Method and device for evaluating green degree of cloud storage service, and electronic equipment

Abstract

The invention relates to the technical field of cloud storage, and discloses a method and device for evaluating the green degree of cloud storage services and electronic device.The method comprises the steps that in a statistical period, the average actual storage capacity of the cloud storage services is obtained, and power consumption data and carbon emission data generated by the cloud storage services are collected; checking the collected power consumption data and carbon emission data to obtain the total power consumption and the total carbon emission of the cloud storage service in a statistical period; calculating the storage energy efficiency and the storage carbon efficiency of the cloud storage service in the statistical period according to the average actual storage capacity, the total power consumption and the total carbon emission of the cloud storage service in the statistical period; and evaluating the green degree of the cloud storage service according to the storage energy efficiency and the storage carbon efficiency of the cloud storage service. And the storage energy efficiency and the storage carbon efficiency of the cloud storage service are calculated more accurately. The efficiency of converting electric energy into storage resources by the cloud storage service and the influence on the environment are known, and the green degree of the cloud storage service is evaluated.

Description

Technical Field

[0001] The present application relates to the field of cloud storage technology, and for example, to a method and device, and an electronic device for evaluating the greenness of a cloud storage service. Background Art

[0002] At present, with the rapid growth of data centers and cloud computing services, global energy consumption and carbon emissions are becoming increasingly serious. The proportion of energy consumption in data centers to global electricity consumption is increasing year by year. Therefore, improving the energy efficiency of data centers and reducing carbon emissions have become global challenges. As an important part of cloud computing, cloud storage services need to receive special attention for their energy consumption and carbon emissions in order to achieve sustainable development. By accurately calculating the energy efficiency and carbon efficiency of cloud storage services, the greenness of cloud storage services can be evaluated, and data center operators can also manage resources more effectively, optimize energy use, and reduce operating costs.

[0003] In the related technology, a method for calculating the energy efficiency and carbon emissions of a data center is proposed, including: obtaining the energy efficiency parameters of the data center, including resource utilization efficiency, computing power utilization rate and computing power energy efficiency; determining the weights corresponding to each energy efficiency parameter of the data center; determining the comprehensive energy efficiency of the data center based on the energy efficiency parameters of the data center and the weights corresponding to each energy efficiency parameter, and the comprehensive energy efficiency is used to calculate the carbon emissions of the data center.

[0004] In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in the related art:

[0005] In related technologies, the calculation scope of energy efficiency and carbon emissions is the entire data center or its sub-modules, and is not directly applicable to specific cloud storage services.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0008] The embodiments of the present disclosure provide a method, an apparatus, and an electronic device for evaluating the greenness of a cloud storage service, which evaluates the greenness of a cloud storage service by calculating more accurate storage energy efficiency and storage carbon efficiency of the cloud storage service.

[0009] In some embodiments, a method for evaluating the greenness of a cloud storage service includes: obtaining an average actual storage capacity of a cloud storage service during a statistical period, and collecting power consumption data and carbon emission data generated by the cloud storage service; calculating the collected power consumption data and carbon emission data to obtain the total power consumption and total carbon emissions of the cloud storage service during the statistical period; calculating the storage energy efficiency and storage carbon efficiency of the cloud storage service during the statistical period based on the average actual storage capacity, total power consumption, and total carbon emissions of the cloud storage service during the statistical period; and evaluating the greenness of the cloud storage service based on the storage energy efficiency and storage carbon efficiency of the cloud storage service.

[0010] Optionally, obtaining the average actual storage capacity of the cloud storage service includes: obtaining logical storage capacity data recorded in all cloud storage service orders within the statistical period; calculating the total logical storage capacity within the statistical period based on the logical storage capacity data in all orders; and calculating the average actual storage capacity based on the total logical storage capacity and the number of orders or the number of users within the statistical period.

[0011] Optionally, power consumption data generated by the cloud storage service is collected, including: collecting power consumption of IT equipment dedicated to the cloud storage service, power consumption of cluster-level public IT equipment, and power consumption of auxiliary equipment in the cloud availability zone; and using the power consumption of IT equipment dedicated to the cloud storage service, power consumption of cluster-level public IT equipment, and power consumption of auxiliary equipment in the cloud availability zone as the power consumption data generated by the cloud storage service.

