CN116610533A - Distributed data center operation and maintenance management method and system - Google Patents
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Abstract
The application provides a distributed data center operation and maintenance management method and a system, which are characterized in that operation information of each data center and task processing requests received by each task receiving end corresponding to each data center are obtained; determining the operation delay amount of each data center according to the network propagation delay parameter and the energy conversion coefficient corresponding to each data center in the operation information by using a preset delay model; determining an operation and maintenance control constraint model according to preset constraint conditions, operation delay amount, operation information and task processing requests; and solving an optimal solution of the operation and maintenance control constraint model by using a preset optimal control algorithm so as to determine and output a power control signal and a task allocation signal for each data center. The method balances the contradiction between the operation time delay and the energy consumption of the data center, solves the technical problem of how to reduce the energy consumption of the data center, and achieves the technical effect of balancing the operation time delay and the energy consumption of the data center.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and a system for operation and maintenance management of a distributed data center.
Background
With the development of digital society, the scale of data centers is continuously expanding, and the technological progress of data centers is developing towards the direction of intelligence and environmental protection. Resource-saving and environment-friendly data centers are also being increasingly valued by all parties.
The existing operation and maintenance management method of the data center generally takes operation performance and system safety as cores to carry out operation and maintenance control, and the control on resource saving is mainly focused on aspects of peak-to-valley joint regulation and control with a power grid or energy storage equipment by staggering peak power consumption and reasonably arranging time of a large-calculation-amount task.
However, in order to fully mobilize the data center for active energy saving, a more advanced research on an operation and maintenance management method thereof is still required.
Disclosure of Invention
The application provides a distributed data center operation and maintenance management method, which aims to solve the technical problem of how to actively reduce the energy consumption of a data center.
In a first aspect, the present application provides a distributed data center operation and maintenance management method, including:
acquiring operation information of each data center and task processing requests received by each task receiving end corresponding to each data center;
determining an operation delay amount of each data center according to the network propagation delay parameter and the energy conversion coefficient corresponding to each data center in the operation information by using a preset delay model, wherein the operation delay amount is used for representing a coupling logic relationship between the operation power of the data center and the amount of the processing tasks born by the operation power;
determining an operation and maintenance control constraint model according to preset constraint conditions, operation delay amount, operation information and task processing requests, wherein the operation and maintenance control constraint model is used for representing a logic constraint relation which needs to be met when power distribution and operation task distribution control are carried out on each data center at each moment;
and solving an optimal solution of the operation and maintenance control constraint model by using a preset optimal control algorithm so as to determine and output a power control signal and a task allocation signal for each data center.
In one possible design, determining the operation delay amount of each data center according to the network propagation delay parameter and the energy conversion coefficient corresponding to each data center in the operation information by using a preset delay model includes:
wherein ,for each data center the amount of computation delay, < >>Operating power at time t for each data center,/-for each data center>For the energy conversion factor corresponding to each data center,/->The amount of processing tasks to be carried out at time t for each data center is +>And (5) the network propagation delay parameter corresponding to each data center.
Alternatively, the energy conversion coefficient includes: the ratio of the first conversion coefficient to the second conversion coefficient, the first conversion coefficient comprising: conversion coefficients between server power and operational resource usage of the data center, the second conversion coefficients comprising: conversion coefficient between server power of the data center and power utilization efficiency of the data center.
In one possible design, the preset constraints include: the delay constraint condition and the operand constraint condition determine an operation and maintenance control constraint model according to a preset constraint condition, an operand, operation information and a task processing request, and the operation and maintenance control constraint model comprises the following steps:
determining a first sub-model of an operation and maintenance control constraint model according to the delay constraint condition, the running power of each data center, the amount of processing tasks born by each data center, the energy conversion coefficient corresponding to each data center and the network propagation delay parameter corresponding to each data center;
and determining a second sub-model of the operation and maintenance control constraint model according to the operand constraint condition, the running power of each data center, the processing task amount born by each data center and the task processing request corresponding to each data center.
