non-target object into an abnormal target object and a normal target object; the processor allocates resources to the normal target object according to the priority, the demand computing power range and the real-time computing power; a quota notification unit: which generates an anomaly early warning upon receiving the anomalous target object.
As an embodiment provided by the present invention, preferably, the resource time control unit calculates the real-time remaining computation power of the virtual machine in real time according to the real-time computation power of the virtual machine obtained by monitoring, and allocates the network resources to the target object in sequence according to the priority by taking the maximum required computation power corresponding to the required computation power range of the target object.
As an embodiment provided by the present invention, preferably, the process of performing the anomaly analysis by the anomaly identification unit according to the habit data of the non-target object and the target object is as follows:
acquiring a network resource occupation value mean value corresponding to each target object stored in a storage library;
acquiring a predicted network resource occupation value and predicted transmission time corresponding to a non-target object;
if the predicted network resource occupation value-network resource occupation value mean value is greater than F1 and the predicted transmission time is greater than F2, the non-target object is an abnormal target object;
otherwise, the non-target object is a normal target object.
As an embodiment provided by the present invention, preferably, the quota notification unit generates an abnormal early warning after receiving the abnormal target object, and displays the abnormal target object through the display unit, and the administrator deletes the abnormal target object or modifies the abnormal target object into a normal target object through the management unit.
As an embodiment provided by the present invention, preferably, the single item obtaining unit is further configured to obtain history data of the request object, and divide the request object into a target object and a non-target object according to the history data, and when dividing the object, the following algorithm is executed:
s1: calling historical data corresponding to the request object from a storage library;
s2: if the corresponding historical data exists, the target object is determined, otherwise, S3 is performed;
s3: preprocessing a request object:
acquiring an IP address of a request object;
performing association analysis on the request object according to a target analysis rule to acquire the association degree Gg of the request object and historical data stored in a storage library; as an embodiment provided by the present invention, preferably, the obtaining manner of the association degree Gg is:
g001: acquiring a predicted transmission path of a request object, and extracting an IP address of a transmission end point;
g002: calling an IP address corresponding to the IP address of the transmission end point from a storage library, and marking the IP address as a historical IP address;
g003: acquiring the corresponding network resource occupation amount of the historical IP address during each scheduling in the last half year, calculating the average value of the occupation amount of the network resources used for one time, and marking the average value as an occupation average value Jz;
g004: a single acquisition unit calls a predicted network resource occupation value Yz of a request object;
g005: calculating a difference value Cz, wherein the Cz is | Jz-Yz |;
g006: if Cz is greater than or equal to C, the degree of correlation
Figure BDA0003507169190000081
Wherein 1 is<α<2；
Otherwise, degree of association
Figure BDA0003507169190000082
Wherein, Zz is the average of single-use network resource occupation amounts corresponding to all IP addresses in the repository, 1<α<β<2；
If the association Gg is less than or equal to G1, the request object is a non-target object, and G1 is a preset value;
otherwise, the request object is the target object.
As an embodiment provided by the present invention, preferably, the method for predicting the expected network resource occupancy Yz by the single acquisition unit is as follows:
acquiring data transmission quantity corresponding to the request object, and marking the data transmission quantity as pre-transmission quantity Qy;
retrieve reference targets from repository: marking a history target object corresponding to the transmission quantity Cy which meets the condition that the absolute value of Cy-Qy is less than or equal to Q1 as a reference target; cy is the transmission amount corresponding to the target object stored in the repository, wherein Q1 is a preset value;
retrieving the network resource occupation value Qzi corresponding to each reference target i from the repository, and calculating the reference network resource occupation value Qcc,
Figure BDA0003507169190000091
i＝1、2、3、…、n；
projected network resource occupancy value
Figure BDA0003507169190000092
If no transmission meeting the condition that the absolute value of Cy-Qy is less than or equal to Q1 existsThe output Cy is 0, and then Qcc is 0;
qcs is the average value of the network resource occupation corresponding to all the target objects stored in the repository.
