CN116232781A - Energy saving method, device and storage medium - Google Patents
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Abstract
The disclosure provides an energy-saving method, an energy-saving device and a storage medium, relates to the technical field of communication, and solves the technical problem that an energy-saving strategy of an edge cloud data center in the related technology has safety risks. The method comprises the following steps: determining the number of physical machines deployed in the edge cloud and the running state of a CEPH system cluster of the distributed storage system deployed in the edge cloud; under the condition that the number of the physical machines meets the requirement and the running state of the CEPH system cluster meets the energy-saving requirement, determining a target physical machine meeting the energy-saving requirement in the physical machines according to the running state of the physical machines; determining the shutdown priority of the target physical machine according to the running state value of the target physical machine; and determining the physical machine to be powered off in the target physical machine according to the shutdown priority of the target physical machine, thereby reducing the safety risk of energy conservation when the edge cloud data center shuts down the physical machine. The method and the device are used in the scene of energy saving in the edge cloud center.
Description
Technical Field
The disclosure relates to the technical field of communication, and in particular relates to an energy saving method, an energy saving device and a storage medium.
Background
The edge cloud data center needs to save energy and reduce emission so as to reduce energy consumption and carbon emission, at present, a common energy-saving method is to collect factors influencing the energy conservation of the edge cloud, perform calculation analysis according to an energy-saving analysis model, and shut down a low-load physical machine under the condition that the normal operation of the edge cloud data center is not influenced, so that the energy-saving purpose is achieved.
However, because the energy-saving analysis model is not fully considered on factors influencing the energy-saving problem of the edge cloud, the determined closeable physical machine is unreasonable according to the calculation and analysis of the energy-saving analysis model, and the energy-saving strategy has a safety risk.
Disclosure of Invention
The disclosure provides an energy-saving method, an energy-saving device and a storage medium, which solve the technical problems that a determined closeable physical machine is unreasonable and an energy-saving strategy has safety risks in the related technology.
In order to achieve the above purpose, the present disclosure adopts the following technical scheme:
in a first aspect, there is provided a method of saving energy, comprising: determining the number of physical machines deployed in the edge cloud and the running state of a CEPH system cluster of the distributed storage system deployed in the edge cloud; under the condition that the number of the physical machines meets the requirement and the running state of the CEPH system cluster meets the energy-saving requirement, determining a target physical machine meeting the energy-saving requirement in the physical machines according to the running state of the physical machines; determining the shutdown priority of the target physical machine according to the running state value of the target physical machine; and determining the physical machine to be powered off in the target physical machine according to the shutdown priority of the target physical machine.
With reference to the first aspect, in one possible implementation manner, the number of physical machines satisfies the requirement includes: the number of physical machines is greater than or equal to a first preset value.
With reference to the first aspect, in one possible implementation manner, the operating state of the CEPH system cluster meets the energy saving requirement, and the method includes at least one of the following: the CEPH system cluster stably operates; the CEPH system cluster operates within a preset time period, and the stability is smaller than a second preset value; the storage utilization rate of the CEPH system cluster in the preset time period is smaller than a third preset value.
With reference to the first aspect, in one possible implementation manner, the target physical machine satisfies at least one of the following: the number of virtual machines deployed in the target physical machine is smaller than a fourth preset value; the average number of virtual machines deployed in a preset time period in the target physical machine is smaller than a fifth preset value; the CPU utilization rate of the target physical machine in the preset time period is smaller than a sixth preset value; the memory utilization rate of the target physical machine in a preset time period is smaller than a seventh preset value; the hard disk reading speed of the target physical machine in a preset time period is smaller than an eighth preset value; the hard disk writing speed of the target physical machine in a preset time period is smaller than a ninth preset value; the average value of the network sending rate of the target physical machine in the preset time period is smaller than a tenth preset value; the average value of the network receiving rate of the target physical machine in the preset time period is smaller than an eleventh preset value.
With reference to the first aspect, in one possible implementation manner, determining the shutdown priority of the target physical machine according to the running state value of the target physical machine includes: determining the operation parameters of each operation state of the target physical machine and the weight value of each operation state; determining tolerance values of operation parameters of each operation state of the target physical machine; determining the score of the target physical machine according to the operation parameters, the weight values and the tolerance values of each operation state of the target physical machine; and determining the shutdown priority of the target physical machine according to the score of the target physical machine. With reference to the first aspect, in one possible implementation manner, the Score of the target physical machine satisfies the following formula:
wherein x is i Operating parameters for each operating state of the target physical machine; x is x Tolerance of Tolerance values of all running state parameters of the target physical machine; y is Weighting of And the weight value of each running state of the target physical machine.
In a second aspect, there is provided an energy saving device comprising: a communication unit and a processing unit; the communication unit is used for determining the number of physical machines deployed in the edge cloud and the running state of a CEPH system cluster of the distributed storage system deployed in the edge cloud; the processing unit is used for determining a target physical machine meeting the energy-saving requirement in the physical machines according to the running state of the physical machines under the condition that the number of the physical machines meets the requirement and the running state of the CEPH system cluster meets the energy-saving requirement; the processing unit is also used for determining the shutdown priority of the target physical machine according to the running state value of the target physical machine; and the processing unit is also used for determining the physical machine to be powered off in the target physical machine according to the power-off priority of the target physical machine.
With reference to the second aspect, in one possible implementation manner, the number of physical machines meets a requirement, including: the number of physical machines is greater than or equal to a first preset value.
With reference to the second aspect, in one possible implementation manner, determining that the operating state of the CEPH system cluster meets the energy saving requirement includes at least one of: determining stable operation of CEPH system clusters; determining that the running stability of the CEPH system cluster in a preset time period is smaller than a second preset value; and determining that the storage utilization rate of the CEPH system cluster in the preset time period is smaller than a third preset value.