[0012] Optionally, the total power consumption of the cloud storage service during the statistical period is calculated according to the following formula:

[0013] C0(t)=R1(t)×C1(t)+R2(t)×C2(t)+R3(t)×C3(t)

[0014] Where t is the statistical period, C0(t) is the total power consumption of the cloud storage service, R1(t) is the proportion of resources used by the cloud storage service, C1(t) is the power consumption of IT equipment dedicated to the cloud storage service, R2(t) is the proportion of cloud storage services in the cluster resources, C2(t) is the power consumption of cluster-level public IT equipment, R3(t) is the proportion of cloud storage services in the available zone resources, and C3(t) is the power consumption of auxiliary equipment in the cloud available zone.

[0015] Optionally, collect carbon emissions data generated by the cloud storage service, including: collecting carbon emissions generated by the power generation process that supports the total power consumption of the cloud storage service and the direct carbon emissions of the cloud storage service; and use the carbon emissions generated by the power generation process that supports the total power consumption of the cloud storage service and the direct carbon emissions of the cloud storage service as the carbon emissions data generated by the cloud storage service.

[0016] Optionally, the total carbon emissions of the cloud storage service during the statistical period are calculated according to the following formula:

[0017] E0(t)=α×[C0(t)-C4(t)]+E1(t)

[0018] Among them, t is the statistical period, E0(t) is the total carbon emissions of cloud storage services, α is the electricity emission factor, C0(t) is the total power consumption of cloud storage services, C4(t) is the self-generated electricity of cloud storage services, and E1(t) is the direct carbon emissions of cloud storage services.

[0019] Optionally, the storage energy efficiency and storage carbon efficiency of the cloud storage service during the statistical period are calculated, including: calculating the storage energy efficiency of the cloud storage service during the statistical period based on the ratio of the average actual storage capacity of the cloud storage service during the statistical period to the total power consumption; and, calculating the storage carbon efficiency of the cloud storage service during the statistical period based on the ratio of the average actual storage capacity of the cloud storage service during the statistical period to the total carbon emissions.

[0020] Optionally, the greenness of the cloud storage service is evaluated based on the storage energy efficiency and storage carbon efficiency of the cloud storage service, including: analyzing the energy utilization efficiency and carbon emission impact of the cloud storage service based on the storage energy efficiency and storage carbon efficiency of the cloud storage service; wherein, the higher the storage energy efficiency, the higher the energy utilization efficiency of the cloud storage service, and the greener the cloud storage service; the higher the storage carbon efficiency, the smaller the carbon emission impact of the cloud storage service, and the greener the cloud storage service.

[0021] In some embodiments, an apparatus for evaluating the greenness of a cloud storage service includes a processor and a memory storing program instructions, and the processor is configured to execute the method for evaluating the greenness of a cloud storage service as described above when running the program instructions.

[0022] In some embodiments, the electronic device includes: an electronic device body; and an apparatus for evaluating the green level of a cloud storage service as described above, installed in the electronic device body.

[0023] The method, device, and electronic device for evaluating the greenness of a cloud storage service provided by the embodiments of the present disclosure can achieve the following technical effects:

[0024] In the disclosed embodiment, the power consumption data and carbon emission data generated by the cloud storage service are calculated to obtain the total power consumption and total carbon emission of the cloud storage service, and then the efficiency corresponding to the power consumption and carbon emission is measured by the average actual storage capacity of the cloud storage service to more accurately calculate the storage energy efficiency and storage carbon efficiency of the cloud storage service. The storage energy efficiency can accurately understand the efficiency of the cloud storage service in converting electrical energy into storage resources, and the storage carbon efficiency can quantify the impact of the cloud storage service on the environment. Therefore, based on the storage energy efficiency and storage carbon efficiency, the greenness of the cloud storage service can be evaluated to promote the development of green cloud services.