As an embodiment provided by the present invention, it is preferable that: the method for estimating the transmission time comprises the following steps:
acquiring data transmission efficiency corresponding to each target object j stored in a storage library, and respectively marking the data transmission efficiency as Xj, wherein j is 1, 2, 3, … and m;
acquiring a predicted network resource occupation value Yz corresponding to a request target;
estimating transmission time
Figure BDA0003507169190000093
As an embodiment provided by the present invention, preferably, the habit data further includes a network resource occupation duration corresponding to the target object and a number of transmission nodes corresponding to the target object, and a process of the single analysis unit acquiring the priority of the target object is as follows:
calling habit data of a historical target object which is the same as the target object from a storage library to serve as pseudo-parameter habit data;
acquiring the maximum network resource occupation duration Tmax in the corresponding historical target object according to the habit-drawing data, and acquiring the average value Tzz of the network resource occupation durations corresponding to all the historical target objects according to the habit data;
acquiring request time of target objects, numbering each target object according to the numbering rule of 0, 1, 0 and 1 … in sequence according to the request time, taking the numbering as a request target time sequence weight, and marking the request target time sequence weight as Sx;
the number D of transmission nodes corresponding to the historical target object which is the same as the target object is called from a storage library;
the priority of the target object is:
Figure BDA0003507169190000101
wherein the content of the first and second substances,
Figure BDA0003507169190000102
presentation pair
Figure BDA0003507169190000103
And rounding to obtain 0.432, 0.352 and 0.216 as preset weights.
A virtual machine network resource allocation system acquires habit data of a target object through a single acquisition unit, calculates a demand calculation power range of the target object according to the habit data, and performs demand analysis according to the habit data of the target object by a single analysis unit to acquire the priority of the target object; the resource time control unit monitors the real-time computing power of the virtual machine and is in communication connection with the processor; the abnormity identification unit performs abnormity analysis on the non-target object according to habit data of the target object, and divides the non-target object into an abnormal target object and a normal target object; the processor allocates resources to the normal target object according to the priority and the demand computing power range and by combining real-time computing power; distributing network resource priority to each target object to ensure the throughput of the target object and the service quality of access application; and abnormal targets are screened out through analysis of historical data, the calculation amount is reduced, and the operation efficiency and the network resource utilization rate are improved.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.

Claims (10)

1. A virtual machine network resource allocation system, comprising:
a single item acquisition unit: the system comprises a habit data acquisition unit, a requirement calculation unit, a single analysis unit and a power consumption calculation unit, wherein the habit data acquisition unit is used for acquiring habit data of a target object, calculating a requirement calculation power range of the target object according to the habit data and uploading the habit data and the requirement calculation power range to the single analysis unit;
single item analysis unit: the method comprises the steps of carrying out demand analysis according to habit data of a target object, obtaining the priority of the target object, and transmitting the priority to a processor;
the resource time control unit: the real-time computing power of the virtual machine is monitored, and the virtual machine is in communication connection with the processor;
an abnormality determination unit: according to habit data of a target object, carrying out abnormity analysis on a non-target object, and dividing the non-target object into an abnormal target object and a normal target object;
the processor allocates resources to the normal target object according to the priority, the demand computing power range and the real-time computing power;
a quota notification unit: which generates an anomaly early warning upon receiving the anomalous target object.
2. The system according to claim 1, wherein the single acquisition unit is further configured to acquire historical data of the request object, and divide the request object into a target object and a non-target object according to the historical data, and when dividing the object, the following algorithm is executed:
s1: calling historical data corresponding to the request object from a storage library;
s2: if the corresponding historical data exists, the target object is obtained, otherwise, S3 is carried out;
s3: preprocessing a request object:
acquiring an IP address of a request object;
performing association analysis on the request object according to a target analysis rule to acquire the association degree Gg of the request object and historical data stored in a storage library;
if the association Gg is less than or equal to G1, the request object is a non-target object, and G1 is a preset value;
otherwise, the request object is the target object.
3. The system according to claim 2, wherein the association Gg is obtained by:
g001: acquiring a predicted transmission path of a request object, and extracting an IP address of a transmission end point;
g002: calling an IP address corresponding to the IP address of the transmission end point from a storage library, and marking the IP address as a historical IP address;
g003: acquiring the corresponding network resource occupation amount of the historical IP address during each scheduling in the last half year, calculating the average value of the occupation amount of the network resources used for one time, and marking the average value as an occupation average value Jz;
g004: a single acquisition unit calls a predicted network resource occupation value Yz of a request object;
g005: calculating a difference value Cz, wherein the Cz is | Jz-Yz |;
g006: if Cz is greater than or equal to C, the degree of correlation
Figure FDA0003507169180000021
Wherein 1 is<α<2；
Otherwise, degree of association
Figure FDA0003507169180000022
Wherein, Zz is the average of single-use network resource occupation amounts corresponding to all IP addresses in the repository, 1<α<β<2。
4. The system according to claim 3, wherein the method for predicting the expected network resource occupation value Yz by the single acquisition unit comprises:
acquiring data transmission quantity corresponding to the request object, and marking the data transmission quantity as pre-transmission quantity Qy;
retrieving a reference target from a repository: marking a history target object corresponding to the transmission quantity Cy which meets the condition that the absolute value of Cy-Qy is less than or equal to Q1 as a reference target; cy is the transmission amount corresponding to the target object stored in the repository, wherein Q1 is a preset value;
retrieving the network resource occupation value Qzi corresponding to each reference target i from the repository, and calculating the reference network resource occupation value Qcc,
Figure FDA0003507169180000023
projected network resource occupancy value
Figure FDA0003507169180000024
If no transmission amount Cy satisfying | Cy-Qy | ≦ Q1 exists, then Qcc is 0;
qcs is the average value of the network resource occupation corresponding to all the target objects stored in the repository.