With reference to the second aspect, in one possible implementation manner, the target physical machine satisfies at least one of the following: determining that the number of virtual machines deployed in the target physical machine is smaller than a fourth preset value; determining that the average number of virtual machines deployed in a preset time period in the target physical machine is smaller than a fifth preset value; determining that the average value of CPU utilization rates of the target physical machine in a preset time period is smaller than a sixth preset value; determining that the average value of the memory utilization rate of the target physical machine in a preset time period is smaller than a seventh preset value; determining that the average value of the hard disk reading speed of the target physical machine in a preset time period is smaller than an eighth preset value; determining that the average value of the hard disk writing speed of the target physical machine in a preset time period is smaller than a ninth preset value; determining that the average value of the network sending rate of the target physical machine in a preset time period is smaller than a tenth preset value; and determining that the network receiving rate average value of the target physical machine in the preset time period is smaller than an eleventh preset value.
With reference to the second aspect, in one possible implementation manner, the communication unit is further configured to determine an operation parameter of each operation state of the target physical machine, and a weight value of each operation state; the communication unit is also used for determining the tolerance value of the operation parameters of each operation state of the target physical machine; the processing unit is also used for determining the score of the target physical machine according to the operation parameters, the weight values and the tolerance values of each operation state of the target physical machine; and the processing unit is also used for determining the shutdown priority of the target physical machine according to the score of the target physical machine.
With reference to the second aspect, in one possible implementation manner, the Score of the target physical machine satisfies the following formula:
wherein x is i Operating parameters for each operating state of the target physical machine; x is x Tolerance of Tolerance values of all running state parameters of the target physical machine; y is Weighting of And the weight value of each running state of the target physical machine.
In a third aspect, there is provided an energy saving device comprising: a processor and a memory; the memory is configured to store computer-executable instructions, and when the energy saving device is operated, the processor executes the computer-executable instructions stored in the memory, so that the energy saving device performs the energy saving method described in the first aspect and any possible implementation manner thereof.
In a fourth aspect, a computer readable storage medium is provided, in which instructions are stored which, when executed by a processor of an energy saving device, cause the energy saving device to perform the energy saving method described in the first aspect and any possible implementation manner thereof.
In the present disclosure, the names of the above-mentioned energy saving devices do not constitute limitations on the devices or functional modules themselves, and in actual implementations, these devices or functional modules may appear under other names. Insofar as the function of each device or functional module is similar to the present disclosure, it is within the scope of the present disclosure and the equivalents thereof.
These and other aspects of the disclosure will be more readily apparent from the following description.
The technical scheme provided by the disclosure at least brings the following beneficial effects: the present disclosure provides an energy saving method applicable in a scenario of edge cloud center energy saving, where an energy saving device determines the number of physical machines deployed in an edge cloud and the running state of a CEPH system cluster of a distributed storage system deployed in the edge cloud; therefore, partial low-load physical machines are ensured to be closed at the moment, and the stable operation of the edge cloud center is not influenced. Under the condition that the number of the physical machines meets the requirement and the running state of the CEPH system cluster meets the energy-saving requirement, determining a target physical machine meeting the energy-saving requirement in the physical machines according to the running state of the physical machines; i.e. a partially low-load physical machine that can be shut down is determined. Determining the shutdown priority of the target physical machine according to the running state value of the target physical machine; and determining the physical machine to be shut down in the target physical machine according to the shutdown priority of the target physical machine, thereby further reducing the safety risk brought by shutting down the physical machine for energy saving.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the prior art, the drawings that are used in the description of the embodiments or the prior art will be briefly described below.
Fig. 1 is a schematic hardware structure of an energy saving device according to an embodiment of the disclosure;
fig. 2 is a schematic diagram of an energy-saving recommendation platform architecture according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an algorithm logic of an energy-saving analysis model according to an embodiment of the disclosure;
fig. 4 is a schematic flow chart of an energy saving method according to an embodiment of the disclosure;
FIG. 5 is a schematic flow chart of another energy saving method according to an embodiment of the disclosure;
fig. 6 is a schematic structural diagram of an energy saving device according to an embodiment of the present disclosure.
Detailed Description
The following describes in detail a method, an apparatus, and a storage medium for saving energy according to embodiments of the present disclosure with reference to the accompanying drawings.
The term "and/or" is herein merely an association relationship describing an associated object, meaning that there may be three relationships, e.g., a and/or B, may represent: a exists alone, A and B exist together, and B exists alone.
The terms "first" and "second" and the like in the description and in the drawings of the present disclosure are used for distinguishing between different objects or for distinguishing between different processes of the same object and not for describing a particular sequential order of objects.
Furthermore, references in the description of this disclosure to the terms "comprise" and "have," and any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.
It should be noted that in the embodiments of the present disclosure, words such as "exemplary" or "such as" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g." in the examples of this disclosure should not be taken as preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "such as" is intended to present related concepts in a concrete fashion.
In the description of the present disclosure, unless otherwise indicated, the meaning of "a plurality" means two or more.
Fig. 1 is a schematic structural diagram of an energy saving device according to an embodiment of the present disclosure, and as shown in fig. 1, the energy saving device 100 includes at least one processor 101, a communication line 102, and at least one communication interface 104, and may further include a memory 103. The processor 101, the memory 103, and the communication interface 104 may be connected through a communication line 102.
The processor 101 may be a central processing unit (central processing unit, CPU), or may be an application specific integrated circuit (application specific integrated circuit, ASIC), or one or more integrated circuits configured to implement embodiments of the present disclosure, such as: one or more digital signal processors (digital signal processor, DSP), or one or more field programmable gate arrays (field programmable gate array, FPGA).
Communication line 102 may include a pathway for communicating information between the aforementioned components.
The communication interface 104, for communicating with other devices or communication networks, may use any transceiver-like device, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), etc.