[0025] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0027] Figure 1 is a schematic diagram of a method for evaluating the greenness of a cloud storage service provided by an embodiment of the present disclosure;

[0028] Figure 2 is a schematic diagram of another method for evaluating the greenness of a cloud storage service provided by an embodiment of the present disclosure;

[0029] Figure 3 is a schematic diagram of power consumption of a cloud storage service provided by an embodiment of the present disclosure;

[0030] Figure 4 is a schematic diagram of carbon emissions generated by a cloud storage service provided by an embodiment of the present disclosure;

[0031] Figure 5 is a schematic diagram of another method for evaluating the greenness of a cloud storage service provided by an embodiment of the present disclosure;

[0032] Figure 6 It is a schematic diagram of a device for evaluating the green level of a cloud storage service provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0033] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0034] The terms "first", "second", etc. in the technical solutions described in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so as to describe the embodiments of the disclosed embodiments described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0035] Unless otherwise stated, the term "plurality" means two or more.

[0036] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0037] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0038] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0039] Combination Figure 1 As shown, the embodiment of the present disclosure provides a method for evaluating the greenness of a cloud storage service. The execution subject of the method may be a processor, and the method includes:

[0040] S101, the processor obtains the average actual storage capacity of the cloud storage service during a statistical period, and collects power consumption data and carbon emission data generated by the cloud storage service.

[0041] S102: The processor calculates the collected power consumption data and carbon emission data to obtain the total power consumption and total carbon emission of the cloud storage service within the statistical period.

[0042] S103, the processor calculates the storage energy efficiency and storage carbon efficiency of the cloud storage service within the statistical period according to the average actual storage capacity, total power consumption, and total carbon emissions of the cloud storage service within the statistical period.

[0043] S104: The processor evaluates the greenness of the cloud storage service based on the storage energy efficiency and storage carbon efficiency of the cloud storage service.

[0044] In the disclosed embodiment, the power consumption data and carbon emission data generated by the cloud storage service are calculated to obtain the total power consumption and total carbon emission of the cloud storage service, and then the efficiency corresponding to the power consumption and carbon emission is measured by the average actual storage capacity of the cloud storage service to more accurately calculate the storage energy efficiency and storage carbon efficiency of the cloud storage service. The storage energy efficiency can accurately understand the efficiency of the cloud storage service in converting electrical energy into storage resources, and the storage carbon efficiency can quantify the impact of the cloud storage service on the environment. Therefore, based on the storage energy efficiency and storage carbon efficiency, the greenness of the cloud storage service can be evaluated to promote the development of green cloud services.

[0045] Optionally, obtaining the average actual storage capacity of the cloud storage service includes: obtaining logical storage capacity data recorded in all cloud storage service orders within the statistical period; calculating the total logical storage capacity within the statistical period based on the logical storage capacity data in all orders; and calculating the average actual storage capacity based on the total logical storage capacity and the number of orders or the number of users within the statistical period.

[0046] Combination Figure 2 As shown, the embodiment of the present disclosure provides another method for evaluating the greenness of a cloud storage service, including:

[0047] S201: The processor obtains logical storage capacity data recorded in all cloud storage service orders within a statistical period.

[0048] S202: The processor calculates the total logical storage capacity within the statistical period according to the logical storage capacity data in all orders.

[0049] S203: The processor calculates the average actual storage capacity according to the total logical storage capacity and the number of orders or the number of users in the statistical period.

[0050] S204: The processor collects power consumption data and carbon emission data generated by the cloud storage service during a statistical period.

[0051] S205: The processor calculates the collected power consumption data and carbon emission data to obtain the total power consumption and total carbon emission of the cloud storage service within the statistical period.

[0052] S206: The processor calculates the storage energy efficiency and storage carbon efficiency of the cloud storage service within the statistical period according to the average actual storage capacity, total power consumption, and total carbon emissions of the cloud storage service within the statistical period.

[0053] S207: The processor evaluates the greenness of the cloud storage service based on the storage energy efficiency and storage carbon efficiency of the cloud storage service.