In one possible design, determining a first sub-model of an operation and maintenance control constraint model according to a delay constraint condition, an operation power of each data center, an amount of processing tasks born by each data center, an energy conversion coefficient corresponding to each data center, and a network propagation delay parameter corresponding to each data center, includes:
according to a first integration format, the operating power of each data center and the amount of processing tasks assumed by each data center are combined into an operating state variable:
wherein ,for operating state variables +.>For the operating power of the ith data center at time t,the processing task amount born by the ith data center at the moment T is represented by a matrix transposition identifier;
combining the energy conversion coefficient corresponding to each data center and the network propagation delay parameter corresponding to each data center into a first coefficient matrix according to the second integration format;
combining the delay upper limit value corresponding to each data center and the network propagation delay parameter into a second coefficient matrix according to a third integration format;
determining a first submodel according to the running state variable, the first coefficient matrix and the second coefficient matrix by using a linear regression formula:
wherein ,for the first sub-model, +.>For the operating state variables, A is a first coefficient matrix, B is a second coefficient matrix, ++>Representing that each element in the first sub-model is less than or equal to zero.
In one possible design, combining the energy conversion coefficients corresponding to each data center and the network propagation delay parameters corresponding to each data center into a first coefficient matrix according to a second integration format includes:
wherein A is a first coefficient matrix,for the energy conversion factor corresponding to the ith data center,/->And the network propagation delay parameter corresponding to the ith data center.
In one possible design, combining the delay upper limit value corresponding to each data center and the network propagation delay parameter into a second coefficient matrix according to a third integration format includes:
wherein B is a second coefficient matrix,for the network propagation delay parameter corresponding to the ith data center,/->The delay upper limit value corresponding to the ith data center.
In one possible design, determining the second sub-model of the operation control constraint model according to the operand constraint condition, the operating power of each data center, the amount of processing tasks assumed by each data center, and the task processing request corresponding to each data center includes:
wherein ,for the second sub-model, +.>For the number of task processing requests corresponding to the h-th task receiving end, k represents that the i-th data center has k task receiving ends in total,/for the h-th task receiving end>For the operating state variable, C is the state correction factor, < ->For the operating power of the ith data center at time t,/>For the amount of processing tasks undertaken by the ith data center at time T, T is the matrix transpose identifier,/->For the length of the sampling period +.>Representing that each element in the second sub-model is less than or equal to zero.
In a second aspect, the present application provides a distributed data center operation and maintenance management system, including:
the acquisition module is used for acquiring the operation information of each data center and the task processing requests received by each task receiving end corresponding to each data center;
a processing module for:
determining an operation delay amount of each data center according to the network propagation delay parameter and the energy conversion coefficient corresponding to each data center in the operation information by using a preset delay model, wherein the operation delay amount is used for representing a coupling logic relationship between the operation power of the data center and the amount of the processing tasks born by the operation power;
determining an operation and maintenance control constraint model according to preset constraint conditions, operation delay amount, operation information and task processing requests, wherein the operation and maintenance control constraint model is used for representing a logic constraint relation which needs to be met when power distribution and operation task distribution control are carried out on each data center at each moment;
and solving an optimal solution of the operation and maintenance control constraint model by using a preset optimal control algorithm so as to determine and output a power control signal and a task allocation signal for each data center.
In a third aspect, the present application provides an electronic device comprising: a processor, a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes the computer-executable instructions stored in the memory to implement any one of the possible distributed data center operation and maintenance management methods provided in the first aspect.
In a fourth aspect, the present application provides a storage medium having stored therein computer-executable instructions which, when executed by a processor, are adapted to carry out any one of the possible distributed data center operation and maintenance management methods provided in the first aspect.
In a fifth aspect, the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements any one of the possible distributed data center operation and maintenance management methods provided in the first aspect.