The memory 103 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (random access memory, RAM) or other type of dynamic storage device that can store information and instructions, or an electrically erasable programmable read-only memory (electrically erasable programmable read-only memory, EEPROM), a compact disc read-only memory (compact disc read-only memory) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to include or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
In a possible design, the memory 103 may exist independent of the processor 101, i.e., the memory 103 may be a memory external to the processor 101, where the memory 103 may be connected to the processor 101 through a communication line 102 for storing execution instructions or application program codes, and the execution is controlled by the processor 101 to implement the energy saving method provided by the embodiments of the disclosure described below. In yet another possible design, the memory 103 may be integrated with the processor 101, i.e., the memory 103 may be an internal memory of the processor 101, e.g., the memory 103 may be a cache, and may be used to temporarily store some data and instruction information, etc.
As one implementation, processor 101 may include one or more CPUs, such as CPU0 and CPU1 in fig. 1. As another implementation, the energy conservation device 100 may include multiple processors, such as the processor 101 and the processor 107 in fig. 1. As yet another implementation, the energy saving apparatus 100 may further include an output device 105 and an input device 106.
From the foregoing description of the embodiments, it will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of the network node is divided into different functional modules to implement all or part of the functions described above. The specific working processes of the above-described system, module and network node may refer to the corresponding processes in the foregoing method embodiments, which are not described herein.
The following explains terms related to the embodiments of the present disclosure, for convenience of the reader.
1. Edge cloud data center
An edge cloud data center is a data center built at the edge of a network. The edge cloud data center platform can directly process or solve specific service requirements locally based on distributed computing and storage capacity by providing storage, computing, network and other resources so as to reduce bandwidth and delay loss caused by network transmission and multistage forwarding.
Along with the rapid development of digital economy, the problem of high energy consumption of an edge cloud data center serving as a bottom support of a digital industry needs to be solved, and a common energy-saving mode is to reduce the number of running servers by migrating virtual machines in a low-load physical machine to other physical machines and closing the low-load physical machine, so that the energy consumption of cloud computing is reduced. However, the energy-saving analysis model may not be considered fully enough for the factors influencing the energy-saving problem of the edge cloud or the weight configured for the factors influencing the energy-saving problem of the edge cloud is unreasonable, so that the closable low-load physical machine determined according to the energy-saving analysis model is unreasonable, and the energy-saving strategy has a safety risk.
2. Promiprusil
Promitus is an open source system monitoring and alarm kit, and is mainly used for monitoring servers and databases.
In a possible implementation manner, as shown in fig. 2, fig. 2 is a schematic diagram of an energy-saving recommendation platform architecture, the present disclosure provides an edge cloud physical machine energy-saving recommendation platform 201, and the edge cloud physical machine energy-saving recommendation platform 201 combines operation characteristics of a CEPH system 2033 and a physical machine 2032 in an edge cloud 203 to obtain operation state parameters of the CEPH system 2033 and the physical machine 2032 in the edge cloud 203 collected by an Exporter service 2034 through a pramipexole monitoring service device 2031 running on the edge cloud 203 by a pramipexole monitoring service center 202; wherein the Exporter service 2034 is configured to provide parameters of monitoring sample data to the promiscuous monitoring service apparatus 2031.
The edge cloud physical machine energy-saving recommendation platform 201 obtains the running state parameters of the CEPH system 2033 and the physical machine 2032 in the edge cloud 203 through the Promitus monitoring service device 2031, and determines an energy-saving strategy for closing the low-load physical machine according to an energy-saving analysis model.
The algorithm logic of the energy saving analysis model is shown in fig. 3 and described in detail below.
S301, collecting operation state data of a physical machine and operation state data of a CEPH system cluster.
S302, judging whether the number of the physical machines meets the tolerance.
The following S311 is performed in the case where the number of physical machines does not satisfy the tolerance. The following S303 is performed in a case where the number of physical machines satisfies the tolerance.
S303, judging whether the running state of the CEPH system cluster meets the tolerance.
The following S304 is performed in case the operational status of the CEPH system cluster satisfies the tolerance. The following S311 is performed in case the operational status of the CEPH system cluster does not meet the tolerance.
S304, traversing all physical machines of the edge cloud.
In the traversal process, it is determined whether the running state of each physical machine satisfies the tolerance.
S305, determining whether all physical machines are traversed.
If not, the following S306 is continued for the physical machine that has not been traversed. If all the physical machines have been traversed, then S308 is performed
S306, judging whether the running state of the physical machine meets the tolerance.
S307, adding the physical machine meeting the tolerance into a physical machine shutdown list.
S308, determining whether the physical machine closeable list is empty.
Under the condition that the physical machine closeable list is empty, the following S311 is executed, the fact that the edge cloud is not suitable for energy saving at the moment is determined, and the analysis is ended.
In the case where the physical machine closeable list is not empty, the following S309 is performed.
S309, sorting scores of the physical machines in the closeable list, and providing decision suggestions for preferential closedown.
And S310, determining that the edge cloud is suitable for energy saving at the moment, and ending the analysis.
And S311, determining that the edge cloud is not suitable for energy saving at the moment, and ending the analysis.
When the algorithm logic of the energy-saving analysis model is executed for analysis, the dimensions of the running states of the CEPH system and the physical machine as shown in the following table 1 and the tolerance sample table of each dimension as shown in the following table 2 are required to be determined, the tolerance sample table of each dimension is used for determining whether the normal running of the edge cloud center is affected by executing the energy-saving strategy, and if the running state parameters of the CEPH system and the physical machine are all within the tolerance ranges of the corresponding dimensions, it can be determined that the normal running of the edge cloud center is not affected by executing the energy-saving strategy at the moment.
Table 1 stores dimension tables of operating states of systems and physical machines
Table 2 tolerance sample table for each dimension
Under the condition that the energy-saving strategy for closing the low-load physical machine is not influenced by the normal operation of the edge cloud center, the weight value sample table of each dimension of the physical machine and the physical machine score calculation formula are determined according to the following table 3 
And calculating to determine the shutdown priority of closing the low-load physical machine, thereby further reducing the safety risk brought by energy saving when closing the physical machine.