[0054] In this embodiment, the average actual storage capacity of the cloud storage service refers to the average value of the total amount of logical storage capacity used by the user group of the cloud storage service during the statistical period. This indicator reflects the size of the storage space provided to users by the cloud storage service in actual operation, and is one of the important parameters for measuring the performance and efficiency of the cloud storage service. By collecting the logical storage capacity data recorded in all cloud storage service orders during the statistical period, and then adding the logical storage capacity in all orders, the total logical storage capacity during the statistical period is obtained, and finally the total logical storage capacity is divided by the number of orders or the number of users during the statistical period to obtain the average actual storage capacity. The average actual storage capacity can be used to calculate the storage energy efficiency and storage carbon efficiency of the cloud storage service. In this way, cloud storage service providers and users can better understand the energy efficiency and environmental impact of the cloud storage service, so as to make more environmentally friendly and economical decisions.

[0055] Optionally, power consumption data generated by the cloud storage service is collected, including: collecting power consumption of IT equipment dedicated to the cloud storage service, power consumption of cluster-level public IT equipment, and power consumption of auxiliary equipment in the cloud availability zone; and using the power consumption of IT equipment dedicated to the cloud storage service, power consumption of cluster-level public IT equipment, and power consumption of auxiliary equipment in the cloud availability zone as the power consumption data generated by the cloud storage service.

[0056] In this embodiment, combined Figure 3 As shown in the figure, the power consumption of cloud storage services can be divided into the power consumption of IT equipment dedicated to cloud storage services, the power consumption of cluster-level public IT equipment, and the power consumption of auxiliary equipment in the cloud availability zone. IT equipment dedicated to cloud storage services includes IT equipment directly used to provide cloud storage services, such as storage servers, data backup equipment, storage network equipment, etc. These devices are usually dedicated to storing data and managing data access. Cluster-level public IT equipment is IT equipment shared by multiple services in the data center, such as network switches, routers, load balancers, etc. Although these devices are not directly involved in data storage, they are crucial to the operation of cloud storage services. Auxiliary equipment in the cloud availability zone includes infrastructure equipment in the data center, such as air conditioning systems, power supply equipment, lighting equipment, dynamic environment systems, and security systems, etc. These devices provide necessary support services for the entire data center, including cloud storage services.

[0057] Optionally, the power consumption of IT equipment dedicated to the cloud storage service includes: the power consumption of all physical servers in the cluster where the cloud storage service functional unit is located during the statistical period.

[0058] In this embodiment, the power consumption of the cloud storage service dedicated IT equipment that provides the cloud storage service functional unit is obtained through the dynamic environment system of the data center, in kilowatt-hours (kWh). The utilization rate, storage load, data transmission activities, etc. of the cloud storage service dedicated IT equipment will affect the power consumption of the equipment. Therefore, in actual applications, it is necessary to adjust the calculation method of the equipment power consumption according to the specific usage of the equipment.

[0059] Optionally, the power consumption of cluster-level public IT equipment includes: the power consumption of non-business functions in the physical cluster where the cloud storage service functional unit is located during the statistical period.

[0060] In this embodiment, cluster-level public IT equipment is used to provide cluster-level public basic services, including software services and hardware services. Software services include services shared by cloud service functional units within the cluster, such as cloud service management and control, scheduling, operation and maintenance, and security systems; hardware services include network equipment, security equipment, and other hardware devices that support the operation of cloud services. The power consumption of hardware equipment can be directly measured, and the power consumption of software services is calculated through the power consumption of the deployed physical equipment, in kilowatt-hours (kWh).

[0061] Optionally, the power consumption of auxiliary devices in the cloud availability zone includes: the power consumption of all non-IT devices in the cloud availability zone where the cloud storage service is located during the statistical period, in kilowatt-hours (kWh). Non-IT devices include refrigeration, power supply, dynamic environment and other equipment.

[0062] Optionally, the total power consumption of the cloud storage service during the statistical period is calculated according to the following formula:

[0063] C0(t)=R1(t)×C1(t)+R2(t)×C2(t)+R3(t)×C3(t)

[0064] Where t is the statistical period, C0(t) is the total power consumption of the cloud storage service, R1(t) is the proportion of resources used by the cloud storage service, C1(t) is the power consumption of IT equipment dedicated to the cloud storage service, R2(t) is the proportion of cloud storage services in the cluster resources, C2(t) is the power consumption of cluster-level public IT equipment, R3(t) is the proportion of cloud storage services in the available zone resources, and C3(t) is the power consumption of auxiliary equipment in the cloud available zone.