The application provides a distributed data center operation and maintenance management method. Acquiring operation information of each data center and a task processing request received by each task receiving end corresponding to each data center; determining the operation delay amount of each data center according to the network propagation delay parameter and the energy conversion coefficient corresponding to each data center in the operation information by using a preset delay model; determining an operation and maintenance control constraint model according to preset constraint conditions, operation delay amount, operation information and task processing requests; and solving an optimal solution of the operation and maintenance control constraint model by using a preset optimal control algorithm so as to determine and output a power control signal and a task allocation signal for each data center. The technical problem of how to actively reduce the energy consumption of the data center is solved, and the technical effect of balancing the contradiction between the operation time delay and the energy consumption of the data center is achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
Fig. 1 is a schematic flow chart of a distributed data center operation and maintenance management method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a possible implementation of step S1031 in the example provided by the application;
fig. 3 is a schematic structural diagram of a hydraulic engineering real-time acquisition data management system according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Specific embodiments of the present application have been shown by way of the above drawings and will be described in more detail below. The drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but rather to illustrate the inventive concepts to those skilled in the art by reference to the specific embodiments.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, including but not limited to combinations of embodiments, which are within the scope of the application, can be made by one of ordinary skill in the art without inventive effort based on the embodiments of the application.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
To solve the technical problem of how to reduce the energy consumption of the data center. The application is characterized in that:
the energy consumption of the distributed data center can be divided into server energy consumption and auxiliary equipment (such as a heat dissipation system) energy consumption, and the server energy consumption is the main energy consumption of each data center. The server power consumption can be adjusted by adjusting the number of servers enabled and the operating dominant frequency of each server. But this tends to cause a delay in operation of the data center services, and thus it is necessary to equalize the delay in operation and the power consumption because they are coupled together. According to the application, by utilizing an optimal control algorithm and solving a real-time operation and maintenance model of a data center, optimal power distribution and operation task distribution are calculated, so that the balance of an information service level and an energy consumption level is realized, and the purposes of reducing energy consumption and carbon emission are finally achieved.
Fig. 1 is a schematic flow chart of a distributed data center operation and maintenance management method according to an embodiment of the present application. As shown in fig. 1, the specific steps of the method include:
s101, acquiring operation information of each data center and task processing requests received by each task receiving end corresponding to each data center.
In this step, each server corresponds to at least one task receiving end, and the task receiving end includes a user terminal that can be directly operated by a user, or a gateway device connected to a plurality of user terminals, and the user generates a task processing request by operating the user terminal.
It should be noted that, each data center is geographically distributed in different regions, and each region is individually powered and connected to form a distributed data center system through a communication network. Each data center has at least one server and auxiliary systems, such as a thermal management system or a cooling and heat dissipation system.
S102, determining the operation delay amount of each data center according to the network propagation delay parameter and the energy conversion coefficient corresponding to each data center in the operation information by using a preset delay model.
In this step, the operation delay amount is used to characterize the coupling logic relationship between the operation power of the data center and the amount of the processing task undertaken, that is, the change of the operation delay amount has two influencing factors, namely, the operation power and the amount of the processing task. The operation delay amount can be expressed as a mathematical function relation: f (x, y), wherein x is a first independent variable corresponding to the operation power, and y is a second independent variable corresponding to the processing task quantity.
In the present embodiment, it is possible to useRepresenting the operation delay amount of the ith data center at the time t byRepresent the firstThe operating power of i data centers at time t is +.>Representing the amount of processing task undertaken by the ith data center at time t, the amount of operational delay for each data center can be represented by equation (1):
（1）
wherein ,for the energy conversion factor corresponding to each data center,/->And (5) the network propagation delay parameter corresponding to each data center.
The operation delay amount, the operation power and the processing task amount are all variables, wherein the operation delay amount is a dependent variable, and the operation power and the processing task amount are independent variables.
Alternatively, the energy conversion coefficient includes: the ratio of the first conversion coefficient to the second conversion coefficient, the first conversion coefficient comprising: conversion coefficients between server power and operational resource usage of the data center, the second conversion coefficients comprising: conversion coefficient between server power of the data center and power utilization efficiency of the data center.
And S103, determining an operation and maintenance control constraint model according to the preset constraint condition, the operation delay amount, the operation information and the task processing request.
In this step, the operation and maintenance control constraint model is used to characterize the logical constraint relationship that needs to be satisfied when the power allocation and the operation task allocation are controlled for each data center at each moment.