Table 3 example table of weight values for each dimension of physical machine
At present, energy conservation and emission reduction are the problems to be solved in the edge cloud data center, and a common energy conservation method is to collect factors influencing the energy conservation of the edge cloud, perform calculation and analysis according to an energy conservation analysis model, and shut down a low-load physical machine under the condition that the normal operation of the edge cloud data center is not influenced, so that the energy conservation purpose is achieved.
However, because the energy-saving analysis model is not fully considered on factors influencing the energy-saving problem of the edge cloud, the determined closeable low-load physical machine is unreasonable according to the calculation and analysis of the energy-saving analysis model, and the energy-saving strategy has safety risks.
In order to solve the technical problems in the related art, the present disclosure provides an energy-saving method, an energy-saving device and a storage medium, wherein the energy-saving device determines the number of physical machines deployed in an edge cloud and the running state of a CEPH system cluster of a distributed storage system deployed in the edge cloud; therefore, partial low-load physical machines are ensured to be closed at the moment, and the stable operation of the edge cloud center is not influenced. Under the condition that the number of the physical machines meets the requirement and the running state of the CEPH system cluster meets the energy-saving requirement, determining a target physical machine meeting the energy-saving requirement in the physical machines according to the running state of the physical machines; i.e. a partially low-load physical machine that can be shut down is determined. Determining the shutdown priority of the target physical machine according to the running state value of the target physical machine; and determining the physical machine to be shut down in the target physical machine according to the shutdown priority of the target physical machine, thereby further reducing the safety risk brought by shutting down the physical machine for energy saving.
As shown in fig. 4, fig. 4 is a schematic diagram illustrating an energy saving method according to an embodiment of the present disclosure, which may be applied to the energy saving device shown in fig. 1, and the method includes the following steps S401 to S404.
S401, the energy-saving device determines the number of physical machines deployed in the edge cloud, and the running state of the CEPH system cluster of the distributed storage system deployed in the edge cloud.
In a possible implementation manner, the energy-saving device can determine the number of physical machines deployed in the edge cloud and the running state of a distributed storage system CEPH system cluster deployed in the edge cloud through the Promitus monitoring device cluster.
It can be understood that, determining the number of physical machines deployed in the edge cloud can determine whether the number of physical machines deployed in the edge cloud meets the requirement of edge cloud operation, and the operation state of the CEPH system cluster can determine from multiple dimensions whether the state of the CEPH system cluster meets the requirement of edge cloud operation at the moment, so that it can determine whether the energy saving of the edge cloud at the moment can influence the stable operation of the edge cloud.
S402, under the condition that the number of the physical machines meets the requirement and the running state of the CEPH system cluster meets the energy-saving requirement, the energy-saving device determines a target physical machine meeting the energy-saving requirement in the physical machines according to the running state of the physical machines.
Optionally, the target physical machine is determined by the energy-saving device, and the physical machine with a significant risk of edge cloud operation caused by energy-saving shutdown is avoided.
Optionally, under the condition that the number of physical machines does not meet the operation requirement of the edge cloud or the operation state of the CEPH system cluster does not meet the energy-saving requirement, the energy-saving device determines that the edge cloud saves energy at the moment, and the edge cloud operation is caused to have a great risk.
S403, the energy-saving device determines the shutdown priority of the target physical machine according to the running state value of the target physical machine.
In a possible implementation manner, the energy-saving device determines the running state value of the target physical machine through the Promitus monitoring device cluster, performs calculation and analysis according to the energy-saving analysis model, and determines the priority order of closing the target physical machine.
S404, the energy-saving device determines the physical machine to be powered off in the target physical machine according to the power-off priority of the target physical machine.
It can be understood that the shutdown priority of the target physical machine is determined according to the running state value of the target physical machine, and the physical machine to be shutdown in the target physical machine is determined according to the shutdown priority of the target physical machine, so that the safety risk of shutting down the physical machine for energy saving can be further reduced.
The technical scheme provided by the embodiment at least has the following beneficial effects: the energy-saving device determines the number of physical machines deployed in the edge cloud and the running state of a CEPH system cluster of the distributed storage system deployed in the edge cloud; therefore, partial low-load physical machines are ensured to be closed at the moment, and the stable operation of the edge cloud center is not influenced. Under the condition that the number of the physical machines meets the requirement and the running state of the CEPH system cluster meets the energy-saving requirement, determining a target physical machine meeting the energy-saving requirement in the physical machines according to the running state of the physical machines; i.e. determines the part of the low-load physical machine that can be shut down. Determining the shutdown priority of the target physical machine according to the running state value of the target physical machine; and determining the physical machine to be shut down in the target physical machine according to the shutdown priority of the target physical machine, thereby further reducing the safety risk brought by shutting down the physical machine for energy saving.
In one possible implementation, the number of physical machines to meet the requirement includes: the number of physical machines is greater than or equal to a first preset value. That is, when the number of physical machines deployed in the edge cloud by the energy-saving device is greater than or equal to the first preset value, it is determined that the number of physical machines deployed in the edge cloud meets the requirement.
Illustratively, the first preset value is 3. That is, in the case where the number of physical machines is greater than or equal to 3, it is determined that the number of physical machines satisfies the demand.
In yet another possible implementation manner, the energy saving device determines that the operating state of the CEPH system cluster meets the energy saving requirement, including at least one of the following:
and (3) stably operating the CEPH system cluster under the condition 1.
And 2, operating stability of the CEPH system cluster in a preset time period is smaller than a second preset value.
And 3, the storage utilization rate of the CEPH system cluster in the preset time period is smaller than a third preset value.
That is, the energy saving device determines that the operating state of the CEPH system cluster meets the energy saving requirement when the operating state of the CEPH system cluster meets the above conditions 1, 2 and 3.
Optionally, for the condition 1, the CEPH system cluster stably operates, if the energy-saving device determines that the stability of the CEPH system cluster is "0", the energy-saving device determines that the CEPH system cluster operates stably at this time.