[0065] In this embodiment, when calculating the total power consumption of the cloud storage service, it is necessary to combine the power consumption of the cloud storage service's dedicated IT equipment, the power consumption of the cluster-level public IT equipment, and the power consumption of the auxiliary equipment in the cloud availability zone, while considering the resource usage ratio of the cloud storage service in each part. By accurately calculating and allocating the power consumption of these three parts, the energy efficiency of the cloud storage service can be more accurately evaluated, providing data support for optimizing energy use and reducing operating costs.

[0066] Optionally, R1(t) can be determined by the average occupancy rate of the cloud storage service functional unit in the physical resources, for example, by the percentage of CPU occupied by the cloud storage service in the physical machine resource manager, that is, the proportion of machines used for storage in the physical machines.

[0067] Optionally, R2(t) may be determined by an average occupancy rate of the business resources of the product cluster in which the cloud storage service functional unit is located.

[0068] Optionally, R3(t) may be determined by an average occupancy rate of business resources of the cloud storage service function unit in the cloud availability zone.

[0069] Optionally, collect carbon emissions data generated by the cloud storage service, including: collecting carbon emissions generated by the power generation process that supports the total power consumption of the cloud storage service and the direct carbon emissions of the cloud storage service; and use the carbon emissions generated by the power generation process that supports the total power consumption of the cloud storage service and the direct carbon emissions of the cloud storage service as the carbon emissions data generated by the cloud storage service.

[0070] In this embodiment, combined Figure 4 As shown in the figure, the carbon emissions generated by cloud storage services include carbon emissions generated by the power generation process that supports the total power consumption of cloud storage services, as well as direct carbon emissions of cloud storage services. In order to support the total power consumption of cloud storage services, power generation activities are required. According to the value of the regional power emission factor, the power generated by power generation activities can be converted into carbon emissions through calculation. The power generated by power generation activities may come from a variety of energy sources such as coal, natural gas, hydropower, solar energy or wind energy. Different energy sources have different carbon emission factors, that is, the amount of carbon dioxide generated per unit of electricity is different. According to the region where the data center is located, the local power emission factor is used to calculate carbon emissions. The power emission factor reflects the average carbon intensity of power production in the region. Direct carbon emissions at the cloud level are generated during the operation of cloud storage services, including: carbon emissions generated during the manufacturing and scrapping of hardware such as servers and storage devices; direct energy consumption within the data center, such as fuel consumption for cooling, lighting and backup generators; other direct emission sources, such as transportation within the data center and employee commuting.

[0071] Optionally, combined Figure 4 As shown, the carbon emissions generated by the power generation process that supports the total power consumption of the cloud storage service include: the carbon emissions corresponding to the total power consumption of the cloud storage service minus the carbon emissions corresponding to the self-generated electricity of the cloud storage service.

[0072] In this embodiment, the total self-generated electricity at the data center level and the ratio of the cloud storage service to the available area resources can be used to determine the self-generated electricity of the cloud storage service. Since this part of the electricity consumption does not generate carbon emissions, the carbon emissions corresponding to the self-generated electricity of the cloud storage service need to be subtracted.

[0073] Optionally, combined Figure 4 As shown in the figure, the direct carbon emissions of cloud storage services include: the direct carbon emissions generated during the operation of cloud storage services minus the carbon emissions corresponding to carbon recovery.

[0074] In this embodiment, the total direct emissions at the cloud level can be apportioned to the cloud storage service by the resource ratio occupied by the cloud storage service, and the direct carbon emissions generated during the operation of the cloud storage service can be obtained. In addition, the cloud storage service will also be equipped with a certain carbon recovery system. When calculating the direct carbon emissions at the cloud level, the carbon emissions corresponding to the carbon recovery need to be subtracted.

[0075] Optionally, the total carbon emissions of the cloud storage service during the statistical period are calculated according to the following formula:

[0076] E0(t)=α×[C0(t)-C4(t)]+E1(t)

[0077] Among them, t is the statistical period, E0(t) is the total carbon emissions of cloud storage services, α is the electricity emission factor, C0(t) is the total power consumption of cloud storage services, C4(t) is the self-generated electricity of cloud storage services, and E1(t) is the direct carbon emissions of cloud storage services.