In this embodiment, the preset constraint conditions include: the delay constraint condition and the operand constraint condition, the corresponding operation and maintenance control constraint model comprises a first sub-model and a second sub-model, and the specific steps comprise:
s1031, determining a first sub-model of the operation and maintenance control constraint model according to the delay constraint condition, the running power of each data center, the amount of processing tasks born by each data center, the energy conversion coefficient corresponding to each data center and the network propagation delay parameter corresponding to each data center.
Fig. 2 is a schematic flow chart of a possible implementation of step S1031 in the embodiment provided by the present application. As shown in fig. 2, this step specifically includes:
s201, according to the first integration format, the running power of each data center and the processing task amount born by each data center are combined into running state variables.
In one possible design, the operating state variable may be represented by equation (2):
（2）
wherein ,for operating state variables +.>For the operating power of the ith data center at time t,the processing task amount born by the ith data center at the time T is represented by a matrix transposition identifier.
S202, according to the second integration format, combining the energy conversion coefficient corresponding to each data center and the network propagation delay parameter corresponding to each data center into a first coefficient matrix.
In this embodiment, the first coefficient matrix may be expressed by the formula (3):
（3）
wherein A is a first coefficient matrix,for the energy conversion factor corresponding to the ith data center,/->And the network propagation delay parameter corresponding to the ith data center.
S203, according to the third integration format, combining the delay upper limit value corresponding to each data center and the network propagation delay parameter into a second coefficient matrix.
In this embodiment, the second coefficient matrix can be expressed by the formula (4):
（4）
wherein B is a second coefficient matrix,for the network propagation delay parameter corresponding to the ith data center,/->The delay upper limit value corresponding to the ith data center.
S204, determining a first sub-model according to the running state variable, the first coefficient matrix and the second coefficient matrix by using a linear regression formula.
In this step, the first sub-model can be represented by equation (5):
（5）
wherein ,for the first sub-model, +.>For the operating state variables, A is a first coefficient matrix, B is a second coefficient matrix, ++>Representing that each element in the first sub-model is less than or equal to zero.
S1032, determining a second sub-model of the operation and maintenance control constraint model according to the operand constraint conditions, the running power of each data center, the processing task amount born by each data center and the task processing request corresponding to each data center.
In this step, the second sub-model can be represented by equation (6):
（6）
wherein ,for the second sub-model, +.>For the number of task processing requests corresponding to the h-th task receiving end, k represents that the i-th data center has k task receiving ends in total,/for the h-th task receiving end>For the operating state variable, C is the state correction factor, < ->For the operating power of the ith data center at time t,/>For the amount of processing tasks undertaken by the ith data center at time T, T is the matrix transpose identifier,/->For the length of the sampling period +.>Representing that each element in the second sub-model is less than or equal to zero.
And S104, solving an optimal solution of the operation and maintenance control constraint model by using a preset optimal control algorithm so as to determine and output a power control signal and a task allocation signal for each data center.
In this step, the operation and maintenance control constraint model may be regarded as an optimal control model to be solved, and the optimal solution of the equation set formed by the equation (5) and the equation (6) is solved through multiple cycle computation by using a preset solving algorithm, i.e., an optimal control algorithm, such as a GEKKO tool kit of Python, so as to obtain an optimal power control signal and a task allocation signal of each data center at the current moment or at one or more subsequent moments.
Therefore, the service efficiency is considered at the same time under the condition of reducing the energy consumption of the distributed data center, and the energy conservation and emission reduction are realized and the service efficiency of the data center is ensured.
The embodiment provides a distributed data center operation and maintenance management method, which comprises the steps of obtaining operation information of each data center and task processing requests received by each task receiving end corresponding to each data center; determining the operation delay amount of each data center according to the network propagation delay parameter and the energy conversion coefficient corresponding to each data center in the operation information by using a preset delay model; determining an operation and maintenance control constraint model according to preset constraint conditions, operation delay amount, operation information and task processing requests; and solving an optimal solution of the operation and maintenance control constraint model by using a preset optimal control algorithm so as to determine and output a power control signal and a task allocation signal for each data center. The technical problem of how to actively reduce the energy consumption of the data center is solved, and the technical effect of balancing the contradiction between the operation time delay and the energy consumption of the data center is achieved.