If the energy-saving device determines that the stability of the CEPH system cluster is 1, the energy-saving device determines that the CEPH system cluster is unstable in operation at the moment, and the edge cloud is energy-saving at the moment, so that an accident can occur in operation of the edge cloud.
And aiming at the condition 2, the running stability of the CEPH system cluster in a preset time period is smaller than a second preset value. If the energy-saving device determines that the running stability of the CEPH system cluster in the preset time period is smaller than the second preset value, the energy-saving device determines that the CEPH system cluster runs stably at the moment.
If the energy-saving device determines that the running stability of the CEPH system is greater than or equal to a second preset value in the preset time period, the energy-saving device determines that the CEPH system cluster is unstable in running at the moment, and the edge cloud is energy-saving at the moment, so that the edge cloud running can possibly be caused to have accidents.
For example, the second preset value is 0.2, and when the stability of operation of the CEPH system cluster in the preset period is less than 0.2, the energy saving device may determine that the state of the CEPH system cluster is stable in operation in the preset period.
And aiming at the condition 3, the storage utilization rate of the CEPH system cluster in a preset time period is smaller than a third preset value. If the energy-saving device determines that the storage utilization rate of the CEPH system cluster in the preset time period is smaller than a third preset value, the energy-saving device determines that the CEPH system cluster is stable in operation at the moment.
If the energy-saving device determines that the storage utilization rate of the CEPH system cluster in the preset time period is greater than or equal to a third preset value, the energy-saving device determines that the CEPH system cluster is unstable in operation at the moment, and the edge cloud is energy-saving at the moment, so that an accident can occur in operation of the edge cloud.
For example, the third preset value is 0.7, and when the storage usage rate of the CEPH system cluster in the preset period is less than 0.7, the energy saving device may determine that the storage usage rate of the CEPH system cluster is low in the preset period, that is, the CEPH system cluster is in a low load state.
In yet another possible implementation manner, the energy saving device determines that a target physical machine in the physical machines meets an energy saving requirement, including at least one of the following:
and 4, the number of deployed virtual machines in the target physical machine is smaller than a fourth preset value.
And 5, the average number of the virtual machines deployed in the preset time period in the target physical machine is smaller than a fifth preset value.
And (6) the average value of the CPU utilization rate of the target physical machine in the preset time period is smaller than a sixth preset value.
And 7, the average value of the memory utilization rate of the target physical machine in the preset time period is smaller than a seventh preset value.
And 8, the average value of the hard disk reading speed of the target physical machine in the preset time period is smaller than an eighth preset value.
And 9, the average value of the hard disk writing speed of the target physical machine in the preset time period is smaller than a ninth preset value.
And (5) under the condition 10, the average value of the network sending rate of the target physical machine in the preset time period is smaller than a tenth preset value.
And the average value of the network receiving rate of the target physical machine in the preset time period is smaller than an eleventh preset value under the condition 11.
That is, the energy saving device determines that the operation state of the physical machine satisfies the energy saving requirement in the case that the operation state of the physical machine satisfies the above conditions 4 to 11.
Optionally, for the above condition 4, the number of virtual machines deployed in the target physical machine is smaller than a fourth preset value. And if the energy-saving device determines that the number of the virtual machines deployed in the target physical machine is smaller than the fourth preset value. The power saving device determines that the target physical machine is in a low load state at this time.
If the energy-saving device determines that the number of virtual machines deployed in the physical machine is greater than or equal to a fourth preset value. The energy-saving device determines that the target physical machine is in a high-load state at the moment, and the edge cloud saves energy at the moment, so that an accident can occur in operation of the edge cloud.
For example, the fourth preset value is 3, and when the number of virtual machines carried by the physical machines is less than 3, the energy saving device may determine that the target physical machine is in the low load state at this time.
For the condition 5, the average number of virtual machines deployed in the preset time period in the target physical machine is smaller than a fifth preset value. If the energy-saving device determines that the average number of virtual machines deployed in the target physical machine within the preset time period is smaller than a fifth preset value, the energy-saving device determines that the target physical machine is in a low-load state at the moment.
If the energy-saving device determines that the average number of virtual machines deployed in the preset time period in the target physical machine is greater than or equal to a fifth preset value, the energy-saving device determines that the target physical machine is in a high-load state at the moment, and at the moment, the edge cloud saves energy, so that an accident can occur in operation of the edge cloud.
For example, the fifth preset value is 2, and when the number of virtual machines carried by the physical machines is less than 2, the energy saving device may determine that the target physical machine is in a low-load state in the preset period of time.
For the condition 6, the average number of virtual machines deployed in the preset time period in the target physical machine is smaller than a fifth preset value. If the energy-saving device determines that the average value of the CPU utilization rates of the target physical machines in the preset time period is smaller than a sixth preset value, the energy-saving device determines that the target physical machines are in a low-load state at the moment.
If the energy-saving device determines that the average value of the CPU utilization rate of the target physical machine in the preset time period is larger than or equal to a sixth preset value, the energy-saving device determines that the target physical machine is in a high-load state at the moment, and at the moment, the edge cloud saves energy, so that an accident can occur in the operation of the edge cloud.
For example, the sixth preset value is 0.6, and when the number of virtual machines carried by the physical machines is less than 0.6, the energy saving device may determine that the target physical machine is in the low load state in the preset period of time.
And aiming at the condition 7, the average value of the memory utilization rate of the target physical machine in a preset time period is smaller than a seventh preset value. If the energy-saving device determines that the average value of the memory usage rate of the target physical machine in the preset time period is smaller than the seventh preset value, the energy-saving device determines that the target physical machine is in a low-load state at the moment.
If the energy-saving device determines that the average value of the memory usage rate of the target physical machine in the preset time period is larger than or equal to a seventh preset value, the energy-saving device determines that the target physical machine is in a high-load state at the moment, and at the moment, the edge cloud saves energy, so that an accident can occur in the operation of the edge cloud.