5. The system according to claim 4, wherein the habit data includes historical transmission efficiency, a network resource occupation value corresponding to each target object j; when the single acquisition unit calculates the required computing power range of the target object according to the habit data, the following algorithm is executed:
estimating transmission time Ty according to historical transmission efficiency;
acquiring Qzj a network resource occupation value corresponding to each target object j stored in the repository;
the required calculated force range is as follows:
Figure FDA0003507169180000031
to is that
Figure FDA0003507169180000032
Wherein, Qzmax is a maximum network resource occupation value corresponding to all target objects stored in the repository.
6. The virtual machine network resource allocation system according to claim 5, wherein: the method for estimating the transmission time comprises the following steps:
acquiring data transmission efficiency corresponding to each target object j stored in a storage library, and respectively marking the data transmission efficiency as Xj, wherein j is 1, 2, 3, … and m;
acquiring a predicted network resource occupation value Yz corresponding to a request target;
estimating transmission time
Figure FDA0003507169180000033
7. The system according to claim 5, wherein: the habit data further comprises network resource occupation duration corresponding to the target object and the number of transmission nodes corresponding to the target object, and the process of acquiring the priority of the target object by the single analysis unit is as follows:
calling habit data of a historical target object which is the same as the target object from a storage library to serve as pseudo-parameter habit data;
acquiring the maximum network resource occupation duration Tmax in the corresponding historical target object according to the habit-drawing data, and acquiring the average value Tzz of the network resource occupation durations corresponding to all historical target objects according to the habit data;
acquiring request time of target objects, numbering each target object according to the numbering rule of 0, 1, 0 and 1 … in sequence according to the request time, taking the numbering as a request target time sequence weight, and marking the request target time sequence weight as Sx;
the number D of transmission nodes corresponding to the historical target object which is the same as the target object is called from a storage library;
the priority of the target object is:
Figure FDA0003507169180000041
wherein the content of the first and second substances,
Figure FDA0003507169180000042
pair of representations
Figure FDA0003507169180000043
And rounding to obtain 0.432, 0.352 and 0.216 as preset weights.
8. The virtual machine network resource allocation system according to claim 7, wherein: the resource time control unit calculates the real-time residual computing power of the virtual machine in real time according to the real-time computing power of the virtual machine obtained through monitoring, the maximum required computing power corresponding to the required computing power range of the target object is taken, and the processor sequentially allocates network resources to the target object according to the priority.
9. The virtual machine network resource allocation system according to claim 7, wherein: the process of carrying out abnormity analysis by the abnormity identification unit according to the habit data of the non-target object combined with the target object is as follows:
acquiring a network resource occupation value mean value corresponding to each target object stored in a storage library;
acquiring a predicted network resource occupation value and predicted transmission time corresponding to a non-target object;
if the predicted network resource occupation value-network resource occupation value mean value is greater than F1 and the predicted transmission time is greater than F2, the non-target object is an abnormal target object;
otherwise, the non-target object is a normal target object, wherein F1 and F2 are preset values.
10. The virtual machine network resource allocation system according to claim 9, wherein: and the quota informing unit generates an abnormal early warning after receiving the abnormal target object and displays the abnormal target object through the display unit, and an administrator deletes the abnormal target object or modifies the abnormal target object into a normal target object through the management unit.
CN202210142928.8A 2022-02-16 2022-02-16 Virtual machine network resource allocation system Pending CN114741154A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117032906A (en) * 2023-10-09 2023-11-10 新立讯科技股份有限公司 Agricultural product basic data resource pool management method and system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117032906A (en) * 2023-10-09 2023-11-10 新立讯科技股份有限公司 Agricultural product basic data resource pool management method and system
CN117032906B (en) * 2023-10-09 2023-12-19 新立讯科技股份有限公司 Agricultural product basic data resource pool management method and system

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