Fig. 3 is a schematic structural diagram of a distributed data center operation and maintenance management system according to an embodiment of the present application. The distributed data center operation and maintenance management system 300 may be implemented in software, hardware, or a combination of both.
As shown in fig. 3, the system includes:
the acquiring module 301 is configured to acquire operation information of each data center and task processing requests received by each task receiving end corresponding to each data center;
a processing module 302, configured to:
determining an operation delay amount of each data center according to the network propagation delay parameter and the energy conversion coefficient corresponding to each data center in the operation information by using a preset delay model, wherein the operation delay amount is used for representing a coupling logic relationship between the operation power of the data center and the amount of the processing tasks born by the operation power;
determining an operation and maintenance control constraint model according to preset constraint conditions, operation delay amount, operation information and task processing requests, wherein the operation and maintenance control constraint model is used for representing a logic constraint relation which needs to be met when power distribution and operation task distribution control are carried out on each data center at each moment;
and solving an optimal solution of the operation and maintenance control constraint model by using a preset optimal control algorithm so as to determine and output a power control signal and a task allocation signal for each data center.
In one possible design, the processing module 302 is configured to determine, according to the network propagation delay parameter and the energy conversion coefficient corresponding to each data center in the operation information, an operation delay amount of each data center by using a preset delay model, where the operation delay amount includes:
wherein ,for each data center the amount of computation delay, < >>Operating power at time t for each data center,/-for each data center>For the energy conversion factor corresponding to each data center,/->The amount of processing tasks to be carried out at time t for each data center is +>And (5) the network propagation delay parameter corresponding to each data center.
Alternatively, the energy conversion coefficient includes: the ratio of the first conversion coefficient to the second conversion coefficient, the first conversion coefficient comprising: conversion coefficients between server power and operational resource usage of the data center, the second conversion coefficients comprising: conversion coefficient between server power of the data center and power utilization efficiency of the data center.
In one possible design, the preset constraints include: delay constraints and operand constraints, a processing module 302, configured to:
determining a first sub-model of an operation and maintenance control constraint model according to the delay constraint condition, the running power of each data center, the amount of processing tasks born by each data center, the energy conversion coefficient corresponding to each data center and the network propagation delay parameter corresponding to each data center;
and determining a second sub-model of the operation and maintenance control constraint model according to the operand constraint condition, the running power of each data center, the processing task amount born by each data center and the task processing request corresponding to each data center.
In one possible design, the processing module 302 is configured to:
according to a first integration format, the operating power of each data center and the amount of processing tasks assumed by each data center are combined into an operating state variable:
wherein ,for operating state variables +.>For the operating power of the ith data center at time t,the processing task amount born by the ith data center at the moment T is represented by a matrix transposition identifier;
combining the energy conversion coefficient corresponding to each data center and the network propagation delay parameter corresponding to each data center into a first coefficient matrix according to the second integration format;
combining the delay upper limit value corresponding to each data center and the network propagation delay parameter into a second coefficient matrix according to a third integration format;
determining a first submodel according to the running state variable, the first coefficient matrix and the second coefficient matrix by using a linear regression formula:
wherein ,for the first sub-model, +.>For the operating state variables, A is a first coefficient matrix, B is a second coefficient matrix, ++>Representing that each element in the first sub-model is less than or equal to zero.
In one possible design, the processing module 302 is configured to combine, according to the second integration format, the energy conversion coefficient corresponding to each data center and the network propagation delay parameter corresponding to each data center into a first coefficient matrix, including:
wherein A is a first coefficient matrix,for the ith data center pairEnergy conversion coefficient of response, +.>And the network propagation delay parameter corresponding to the ith data center.
In one possible design, the processing module 302 is configured to combine, according to the third integration format, the delay upper limit value corresponding to each data center and the network propagation delay parameter into a second coefficient matrix, where the processing module includes:
wherein B is a second coefficient matrix,for the network propagation delay parameter corresponding to the ith data center,/->The delay upper limit value corresponding to the ith data center.