For example, the seventh preset value is 0.6, and when the average value of the memory usage of the target physical machine in the preset period is less than 0.6, the energy saving device may determine that the target physical machine is in the low load state in the preset period.
And aiming at the condition 8, the average value of the memory utilization rate of the target physical machine in a preset time period is smaller than a seventh preset value. If the energy-saving device determines that the average value of the hard disk reading speed of the target physical machine in the preset time period is smaller than the eighth preset value, the energy-saving device determines that the target physical machine is in a low-load state at the moment.
If the energy-saving device determines that the average value of the hard disk reading speed of the target physical machine in the preset time period is larger than or equal to an eighth preset value, the energy-saving device determines that the target physical machine is in a high-load state at the moment, and at the moment, the edge cloud saves energy, so that an accident can occur in the operation of the edge cloud.
For example, the eighth preset value is 2Mpbs, and when the average value of the hard disk reading rate of the target physical machine in the preset period is less than 2Mpbs, the energy saving device may determine that the target physical machine is in the low load state in the preset period.
Aiming at the condition 9, the average value of the hard disk writing speed of the target physical machine in a preset time period is smaller than a ninth preset value. If the energy-saving device determines that the average value of the hard disk writing speed of the target physical machine in the preset time period is smaller than the ninth preset value, the energy-saving device determines that the target physical machine is in a low-load state at the moment.
The average value of the hard disk writing speed of the target physical machine in the preset time period is larger than or equal to a ninth preset value, and the energy-saving device determines that the target physical machine is in a high-load state at the moment, and at the moment, the edge cloud saves energy, so that the edge cloud operation can possibly be caused to have accidents.
For example, the ninth preset value is 2Mpbs, and when the average value of the hard disk writing speed of the target physical machine in the preset time period is smaller than 2Mpbs, the energy saving device may determine that the target physical machine is in the low load state in the preset time period.
Aiming at the condition 10, the average value of the network sending rate of the target physical machine in the preset time period is smaller than a tenth preset value. If the energy-saving device determines that the average value of the network sending rate of the target physical machine in the preset time period is smaller than a tenth preset value, the energy-saving device determines that the target physical machine is in a low-load state at the moment.
If the average value of the network sending rate of the target physical machine in the preset time period is greater than or equal to a tenth preset value, the energy-saving device determines that the target physical machine is in a high-load state at the moment, and the edge cloud saves energy at the moment, so that an accident can occur in the operation of the edge cloud.
For example, the tenth preset value is 2Mpbs, and when the average value of the network transmission rate of the target physical machine in the preset period is less than 2Mpbs, the energy saving device may determine that the target physical machine is in the low load state in the preset period.
For the condition 11, the average value of the network receiving rate of the target physical machine in the preset time period is smaller than an eleventh preset value. If the energy-saving device determines that the average value of the network receiving rate of the target physical machine in the preset time period is smaller than the eleventh preset value, the energy-saving device determines that the target physical machine is in a low-load state at the moment.
If the average value of the network receiving rates of the target physical machine in the preset time period is greater than or equal to an eleventh preset value, the energy-saving device determines that the target physical machine is in a high-load state at the moment, and the edge cloud saves energy at the moment, so that an accident can occur in the operation of the edge cloud.
For example, the eleventh preset value is 2Mpbs, and the energy saving device may determine that the target physical machine is in the low load state during the preset period when the average value of the network receiving rate of the target physical machine during the preset period is less than 2 Mpbs.
The above provides an energy-saving method for the embodiments of the present disclosure.
The specific implementation method of the energy saving method is explained in detail below.
In a possible implementation manner, as shown in fig. 5 in conjunction with fig. 4, the energy saving device in S403 determines the shutdown priority of the target physical machine according to the operation state value of the target physical machine, which is specifically described in the following S501-S504.
S501, the energy-saving device determines the operation parameters of each operation state of the target physical machine and the weight value of each operation state.
Exemplary operating parameters and weight values for each operating state include:
The number of virtual machines deployed in the physical machine has a weight value of 10.
The average number of the virtual machines deployed in the preset time period is 10.
The average value of CPU usage in the preset time period has a weight value of 10.
The average value of the memory usage in the preset time period has a weight value of 10.
The average value of the hard disk reading speed in the preset time period is 5.
The average value of the hard disk writing speed in the preset time period is 5.
And the average value of the network sending rate in the preset time period has a weight value of 5.
The average value of the network receiving rate in the preset time period has a weight value of 5.
S502, the energy-saving device determines tolerance values of operation parameters of all operation states of the target physical machine.
Illustratively, the tolerance values for the respective operating states include:
the number of virtual machines deployed in the physical machine has a tolerance value of 3.
The average number of virtual machines deployed in a preset time period is 2.
The average value of CPU usage in the preset time period has a tolerance value of 0.6.
The average value of the memory usage rate in the preset time period has a tolerance value of 0.6.
The average value of the hard disk reading speed in the preset time period is 2Mpbs.
The average value of the hard disk writing speed in the preset time period is 2Mpbs.
The average value of the network sending rate in the preset time period has a tolerance value of 2Mpbs.
And the average value of the network receiving rate in the preset time period is 2Mpbs.
S503, the energy-saving device determines the score of the target physical machine according to the operation parameters, the weight values and the tolerance values of each operation state of the target physical machine.
In a possible implementation manner, the score of the target physical machine in S403 satisfies the following formula:
wherein x is i Operating parameters for each operating state of the target physical machine; x is x Tolerance of Tolerance values of all running state parameters of the target physical machine; y is Weighting of For each of the target physical machinesWeight value of the running state.
S504, the energy-saving device determines the shutdown priority of the target physical machine according to the score of the target physical machine.
In one possible implementation manner, the energy-saving device sorts the shutdown priorities of the target physical machines according to the scores of the target physical machines from high to low, and determines that the physical machines with high scores are preferentially shut down.