[0078] Alternatively, the electricity emission factor can use data provided by national authorities. For cloud services deployed overseas, the emission data provided by local relevant departments or companies can be used.

[0079] Optionally, the storage energy efficiency and storage carbon efficiency of the cloud storage service during the statistical period are calculated, including: calculating the storage energy efficiency of the cloud storage service during the statistical period based on the ratio of the average actual storage capacity of the cloud storage service during the statistical period to the total power consumption; and, calculating the storage carbon efficiency of the cloud storage service during the statistical period based on the ratio of the average actual storage capacity of the cloud storage service during the statistical period to the total carbon emissions.

[0080] In this embodiment, storage energy efficiency refers to the ratio of the average actual storage capacity of the cloud storage service during the statistical period to the total power consumption of the cloud storage service. Storage energy efficiency reflects the efficiency of the cloud storage service in converting energy into storage resources. The higher the storage energy efficiency, the higher the energy utilization efficiency. Storage carbon efficiency refers to the ratio of the average actual storage capacity of the cloud storage service during the statistical period to the total carbon emissions generated by the cloud storage service. Storage carbon efficiency reflects the impact of storage resources provided by storage vendors on the environment. The higher the storage carbon efficiency, the smaller the impact of providing the same storage resources on the environment.

[0081] Optionally, the storage energy efficiency of the cloud storage service during the statistical period is calculated according to the following formula:

[0082]

[0083] Among them, t is the statistical period, EE(t) is the storage energy efficiency of the cloud storage service, SC(t) is the average actual storage capacity of the cloud storage service, and C0(t) is the total power consumption of the cloud storage service.

[0084] Optionally, the storage carbon efficiency of the cloud storage service during the statistical period is calculated according to the following formula:

[0085]

[0086] Among them, t is the statistical period, CE(t) is the storage carbon efficiency of cloud storage services, SC(t) is the average actual storage capacity of cloud storage services, and E0(t) is the total carbon emissions of cloud storage services.

[0087] In this embodiment, when calculating storage energy efficiency and storage carbon efficiency, the time granularity of data collection can be increased, for example, from daily to hourly and minutely, to more accurately capture changes in energy consumption and carbon emissions. More fine-grained device-level data can also be collected, such as energy consumption and carbon emissions of different types of storage devices.

[0088] Optionally, when calculating storage energy efficiency and storage carbon efficiency, weight factors are introduced for different types of storage devices; and / or, the proportion of renewable energy used in the data center is considered.

[0089] In this embodiment, weight factors are introduced for different types of storage devices to reflect the different efficiencies of different devices in terms of energy consumption and carbon emissions. Considering the proportion of renewable energy used in data centers can further improve the calculation accuracy of storage carbon efficiency.

[0090] Optionally, the storage energy efficiency of the cloud storage service during the statistical period is calculated according to the following formula:

[0091]

[0092] Where t is the statistical period, EE(t) is the storage energy efficiency of the cloud storage service, SC i (t) is the average actual storage capacity of the cloud storage service provided to the i-th device, ω i is the weight factor of the ith device, and C0(t) is the total power consumption of the cloud storage service.

[0093] Optionally, the storage carbon efficiency of the cloud storage service during the statistical period is calculated according to the following formula:

[0094]

[0095] Among them, t is the statistical period, CE(t) is the storage carbon efficiency of cloud storage services, SC i (t) is the average actual storage capacity of the cloud storage service provided to the i-th device, ω i is the weight factor of the ith device, E0(t) is the total carbon emissions of the cloud storage service, and r is the proportion of renewable energy.

[0096] Optionally, the greenness of the cloud storage service is evaluated based on the storage energy efficiency and storage carbon efficiency of the cloud storage service, including: analyzing the energy utilization efficiency and carbon emission impact of the cloud storage service based on the storage energy efficiency and storage carbon efficiency of the cloud storage service; wherein, the higher the storage energy efficiency, the higher the energy utilization efficiency of the cloud storage service, and the greener the cloud storage service; the higher the storage carbon efficiency, the smaller the carbon emission impact of the cloud storage service, and the greener the cloud storage service.