In one possible design, the processing module 302 is configured to determine a second sub-model of the operation control constraint model according to the operand constraint condition, the running power of each data center, the amount of processing tasks assumed by each data center, and the task processing request corresponding to each data center, where the second sub-model includes:
wherein ,for the second sub-model, +.>For the number of task processing requests corresponding to the h-th task receiving end, k represents that the i-th data center has k task receiving ends in total,/for the h-th task receiving end>For the operating state variable, C is the state correction factor, < ->For the operating power of the ith data center at time t,/>For the amount of processing tasks undertaken by the ith data center at time T, T is the matrix transpose identifier,/->For the length of the sampling period +.>Representing that each element in the second sub-model is less than or equal to zero.
It should be noted that, the system provided in the embodiment shown in fig. 3 may perform the method provided in any of the above method embodiments, and the specific implementation principles, technical features, explanation of terms, and technical effects are similar, and are not repeated herein.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 4, the electronic device 400 may include: at least one processor 401 and a memory 402. Fig. 4 shows an apparatus for example a processor.
A memory 402 for storing a program. In particular, the program may include program code including computer-operating instructions.
Memory 402 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The processor 401 is configured to execute computer-executable instructions stored in the memory 402 to implement the methods described in the above method embodiments.
The processor 401 may be a central processing unit (central processing unit, abbreviated as CPU), or an application specific integrated circuit (application specific integrated circuit, abbreviated as ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
Alternatively, the memory 402 may be separate or integrated with the processor 401. When the memory 402 is a device independent from the processor 401, the electronic apparatus 400 may further include:
a bus 403 for connecting the processor 401 and the memory 402. The bus may be an industry standard architecture (industry standard architecture, abbreviated ISA) bus, an external device interconnect (peripheral component, PCI) bus, or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. Buses may be divided into address buses, data buses, control buses, etc., but do not represent only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 402 and the processor 401 are integrated on a chip, the memory 402 and the processor 401 may complete communication through an internal interface.
Embodiments of the present application also provide a computer-readable storage medium, which may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, and specifically, the computer readable storage medium stores program instructions for the methods in the above method embodiments.
The embodiments of the present application also provide a computer program product comprising a computer program which, when executed by a processor, implements the method of the above-described method embodiments.
Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the application.
Claims (10)
1. A distributed data center operation and maintenance management method, comprising:
acquiring operation information of each data center and task processing requests received by each task receiving end corresponding to each data center;
determining an operation time delay amount of each data center according to a network propagation time delay parameter and an energy conversion coefficient corresponding to each data center in the operation information by using a preset time delay model, wherein the operation time delay amount is used for representing a coupling logic relationship between the operation power of the data center and the amount of the processing tasks born by the data center;
determining an operation and maintenance control constraint model according to preset constraint conditions, the operation delay amount, the operation information and the task processing request, wherein the operation and maintenance control constraint model is used for representing a logic constraint relation which needs to be met when power distribution and operation task distribution control are carried out on each data center at each moment;
and solving an optimal solution of the operation and maintenance control constraint model by using a preset optimal control algorithm so as to determine and output a power control signal and a task allocation signal for each data center.
2. The method for managing operation and maintenance of distributed data centers according to claim 1, wherein determining the operation delay amount of each data center according to the network propagation delay parameter and the energy conversion coefficient corresponding to each data center in the operation information by using a preset delay model includes:
，
wherein ,for said amount of computation delay per said data center,/for each said data center>For each of the data centers, the operating power at time t is +.>For each of said data centers corresponding said energy conversion factor,/for each of said data centers>For each of the data centers, the amount of processing tasks to be carried out at time t is +.>And the network propagation delay parameters corresponding to each data center are obtained.
3. The distributed data center operation and maintenance management method according to claim 1 or 2, wherein the energy conversion coefficient includes: a ratio of a first conversion coefficient to a second conversion coefficient, the first conversion coefficient comprising: a conversion factor between server power and operational resource usage of the data center, the second conversion factor comprising: conversion coefficients between the server power of the data center and the power utilization efficiency of the data center.