The technical scheme provided by the embodiment at least has the following beneficial effects: the energy-saving device determines the number of physical machines deployed in the edge cloud and the running state of a CEPH system cluster of the distributed storage system deployed in the edge cloud; therefore, partial low-load physical machines are ensured to be closed at the moment, and the stable operation of the edge cloud center is not influenced. Under the condition that the number of the physical machines meets the requirement and the running state of the CEPH system cluster meets the energy-saving requirement, determining a target physical machine meeting the energy-saving requirement in the physical machines according to the running state of the physical machines; i.e. a partially low-load physical machine that can be shut down is determined. Calculating according to the running state value of the target physical machine through a formula, and determining the shutdown priority of the target physical machine; and determining the physical machine to be shut down in the target physical machine according to the shutdown priority of the target physical machine, thereby further reducing the safety risk brought by shutting down the physical machine for energy saving.
The energy saving method according to the embodiment of the present disclosure is described in detail above.
It can be seen that the foregoing description has mainly been presented with respect to a method of providing a technical solution according to an embodiment of the present disclosure. To achieve the above functions, it includes corresponding hardware structures and/or software modules that perform the respective functions. Those of skill in the art will readily appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The embodiments of the present disclosure may divide the functional modules of the energy saving device according to the above method examples, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated in one processing module. The integrated modules may be implemented in hardware or in software functional modules. Optionally, the division of the modules in the embodiments of the present disclosure is schematic, which is merely a logic function division, and other division manners may be actually implemented.
Fig. 6 is a schematic structural diagram of an energy saving device 600 according to an embodiment of the disclosure. The energy-saving device comprises: a communication unit 601 and a processing unit 602; a communication unit 601, configured to determine the number of physical machines deployed in the edge cloud, and an operation state of a CEPH system cluster of the distributed storage system deployed in the edge cloud; the processing unit 602 is configured to determine, according to the operation states of the physical machines, a target physical machine that meets the energy-saving requirement in the physical machines, when the number of physical machines meets the requirement and the operation states of the CEPH system cluster meet the energy-saving requirement; the processing unit 602 is further configured to determine a shutdown priority of the target physical machine according to the operation state value of the target physical machine; the processing unit 602 is further configured to determine, according to the shutdown priority of the target physical machine, a physical machine to be shutdown in the target physical machine.
In one possible implementation, the number of physical machines satisfies the requirement, including: the number of physical machines is greater than or equal to a first preset value.
In a possible implementation manner, the processing unit 602 determines that the operating state of the CEPH system cluster meets the energy saving requirement, including at least one of the following: determining stable operation of CEPH system clusters; determining that the running stability of the CEPH system cluster in a preset time period is smaller than a second preset value; and determining that the storage utilization rate of the CEPH system cluster in the preset time period is smaller than a third preset value.
In a possible implementation manner, the processing unit 602 determines that a target physical machine of the physical machines meets a power saving requirement, including at least one of the following: determining that the number of virtual machines deployed in the target physical machine is smaller than a fourth preset value; determining that the average number of virtual machines deployed in a preset time period in the target physical machine is smaller than a fifth preset value; determining that the average value of CPU utilization rates of the target physical machine in a preset time period is smaller than a sixth preset value; determining that the average value of the memory utilization rate of the target physical machine in a preset time period is smaller than a seventh preset value; determining that the average value of the hard disk reading speed of the target physical machine in a preset time period is smaller than an eighth preset value; determining that the average value of the hard disk writing speed of the target physical machine in a preset time period is smaller than a ninth preset value; determining that the average value of the network sending rate of the target physical machine in a preset time period is smaller than a tenth preset value; and determining that the network receiving rate average value of the target physical machine in the preset time period is smaller than an eleventh preset value.
In a possible implementation manner, the communication unit 601 is further configured to determine an operation parameter of each operation state of the target physical machine, and a weight value of each operation state; the communication unit 601 is further configured to determine tolerance values of operation parameters of each operation state of the target physical machine; the processing unit 602 is further configured to determine a score of the target physical machine according to the operation parameters, the weight values, and the tolerance values of each operation state of the target physical machine; the processing unit 602 is further configured to determine a shutdown priority of the target physical machine according to the score of the target physical machine.
In one possible implementation, the Score of the target physical machine satisfies the following formula:
wherein x is i Operating parameters for each operating state of the target physical machine; x is x Tolerance of Tolerance values of all running state parameters of the target physical machine; y is Weighting of And the weight value of each running state of the target physical machine.
The embodiment of the disclosure also provides an energy-saving device, which comprises a processor and a memory; the memory is used for storing computer-executable instructions, and when the energy-saving device runs, the processor executes the computer-executable instructions stored in the memory, so that the energy-saving device executes the energy-saving method described in the embodiment of the disclosure.
The disclosed embodiments also provide a computer-readable storage medium having instructions stored therein, which when executed by a processor of an energy saving device, cause the energy saving device to perform the energy saving method described in the disclosed embodiments.
Embodiments of the present disclosure provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the energy saving method of the method embodiments described above.
Embodiments of the present disclosure provide a chip comprising a processor and a communication interface coupled to the processor for running a computer program or instructions to implement the energy saving method as in the method embodiments described above.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: electrical connections having one or more wires, portable computer diskette, hard disk. Random access Memory (Random Access Memory, RAM), read-Only Memory (ROM), erasable programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), registers, hard disk, optical fiber, portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any other form of computer-readable storage medium suitable for use by a person or combination of the foregoing, or as a numerical value in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an application specific integrated circuit (Application Specific Integ rated Circuit, ASIC). In the disclosed embodiments, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
Since the apparatus, device, computer readable storage medium, and computer program product in the embodiments of the present disclosure may be applied to the above-mentioned method, the technical effects that may be obtained by the apparatus, device, computer readable storage medium, and computer program product may also refer to the above-mentioned method embodiments, and the embodiments of the present disclosure are not repeated herein.