[0097] Combination Figure 5 As shown, the embodiment of the present disclosure provides another method for evaluating the greenness of a cloud storage service, including:

[0098] S501, the processor obtains the average actual storage capacity of the cloud storage service during a statistical period, and collects power consumption data and carbon emission data generated by the cloud storage service.

[0099] S502: The processor calculates the collected power consumption data and carbon emission data to obtain the total power consumption and total carbon emission of the cloud storage service within the statistical period.

[0100] S503: The processor calculates the storage energy efficiency and storage carbon efficiency of the cloud storage service within the statistical period according to the average actual storage capacity, total power consumption, and total carbon emissions of the cloud storage service within the statistical period.

[0101] S504, the processor analyzes the energy utilization efficiency and carbon emission impact of the cloud storage service based on the storage energy efficiency and storage carbon efficiency of the cloud storage service; wherein, the higher the storage energy efficiency, the higher the energy utilization efficiency of the cloud storage service, and the higher the greenness of the cloud storage service; the higher the storage carbon efficiency, the smaller the carbon emission impact of the cloud storage service, and the higher the greenness of the cloud storage service.

[0102] In this embodiment, quantitative calculation is used to obtain specific and reliable storage energy efficiency and carbon efficiency values, which can be used as a quantitative reflection of the greenness of cloud storage services. Cloud storage services are analyzed based on the storage energy efficiency and storage carbon efficiency of cloud storage services, so that cloud service providers and users can understand the carbon emission intensity and total amount of cloud storage services. Accurate energy efficiency and carbon efficiency data can help cloud service providers and users make more informed decisions, such as choosing more environmentally friendly cloud service options, optimizing resource allocation, and improving energy management strategies. Storage carbon efficiency can also provide accurate carbon footprint data, which helps cloud service providers fulfill their environmental responsibilities, meet increasingly stringent environmental regulations and policy requirements, and improve corporate compliance. By demonstrating the high energy efficiency and low carbon emissions of their cloud services, cloud service providers can also gain a competitive advantage in the market and attract customers who have requirements for environmental impact. In addition, accurate energy efficiency and carbon efficiency data are the basis for achieving the goal of carbon neutrality. Through accurate storage energy efficiency and storage carbon efficiency data, enterprises can quantify their carbon emissions, formulate emission reduction strategies, and achieve carbon neutrality.

[0103] Combination Figure 6 As shown, an embodiment of the present disclosure provides a device 600 for evaluating the greenness of a cloud storage service, including a processor 700 and a memory 701. Optionally, the device may also include a communication interface 702 and a bus 703. The processor 700, the communication interface 702, and the memory 701 may communicate with each other through the bus 703. The communication interface 702 may be used for information transmission. The processor 700 may call the logic instructions in the memory 701 to execute the method for evaluating the greenness of a cloud storage service of the above embodiment.

[0104] In addition, the logic instructions in the memory 701 described above may be implemented in the form of software functional units and when sold or used as independent products, may be stored in a computer-readable storage medium.

[0105] The memory 701 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 700 executes the function application and data processing by running the program instructions / modules stored in the memory 701, that is, the method for evaluating the greenness of the cloud storage service in the above embodiment is implemented.

[0106] The memory 701 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 701 may include a high-speed random access memory and may also include a non-volatile memory.

[0107] An embodiment of the present disclosure provides an electronic device, including: an electronic device body, and the above-mentioned device for evaluating the green level of a cloud storage service. The device for evaluating the green level of a cloud storage service is installed in the electronic device body. The installation relationship described here is not limited to placement inside the electronic device, but also includes installation connections with other components of the electronic device, including but not limited to physical connections, electrical connections, or signal transmission connections. It can be understood by those skilled in the art that the device for evaluating the green level of a cloud storage service can be adapted to a feasible electronic device body, thereby realizing other feasible embodiments.

[0108] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned method for evaluating the greenness of a cloud storage service.