4. The distributed data center operation and maintenance management method according to claim 1 or 2, wherein the preset constraint condition includes: delay constraint conditions and operand constraint conditions, wherein determining an operation and maintenance control constraint model according to a preset constraint condition, the operand, the operation information and the task processing request comprises the following steps:
determining a first sub-model of the operation and maintenance control constraint model according to the delay constraint condition, the running power of each data center, the processing task amount born by each data center, the energy conversion coefficient corresponding to each data center and the network propagation delay parameter corresponding to each data center;
and determining a second sub-model of the operation and maintenance control constraint model according to the operand constraint conditions, the running power of each data center, the processing task amount born by each data center and the task processing request corresponding to each data center.
5. The method according to claim 4, wherein determining the first sub-model of the operation and maintenance control constraint model according to the delay constraint condition, the running power of each data center, the amount of processing tasks assumed by each data center, the energy conversion coefficient corresponding to each data center, and the network propagation delay parameter corresponding to each data center comprises:
combining the operating power of each of the data centers and the amount of processing tasks undertaken by each of the data centers into an operating state variable according to a first integration format:
，
wherein ,for the operating state variable, +.>For the operating power of the ith data center at time t, +.>The processing task amount born by the ith data center at the moment T is represented by a matrix transposition identifier;
combining the energy conversion coefficient corresponding to each data center and the network propagation delay parameter corresponding to each data center into a first coefficient matrix according to a second integration format;
combining the delay upper limit value corresponding to each data center and the network propagation delay parameter into a second coefficient matrix according to a third integration format;
determining the first sub-model according to the running state variable, the first coefficient matrix and the second coefficient matrix by using a linear regression formula:
，
wherein ,for the first sub-model, +.>For the operating state variables, A is the first coefficient matrix, B is the second coefficient matrix, ++>Representing that each element in the first sub-model is less than or equal to zero.
6. The method according to claim 5, wherein the combining the energy conversion coefficient corresponding to each data center and the network propagation delay parameter corresponding to each data center into a first coefficient matrix according to a second integration format comprises:
，
wherein A is the first coefficient matrix,for the energy conversion coefficient corresponding to the ith data center,/th data center>And the network propagation delay parameter corresponding to the ith data center is obtained.
7. The method according to claim 5, wherein combining the delay upper limit value corresponding to each data center and the network propagation delay parameter into a second coefficient matrix according to a third integration format comprises:
，
wherein B is the second coefficient matrix,for the network propagation delay parameter corresponding to the ith data center,and the delay upper limit value corresponding to the ith data center.
8. The method according to claim 4, wherein determining the second sub-model of the operation control constraint model according to the operand constraint condition, the running power of each data center, the processing task amount assumed by each data center, and the task processing request corresponding to each data center comprises:
，
wherein ,for the second sub-model, +.>For the number of task processing requests corresponding to the h th task receiving end, k represents that the ith data center has k task receiving ends in total, and the number of the task receiving ends is->For the operating state variable, C is a state correction factor,>for the operating power of the ith data center at time t, +.>For the amount of processing tasks undertaken by the ith said data center at time T, T is a matrix transpose identifier,for the length of the sampling period +.>Representing that each element in the second sub-model is less than or equal to zero.
9. A distributed data center operation and maintenance management system, comprising:
the acquisition module is used for acquiring the operation information of each data center and the task processing requests received by each task receiving end corresponding to each data center;
a processing module for:
determining an operation time delay amount of each data center according to a network propagation time delay parameter and an energy conversion coefficient corresponding to each data center in the operation information by using a preset time delay model, wherein the operation time delay amount is used for representing a coupling logic relationship between the operation power of the data center and the amount of the processing tasks born by the data center;
determining an operation and maintenance control constraint model according to preset constraint conditions, the operation delay amount, the operation information and the task processing request, wherein the operation and maintenance control constraint model is used for representing a logic constraint relation which needs to be met when power distribution and operation task distribution control are carried out on each data center at each moment;
and solving an optimal solution of the operation and maintenance control constraint model by using a preset optimal control algorithm so as to determine and output a power control signal and a task allocation signal for each data center.
10. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the distributed data center operation and maintenance management method as claimed in any one of claims 1 to 8.
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