The foregoing is merely illustrative of specific embodiments of the present disclosure, but the scope of the present disclosure is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.
Claims (14)
1. A method of conserving energy, comprising:
determining the number of physical machines deployed in an edge cloud and the running state of a distributed storage system CEPH system cluster deployed in the edge cloud;
under the condition that the number of the physical machines meets the requirement and the running state of the CEPH system cluster meets the energy-saving requirement, determining a target physical machine meeting the energy-saving requirement in the physical machines according to the running state of the physical machines;
determining the shutdown priority of the target physical machine according to the running state value of the target physical machine;
And determining the physical machine to be powered off in the target physical machine according to the shutdown priority of the target physical machine.
2. The method of claim 1, wherein the number of physical machines satisfying a demand comprises:
the number of the physical machines is larger than or equal to a first preset value.
3. The method of claim 1, wherein the operating state of the CEPH system cluster meets energy conservation requirements, comprising at least one of:
the CEPH system cluster stably operates;
the CEPH system cluster operates within a preset time period, and the stability is smaller than a second preset value;
and the storage utilization rate of the CEPH system cluster in the preset time period is smaller than a third preset value.
4. The method of claim 1, wherein the target physical machine satisfies at least one of:
the number of virtual machines deployed in the target physical machine is smaller than a fourth preset value;
the average number of virtual machines deployed in a preset time period in the target physical machine is smaller than a fifth preset value;
the average value of the CPU utilization rate of the target physical machine in the preset time period is smaller than a sixth preset value;
the average value of the memory utilization rate of the target physical machine in a preset time period is smaller than a seventh preset value;
The average value of the hard disk reading speed of the target physical machine in a preset time period is smaller than an eighth preset value;
the average value of the hard disk writing speed of the target physical machine in a preset time period is smaller than a ninth preset value;
the average value of the network sending rate of the target physical machine in the preset time period is smaller than a tenth preset value;
and the average value of the network receiving rate of the target physical machine in the preset time period is smaller than an eleventh preset value.
5. The method of claim 4, wherein determining the shutdown priority of the target physical machine based on the operational status value of the target physical machine comprises:
determining operation parameters of each operation state of the target physical machine and weight values of each operation state;
determining tolerance values of operation parameters of each operation state of the target physical machine;
determining the score of the target physical machine according to the operation parameters, the weight values and the tolerance values of each operation state of the target physical machine;
and determining the shutdown priority of the target physical machine according to the score of the target physical machine.
6. The method of claim 5, wherein the Score of the target physical machine satisfies the following formula:
Wherein x is i Operating parameters for each operating state of the target physical machine; x is x Tolerance of Tolerance values of all running state parameters of the target physical machine; y is Weighting of And the weight value of each running state of the target physical machine.
7. An energy saving device, comprising: a communication unit and a processing unit;
the communication unit is used for determining the number of physical machines deployed in the edge cloud and the running state of a CEPH system cluster of the distributed storage system deployed in the edge cloud;
the processing unit is used for determining a target physical machine meeting the energy-saving requirement in the physical machines according to the running state of the physical machines under the condition that the number of the physical machines meets the requirement and the running state of the CEPH system cluster meets the energy-saving requirement;
the processing unit is further used for determining the shutdown priority of the target physical machine according to the running state value of the target physical machine;
the processing unit is further configured to determine a physical machine to be powered off in the target physical machine according to the shutdown priority of the target physical machine.
8. The apparatus of claim 7, wherein the number of physical machines meets a demand, comprising:
The number of the physical machines is larger than or equal to a first preset value.
9. The apparatus of claim 7, wherein the determining that the operational status of the CEPH system cluster meets the power saving requirement comprises at least one of:
determining stable operation of the CEPH system cluster;
determining that the running stability of the CEPH system cluster in a preset time period is smaller than a second preset value;
and determining that the storage utilization rate of the CEPH system cluster in the preset time period is smaller than a third preset value.
10. The apparatus of claim 7, wherein the target physical machine satisfies at least one of:
determining that the number of virtual machines deployed in the target physical machine is smaller than a fourth preset value;
determining that the average number of virtual machines deployed in a preset time period in the target physical machine is smaller than a fifth preset value;
determining that the average value of CPU utilization rates of the target physical machine in a preset time period is smaller than a sixth preset value;
determining that the average value of the memory usage rate of the target physical machine in a preset time period is smaller than a seventh preset value;
determining that the average value of the hard disk reading speed of the target physical machine in a preset time period is smaller than an eighth preset value;
Determining that the average value of the hard disk writing speed of the target physical machine in a preset time period is smaller than a ninth preset value;
determining that the average value of the network sending rate of the target physical machine in a preset time period is smaller than a tenth preset value;
and determining that the network receiving rate average value of the target physical machine in the preset time period is smaller than an eleventh preset value.
11. The apparatus of claim 10, wherein the communication unit is further configured to determine an operational parameter for each operational state of the target physical machine, and a weight value for each operational state;
the communication unit is further used for determining tolerance values of operation parameters of each operation state of the target physical machine;
the processing unit is further used for determining the score of the target physical machine according to the operation parameters, the weight values and the tolerance values of each operation state of the target physical machine;
and the processing unit is also used for determining the shutdown priority of the target physical machine according to the score of the target physical machine.
12. The apparatus of claim 11, wherein the Score of the target physical machine satisfies the following formula:
wherein x is i Operating parameters for each operating state of the target physical machine; x is x Tolerance of Tolerance values of all running state parameters of the target physical machine; y is Weighting of And the weight value of each running state of the target physical machine.
13. An energy saving device, comprising: a processor and a memory; wherein the memory is configured to store computer-executable instructions that, when executed by the energy conservation device, cause the energy conservation device to perform the energy conservation method of any one of claims 1-6.
14. A computer readable storage medium having instructions stored therein, which when executed by a processor of an energy saving device, cause the energy saving device to perform the energy saving method of any of claims 1-6.
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