[0109] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, including: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0110] The above description and accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent possible changes only. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the technical solutions recorded in this application. As used in the technical solutions recorded in this application, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0111] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0112] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0113] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for evaluating the greenness of a cloud storage service, characterized in that: include: During the statistical period, obtain the average actual storage capacity of the cloud storage service, and collect the power consumption data and carbon emission data generated by the cloud storage service; Calculate the collected power consumption data and carbon emission data to obtain the total power consumption and total carbon emission of the cloud storage service during the statistical period; Calculate the storage energy efficiency and storage carbon efficiency of the cloud storage service during the statistical period based on the average actual storage capacity, total power consumption, and total carbon emissions of the cloud storage service during the statistical period; Evaluate the greenness of cloud storage services based on their storage energy efficiency and storage carbon efficiency.

2. The method according to claim 1, characterized in that Get the average actual storage capacity of cloud storage services, including: Obtain the logical storage capacity data recorded in all cloud storage service orders within the statistical period; Calculate the total logical storage capacity within the statistical period based on the logical storage capacity data in all orders; Calculate the average actual storage capacity based on the total logical storage capacity and the number of orders or users during the statistical period.

3. The method according to claim 1, characterized in that Collect power consumption data generated by cloud storage services, including: Collect the power consumption of IT equipment dedicated to cloud storage services, the power consumption of cluster-level public IT equipment, and the power consumption of auxiliary equipment in the cloud availability zone; The power consumption of IT equipment dedicated to the cloud storage service, the power consumption of cluster-level public IT equipment, and the power consumption of auxiliary equipment in the cloud availability zone are used as the power consumption data generated by the cloud storage service.

4. The method according to claim 3, characterized in that The total power consumption of the cloud storage service during the statistical period is calculated according to the following formula: C0(t)=R1(t)×C1(t)+R2(t)×C2(t)+R3(t)×C3(t) Where t is the statistical period, C0(t) is the total power consumption of the cloud storage service, R1(t) is the proportion of resources used by the cloud storage service, C1(t) is the power consumption of IT equipment dedicated to the cloud storage service, R2(t) is the proportion of cloud storage services in the cluster resources, C2(t) is the power consumption of cluster-level public IT equipment, R3(t) is the proportion of cloud storage services in the available zone resources, and C3(t) is the power consumption of auxiliary equipment in the cloud available zone.

5. The method according to claim 1, characterized in that Collect carbon emissions data generated by cloud storage services, including: Collect the carbon emissions generated by the power generation process that supports the total electricity consumption of cloud storage services and the direct carbon emissions of cloud storage services; The carbon emissions generated by the power generation process that supports the total power consumption of the cloud storage service and the direct carbon emissions of the cloud storage service are used as the carbon emissions data generated by the cloud storage service.

6. The method according to claim 5, characterized in that The total carbon emissions of cloud storage services during the statistical period are calculated using the following formula: E0(t)=α×[C0(t)-C4(t)]+E1(t) Among them, t is the statistical period, E0(t) is the total carbon emissions of cloud storage services, α is the electricity emission factor, C0(t) is the total power consumption of cloud storage services, C4(t) is the self-generated electricity of cloud storage services, and E1(t) is the direct carbon emissions of cloud storage services.

7. The method according to any one of claims 1 to 6, characterized in that: Calculate the storage energy efficiency and storage carbon efficiency of cloud storage services during the statistical period, including: Calculate the storage energy efficiency of the cloud storage service during the statistical period based on the ratio of the average actual storage capacity of the cloud storage service to the total power consumption during the statistical period; and, The storage carbon efficiency of cloud storage services during the statistical period is calculated based on the ratio of the average actual storage capacity of cloud storage services during the statistical period to the total carbon emissions.

8. The method according to any one of claims 1 to 6, characterized in that: Evaluate the greenness of cloud storage services based on their storage energy efficiency and storage carbon efficiency, including: Analyze the energy efficiency and carbon emission impact of cloud storage services based on their storage energy efficiency and storage carbon efficiency; Among them, the higher the storage energy efficiency, the higher the energy utilization efficiency of the cloud storage service, and the greener the cloud storage service is; the higher the storage carbon efficiency, the smaller the carbon emission impact of the cloud storage service, and the greener the cloud storage service is.

9. A device for evaluating the greenness of a cloud storage service, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for evaluating the greenness of a cloud storage service as described in any one of claims 1 to 8 when running the program instructions.

10. An electronic device, characterized in that: include: Electronic device body; The device for evaluating the greenness of a cloud storage service as described in claim 9 is installed in the electronic device body.

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